Update 'Concept Paper 2021 09 03'

Concept-Paper-2021-09-03.md

@@ -32,31 +32,35 @@ The objective of this research project is to estimate
 ## Potential Topics
 Some areas of interest.
 
-    1. Estimate Probability of Success of moving between Phases. 
+1. Estimate Probability of Success of moving between Phases. 
     1. Depends on either FDA data or use of an ML matching process (Hard to do right).
-        2. Follow E.K.’s approach of splitting Technical vs Scientific failures to progress.
-        3. Denormalize the “process” of moving between phase 1 → phase 2 → phase 3 that is talked about and actually represent the real paths taken.
+2. Follow E.K.’s approach of splitting Technical vs Scientific failures to progress.
+3. Denormalize the “process” of moving between phase 1 → phase 2 → phase 3 that is talked about and actually represent the real paths taken.
     1. As selection from a set of paths. 
     2. Seeking approval for a new indication may not require initial phase trials. This “extraneous” information may be hard to capture/model/control for.
     3. Denormalizing this path may work as a proxy for changes in regulatory strictness?
         1. Probably not, because choice of path is probably dependent on already known information on safety.
-    2. Estimate probability of success for approval based on trial data? How do biologics differ from standard small-molecule drugs?
-        1. Haven’t put much thought in to this, not sure what the value proposition would be.
-    3. Estimate entry rates/Probability Of Success of Biologics/biosimilars 
-        1. There appear to be lots of competing biosimilars in insulin.
-    4. Develop “diagnostic tests” that will allow effects of policy to be forecast partway through implementation.
-        1. Something such as measures of phase 1 trials and phase 2 trials to predict phase 3 & approval rates.
-Possible Hypotheses
+2. Estimate probability of success for approval based on trial data? How do biologics differ from standard small-molecule drugs?
+1. Haven’t put much thought in to this, not sure what the value proposition would be.
+3. Estimate entry rates/Probability Of Success of Biologics/biosimilars 
+1. There appear to be lots of competing biosimilars in insulin.
+4. Develop “diagnostic tests” that will allow effects of policy to be forecast partway through implementation.
+1. Something such as measures of phase 1 trials and phase 2 trials to predict phase 3 & approval rates.
+
+
+## Possible Hypotheses
 Do Probabilities of Success differ significantly when estimated on normalized or denormalized paths?
-    • With the FDA data, this will be uniquely answerable. Not sure though what those implications of interest are though.
-    • The value proposition here is that it might “measure” the risk reducing effect of that extra knowledge from previous studies, evidence, etc. I wonder if this could act as an instrument on the effect of unknown risk when starting a new compound? 
+
+• With the FDA data, this will be uniquely answerable. Not sure though what those implications of interest are though.
+• The value proposition here is that it might “measure” the risk reducing effect of that extra knowledge from previous studies, evidence, etc. I wonder if this could act as an instrument on the effect of unknown risk when starting a new compound? 
     ◦ The underlying assumption is that the path depends heavily on whether the compound has been studied previously or not. That may not be true.
     ◦ Provides the “risk value” of prior phases. Maybe allows modeling as net future value?
 Do Probabilities of Success depend on reimbursment rate?
-    • The thought here is that if reimbursement rates increase, more R&D is spent on “long-shot” drugs that are likely to develop new fields of research.
-    • Requires some sort of reimbursement/pricing data. Not necessarily available, and would require a natural experiment.
+• The thought here is that if reimbursement rates increase, more R&D is spent on “long-shot” drugs that are likely to develop new fields of research.
+• Requires some sort of reimbursement/pricing data. Not necessarily available, and would require a natural experiment.
 
 Intermediate goals
+
     1. Find literature that describes each difficult section of the R&D to successful launch process, focusing on the second section.
 
 Policy questions/ topics 
@@ -65,14 +69,15 @@ Precision medicines & trials
 
 
 Insurers paying for participants in a clinical trial. Medicare pays for many, but what effect does this have on recruitment? Possible variation across drugs (legislatively), as identification.
-    • Endpoint doesn’t matter for recruiting
-    • Our data may show the “rate” at which people join trials.
-        ◦ Does medicare coverage for the trial speed that up?
+- Endpoint doesn’t matter for recruiting
+- Our data may show the “rate” at which people join trials.
+    - Does medicare coverage for the trial speed that up?
 
 Use of surragate endpoints. Risk sharing between insurer and developer.
-    • Cancer: survival rates vs what happened to the cancer
-    • Reduces length of trial, but requires phase 4 trials
-    • Effects on recruiting/timing
+
+- Cancer: survival rates vs what happened to the cancer
+- Reduces length of trial, but requires phase 4 trials
+- Effects on recruiting/timing
 
 Possible Tasks
 Lit review