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ClinicalTrialsEstimation
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youainti:lognorm_sigma_prior
youainti:main
youainti:exploring_fixed_effects
youainti:check_reversion
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youainti:exploring_fixed_effects
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youainti:v1.0.0

23 Commits
v1.0.0 ... main

Author SHA1 Message Date
will king 5b271a41c5 Merge branch 'lognorm_sigma_prior' 1 year ago
will king f17f88728f Merge branch 'exploring_fixed_effects' 1 year ago
Will King cd90d8e28b reran analysis to get up to date stuff 1 year ago
Will King 7074fa6c32 Updated graphing code 1 year ago
Will King 6fcc8cda22 Fixed column issue in enrollment delay 1 year ago
Will King be77d1d38a Fixed error in column order that invalidated results 
The columns for competitor drugs were placed in the wrong spot.
 1 year ago
Will King c1ebd42639 Began exploring fixed effects' interpretation 1 year ago
will king 73407ac215 Merge branch 'main' of https://git.youainti.com/youainti/ClinicalTrialsEstimation 1 year ago
will king c29d696e5e recording home-pc version of recruitment status 1 year ago
Will 2f71e63e22 Removed results (images) from prior analyses. 
There are a lot of images that have been collected from prior analyses.
I'm cleaning that up and changing the directory tree where they are
stored.
 1 year ago
Will King 87d2077f6e Got new images for analysis 
Based on updated data.
 1 year ago
Will King 5a7e6ff299 Merge branch 'main' of https://git.youainti.com/youainti/ClinicalTrialsEstimation 1 year ago
Will King 26b3ce9b17 Updated analysis graphs and interpretation. 
Also renamed some images to match what they represent.
The new .png files are from that. The renamed ones are from running the
analysis earlier.
 1 year ago
will king c255652744 Merge branch 'main' of ssh://git.youainti.com:3022/youainti/ClinicalTrialsEstimation 2 years ago
will king 9656a59a19 removed unused Julia models 2 years ago
Will King d25f5c2a0e Ran an analysis for pharma and included all the output. 2 years ago
Will King 0d0225ecdb recording current model 3 years ago
Will King eabf858955 Got direct effects model working 3 years ago
Will King 98fc18e200 updated analysis 3 years ago
Will King a7e1f71e12 Merge branch 'main' of https://git.youainti.com/youainti/ClinicalTrialsEstimation 3 years ago
Will King 63229d3a99 Updated to keep current analysis 3 years ago
will king f2cfb776a0 Merge branch 'main' of ssh://git.youainti.com:3022/youainti/ClinicalTrialsEstimation 3 years ago
will king 05b1b0c2ef added julia models-incomplete 3 years ago
89 changed files with 11941 additions and 5916 deletions
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.gitignore vendored
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.Rproj.user
*/*_files/*
#R stuff
.Rhistory
.RData

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ClinicalTrialsEstimation.Rproj
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Version: 1.0
RestoreWorkspace: Default
SaveWorkspace: Default
AlwaysSaveHistory: Default
EnableCodeIndexing: Yes
UseSpacesForTab: Yes
NumSpacesForTab: 2
Encoding: UTF-8
RnwWeave: Sweave
LaTeX: pdfLaTeX

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Julia-turing models/Model.jl
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using Turing, StatsFuns
using Distributions, StatsPlots
using FillArrays
begin #creating synthetic data
βs = [1 2 3]'
x = Matrix([1:10 1:2:20 1:3:30])
X = (x .- mean(x,dims=1)) ./ std(x,dims=1)
t = [x for x=1:0.5:5.5]
s = rand([1,2],10)
σ= [2 3]
rand_draw1 = randn(10)
y = x*βs .+ σ[s]
end
@model function JointDurationStateModel(
DeviationFromExpectedDuration,
ConclusionStatus,
SnapshotState,
CurrentDuration,
)
# get dimensions
n,k = size(SnapshotState)
#hyperpriors priors
#Heirarchal parameters
#β ~ MvNormal(Fill(0,k),2)
η ~ MvNormal(Fill(0,k),2)
#Direct Priors
#σ_DFED ~ filldist(Exponential(1),2) #TODO: check implication of this form
#model
#μ = SnapshotState * β
p = StatsFuns.logistic.(SnapshotState * η)
#estimate ConclusionStatus model
ConclusionStatus .~ Bernoulli(p)
#Estimate DFED model
#=
for i in eachindex(ConclusionStatus)
DeviationFromExpectedDuration ~ Normal(
μ[ConclusionStatus[i]],
σ_DFED[ConclusionStatus[i]]
)
end
=#
end
model = JointDurationStateModel(y,s,X,t)
prior = JointDurationStateModel(fill(missing,size(y)),fill(missing,size(s)),X,t)
chain = sample(model,NUTS(0.85),2000)
plot(chain)

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r-analysis/.Rhistory
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r-analysis/DataMangement.R
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library(bayesplot)
available_mcmc(pattern = "_nuts_")
library(ggplot2)
library(rstan)
#Resources: https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started
#save unchanged models instead of recompiling
rstan_options(auto_write = TRUE)
#allow for multithreaded sampling
options(mc.cores = parallel::detectCores())
#test installation, shouldn't get any errors
#example(stan_model, package = "rstan", run.dontrun = TRUE)
################ Pull data from database ######################
library(RPostgreSQL)
driver <- dbDriver("PostgreSQL")
con <- dbConnect(
driver,
user='root',
password='root',
dbname='aact_db',
host='will-office'
)
query <- dbSendQuery(
con,
"select * from formatted_data_with_planned_enrollment;"
)
df <- fetch(query)
df <- na.omit(df)
query2 <-dbSendQuery(con,"select count(*) from \"DiseaseBurden\".icd10_categories ic ;")
n_categories <- fetch(query2)
################ Format Data ###########################
categories <- df["category_id"]
x <- df["elapsed_duration"]
x["log_n_brands"] <- log(df$n_brands + 1)
x["h_sdi_val"] <- log(df$h_sdi_val + 1)
x["hm_sdi_val"] <- log(df$hm_sdi_val + 1)
x["m_sdi_val"] <- log(df$m_sdi_val + 1)
x["lm_sdi_val"] <- log(df$lm_sdi_val + 1)
x["l_sdi_val"] <- log(df$l_sdi_val + 1)
x["enrollment"] <- df["current_enrollment"] / df["planned_enrollment"]
#square terms
#x["enrollment^2"] <- x["enrollment"]^2
#x["elapsed_duration^2"] <- x["elapsed_duration"]^2
#x["n_brands^2"] <- x["n_brands"]^2
#break out
x["status_NYR"] <- ifelse(df["current_status"]=="Not yet recruiting",1,0)
x["status_Rec"] <- ifelse(df["current_status"]=="Recruiting",1,0)
x["status_ANR"] <- ifelse(df["current_status"]=="Active, not recruiting",1,0)
x["status_EBI"] <- ifelse(df["current_status"]=="Enrolling by invitation",1,0)
y <- ifelse(df["final_status"]=="Terminated",1,0)
################################# DATA EXPLORATION ############################
#Plot terminated vs completed
#Plot duration for terminated vs completed
#Plot different times of
################################# FIT #########################################
#setup data (named list)
trials_data <- list(
D = ncol(x),#
N = nrow(x),
# L = n_categories$count,
y = as.vector(y),
# ll = as.vector(categories$category_id),
x = as.matrix(x)
)
#fit <- stan(file='Logistic.stan', data = trials_data) #attempt at simple model
fit <- stan(file='Hierarchal_Logistic.stan', data = trials_data)
################################# ANALYZE #####################################
print(fit)
color_scheme_set("darkgray")
#trace plots
plot(fit, pars=c("mu"), plotfun="trace")
plot(fit, pars=c("sigma"), plotfun="trace")
#other diagnostics
logpost <- log_posterior(fit)
nuts_prmts <- nuts_params(fit)
posterior <- as.array(fit)
mcmc_parcoord(posterior,pars=c(
"mu[1]",
"mu[2]",
"mu[3]",
"mu[4]",
"mu[5]",
"mu[6]",
"mu[7]",
"mu[8]",
"mu[9]",
"mu[10]",
"mu[11]",
"mu[12]"
),
np=nuts_prmts
)
mcmc_pairs(
posterior,
np = nuts_prmts,
pars=c(
"mu[1]",
"mu[2]",
"sigma[1]",
"sigma[2]"
),
off_diag_args = list(size = 0.75)
)
mcmc_parcoord(posterior,pars=c(
"sigma[1]",
"sigma[2]",
"sigma[3]",
"sigma[4]",
"sigma[5]",
"sigma[6]",
"sigma[7]",
"sigma[8]",
"sigma[9]",
"sigma[10]",
"sigma[11]",
"sigma[12]"
),
np=nuts_prmts
)
# check model estimates
#mu
plot(fit, pars=c("mu"), show_density =TRUE, ci_level=0.8)
plot(fit, pars=c("mu"), plotfun = "hist")
#sigma
plot(fit, pars=c("sigma"), show_density =TRUE, ci_level=0.8)
plot(fit, pars=c("sigma"), plotfun = "hist")

