Adding sql that was sitting on home computer
will king
2 years ago
parent
9aaf007791
commit
dfb041d12b
@ -0,0 +1,308 @@
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select * from formatted_data_with_planned_enrollment fdwpe
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;
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select * from formatted_data_mat fdm
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;
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select count(distinct condition ) from formatted_data_mat fdm
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select nct_id, fdm.current_status , count(*)
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from formatted_data_mat fdm
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group by nct_id , fdm.current_status
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order by nct_id
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;
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select * from formatted_data_mat fdm ;
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-- group with trial split
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with cte as (
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select nct_id
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from formatted_data_mat fdm
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group by nct_id
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having count(distinct current_status) > 1
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order by nct_id
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)
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select
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fdm.nct_id
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, current_status
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, earliest_date_observed
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, elapsed_duration
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, n_brands
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, category_id
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, h_sdi_val
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, h_sdi_u95
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, h_sdi_l95
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, hm_sdi_val
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, hm_sdi_u95
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, hm_sdi_l95
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, m_sdi_val
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, m_sdi_u95
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, m_sdi_l95
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, lm_sdi_val
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, lm_sdi_u95
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, lm_sdi_l95
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, l_sdi_val
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, l_sdi_u95
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, l_sdi_l95
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from formatted_data_mat fdm
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join cte on cte.nct_id = fdm.nct_id
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group by
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fdm.nct_id
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, current_status
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, earliest_date_observed
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, elapsed_duration
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, n_brands
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, category_id
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, h_sdi_val
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, h_sdi_u95
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, h_sdi_l95
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, hm_sdi_val
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, hm_sdi_u95
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, hm_sdi_l95
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, m_sdi_val
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, m_sdi_u95
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, m_sdi_l95
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, lm_sdi_val
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, lm_sdi_u95
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, lm_sdi_l95
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, l_sdi_val
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, l_sdi_u95
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, l_sdi_l95
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order by nct_id , earliest_date_observed
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;
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select count(distinct category_id ) from
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select distinct category_id from formatted_data_mat fdm
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;
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-- group with trial split
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with cte as (
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select nct_id
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from formatted_data_mat fdm
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group by nct_id
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having count(distinct current_status) > 1
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order by nct_id
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)
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select
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fdm.nct_id
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, current_status
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, earliest_date_observed
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, elapsed_duration
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, n_brands
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, category_id
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, h_sdi_val
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, h_sdi_u95
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, h_sdi_l95
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, hm_sdi_val
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, hm_sdi_u95
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, hm_sdi_l95
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, m_sdi_val
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, m_sdi_u95
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, m_sdi_l95
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, lm_sdi_val
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, lm_sdi_u95
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, lm_sdi_l95
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, l_sdi_val
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, l_sdi_u95
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, l_sdi_l95
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from formatted_data_mat fdm
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join cte on cte.nct_id = fdm.nct_id
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group by
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fdm.nct_id
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, current_status
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, earliest_date_observed
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, elapsed_duration
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, n_brands
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, category_id
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, h_sdi_val
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, h_sdi_u95
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, h_sdi_l95
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, hm_sdi_val
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, hm_sdi_u95
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, hm_sdi_l95
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, m_sdi_val
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, m_sdi_u95
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, m_sdi_l95
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, lm_sdi_val
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, lm_sdi_u95
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, lm_sdi_l95
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, l_sdi_val
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, l_sdi_u95
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, l_sdi_l95
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order by nct_id , earliest_date_observed
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; --TODO: join to usp dc dataset
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WITH trialncts AS (
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SELECT DISTINCT ts.nct_id
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FROM history.trial_snapshots ts