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r-analysis/EffectsOfMarketConditions.qmd
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---
title: "The Effects of market conditions on recruitment and completion of clinical trials"
author: "Will King"
format: html
editor: source
---
```{r}
library(bayesplot)
available_mcmc(pattern = "_nuts_")
library(ggplot2)
library(rstan)
#Resources: https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started
#save unchanged models instead of recompiling
rstan_options(auto_write = TRUE)
#allow for multithreaded sampling
options(mc.cores = parallel::detectCores())
#test installation, shouldn't get any errors
#example(stan_model, package = "rstan", run.dontrun = TRUE)
```
```{r}
################ Pull data from database ######################
library(RPostgreSQL)
driver <- dbDriver("PostgreSQL")
get_data <- function(driver) {
con <- dbConnect(
driver,
user='root',
password='root',
dbname='aact_db',
host='will-office'
)
on.exit(dbDisconnect(con))
query <- dbSendQuery(
con,
"select * from formatted_data_with_planned_enrollment;"
)
df <- fetch(query, n = -1)
df <- na.omit(df)
query2 <-dbSendQuery(con,"select count(*) from \"DiseaseBurden\".icd10_categories ic where \"level\"=1;")
n_categories <- fetch(query2, n = -1)
return(list(data=df,ncat=n_categories))
}
d <- get_data(driver)
df <- d$data
n_categories <- d$ncat
################ Format Data ###########################
categories <- df["category_id"]
x <- df["elapsed_duration"]
x["log_n_brands"] <- asinh(df$n_brands) #todo - convert to arcsinh() transform
x["h_sdi_val"] <- asinh(df$h_sdi_val)
x["hm_sdi_val"] <- asinh(df$hm_sdi_val)
x["m_sdi_val"] <- asinh(df$m_sdi_val)
x["lm_sdi_val"] <- asinh(df$lm_sdi_val)
x["l_sdi_val"] <- asinh(df$l_sdi_val)
#x["enrollment"] <- df["current_enrollment"] / df["planned_enrollment"]
#square terms
#x["enrollment^2"] <- x["enrollment"]^2
#x["elapsed_duration^2"] <- x["elapsed_duration"]^2
#x["n_brands^2"] <- x["n_brands"]^2
#break out
#x["status_NYR"] <- ifelse(df["current_status"]=="Not yet recruiting",1,0)
#x["status_Rec"] <- ifelse(df["current_status"]=="Recruiting",1,0)
#x["status_ANR"] <- ifelse(df["current_status"]=="Active, not recruiting",1,0)
#x["status_EBI"] <- ifelse(df["current_status"]=="Enrolling by invitation",1,0)
y <- ifelse(df["final_status"]=="Terminated",1,0)
```
```{r}
################################# DATA EXPLORATION ############################
driver <- dbDriver("PostgreSQL")
con <- dbConnect(
driver,
user='root',
password='root',
dbname='aact_db',
host='will-office'
)
#Plot histogram of count of snapshots
df3 <- dbGetQuery(
con,
"select nct_id,final_status,count(*) from formatted_data_with_planned_enrollment fdwpe
group by nct_id,final_status ;"
)
#df3 <- fetch(query3, n = -1)
ggplot(data=df3, aes(x=count, fill=final_status)) +
geom_histogram(binwidth=1) +
ggtitle("Histogram of snapshots per trial (matched trials)") +
xlab("Snapshots per trial")
#Plot duration for terminated vs completed
df4 <- dbGetQuery(
con,
"
select
nct_id,
start_date ,
primary_completion_date,
overall_status ,
primary_completion_date - start_date as duration
from ctgov.studies s
where nct_id in (select distinct nct_id from http.download_status ds)
;"
)
#df4 <- fetch(query4, n = -1)
ggplot(data=df4, aes(x=duration,fill=overall_status)) +
geom_histogram()+
ggtitle("Histogram of trial durations") +
xlab("duration")+
facet_wrap(~overall_status)
df5 <- dbGetQuery(
con,
"
with cte1 as (
select
nct_id,
start_date ,
primary_completion_date,
overall_status ,
primary_completion_date - start_date as duration
from ctgov.studies s
where nct_id in (select distinct nct_id from http.download_status ds)
), cte2 as (
select nct_id,count(*) as snapshot_count from formatted_data_with_planned_enrollment fdwpe
group by nct_id
)
select a.nct_id, a.overall_status, a.duration,b.snapshot_count
from cte1 as a
join cte2 as b
on a.nct_id=b.nct_id
;"
)
#df5 <- fetch(query5, n = -1)
ggplot(data=df5, aes(x=duration,y=snapshot_count,color=overall_status)) +
geom_point() +
ggtitle("duration, status, and snapshot_count") +
xlab("duration") +
ylab("snapshot count")
dbDisconnect(con)
```
```{r}
################################# FIT #########################################
#setup data (named list)
trials_data <- list(
D = ncol(x),#
N = nrow(x),
L = n_categories$count,
y = as.vector(y),
ll = as.vector(categories$category_id),
x = as.matrix(x)
)
#fit <- stan(file='Logistic.stan', data = trials_data) #attempt at simple model
fit <- stan(
file='Hierarchal_Logistic.stan',
data = trials_data,
chains = 6,
iter = 5000,
seed = 11021585
)
```
```{r}
################################# ANALYZE #####################################
print(fit)
```
```{r}
color_scheme_set("darkgray")
#trace plots
plot(fit, pars=c("mu"), plotfun="trace")
color_scheme_set("viridis")
mcmc_rank_overlay(
fit,
pars=c(
"mu[1]",
"mu[2]",
"mu[3]",
"mu[4]",
"mu[5]",
"mu[6]",
"mu[7]"
),
n_bins=100
)+ legend_move("top")
```
```{r}
color_scheme_set("viridis")
plot(fit, pars=c("sigma"), plotfun="trace")
color_scheme_set("viridis")
mcmc_rank_overlay(
fit,
pars=c(
"sigma[1]",
"sigma[2]",
"sigma[3]",
"sigma[4]",
"sigma[5]",
"sigma[6]",
"sigma[7]"
),
n_bins=100
)+ legend_move("top")
```
```{r}
#other diagnostics
logpost <- log_posterior(fit)
nuts_prmts <- nuts_params(fit)
posterior <- as.array(fit)
```
```{r}
color_scheme_set("darkgray")
mcmc_parcoord(posterior,pars=c(
"mu[1]",
"mu[2]",
"mu[3]",
"mu[4]",
"mu[5]",
"mu[6]",
"mu[7]"
),
np=nuts_prmts
)
```
```{r}
mcmc_pairs(
posterior,
np = nuts_prmts,
pars=c(
"mu[1]",
"mu[2]",
"mu[3]",
"mu[4]",
"mu[5]",
"mu[6]",
"mu[7]"
),
off_diag_args = list(size = 0.75)
)
```
```{r}
mcmc_parcoord(posterior,pars=c(
"sigma[1]",
"sigma[2]",
"sigma[3]",
"sigma[4]",
"sigma[5]",
"sigma[6]",
"sigma[7]"
),
np=nuts_prmts
)
```
```{r}
mcmc_pairs(
posterior,
np = nuts_prmts,
pars=c(
"sigma[1]",
"sigma[2]",
"sigma[3]",
"sigma[4]",
"sigma[5]",
"sigma[6]",
"sigma[7]"
),
off_diag_args = list(size = 0.75)
)
```
```{r}
# check model estimates
#mu
plot(fit, pars=c("mu"), show_density =TRUE, ci_level=0.8)
plot(fit, pars=c("mu"), plotfun = "hist", bins=50)
```
```{r}
#sigma
plot(fit, pars=c("sigma"), show_density =TRUE, ci_level=0.8)
plot(fit, pars=c("sigma"), plotfun = "hist",bins=50)
```

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r-analysis/Hierarchal_Logistic.stan
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// The input data is a vector 'y' of length 'N'.
data {
int<lower=1> D;
int<lower=1> N;
int<lower=1> L;
int<lower=0, upper=1> y[N];
int<lower=1, upper=L> ll[N];
row_vector[D] x[N];
int<lower=1> D; // number of features
int<lower=1> N; // number of observations
int<lower=1> L; // number of categories
array[N] int<lower=0, upper=1> y; // vector of dependent variables
array[N] int<lower=1, upper=L> ll; // vector of categories
array[N] row_vector[D] x; // independent variables
real mu_mean; //hyperprior
real<lower=0> mu_stdev; //hyperprior
real sigma_location; //hyperprior
real<lower=0> sigma_scale; //hyperprior
//counterfactuals
int<lower=0> Nx;
array[Nx] int<lower=1, upper=L> llx;//vec of categories
array[Nx] row_vector[D] counterfact_x_tilde; // Posterior Prediction intervention
array[Nx] row_vector[D] counterfact_x; // Posterior Prediction intervention
//the two statuses to catch relative difference
array[2] int status_indexes; //the two status indexes to compare (order is x[1] - x[2])
}
parameters {
real mu[D];
real<lower=0> sigma[D];
vector[D] beta[L];
array[D] real mu;
array[D] real<lower=0> sigma;
array[L] vector[D] beta;
}
model {
sigma ~ gamma(2,1); //shape and inverse scale
mu ~ normal(0, 1); //convert to mvnormal
sigma ~ lognormal(sigma_location,sigma_scale); //hyperprior for stdev: shape and inverse scale
mu ~ normal(mu_mean, mu_stdev); //hyperprior for mean //TODO: convert to mvnormal
for (l in 1:L) {
beta[l] ~ normal(mu, sigma);
}
@ -37,4 +48,55 @@ model {
y ~ bernoulli_logit(x_beta_ll);
}
}
generated quantities {
//SETUP PRIOR PREDICTION
//preallocate
real mu_prior[D];
real sigma_prior[D];
real p_prior[N]; // what I have priors about
real p_predicted[N]; //predicted p_values
//intervention
real p_predicted_default[Nx]; //predicted p_values
real p_predicted_intervention[Nx]; //predicted p_values
real predicted_difference[Nx]; //difference in predicted values
//collect array of relative status differences between
array[L] real status_diff;
//GENERATE RELATIVE DIFFERENCES BETWEEN STATUSES
for (l in 1:L) {
status_diff[l] = beta[l,status_indexes[1]] - beta[l,status_indexes[2]];
}
//GENERATE PRIOR PREDICTIONS
{
vector[D] beta_prior[L];//local var
//sample parameters
for (d in 1:D) {
mu_prior[d] = normal_rng(mu_mean,mu_stdev);
sigma_prior[d] = lognormal_rng(sigma_location,sigma_scale);
}
for (l in 1:L) {
for (d in 1:D) {
beta_prior[l,d] = normal_rng(mu_prior[d],sigma_prior[d]);
}
}
//generate probabilities
vector[D] b_prior[N];//local var
for (n in 1:N){
b_prior[n] = beta_prior[ll[n]];
p_prior[n] = inv_logit( x[n] * b_prior[n] );
}
}
//GENERATE POSTERIOR PREDICTIONS
for (n in 1:N) {
p_predicted[n] = inv_logit( x[n] * beta[ll[n]] );
}
//GENERATE POSTERIOR DISTRIBUTION OF DIFFERENCES
for (n in 1:Nx) {
p_predicted_default[n] = inv_logit( counterfact_x[n] * beta[llx[n]] );
p_predicted_intervention[n] = inv_logit( counterfact_x_tilde[n] * beta[llx[n]] ); //intervention
//intervention - base case
predicted_difference[n] = p_predicted_intervention[n] - p_predicted_default[n];
}
}

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r-analysis/Logistic.stan
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//
// This Stan program defines a simple model, with a
// vector of values 'y' modeled as normally distributed
// with mean 'mu' and standard deviation 'sigma'.
//
// Learn more about model development with Stan at:
//
// http://mc-stan.org/users/interfaces/rstan.html
// https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started
//
// The input data is a vector 'y' of length 'N'.
data {
int<lower=1> D;
int<lower=1> N;
int<lower=0, upper=1> y[N];
row_vector[D] x[N];
}
parameters {
real mu[D];
real<lower=0> sigma[D];
}
model {
sigma ~ gamma(2,0.1);
mu ~ normal(0, sigma); //convert to mvnormal
for (n in 1:N) {
y[n] ~ bernoulli_logit(x[n] * mu);
}
}

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//
// This Stan program defines a simple model, with a
// vector of values 'y' modeled as normally distributed
// with mean 'mu' and standard deviation 'sigma'.
//
// Learn more about model development with Stan at:
//
// http://mc-stan.org/users/interfaces/rstan.html
// https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started
//
// The input data is a vector 'y' of length 'N'.
data {
int<lower=0> N;
int<lower=0> k;
matrix[N,k] X;
vector[N] int<lower=0, upper=1> y;
}
// The parameters accepted by the model. Our model
// accepts two parameters 'mu' and 'sigma'.
parameters {
vector[k] beta;
}
// The model to be estimated. We model the output
// 'y' to be normally distributed with mean 'mu'
// and standard deviation 'sigma'.
model {
y ~ bernoulli_logit( X * beta);
}

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% latex table generated in R 4.4.2 by xtable 1.8-4 package
% Sun Feb 2 01:37:36 2025
\begin{table}[ht]
\centering
\begin{tabular}{lrrrrrrrrr}
\hline
ICD-10 Category & mean & P(≥0) & 2.5\% & 5\% & 25\% & 50\% median & 75\% & 95\% & 97.5\% \\
\hline
Blood \& Immune system & 0.061 & 0.624 & -0.333 & -0.267 & -0.071 & 0.061 & 0.194 & 0.388 & 0.454 \\
Circulatory & 0.037 & 0.577 & -0.362 & -0.293 & -0.094 & 0.038 & 0.170 & 0.363 & 0.426 \\
Congential & 0.063 & 0.628 & -0.332 & -0.264 & -0.068 & 0.063 & 0.195 & 0.391 & 0.456 \\
Contact with Healthcare & 0.063 & 0.627 & -0.335 & -0.267 & -0.070 & 0.064 & 0.194 & 0.390 & 0.454 \\
Digestive & 0.060 & 0.621 & -0.339 & -0.270 & -0.071 & 0.059 & 0.192 & 0.388 & 0.454 \\
Ear and Mastoid & 0.063 & 0.625 & -0.332 & -0.263 & -0.069 & 0.064 & 0.196 & 0.393 & 0.457 \\
Endocrine, Nutritional, and Metabolic & 0.177 & 0.820 & -0.201 & -0.137 & 0.045 & 0.173 & 0.304 & 0.505 & 0.573 \\
External Causes & 0.063 & 0.625 & -0.330 & -0.266 & -0.070 & 0.064 & 0.194 & 0.392 & 0.460 \\
Eye and Adnexa & 0.061 & 0.624 & -0.333 & -0.265 & -0.069 & 0.062 & 0.192 & 0.386 & 0.452 \\
Genitourinary & 0.063 & 0.627 & -0.337 & -0.270 & -0.070 & 0.065 & 0.195 & 0.389 & 0.456 \\
Infections \& Parasites & 0.135 & 0.772 & -0.223 & -0.163 & 0.013 & 0.134 & 0.257 & 0.438 & 0.498 \\
Injury etc. & 0.063 & 0.625 & -0.337 & -0.268 & -0.069 & 0.062 & 0.196 & 0.392 & 0.459 \\
Mental \& Behavioral & 0.142 & 0.767 & -0.239 & -0.175 & 0.010 & 0.140 & 0.270 & 0.469 & 0.537 \\
Musculoskeletal & 0.159 & 0.794 & -0.218 & -0.156 & 0.027 & 0.156 & 0.286 & 0.482 & 0.550 \\
Neoplasms & 0.211 & 0.902 & -0.107 & -0.056 & 0.099 & 0.208 & 0.320 & 0.489 & 0.546 \\
Nervous System & 0.009 & 0.524 & -0.383 & -0.315 & -0.120 & 0.011 & 0.140 & 0.327 & 0.389 \\
Perinatal Period & 0.062 & 0.626 & -0.338 & -0.270 & -0.068 & 0.062 & 0.194 & 0.390 & 0.457 \\
Pregancy, Childbirth, \& Puerperium & 0.061 & 0.622 & -0.336 & -0.270 & -0.071 & 0.061 & 0.194 & 0.392 & 0.458 \\
Respiratory & 0.061 & 0.622 & -0.335 & -0.269 & -0.072 & 0.061 & 0.195 & 0.391 & 0.458 \\
Skin \& Subcutaneaous tissue & 0.104 & 0.704 & -0.283 & -0.220 & -0.027 & 0.103 & 0.234 & 0.429 & 0.494 \\
Special Purposes & 0.062 & 0.625 & -0.335 & -0.268 & -0.070 & 0.062 & 0.194 & 0.391 & 0.456 \\
Symptoms, Signs etc. & 0.062 & 0.625 & -0.332 & -0.265 & -0.069 & 0.062 & 0.194 & 0.390 & 0.456 \\
\hline
\end{tabular}
\end{table}

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10
r-analysis/output/EffectsOfEnrollmentDelay/betas/parameter_across_groups/parameters_tables_10_status_EBI.tex
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c("Blood & Immune system", "Circulatory", "Congential", "Contact with Healthcare", "Digestive", "Ear and Mastoid", "Endocrine, Nutritional, and Metabolic", "External Causes", "Eye and Adnexa", "Genitourinary", "Infections & Parasites", "Injury etc.", "Mental & Behavioral", "Musculoskeletal", "Neoplasms", "Nervous System", "Perinatal Period", "Pregancy, Childbirth, & Puerperium", "Respiratory", "Skin & Subcutaneaous tissue", "Special Purposes", "Symptoms, Signs etc.")
c(-0.00287140656905416, -0.0020229135961933, -0.00252250078632693, -0.00209236160817254, -0.00233302952373521, -0.00216616648896081, -0.00681543732790809, -0.00273617241938979, -0.00219896995482389, -0.00310971192809426, -0.00900529540694934, -0.00252589644802287, -0.00444905498743218, -0.00275503221432378, -0.00295173909211749, -0.00517241124667845, -0.00201260276746087, -0.00257497154836192, -0.00242272398017638, -0.00267620974013439, -0.00248385412497201, -0.00206395314166231)
c(`2.5%` = -0.275944463803495, `2.5%` = -0.272921161056328, `2.5%` = -0.272430725186905, `2.5%` = -0.272911375314851, `2.5%` = -0.27286212768348, `2.5%` = -0.276406977245084, `2.5%` = -0.277987677303119, `2.5%` = -0.270429258845101, `2.5%` = -0.272173859797933, `2.5%` = -0.270957011640022, `2.5%` = -0.281051472827658, `2.5%` = -0.274123164780341, `2.5%` = -0.273927630545993, `2.5%` = -0.272259113214428, `2.5%` = -0.271867873121147, `2.5%` = -0.275526164264352, `2.5%` = -0.275736741086972, `2.5%` = -0.274025059638234,
`2.5%` = -0.271895651649722, `2.5%` = -0.274439135854432, `2.5%` = -0.272777026543343, `2.5%` = -0.274505781494588)
c(`97.5%` = 0.268525663885088, `97.5%` = 0.269687555821123, `97.5%` = 0.263988376114375, `97.5%` = 0.268376674723849, `97.5%` = 0.271595831106391, `97.5%` = 0.27398054865504, `97.5%` = 0.26497995981819, `97.5%` = 0.264914777867797, `97.5%` = 0.268729163024419, `97.5%` = 0.266687930004995, `97.5%` = 0.260632526818398, `97.5%` = 0.270701536658997, `97.5%` = 0.264287404395679, `97.5%` = 0.266631371530776, `97.5%` = 0.265171319133342, `97.5%` = 0.267696135908434, `97.5%` = 0.269792405742312, `97.5%` = 0.267738792080357,
`97.5%` = 0.270026733830649, `97.5%` = 0.267892275489918, `97.5%` = 0.266305005411752, `97.5%` = 0.271501986023693)
c(`5%` = -0.228422342352796, `5%` = -0.225192615705392, `5%` = -0.225155935562034, `5%` = -0.227577347541465, `5%` = -0.226336678780408, `5%` = -0.227963356496732, `5%` = -0.230488468962024, `5%` = -0.226216509362732, `5%` = -0.224813386072165, `5%` = -0.224824888562106, `5%` = -0.232874112114568, `5%` = -0.227479714082238, `5%` = -0.227858560023144, `5%` = -0.22530608055078, `5%` = -0.226179910621409, `5%` = -0.228284369418676, `5%` = -0.228444224989461, `5%` = -0.226622261032726, `5%` = -0.224288571128251,
`5%` = -0.227590395464131, `5%` = -0.225531022618535, `5%` = -0.227689137616937)
c(`95%` = 0.2234974899362, `95%` = 0.222074828728455, `95%` = 0.218357594565282, `95%` = 0.22222430007764, `95%` = 0.222289991457749, `95%` = 0.223327488400761, `95%` = 0.216270931014924, `95%` = 0.220095617146775, `95%` = 0.221009192368411, `95%` = 0.218989193656767, `95%` = 0.213804656879867, `95%` = 0.222889299900387, `95%` = 0.220133939553423, `95%` = 0.21899037055656, `95%` = 0.218796743068272, `95%` = 0.220217569663176, `95%` = 0.224044557893232, `95%` = 0.220936167501407, `95%` = 0.222159099161132,
`95%` = 0.220911069519647, `95%` = 0.220781640962889, `95%` = 0.223599356920508)

10
r-analysis/output/EffectsOfEnrollmentDelay/betas/parameter_across_groups/parameters_tables_11_status_Rec.tex
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c("Blood & Immune system", "Circulatory", "Congential", "Contact with Healthcare", "Digestive", "Ear and Mastoid", "Endocrine, Nutritional, and Metabolic", "External Causes", "Eye and Adnexa", "Genitourinary", "Infections & Parasites", "Injury etc.", "Mental & Behavioral", "Musculoskeletal", "Neoplasms", "Nervous System", "Perinatal Period", "Pregancy, Childbirth, & Puerperium", "Respiratory", "Skin & Subcutaneaous tissue", "Special Purposes", "Symptoms, Signs etc.")
c(0.00652882287222823, -0.0121276026193075, 0.00806097077518296, 0.00921072076176892, 0.00511136013511625, 0.00923452323640744, 0.0756452851959799, 0.00901967004583639, 0.00596509106734287, 0.00869308015726206, 0.0234704825434495, 0.00891347968211811, 0.0410008650026277, 0.0493113026890666, -0.0318651891745962, -0.0230137010884716, 0.00907668265519354, 0.0082773304353959, 0.00672386515176627, 0.0229027326955782, 0.00903950397151059, 0.00840626979762671)
c(`2.5%` = -0.26412401679988, `2.5%` = -0.284982548735974, `2.5%` = -0.262583809560454, `2.5%` = -0.259623047912961, `2.5%` = -0.267223017905891, `2.5%` = -0.262824783073146, `2.5%` = -0.182738362310984, `2.5%` = -0.261194421023313, `2.5%` = -0.264838971088612, `2.5%` = -0.263984097470059, `2.5%` = -0.233328888684629, `2.5%` = -0.260381735988535, `2.5%` = -0.219982381931615, `2.5%` = -0.209178899422793, `2.5%` = -0.272496159194591, `2.5%` = -0.293189864865698, `2.5%` = -0.259442015177563, `2.5%` = -0.265643699376373,
`2.5%` = -0.264848607612733, `2.5%` = -0.243262927814473, `2.5%` = -0.263583321279326, `2.5%` = -0.260710033426863)
c(`97.5%` = 0.279506924359323, `97.5%` = 0.25166727687992, `97.5%` = 0.279367210952163, `97.5%` = 0.278659096264822, `97.5%` = 0.274044451638933, `97.5%` = 0.280562384576216, `97.5%` = 0.359198759844572, `97.5%` = 0.282464614440408, `97.5%` = 0.274146522142649, `97.5%` = 0.279840596472466, `97.5%` = 0.282219317765516, `97.5%` = 0.279650795194328, `97.5%` = 0.3146382824935, `97.5%` = 0.324504387608479, `97.5%` = 0.198395249398707, `97.5%` = 0.238015436387141, `97.5%` = 0.279664049019525, `97.5%` = 0.282472259620116,
`97.5%` = 0.280170359985892, `97.5%` = 0.296090615801029, `97.5%` = 0.278827396276502, `97.5%` = 0.276519814652463)
c(`5%` = -0.217405377189081, `5%` = -0.235700053008877, `5%` = -0.215580888141684, `5%` = -0.212836780879927, `5%` = -0.220927862811969, `5%` = -0.215245283188846, `5%` = -0.139947007794807, `5%` = -0.214372400370517, `5%` = -0.216692707544591, `5%` = -0.215840640492215, `5%` = -0.190195428873606, `5%` = -0.214974248730335, `5%` = -0.175242262948575, `5%` = -0.166101667860902, `5%` = -0.228981038446028, `5%` = -0.246615923137647, `5%` = -0.214004181031889, `5%` = -0.218493362752846, `5%` = -0.21829426001738,
`5%` = -0.197184265526973, `5%` = -0.216831943794109, `5%` = -0.21336683632312)
c(`95%` = 0.231060746721114, `95%` = 0.206639994505824, `95%` = 0.232261579219567, `95%` = 0.231058781781102, `95%` = 0.228526975656424, `95%` = 0.233653720301541, `95%` = 0.306122759153545, `95%` = 0.234530990151044, `95%` = 0.226800701259279, `95%` = 0.230966741806046, `95%` = 0.237452575327, `95%` = 0.23322865073513, `95%` = 0.264306220644107, `95%` = 0.274559112084226, `95%` = 0.161143364484194, `95%` = 0.195044859439803, `95%` = 0.232513302970324, `95%` = 0.233960896032084, `95%` = 0.232756864140852,
`95%` = 0.246925692056239, `95%` = 0.231685551132494, `95%` = 0.230370019187908)

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r-analysis/output/EffectsOfEnrollmentDelay/betas/parameter_across_groups/parameters_tables_12_status_ANR.tex
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c("Blood & Immune system", "Circulatory", "Congential", "Contact with Healthcare", "Digestive", "Ear and Mastoid", "Endocrine, Nutritional, and Metabolic", "External Causes", "Eye and Adnexa", "Genitourinary", "Infections & Parasites", "Injury etc.", "Mental & Behavioral", "Musculoskeletal", "Neoplasms", "Nervous System", "Perinatal Period", "Pregancy, Childbirth, & Puerperium", "Respiratory", "Skin & Subcutaneaous tissue", "Special Purposes", "Symptoms, Signs etc.")
c(-0.0543082831893662, -0.0491179148659016, -0.0550797452225853, -0.0532946953959887, -0.0546326964547788, -0.054263670489829, -0.101183732558573, -0.0537419015558274, -0.055296341966815, -0.0541521176630153, -0.111933746601916, -0.0536195743237358, -0.101358093383466, -0.109295568325447, -0.242704066590673, -0.0324576251191776, -0.0530684599999257, -0.0529707690770896, -0.0545919114607711, -0.0809466615050335, -0.0530673747164349, -0.0537090394695628)
c(`2.5%` = -0.3460926221197, `2.5%` = -0.340513734259116, `2.5%` = -0.345063660691308, `2.5%` = -0.343936968361375, `2.5%` = -0.347086498849369, `2.5%` = -0.346730664863283, `2.5%` = -0.402573667581036, `2.5%` = -0.344748248866169, `2.5%` = -0.348019495991554, `2.5%` = -0.343259904607912, `2.5%` = -0.397011060181461, `2.5%` = -0.347299904964005, `2.5%` = -0.398857333530158, `2.5%` = -0.407252271853641, `2.5%` = -0.543673509898535, `2.5%` = -0.315411836398564, `2.5%` = -0.344614905885746, `2.5%` = -0.345987208689716,
`2.5%` = -0.34467924126698, `2.5%` = -0.37464941870207, `2.5%` = -0.346982193867717, `2.5%` = -0.34255968704254)
c(`97.5%` = 0.23859370882391, `97.5%` = 0.247596369898235, `97.5%` = 0.236924146027621, `97.5%` = 0.239608837398885, `97.5%` = 0.23894631939739, `97.5%` = 0.238196795290067, `97.5%` = 0.180340815442861, `97.5%` = 0.240218974320717, `97.5%` = 0.235175513197134, `97.5%` = 0.237777766149638, `97.5%` = 0.156402075952097, `97.5%` = 0.242638519639189, `97.5%` = 0.182559370226462, `97.5%` = 0.170699887327904, `97.5%` = 0.0114150292406248, `97.5%` = 0.259944925352525, `97.5%` = 0.242795694071721, `97.5%` = 0.242602907693202,
`97.5%` = 0.239492781782234, `97.5%` = 0.204585540113804, `97.5%` = 0.238328250302865, `97.5%` = 0.240534823211121)
c(`5%` = -0.295316756003101, `5%` = -0.29096401042673, `5%` = -0.294243091294835, `5%` = -0.293355784827148, `5%` = -0.296193852536529, `5%` = -0.294348519148639, `5%` = -0.347024621453625, `5%` = -0.293279771169653, `5%` = -0.297136938616546, `5%` = -0.2933097076324, `5%` = -0.347474604177594, `5%` = -0.294318945943837, `5%` = -0.344628024841975, `5%` = -0.352532667961251, `5%` = -0.488379411138948, `5%` = -0.266550224886603, `5%` = -0.293396519481707, `5%` = -0.295913858425759, `5%` = -0.294475437187543,
`5%` = -0.322719512364534, `5%` = -0.294234511411049, `5%` = -0.291661527567577)
c(`95%` = 0.186170625673217, `95%` = 0.194446427538144, `95%` = 0.18316007227917, `95%` = 0.185398130082336, `95%` = 0.18656668417393, `95%` = 0.186918825584728, `95%` = 0.133329444707804, `95%` = 0.188140180351712, `95%` = 0.186846293810823, `95%` = 0.188406047073467, `95%` = 0.112535221760718, `95%` = 0.191176019972506, `95%` = 0.132004530075709, `95%` = 0.124477125901656, `95%` = -0.0267022930175043, `95%` = 0.205343484029368, `95%` = 0.187850282416095, `95%` = 0.190035921589226, `95%` = 0.188021330440522,
`95%` = 0.155090212003947, `95%` = 0.188333556494078, `95%` = 0.187492448246279)

10
r-analysis/output/EffectsOfEnrollmentDelay/betas/parameter_across_groups/parameters_tables_1_Elapsed Duration.tex
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c("Blood & Immune system", "Circulatory", "Congential", "Contact with Healthcare", "Digestive", "Ear and Mastoid", "Endocrine, Nutritional, and Metabolic", "External Causes", "Eye and Adnexa", "Genitourinary", "Infections & Parasites", "Injury etc.", "Mental & Behavioral", "Musculoskeletal", "Neoplasms", "Nervous System", "Perinatal Period", "Pregancy, Childbirth, & Puerperium", "Respiratory", "Skin & Subcutaneaous tissue", "Special Purposes", "Symptoms, Signs etc.")
c(-0.0505321895701756, -0.0561236542390722, -0.0499660268658585, -0.0481574325504504, -0.0505967062178304, -0.0480668691756736, -0.0585983827703775, -0.047133883963608, -0.0507912436647474, -0.0478593490739956, -0.0816317285457012, -0.0482822682398582, -0.0774004055701482, -0.016726156695298, -0.315526037006588, -0.0578472700681722, -0.0475268941302727, -0.0480850373469818, -0.0494676291479126, -0.0931186973375372, -0.0479483938019099, -0.0486654863146441)
c(`2.5%` = -0.351333711658477, `2.5%` = -0.358192918691032, `2.5%` = -0.350165746673952, `2.5%` = -0.348260904879301, `2.5%` = -0.355213104294602, `2.5%` = -0.349537890844496, `2.5%` = -0.356636178298712, `2.5%` = -0.348967122811696, `2.5%` = -0.356770047469594, `2.5%` = -0.348523219782961, `2.5%` = -0.369067201876791, `2.5%` = -0.352751358464831, `2.5%` = -0.384222770661754, `2.5%` = -0.310459552529128, `2.5%` = -0.642054194627079, `2.5%` = -0.359167198818045, `2.5%` = -0.351037786426624, `2.5%` = -0.35686171619278,
`2.5%` = -0.353960823288628, `2.5%` = -0.398212841275992, `2.5%` = -0.351411960176182, `2.5%` = -0.352062830672535)
c(`97.5%` = 0.252996678658013, `97.5%` = 0.245199739323497, `97.5%` = 0.252568610015387, `97.5%` = 0.256300675128962, `97.5%` = 0.256220116158008, `97.5%` = 0.256880194696558, `97.5%` = 0.237496943950342, `97.5%` = 0.260731873495491, `97.5%` = 0.255694095814266, `97.5%` = 0.258199105421347, `97.5%` = 0.196240544658593, `97.5%` = 0.256780445660338, `97.5%` = 0.218570279154564, `97.5%` = 0.295751559042532, `97.5%` = -0.0498087194116947, `97.5%` = 0.238629751570793, `97.5%` = 0.258078396538427, `97.5%` = 0.259647869046876,
`97.5%` = 0.256663885447249, `97.5%` = 0.195728293212208, `97.5%` = 0.259664751280795, `97.5%` = 0.257403541589351)
c(`5%` = -0.29803422118825, `5%` = -0.304624731177692, `5%` = -0.298500654906565, `5%` = -0.295625102451718, `5%` = -0.300950367218894, `5%` = -0.296909500653078, `5%` = -0.304028644400323, `5%` = -0.295898014297575, `5%` = -0.301053900765771, `5%` = -0.297395770121559, `5%` = -0.318536205133514, `5%` = -0.298729047781782, `5%` = -0.328832973524529, `5%` = -0.260408972551825, `5%` = -0.580798660746861, `5%` = -0.303098555574414, `5%` = -0.297724180258084, `5%` = -0.300789071762739, `5%` = -0.29885389568916,
`5%` = -0.341186799958823, `5%` = -0.297555789883245, `5%` = -0.297336353504652)
c(`95%` = 0.199805705380915, `95%` = 0.193775727317953, `95%` = 0.20049984396355, `95%` = 0.203214802673669, `95%` = 0.202603902019158, `95%` = 0.203401989386427, `95%` = 0.187816936293634, `95%` = 0.205427481892033, `95%` = 0.200369696005793, `95%` = 0.203709821019864, `95%` = 0.149606773044668, `95%` = 0.201078718458991, `95%` = 0.168803960348435, `95%` = 0.23786658788661, `95%` = -0.0876108144400803, `95%` = 0.186931912303368, `95%` = 0.204472031901114, `95%` = 0.205112704275416, `95%` = 0.201106127451977,
`95%` = 0.145913702571692, `95%` = 0.204425554740135, `95%` = 0.202755710634796)

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r-analysis/output/EffectsOfEnrollmentDelay/betas/parameter_across_groups/parameters_tables_2_asinh(Generic Brands).tex
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c("Blood & Immune system", "Circulatory", "Congential", "Contact with Healthcare", "Digestive", "Ear and Mastoid", "Endocrine, Nutritional, and Metabolic", "External Causes", "Eye and Adnexa", "Genitourinary", "Infections & Parasites", "Injury etc.", "Mental & Behavioral", "Musculoskeletal", "Neoplasms", "Nervous System", "Perinatal Period", "Pregancy, Childbirth, & Puerperium", "Respiratory", "Skin & Subcutaneaous tissue", "Special Purposes", "Symptoms, Signs etc.")
c(-0.0374509867623648, -0.114801759701441, -0.0421991751741288, -0.0268927396537672, -0.0431305204973958, -0.0260913808525182, -0.147540114797909, -0.027116517230545, -0.0572148794918185, -0.0567196164994895, -0.0431781674057524, -0.0247442525263859, -0.148857275259831, 0.173176112299651, -0.745438348426778, 0.514928808041493, -0.0262970064646734, -0.0273698856833142, -0.0519713531203428, -0.0813059076860515, -0.0258722297716816, -0.0348374667962509)
c(`2.5%` = -0.519252573751591, `2.5%` = -0.588446720987112, `2.5%` = -0.518375863151681, `2.5%` = -0.504192330494074, `2.5%` = -0.521537560181517, `2.5%` = -0.501531706057096, `2.5%` = -0.605455988019874, `2.5%` = -0.50319865217871, `2.5%` = -0.529409792355877, `2.5%` = -0.537648621232678, `2.5%` = -0.392971765289971, `2.5%` = -0.499643963973294, `2.5%` = -0.654106115127152, `2.5%` = -0.223573014616573, `2.5%` = -1.12441685579914, `2.5%` = 0.0216592886163218, `2.5%` = -0.492165903236766, `2.5%` = -0.506906607161406,
`2.5%` = -0.530569224373654, `2.5%` = -0.560352091813988, `2.5%` = -0.502878744623734, `2.5%` = -0.516492014350377)
c(`97.5%` = 0.438156824615533, `97.5%` = 0.326892147498506, `97.5%` = 0.439646865645069, `97.5%` = 0.45479809614294, `97.5%` = 0.429980507934071, `97.5%` = 0.452354448697589, `97.5%` = 0.271710367913333, `97.5%` = 0.452356456142033, `97.5%` = 0.408657439741233, `97.5%` = 0.413237173559078, `97.5%` = 0.304781682760948, `97.5%` = 0.462872494668127, `97.5%` = 0.307321465786219, `97.5%` = 0.625146449622561, `97.5%` = -0.396240166662077, `97.5%` = 1.17318649525454, `97.5%` = 0.448797338514388, `97.5%` = 0.456641082563543,
`97.5%` = 0.416128814653975, `97.5%` = 0.375467600073314, `97.5%` = 0.455154502226114, `97.5%` = 0.445072841208461)
c(`5%` = -0.43284681982114, `5%` = -0.498779889626974, `5%` = -0.434007392588596, `5%` = -0.417440686797088, `5%` = -0.43620401741364, `5%` = -0.415561086942261, `5%` = -0.520616290327113, `5%` = -0.415868472350007, `5%` = -0.445215274649135, `5%` = -0.450348871660559, `5%` = -0.335854611911698, `5%` = -0.417370214498537, `5%` = -0.55799947646926, `5%` = -0.165197877092618, `5%` = -1.05984134753029, `5%` = 0.0851401986791414, `5%` = -0.412294843116399, `5%` = -0.421537839042216, `5%` = -0.443110847973097,
`5%` = -0.472054693822914, `5%` = -0.4154217625395, `5%` = -0.430050287469576)
c(`95%` = 0.353300713490598, `95%` = 0.251902327224973, `95%` = 0.353130963560884, `95%` = 0.367385797642419, `95%` = 0.346576238409847, `95%` = 0.364713198282056, `95%` = 0.20347262465658, `95%` = 0.366348980622731, `95%` = 0.328590570058774, `95%` = 0.329135185773538, `95%` = 0.24519101313148, `95%` = 0.372694013964094, `95%` = 0.231568717349548, `95%` = 0.544725565756189, `95%` = -0.449885315105183, `95%` = 1.03873143227049, `95%` = 0.364171369034129, `95%` = 0.369255306736876, `95%` = 0.335218886894626,
`95%` = 0.30207955992225, `95%` = 0.367493122771963, `95%` = 0.360231402349028)

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r-analysis/output/EffectsOfEnrollmentDelay/betas/parameter_across_groups/parameters_tables_3_asinh(Competitors USPDC).tex
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c("Blood & Immune system", "Circulatory", "Congential", "Contact with Healthcare", "Digestive", "Ear and Mastoid", "Endocrine, Nutritional, and Metabolic", "External Causes", "Eye and Adnexa", "Genitourinary", "Infections & Parasites", "Injury etc.", "Mental & Behavioral", "Musculoskeletal", "Neoplasms", "Nervous System", "Perinatal Period", "Pregancy, Childbirth, & Puerperium", "Respiratory", "Skin & Subcutaneaous tissue", "Special Purposes", "Symptoms, Signs etc.")
c(0.0177091826256191, -0.0174830982131017, 0.0197148498472504, 0.0267207484850693, 0.01865599931714, 0.0273355925564759, -0.0557624513358899, 0.0285704704444676, 0.0105781672515, 0.0120682740554864, 0.117270560237318, 0.0273743158412062, 0.0202098736769506, -0.0761301686125453, 0.14948178706425, 0.363548114620147, 0.027441418729276, 0.0271631725930479, 0.0175175823493799, 0.0472688930727053, 0.0278787334069728, 0.0236727211994869)
c(`2.5%` = -0.321412408332877, `2.5%` = -0.360033194654434, `2.5%` = -0.317756875891385, `2.5%` = -0.310112746266069, `2.5%` = -0.321047087440019, `2.5%` = -0.307177857312271, `2.5%` = -0.406584875693101, `2.5%` = -0.302290577496734, `2.5%` = -0.329787608837027, `2.5%` = -0.325741416529682, `2.5%` = -0.15743258849236, `2.5%` = -0.31119118357692, `2.5%` = -0.317021340734379, `2.5%` = -0.431037121901406, `2.5%` = -0.095511016993371, `2.5%` = 0.02041143019079, `2.5%` = -0.305991043265743, `2.5%` = -0.30655610684263,
`2.5%` = -0.319852685329391, `2.5%` = -0.276270824232982, `2.5%` = -0.304861628467873, `2.5%` = -0.311752202314792)
c(`97.5%` = 0.343837322664501, `97.5%` = 0.295622079182328, `97.5%` = 0.349107813430285, `97.5%` = 0.355203673240806, `97.5%` = 0.349024698020337, `97.5%` = 0.360808144158849, `97.5%` = 0.249517798279291, `97.5%` = 0.358750370961986, `97.5%` = 0.33508908205543, `97.5%` = 0.337658598779989, `97.5%` = 0.421101378052922, `97.5%` = 0.354555256028663, `97.5%` = 0.350403071909054, `97.5%` = 0.22676112069751, `97.5%` = 0.422731018753381, `97.5%` = 0.85608235301133, `97.5%` = 0.355759840642227, `97.5%` = 0.356934774044409,
`97.5%` = 0.341627016198812, `97.5%` = 0.376205200194986, `97.5%` = 0.358952915535868, `97.5%` = 0.352180233725197)
c(`5%` = -0.25720973558682, `5%` = -0.294903363605129, `5%` = -0.257137304097466, `5%` = -0.248399306392637, `5%` = -0.25489751531943, `5%` = -0.247102777254339, `5%` = -0.339933290032092, `5%` = -0.242533928470576, `5%` = -0.263620928816618, `5%` = -0.263152155503003, `5%` = -0.112232006084965, `5%` = -0.246916752820145, `5%` = -0.255886271822305, `5%` = -0.360056361494536, `5%` = -0.0582870053082083, `5%` = 0.0634526303087552, `5%` = -0.245278803753055, `5%` = -0.246842321323342, `5%` = -0.256280286172,
`5%` = -0.217436243551559, `5%` = -0.244210801606863, `5%` = -0.250885342909289)
c(`95%` = 0.285370104912777, `95%` = 0.243010994205006, `95%` = 0.289287710051617, `95%` = 0.297571077262736, `95%` = 0.291325959161563, `95%` = 0.299270475058976, `95%` = 0.200063580704579, `95%` = 0.298824142626635, `95%` = 0.27722780854366, `95%` = 0.278261424943046, `95%` = 0.365441233493439, `95%` = 0.297355276712413, `95%` = 0.290718931165702, `95%` = 0.178465709918192, `95%` = 0.373392398003416, `95%` = 0.753336516739962, `95%` = 0.297312097796899, `95%` = 0.297460388207995, `95%` = 0.285527407612361,
`95%` = 0.317952486884081, `95%` = 0.296773071637121, `95%` = 0.292674820916317)

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r-analysis/output/EffectsOfEnrollmentDelay/betas/parameter_across_groups/parameters_tables_9_status_NYR.tex
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c("Blood & Immune system", "Circulatory", "Congential", "Contact with Healthcare", "Digestive", "Ear and Mastoid", "Endocrine, Nutritional, and Metabolic", "External Causes", "Eye and Adnexa", "Genitourinary", "Infections & Parasites", "Injury etc.", "Mental & Behavioral", "Musculoskeletal", "Neoplasms", "Nervous System", "Perinatal Period", "Pregancy, Childbirth, & Puerperium", "Respiratory", "Skin & Subcutaneaous tissue", "Special Purposes", "Symptoms, Signs etc.")
c(-0.00521081730211856, 0.0102255609545677, -0.00496344785285301, -0.00412970481483706, -0.00482782618675194, -0.0050365287595942, -0.0244992731496998, -0.00474024131164134, -0.00556304034621756, -0.00514633902980756, 0.0549642831190738, -0.00505116955549756, 0.000222710855606745, -0.0139890440514832, -0.101150807225687, 0.00328213613579688, -0.00511716442489377, -0.00389750763874108, -0.00529064338687045, 0.00342256724316399, -0.00450674475013338, -0.00565678046765618)
c(`2.5%` = -0.283907488368931, `2.5%` = -0.264718520415862, `2.5%` = -0.281159012081347, `2.5%` = -0.28202830088485, `2.5%` = -0.284419420868419, `2.5%` = -0.281327857726233, `2.5%` = -0.301921658351706, `2.5%` = -0.281271371584173, `2.5%` = -0.284249194010694, `2.5%` = -0.284012477531593, `2.5%` = -0.210012739588992, `2.5%` = -0.283029586218827, `2.5%` = -0.27410283519672, `2.5%` = -0.290631236010586, `2.5%` = -0.392051441442553, `2.5%` = -0.267503827888536, `2.5%` = -0.281737736791382, `2.5%` = -0.279312077145091,
`2.5%` = -0.282292027611709, `2.5%` = -0.271858847048387, `2.5%` = -0.282095546296768, `2.5%` = -0.283135697264375)
c(`97.5%` = 0.273419332183028, `97.5%` = 0.291348978934726, `97.5%` = 0.271017033043418, `97.5%` = 0.274111097548879, `97.5%` = 0.273436192936731, `97.5%` = 0.269026268053048, `97.5%` = 0.248170504583118, `97.5%` = 0.271591216677986, `97.5%` = 0.273523419916202, `97.5%` = 0.274995321113636, `97.5%` = 0.342573757255475, `97.5%` = 0.272937984720548, `97.5%` = 0.274132679138622, `97.5%` = 0.258354914663837, `97.5%` = 0.156606469826098, `97.5%` = 0.279853103150131, `97.5%` = 0.270902691342387, `97.5%` = 0.272104479124203,
`97.5%` = 0.273263017556272, `97.5%` = 0.279847543183877, `97.5%` = 0.272441175448396, `97.5%` = 0.274036307386916)
c(`5%` = -0.233812893025361, `5%` = -0.217746444191205, `5%` = -0.233921233193929, `5%` = -0.232257851816202, `5%` = -0.233567219181413, `5%` = -0.233477104858465, `5%` = -0.252962421653837, `5%` = -0.232486743487047, `5%` = -0.235070814746299, `5%` = -0.234533692066961, `5%` = -0.167107728841027, `5%` = -0.234659301068703, `5%` = -0.226762629571076, `5%` = -0.243190415663175, `5%` = -0.337956677281885, `5%` = -0.221951335704386, `5%` = -0.233061969747887, `5%` = -0.230188221811804, `5%` = -0.234247825650107,
`5%` = -0.223218309293191, `5%` = -0.235479873959263, `5%` = -0.234291225207645)
c(`95%` = 0.225018049213407, `95%` = 0.240749705129402, `95%` = 0.223576635602984, `95%` = 0.22490531538246, `95%` = 0.225150662214719, `95%` = 0.222530040783611, `95%` = 0.200572258529045, `95%` = 0.224085208533272, `95%` = 0.223765301903729, `95%` = 0.224205184960909, `95%` = 0.289724198406613, `95%` = 0.224221721966219, `95%` = 0.226948329518661, `95%` = 0.212010272021156, `95%` = 0.117095735429865, `95%` = 0.231832837318039, `95%` = 0.222364411332351, `95%` = 0.223927230144499, `95%` = 0.223829194578265,
`95%` = 0.231111983676153, `95%` = 0.225668354476114, `95%` = 0.223106259041378)

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r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_table_10_Respiratory.tex
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c("Elapsed Duration", "asinh(Competitors USPDC)", "asinh(Generic Brands)", "asinh(High SDI)", "asinh(High-Medium SDI)", "asinh(Low SDI)", "asinh(Low-Medium SDI)", "asinh(Medium SDI)", "status_ANR", "status_EBI", "status_NYR", "status_Rec")
c(-0.0494676291479126, 0.0175175823493799, -0.0519713531203428, -0.141092626419518, -0.100269478864353, -0.0483011770497731, -0.0706566951944514, -0.0903067809957733, -0.0545919114607711, -0.00242272398017638, -0.00529064338687045, 0.00672386515176627)
c(`2.5%` = -0.353960823288628, `2.5%` = -0.319852685329391, `2.5%` = -0.530569224373654, `2.5%` = -0.422086178248832, `2.5%` = -0.35533612512543, `2.5%` = -0.30969751701722, `2.5%` = -0.328762771558244, `2.5%` = -0.347450401239575, `2.5%` = -0.34467924126698, `2.5%` = -0.271895651649722, `2.5%` = -0.282292027611709, `2.5%` = -0.264848607612733)
c(`97.5%` = 0.256663885447249, `97.5%` = 0.341627016198812, `97.5%` = 0.416128814653975, `97.5%` = 0.122884801270876, `97.5%` = 0.140134372918192, `97.5%` = 0.193515113672737, `97.5%` = 0.167756086960966, `97.5%` = 0.148714819749431, `97.5%` = 0.239492781782234, `97.5%` = 0.270026733830649, `97.5%` = 0.273263017556272, `97.5%` = 0.280170359985892)
c(`5%` = -0.29885389568916, `5%` = -0.256280286172, `5%` = -0.443110847973097, `5%` = -0.3719291117473, `5%` = -0.309480163754538, `5%` = -0.2632490885577, `5%` = -0.281813066410411, `5%` = -0.302200597441508, `5%` = -0.294475437187543, `5%` = -0.224288571128251, `5%` = -0.234247825650107, `5%` = -0.21829426001738)
c(`95%` = 0.201106127451977, `95%` = 0.285527407612361, `95%` = 0.335218886894626, `95%` = 0.0802166258592332, `95%` = 0.100605252625849, `95%` = 0.155768362847512, `95%` = 0.129300525656481, `95%` = 0.109558120218469, `95%` = 0.188021330440522, `95%` = 0.222159099161132, `95%` = 0.223829194578265, `95%` = 0.232756864140852)

6
r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_table_11_Digestive.tex
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c("Elapsed Duration", "asinh(Competitors USPDC)", "asinh(Generic Brands)", "asinh(High SDI)", "asinh(High-Medium SDI)", "asinh(Low SDI)", "asinh(Low-Medium SDI)", "asinh(Medium SDI)", "status_ANR", "status_EBI", "status_NYR", "status_Rec")
c(-0.0505967062178304, 0.01865599931714, -0.0431305204973958, -0.164813949931452, -0.115970648900297, -0.0569474070992618, -0.0803009641524095, -0.102290027233277, -0.0546326964547788, -0.00233302952373521, -0.00482782618675194, 0.00511136013511625)
c(`2.5%` = -0.355213104294602, `2.5%` = -0.321047087440019, `2.5%` = -0.521537560181517, `2.5%` = -0.440159920047502, `2.5%` = -0.373679807524352, `2.5%` = -0.324379242419328, `2.5%` = -0.338605161726642, `2.5%` = -0.358079997022996, `2.5%` = -0.347086498849369, `2.5%` = -0.27286212768348, `2.5%` = -0.284419420868419, `2.5%` = -0.267223017905891)
c(`97.5%` = 0.256220116158008, `97.5%` = 0.349024698020337, `97.5%` = 0.429980507934071, `97.5%` = 0.0934098582169743, `97.5%` = 0.122782511673881, `97.5%` = 0.187764012771118, `97.5%` = 0.163449179706992, `97.5%` = 0.135463410274458, `97.5%` = 0.23894631939739, `97.5%` = 0.271595831106391, `97.5%` = 0.273436192936731, `97.5%` = 0.274044451638933)
c(`5%` = -0.300950367218894, `5%` = -0.25489751531943, `5%` = -0.43620401741364, `5%` = -0.392282017795443, `5%` = -0.327592131875321, `5%` = -0.276552341418244, `5%` = -0.293674641980593, `5%` = -0.312900506044298, `5%` = -0.296193852536529, `5%` = -0.226336678780408, `5%` = -0.233567219181413, `5%` = -0.220927862811969)
c(`95%` = 0.202603902019158, `95%` = 0.291325959161563, `95%` = 0.346576238409847, `95%` = 0.053045778552792, `95%` = 0.0838312787329803, `95%` = 0.149008753739683, `95%` = 0.122846563348083, `95%` = 0.0964502516045677, `95%` = 0.18656668417393, `95%` = 0.222289991457749, `95%` = 0.225150662214719, `95%` = 0.228526975656424)

6
r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_table_12_Skin & Subcutaneaous tissue.tex
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c("Elapsed Duration", "asinh(Competitors USPDC)", "asinh(Generic Brands)", "asinh(High SDI)", "asinh(High-Medium SDI)", "asinh(Low SDI)", "asinh(Low-Medium SDI)", "asinh(Medium SDI)", "status_ANR", "status_EBI", "status_NYR", "status_Rec")
c(-0.0931186973375372, 0.0472688930727053, -0.0813059076860515, -0.115696459653843, -0.0668043254017594, 0.0225267378773846, -0.00783924736360468, -0.0287628443176525, -0.0809466615050335, -0.00267620974013439, 0.00342256724316399, 0.0229027326955782)
c(`2.5%` = -0.398212841275992, `2.5%` = -0.276270824232982, `2.5%` = -0.560352091813988, `2.5%` = -0.369871784607321, `2.5%` = -0.303609657008891, `2.5%` = -0.217526831000879, `2.5%` = -0.243184811863137, `2.5%` = -0.259649581484174, `2.5%` = -0.37464941870207, `2.5%` = -0.274439135854432, `2.5%` = -0.271858847048387, `2.5%` = -0.243262927814473)
c(`97.5%` = 0.195728293212208, `97.5%` = 0.376205200194986, `97.5%` = 0.375467600073314, `97.5%` = 0.133967945394652, `97.5%` = 0.165983963699542, `97.5%` = 0.265350540741357, `97.5%` = 0.230601337710681, `97.5%` = 0.207050118076093, `97.5%` = 0.204585540113804, `97.5%` = 0.267892275489918, `97.5%` = 0.279847543183877, `97.5%` = 0.296090615801029)
c(`5%` = -0.341186799958823, `5%` = -0.217436243551559, `5%` = -0.472054693822914, `5%` = -0.32668730633076, `5%` = -0.26256729924706, `5%` = -0.178033167408505, `5%` = -0.204261113664472, `5%` = -0.222392523721118, `5%` = -0.322719512364534, `5%` = -0.227590395464131, `5%` = -0.223218309293191, `5%` = -0.197184265526973)
c(`95%` = 0.145913702571692, `95%` = 0.317952486884081, `95%` = 0.30207955992225, `95%` = 0.093183366481975, `95%` = 0.125927251547133, `95%` = 0.224177921805955, `95%` = 0.189346878001297, `95%` = 0.165962633366688, `95%` = 0.155090212003947, `95%` = 0.220911069519647, `95%` = 0.231111983676153, `95%` = 0.246925692056239)

6
r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_table_13_Musculoskeletal.tex
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c("Elapsed Duration", "asinh(Competitors USPDC)", "asinh(Generic Brands)", "asinh(High SDI)", "asinh(High-Medium SDI)", "asinh(Low SDI)", "asinh(Low-Medium SDI)", "asinh(Medium SDI)", "status_ANR", "status_EBI", "status_NYR", "status_Rec")
c(-0.016726156695298, -0.0761301686125453, 0.173176112299651, -0.0875007238905653, -0.0663531879724985, 0.0263716192287277, -0.00242271539233981, -0.0381494869327325, -0.109295568325447, -0.00275503221432378, -0.0139890440514832, 0.0493113026890666)
c(`2.5%` = -0.310459552529128, `2.5%` = -0.431037121901406, `2.5%` = -0.223573014616573, `2.5%` = -0.340476599617661, `2.5%` = -0.305174376069712, `2.5%` = -0.214602193292334, `2.5%` = -0.238403789666391, `2.5%` = -0.273459885159963, `2.5%` = -0.407252271853641, `2.5%` = -0.272259113214428, `2.5%` = -0.290631236010586, `2.5%` = -0.209178899422793)
c(`97.5%` = 0.295751559042532, `97.5%` = 0.22676112069751, `97.5%` = 0.625146449622561, `97.5%` = 0.167253531302005, `97.5%` = 0.166163888071297, `97.5%` = 0.270408213531531, `97.5%` = 0.239032427936572, `97.5%` = 0.196051431168698, `97.5%` = 0.170699887327904, `97.5%` = 0.266631371530776, `97.5%` = 0.258354914663837, `97.5%` = 0.324504387608479)
c(`5%` = -0.260408972551825, `5%` = -0.360056361494536, `5%` = -0.165197877092618, `5%` = -0.295749142104736, `5%` = -0.263170921158664, `5%` = -0.174300938748019, `5%` = -0.199357322617647, `5%` = -0.232838007090428, `5%` = -0.352532667961251, `5%` = -0.22530608055078, `5%` = -0.243190415663175, `5%` = -0.166101667860902)
c(`95%` = 0.23786658788661, `95%` = 0.178465709918192, `95%` = 0.544725565756189, `95%` = 0.123888229912759, `95%` = 0.128681125268866, `95%` = 0.228097063420235, `95%` = 0.196129167554776, `95%` = 0.155695563618615, `95%` = 0.124477125901656, `95%` = 0.21899037055656, `95%` = 0.212010272021156, `95%` = 0.274559112084226)

6
r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_table_14_Genitourinary.tex
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c("Elapsed Duration", "asinh(Competitors USPDC)", "asinh(Generic Brands)", "asinh(High SDI)", "asinh(High-Medium SDI)", "asinh(Low SDI)", "asinh(Low-Medium SDI)", "asinh(Medium SDI)", "status_ANR", "status_EBI", "status_NYR", "status_Rec")
c(-0.0478593490739956, 0.0120682740554864, -0.0567196164994895, -0.126233602235583, -0.0888817238478321, -0.0319840844510598, -0.055198586240614, -0.0772497283445443, -0.0541521176630153, -0.00310971192809426, -0.00514633902980756, 0.00869308015726206)
c(`2.5%` = -0.348523219782961, `2.5%` = -0.325741416529682, `2.5%` = -0.537648621232678, `2.5%` = -0.408043017285209, `2.5%` = -0.343971909458341, `2.5%` = -0.293935837353889, `2.5%` = -0.316931983669652, `2.5%` = -0.333641043439836, `2.5%` = -0.343259904607912, `2.5%` = -0.270957011640022, `2.5%` = -0.284012477531593, `2.5%` = -0.263984097470059)
c(`97.5%` = 0.258199105421347, `97.5%` = 0.337658598779989, `97.5%` = 0.413237173559078, `97.5%` = 0.141325836240884, `97.5%` = 0.154268857189929, `97.5%` = 0.215841474017224, `97.5%` = 0.188621888649733, `97.5%` = 0.163079486802808, `97.5%` = 0.237777766149638, `97.5%` = 0.266687930004995, `97.5%` = 0.274995321113636, `97.5%` = 0.279840596472466)
c(`5%` = -0.297395770121559, `5%` = -0.263152155503003, `5%` = -0.450348871660559, `5%` = -0.357452526309299, `5%` = -0.298995454954135, `5%` = -0.246714915420225, `5%` = -0.268903804112122, `5%` = -0.285856831702031, `5%` = -0.2933097076324, `5%` = -0.224824888562106, `5%` = -0.234533692066961, `5%` = -0.215840640492215)
c(`95%` = 0.203709821019864, `95%` = 0.278261424943046, `95%` = 0.329135185773538, `95%` = 0.0975164848465818, `95%` = 0.11201114117772, `95%` = 0.174468036775523, `95%` = 0.148448833659993, `95%` = 0.122321755852338, `95%` = 0.188406047073467, `95%` = 0.218989193656767, `95%` = 0.224205184960909, `95%` = 0.230966741806046)

6
r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_table_17_Congential.tex
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c("Elapsed Duration", "asinh(Competitors USPDC)", "asinh(Generic Brands)", "asinh(High SDI)", "asinh(High-Medium SDI)", "asinh(Low SDI)", "asinh(Low-Medium SDI)", "asinh(Medium SDI)", "status_ANR", "status_EBI", "status_NYR", "status_Rec")
c(-0.0499660268658585, 0.0197148498472504, -0.0421991751741288, -0.13476563953491, -0.0956058294147287, -0.0442534026160268, -0.0662861597218824, -0.0865335314057803, -0.0550797452225853, -0.00252250078632693, -0.00496344785285301, 0.00806097077518296)
c(`2.5%` = -0.350165746673952, `2.5%` = -0.317756875891385, `2.5%` = -0.518375863151681, `2.5%` = -0.414723657705109, `2.5%` = -0.35057979600666, `2.5%` = -0.306304745881982, `2.5%` = -0.323256686118706, `2.5%` = -0.342841852065351, `2.5%` = -0.345063660691308, `2.5%` = -0.272430725186905, `2.5%` = -0.281159012081347, `2.5%` = -0.262583809560454)
c(`97.5%` = 0.252568610015387, `97.5%` = 0.349107813430285, `97.5%` = 0.439646865645069, `97.5%` = 0.129542894513277, `97.5%` = 0.145184905982789, `97.5%` = 0.197603443786488, `97.5%` = 0.173229891076501, `97.5%` = 0.153760033992607, `97.5%` = 0.236924146027621, `97.5%` = 0.263988376114375, `97.5%` = 0.271017033043418, `97.5%` = 0.279367210952163)
c(`5%` = -0.298500654906565, `5%` = -0.257137304097466, `5%` = -0.434007392588596, `5%` = -0.363849520269935, `5%` = -0.30377336167253, `5%` = -0.259427189054046, `5%` = -0.277378887591161, `5%` = -0.29778671716257, `5%` = -0.294243091294835, `5%` = -0.225155935562034, `5%` = -0.233921233193929, `5%` = -0.215580888141684)
c(`95%` = 0.20049984396355, `95%` = 0.289287710051617, `95%` = 0.353130963560884, `95%` = 0.0859110309743907, `95%` = 0.105692185919512, `95%` = 0.15909647715405, `95%` = 0.13405804651131, `95%` = 0.114598752695363, `95%` = 0.18316007227917, `95%` = 0.218357594565282, `95%` = 0.223576635602984, `95%` = 0.232261579219567)

6
r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_table_18_Symptoms, Signs etc..tex
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c("Elapsed Duration", "asinh(Competitors USPDC)", "asinh(Generic Brands)", "asinh(High SDI)", "asinh(High-Medium SDI)", "asinh(Low SDI)", "asinh(Low-Medium SDI)", "asinh(Medium SDI)", "status_ANR", "status_EBI", "status_NYR", "status_Rec")
c(-0.0486654863146441, 0.0236727211994869, -0.0348374667962509, -0.115895916505032, -0.0800513158310921, -0.023475914516512, -0.0466582461817656, -0.0684715865431922, -0.0537090394695628, -0.00206395314166231, -0.00565678046765618, 0.00840626979762671)
c(`2.5%` = -0.352062830672535, `2.5%` = -0.311752202314792, `2.5%` = -0.516492014350377, `2.5%` = -0.394971716793324, `2.5%` = -0.333434811543346, `2.5%` = -0.286384363759695, `2.5%` = -0.304309764526848, `2.5%` = -0.325218632099741, `2.5%` = -0.34255968704254, `2.5%` = -0.274505781494588, `2.5%` = -0.283135697264375, `2.5%` = -0.260710033426863)
c(`97.5%` = 0.257403541589351, `97.5%` = 0.352180233725197, `97.5%` = 0.445072841208461, `97.5%` = 0.154546638365113, `97.5%` = 0.162204869037247, `97.5%` = 0.222312513558577, `97.5%` = 0.200387773947608, `97.5%` = 0.175953830420771, `97.5%` = 0.240534823211121, `97.5%` = 0.271501986023693, `97.5%` = 0.274036307386916, `97.5%` = 0.276519814652463)
c(`5%` = -0.297336353504652, `5%` = -0.250885342909289, `5%` = -0.430050287469576, `5%` = -0.345255477437627, `5%` = -0.288017242271703, `5%` = -0.239293183426376, `5%` = -0.258607586912473, `5%` = -0.280518378138114, `5%` = -0.291661527567577, `5%` = -0.227689137616937, `5%` = -0.234291225207645, `5%` = -0.21336683632312)
c(`95%` = 0.202755710634796, `95%` = 0.292674820916317, `95%` = 0.360231402349028, `95%` = 0.109134691618903, `95%` = 0.123319005948664, `95%` = 0.182494091485978, `95%` = 0.158914432925447, `95%` = 0.135737605682531, `95%` = 0.187492448246279, `95%` = 0.223599356920508, `95%` = 0.223106259041378, `95%` = 0.230370019187908)

6
r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_table_1_Infections & Parasites.tex
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c("Elapsed Duration", "asinh(Competitors USPDC)", "asinh(Generic Brands)", "asinh(High SDI)", "asinh(High-Medium SDI)", "asinh(Low SDI)", "asinh(Low-Medium SDI)", "asinh(Medium SDI)", "status_ANR", "status_EBI", "status_NYR", "status_Rec")
c(-0.0816317285457012, 0.117270560237318, -0.0431781674057524, -0.322939255980837, -0.0445097363713965, 0.0831185962473774, 0.054469735486804, 0.00439061656478571, -0.111933746601916, -0.00900529540694934, 0.0549642831190738, 0.0234704825434495)
c(`2.5%` = -0.369067201876791, `2.5%` = -0.15743258849236, `2.5%` = -0.392971765289971, `2.5%` = -0.495385786097959, `2.5%` = -0.279907428049715, `2.5%` = -0.126136965980224, `2.5%` = -0.164686552393619, `2.5%` = -0.221766129169443, `2.5%` = -0.397011060181461, `2.5%` = -0.281051472827658, `2.5%` = -0.210012739588992, `2.5%` = -0.233328888684629)
c(`97.5%` = 0.196240544658593, `97.5%` = 0.421101378052922, `97.5%` = 0.304781682760948, `97.5%` = -0.159810477185182, `97.5%` = 0.194502628737617, `97.5%` = 0.305301279386911, `97.5%` = 0.287025096307068, `97.5%` = 0.239372401831851, `97.5%` = 0.156402075952097, `97.5%` = 0.260632526818398, `97.5%` = 0.342573757255475, `97.5%` = 0.282219317765516)
c(`5%` = -0.318536205133514, `5%` = -0.112232006084965, `5%` = -0.335854611911698, `5%` = -0.466181244375375, `5%` = -0.239813977632262, `5%` = -0.0931509430887361, `5%` = -0.13026348611388, `5%` = -0.18443291032113, `5%` = -0.347474604177594, `5%` = -0.232874112114568, `5%` = -0.167107728841027, `5%` = -0.190195428873606)
c(`95%` = 0.149606773044668, `95%` = 0.365441233493439, `95%` = 0.24519101313148, `95%` = -0.184792569485545, `95%` = 0.15345194844924, `95%` = 0.266763338434242, `95%` = 0.246022644974912, `95%` = 0.199837947046417, `95%` = 0.112535221760718, `95%` = 0.213804656879867, `95%` = 0.289724198406613, `95%` = 0.237452575327)

6
r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_table_2_Neoplasms.tex
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c("Elapsed Duration", "asinh(Competitors USPDC)", "asinh(Generic Brands)", "asinh(High SDI)", "asinh(High-Medium SDI)", "asinh(Low SDI)", "asinh(Low-Medium SDI)", "asinh(Medium SDI)", "status_ANR", "status_EBI", "status_NYR", "status_Rec")
c(-0.315526037006588, 0.14948178706425, -0.745438348426778, 0.0393966359152392, -0.0457348007757995, 0.064106280747042, -0.0140917847275379, -0.0473660509007094, -0.242704066590673, -0.00295173909211749, -0.101150807225687, -0.0318651891745962)
c(`2.5%` = -0.642054194627079, `2.5%` = -0.095511016993371, `2.5%` = -1.12441685579914, `2.5%` = -0.194160502625767, `2.5%` = -0.277926578990489, `2.5%` = -0.162654732653132, `2.5%` = -0.246821463107613, `2.5%` = -0.281736932939805, `2.5%` = -0.543673509898535, `2.5%` = -0.271867873121147, `2.5%` = -0.392051441442553, `2.5%` = -0.272496159194591)
c(`97.5%` = -0.0498087194116947, `97.5%` = 0.422731018753381, `97.5%` = -0.396240166662077, `97.5%` = 0.291104489058118, `97.5%` = 0.186629528620266, `97.5%` = 0.30340806969423, `97.5%` = 0.218889942875867, `97.5%` = 0.182247266485996, `97.5%` = 0.0114150292406248, `97.5%` = 0.265171319133342, `97.5%` = 0.156606469826098, `97.5%` = 0.198395249398707)
c(`5%` = -0.580798660746861, `5%` = -0.0582870053082083, `5%` = -1.05984134753029, `5%` = -0.157477096682293, `5%` = -0.237601446017153, `5%` = -0.12601793998578, `5%` = -0.207070786292008, `5%` = -0.242358894302708, `5%` = -0.488379411138948, `5%` = -0.226179910621409, `5%` = -0.337956677281885, `5%` = -0.228981038446028)
c(`95%` = -0.0876108144400803, `95%` = 0.373392398003416, `95%` = -0.449885315105183, `95%` = 0.246925701935553, `95%` = 0.146465971010273, `95%` = 0.261260827352338, `95%` = 0.178110810416068, `95%` = 0.144856415039965, `95%` = -0.0267022930175043, `95%` = 0.218796743068272, `95%` = 0.117095735429865, `95%` = 0.161143364484194)

6
r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_table_3_Blood & Immune system.tex
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c("Elapsed Duration", "asinh(Competitors USPDC)", "asinh(Generic Brands)", "asinh(High SDI)", "asinh(High-Medium SDI)", "asinh(Low SDI)", "asinh(Low-Medium SDI)", "asinh(Medium SDI)", "status_ANR", "status_EBI", "status_NYR", "status_Rec")
c(-0.0505321895701756, 0.0177091826256191, -0.0374509867623648, -0.125776066126894, -0.0902292099576031, -0.036944329702934, -0.0591309519669422, -0.0793088083304893, -0.0543082831893662, -0.00287140656905416, -0.00521081730211856, 0.00652882287222823)
c(`2.5%` = -0.351333711658477, `2.5%` = -0.321412408332877, `2.5%` = -0.519252573751591, `2.5%` = -0.406597880159647, `2.5%` = -0.344254022808293, `2.5%` = -0.297343859139922, `2.5%` = -0.315544785108668, `2.5%` = -0.331413856081863, `2.5%` = -0.3460926221197, `2.5%` = -0.275944463803495, `2.5%` = -0.283907488368931, `2.5%` = -0.26412401679988)
c(`97.5%` = 0.252996678658013, `97.5%` = 0.343837322664501, `97.5%` = 0.438156824615533, `97.5%` = 0.142015362351499, `97.5%` = 0.154012266834766, `97.5%` = 0.207443984276725, `97.5%` = 0.181870296298027, `97.5%` = 0.158756949640892, `97.5%` = 0.23859370882391, `97.5%` = 0.268525663885088, `97.5%` = 0.273419332183028, `97.5%` = 0.279506924359323)
c(`5%` = -0.29803422118825, `5%` = -0.25720973558682, `5%` = -0.43284681982114, `5%` = -0.356137939234695, `5%` = -0.299115224405783, `5%` = -0.252322537371494, `5%` = -0.269748806995291, `5%` = -0.286403912298463, `5%` = -0.295316756003101, `5%` = -0.228422342352796, `5%` = -0.233812893025361, `5%` = -0.217405377189081)
c(`95%` = 0.199805705380915, `95%` = 0.285370104912777, `95%` = 0.353300713490598, `95%` = 0.0994967769999591, `95%` = 0.113509581613765, `95%` = 0.167490802315082, `95%` = 0.14240768840267, `95%` = 0.120221139002118, `95%` = 0.186170625673217, `95%` = 0.2234974899362, `95%` = 0.225018049213407, `95%` = 0.231060746721114)

6
r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_table_4_Endocrine, Nutritional, and Metabolic.tex
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c("Elapsed Duration", "asinh(Competitors USPDC)", "asinh(Generic Brands)", "asinh(High SDI)", "asinh(High-Medium SDI)", "asinh(Low SDI)", "asinh(Low-Medium SDI)", "asinh(Medium SDI)", "status_ANR", "status_EBI", "status_NYR", "status_Rec")
c(-0.0585983827703775, -0.0557624513358899, -0.147540114797909, -0.0438548071229014, -0.0394212013879011, 0.0495779683778335, -0.000453054277997355, -0.0472027857512424, -0.101183732558573, -0.00681543732790809, -0.0244992731496998, 0.0756452851959799)
c(`2.5%` = -0.356636178298712, `2.5%` = -0.406584875693101, `2.5%` = -0.605455988019874, `2.5%` = -0.295750042124458, `2.5%` = -0.273112681569153, `2.5%` = -0.186331398654033, `2.5%` = -0.236827961184312, `2.5%` = -0.283031319821082, `2.5%` = -0.402573667581036, `2.5%` = -0.277987677303119, `2.5%` = -0.301921658351706, `2.5%` = -0.182738362310984)
c(`97.5%` = 0.237496943950342, `97.5%` = 0.249517798279291, `97.5%` = 0.271710367913333, `97.5%` = 0.217593297859276, `97.5%` = 0.19504307737622, `97.5%` = 0.294769414462677, `97.5%` = 0.239952858853215, `97.5%` = 0.184326204801509, `97.5%` = 0.180340815442861, `97.5%` = 0.26497995981819, `97.5%` = 0.248170504583118, `97.5%` = 0.359198759844572)
c(`5%` = -0.304028644400323, `5%` = -0.339933290032092, `5%` = -0.520616290327113, `5%` = -0.254196188061864, `5%` = -0.232559443411886, `5%` = -0.147666860670196, `5%` = -0.197308104756834, `5%` = -0.242030280687931, `5%` = -0.347024621453625, `5%` = -0.230488468962024, `5%` = -0.252962421653837, `5%` = -0.139947007794807)
c(`95%` = 0.187816936293634, `95%` = 0.200063580704579, `95%` = 0.20347262465658, `95%` = 0.173544980297132, `95%` = 0.155038635197841, `95%` = 0.25235933990905, `95%` = 0.196860730183825, `95%` = 0.146126493852613, `95%` = 0.133329444707804, `95%` = 0.216270931014924, `95%` = 0.200572258529045, `95%` = 0.306122759153545)

6
r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_table_5_Mental & Behavioral.tex
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c("Elapsed Duration", "asinh(Competitors USPDC)", "asinh(Generic Brands)", "asinh(High SDI)", "asinh(High-Medium SDI)", "asinh(Low SDI)", "asinh(Low-Medium SDI)", "asinh(Medium SDI)", "status_ANR", "status_EBI", "status_NYR", "status_Rec")
c(-0.0774004055701482, 0.0202098736769506, -0.148857275259831, -0.034756624929762, -0.0209198662802634, 0.0737245631655913, 0.0355843232922172, -0.0257770506361907, -0.101358093383466, -0.00444905498743218, 0.000222710855606745, 0.0410008650026277)
c(`2.5%` = -0.384222770661754, `2.5%` = -0.317021340734379, `2.5%` = -0.654106115127152, `2.5%` = -0.28447613031784, `2.5%` = -0.251665449535869, `2.5%` = -0.161795305781745, `2.5%` = -0.197888724987928, `2.5%` = -0.256373204834308, `2.5%` = -0.398857333530158, `2.5%` = -0.273927630545993, `2.5%` = -0.27410283519672, `2.5%` = -0.219982381931615)
c(`97.5%` = 0.218570279154564, `97.5%` = 0.350403071909054, `97.5%` = 0.307321465786219, `97.5%` = 0.226059193399154, `97.5%` = 0.218536405219445, `97.5%` = 0.325200306839703, `97.5%` = 0.279312654411686, `97.5%` = 0.207355980521146, `97.5%` = 0.182559370226462, `97.5%` = 0.264287404395679, `97.5%` = 0.274132679138622, `97.5%` = 0.3146382824935)
c(`5%` = -0.328832973524529, `5%` = -0.255886271822305, `5%` = -0.55799947646926, `5%` = -0.243764242401936, `5%` = -0.214018963569841, `5%` = -0.12427108412664, `5%` = -0.159713441998239, `5%` = -0.217062488812283, `5%` = -0.344628024841975, `5%` = -0.227858560023144, `5%` = -0.226762629571076, `5%` = -0.175242262948575)
c(`95%` = 0.168803960348435, `95%` = 0.290718931165702, `95%` = 0.231568717349548, `95%` = 0.181638021717836, `95%` = 0.175300930108115, `95%` = 0.280768005851167, `95%` = 0.236582791839493, `95%` = 0.16844496482049, `95%` = 0.132004530075709, `95%` = 0.220133939553423, `95%` = 0.226948329518661, `95%` = 0.264306220644107)

6
r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_table_6_Nervous System.tex
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c("Elapsed Duration", "asinh(Competitors USPDC)", "asinh(Generic Brands)", "asinh(High SDI)", "asinh(High-Medium SDI)", "asinh(Low SDI)", "asinh(Low-Medium SDI)", "asinh(Medium SDI)", "status_ANR", "status_EBI", "status_NYR", "status_Rec")
c(-0.0578472700681722, 0.363548114620147, 0.514928808041493, -0.179789393683086, -0.0908978444606774, 0.0485959237871361, -0.00145174053575294, -0.0416302406262986, -0.0324576251191776, -0.00517241124667845, 0.00328213613579688, -0.0230137010884716)
c(`2.5%` = -0.359167198818045, `2.5%` = 0.02041143019079, `2.5%` = 0.0216592886163218, `2.5%` = -0.430812731714158, `2.5%` = -0.333639714164675, `2.5%` = -0.181367177838171, `2.5%` = -0.231956837333007, `2.5%` = -0.27766810096831, `2.5%` = -0.315411836398564, `2.5%` = -0.275526164264352, `2.5%` = -0.267503827888536, `2.5%` = -0.293189864865698)
c(`97.5%` = 0.238629751570793, `97.5%` = 0.85608235301133, `97.5%` = 1.17318649525454, `97.5%` = 0.0570528188192439, `97.5%` = 0.140764053180901, `97.5%` = 0.287579065151981, `97.5%` = 0.231839325930911, `97.5%` = 0.196383580001578, `97.5%` = 0.259944925352525, `97.5%` = 0.267696135908434, `97.5%` = 0.279853103150131, `97.5%` = 0.238015436387141)
c(`5%` = -0.303098555574414, `5%` = 0.0634526303087552, `5%` = 0.0851401986791414, `5%` = -0.386505748941182, `5%` = -0.290084333457996, `5%` = -0.143728029163889, `5%` = -0.193940136889492, `5%` = -0.237273417600727, `5%` = -0.266550224886603, `5%` = -0.228284369418676, `5%` = -0.221951335704386, `5%` = -0.246615923137647)
c(`95%` = 0.186931912303368, `95%` = 0.753336516739962, `95%` = 1.03873143227049, `95%` = 0.0190438837776769, `95%` = 0.102564973004199, `95%` = 0.243855515697918, `95%` = 0.193187180905901, `95%` = 0.155692997386599, `95%` = 0.205343484029368, `95%` = 0.220217569663176, `95%` = 0.231832837318039, `95%` = 0.195044859439803)

6
r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_table_7_Eye and Adnexa.tex
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c("Elapsed Duration", "asinh(Competitors USPDC)", "asinh(Generic Brands)", "asinh(High SDI)", "asinh(High-Medium SDI)", "asinh(Low SDI)", "asinh(Low-Medium SDI)", "asinh(Medium SDI)", "status_ANR", "status_EBI", "status_NYR", "status_Rec")
c(-0.0507912436647474, 0.0105781672515, -0.0572148794918185, -0.161324485000642, -0.11718865552377, -0.0580549952623519, -0.0821974616540054, -0.105025588883719, -0.055296341966815, -0.00219896995482389, -0.00556304034621756, 0.00596509106734287)
c(`2.5%` = -0.356770047469594, `2.5%` = -0.329787608837027, `2.5%` = -0.529409792355877, `2.5%` = -0.44209572260938, `2.5%` = -0.373553837099913, `2.5%` = -0.325433458075037, `2.5%` = -0.344635367302557, `2.5%` = -0.361334615741075, `2.5%` = -0.348019495991554, `2.5%` = -0.272173859797933, `2.5%` = -0.284249194010694, `2.5%` = -0.264838971088612)
c(`97.5%` = 0.255694095814266, `97.5%` = 0.33508908205543, `97.5%` = 0.408657439741233, `97.5%` = 0.101657962161228, `97.5%` = 0.11864634098921, `97.5%` = 0.186079165001043, `97.5%` = 0.157815589258783, `97.5%` = 0.131395254082946, `97.5%` = 0.235175513197134, `97.5%` = 0.268729163024419, `97.5%` = 0.273523419916202, `97.5%` = 0.274146522142649)
c(`5%` = -0.301053900765771, `5%` = -0.263620928816618, `5%` = -0.445215274649135, `5%` = -0.391137998579351, `5%` = -0.328258365252273, `5%` = -0.276463334286846, `5%` = -0.297022738992198, `5%` = -0.315922917337871, `5%` = -0.297136938616546, `5%` = -0.224813386072165, `5%` = -0.235070814746299, `5%` = -0.216692707544591)
c(`95%` = 0.200369696005793, `95%` = 0.27722780854366, `95%` = 0.328590570058774, `95%` = 0.0578686050827066, `95%` = 0.0817009261712685, `95%` = 0.147963763976249, `95%` = 0.119977584917821, `95%` = 0.0949818456263718, `95%` = 0.186846293810823, `95%` = 0.221009192368411, `95%` = 0.223765301903729, `95%` = 0.226800701259279)

6
r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_table_9_Circulatory.tex
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c("Elapsed Duration", "asinh(Competitors USPDC)", "asinh(Generic Brands)", "asinh(High SDI)", "asinh(High-Medium SDI)", "asinh(Low SDI)", "asinh(Low-Medium SDI)", "asinh(Medium SDI)", "status_ANR", "status_EBI", "status_NYR", "status_Rec")
c(-0.0561236542390722, -0.0174830982131017, -0.114801759701441, -0.0489532404314588, -0.00995833322809683, 0.0764993233097176, 0.0602798840227049, 0.0304789175922322, -0.0491179148659016, -0.0020229135961933, 0.0102255609545677, -0.0121276026193075)
c(`2.5%` = -0.358192918691032, `2.5%` = -0.360033194654434, `2.5%` = -0.588446720987112, `2.5%` = -0.297868077807792, `2.5%` = -0.240899712415657, `2.5%` = -0.162465078674946, `2.5%` = -0.17414438609744, `2.5%` = -0.201017363333911, `2.5%` = -0.340513734259116, `2.5%` = -0.272921161056328, `2.5%` = -0.264718520415862, `2.5%` = -0.284982548735974)
c(`97.5%` = 0.245199739323497, `97.5%` = 0.295622079182328, `97.5%` = 0.326892147498506, `97.5%` = 0.210710410280741, `97.5%` = 0.229119750480018, `97.5%` = 0.332077723102122, `97.5%` = 0.312602029054938, `97.5%` = 0.277230334088767, `97.5%` = 0.247596369898235, `97.5%` = 0.269687555821123, `97.5%` = 0.291348978934726, `97.5%` = 0.25166727687992)
c(`5%` = -0.304624731177692, `5%` = -0.294903363605129, `5%` = -0.498779889626974, `5%` = -0.256277233535303, `5%` = -0.202637633223219, `5%` = -0.125070759586198, `5%` = -0.13517180498734, `5%` = -0.163740873087956, `5%` = -0.29096401042673, `5%` = -0.225192615705392, `5%` = -0.217746444191205, `5%` = -0.235700053008877)
c(`95%` = 0.193775727317953, `95%` = 0.243010994205006, `95%` = 0.251902327224973, `95%` = 0.165019140778464, `95%` = 0.188705917246998, `95%` = 0.287176225295342, `95%` = 0.267926957380469, `95%` = 0.233147083774771, `95%` = 0.194446427538144, `95%` = 0.222074828728455, `95%` = 0.240749705129402, `95%` = 0.206639994505824)

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33
r-analysis/output/EffectsOfEnrollmentDelay/distdiff_5percentile_table.tex
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@ -0,0 +1,33 @@
% latex table generated in R 4.4.2 by xtable 1.8-4 package
% Sun Feb 2 01:37:17 2025
\begin{table}[ht]
\centering
\begin{tabular}{r}
\hline
Value \\
\hline
-0.226 \\
-0.013 \\
-0.005 \\
-0.001 \\
-0.000 \\
0.000 \\
0.000 \\
0.002 \\
0.005 \\
0.008 \\
0.011 \\
0.014 \\
0.018 \\
0.022 \\
0.027 \\
0.032 \\
0.039 \\
0.047 \\
0.057 \\
0.073 \\
0.272 \\
\hline
\end{tabular}
\end{table}

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