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), nct_to_cui AS (
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SELECT bi.nct_id,
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bi.downcase_mesh_term,
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rr.tty2,
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rr.rxcui2 AS approved_drug_rxcui,
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count(*) AS count
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FROM ctgov.browse_interventions bi
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LEFT JOIN rxnorm_migrated.rxnorm_props rp ON bi.downcase_mesh_term::text = rp.propvalue1::text
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LEFT JOIN rxnorm_migrated.rxnorm_relations rr ON rr.rxcui1 = rp.rxcui
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WHERE (bi.nct_id::text IN ( SELECT trialncts.nct_id
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FROM trialncts)) AND bi.mesh_type::text = 'mesh-list'::text AND rp.propname::text = 'Active_ingredient_name'::text AND (rr.tty2::text = ANY (ARRAY['BPCK'::text, 'SCD'::text, 'SBD'::text, 'GPCK'::text]))
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GROUP BY bi.nct_id, bi.downcase_mesh_term, rr.tty2, rr.rxcui2
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)
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SELECT nct_to_cui.nct_id,
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ud."USP Category",
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ud."USP Class"
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FROM nct_to_cui
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JOIN "Formularies".usp_dc ud ON ud.rxcui::bpchar = nct_to_cui.approved_drug_rxcui
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GROUP BY nct_to_cui.nct_id, ud."USP Category", ud."USP Class"
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ORDER BY nct_to_cui.nct_id;
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CREATE MATERIALIZED VIEW "Formularies".nct_to_brands_through_uspdc
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AS
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WITH trialncts AS (
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SELECT DISTINCT ts.nct_id
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FROM history.trial_snapshots ts
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)
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SELECT
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bi.nct_id,
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count( distinct rr2.rxcui2 ) as brand_name_count
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FROM ctgov.browse_interventions bi
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LEFT JOIN rxnorm_migrated.rxnorm_props rp ON bi.downcase_mesh_term::text = rp.propvalue1::text --match mesh terms to rxcui
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LEFT JOIN rxnorm_migrated.rxnorm_relations rr ON rr.rxcui1 = rp.rxcui -- match rxcui to relations between rxcuis
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LEFT JOIN rxnorm_migrated.rxnorm_relations rr2 ON rr.rxcui2 = rr2.rxcui1 -- match rxcui to relations between rxcuis
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WHERE
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(bi.nct_id::text IN (SELECT trialncts.nct_id FROM trialncts)) --check the nct_id is in our list
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AND
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bi.mesh_type::text = 'mesh-list'::text --we are only looking at mesh "list" rxcuis
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AND rp.propname::text = 'Active_ingredient_name'::text --and we only care about active ingredients linked to \/\/\/\/\/
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AND (rr.tty2::text = ANY (ARRAY['BPCK'::text, 'SCD'::text, 'SBD'::text, 'GPCK'::text])) --and we are linking from active ingredients ^^^^ to branded packs
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AND (rr2.tty2::text = 'BN') --and from branded packs back to brand names
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GROUP BY bi.nct_id --remove duplicates
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;
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/*
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*
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*/
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select
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fdqpe.nct_id
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--,fdqpe.start_date
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--,fdqpe.current_enrollment
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--,fdqpe.enrollment_category
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,fdqpe.current_status
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,fdqpe.earliest_date_observed
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,fdqpe.elapsed_duration
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,fdqpe.n_brands as identical_brands
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,ntbtu.brand_name_count
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,fdqpe.category_id
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,fdqpe.final_status
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,fdqpe.h_sdi_val
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--,fdqpe.h_sdi_u95
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--,fdqpe.h_sdi_l95
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,fdqpe.hm_sdi_val
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--,fdqpe.hm_sdi_u95
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--,fdqpe.hm_sdi_l95
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,fdqpe.m_sdi_val
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--,fdqpe.m_sdi_u95
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--,fdqpe.m_sdi_l95
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,fdqpe.lm_sdi_val
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--,fdqpe.lm_sdi_u95
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--,fdqpe.lm_sdi_l95
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,fdqpe.l_sdi_val
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--,fdqpe.l_sdi_u95
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--,fdqpe.l_sdi_l95
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from formatted_data_mat fdqpe
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join "Formularies".nct_to_brands_through_uspdc ntbtu
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on fdqpe.nct_id = ntbtu.nct_id
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;
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--example of multiple reopenings
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select *
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from formatted_data_mat fdm
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where nct_id = 'NCT01239797'
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--attempt to automatically find transition periods
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with cte1 as (
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select nct_id, min(earliest_date_observed) over (partition by nct_id) as earliest_closed_enrollment
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from formatted_data_mat fdm
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where current_status = 'Active, not recruiting'
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), cte2 as (
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select nct_id, max(earliest_date_observed) over (partition by nct_id) latest_open_enrollment
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from formatted_data_mat fdm
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where current_status != 'Active, not recruiting'
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)
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select
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cte1.nct_id
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,cte1.earliest_closed_enrollment
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,cte2.latest_open_enrollment
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,cte1.earliest_closed_enrollment - cte2.latest_open_enrollment
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from cte1
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join cte2 on cte1.nct_id = cte2.nct_id
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/*group by
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cte1.nct_id
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,cte1.earliest_closed_enrollment
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,cte2.latest_open_enrollment
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*/
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/* So ocassionally a study reopens enrollment.
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* If that didn't happen, then I could just find the first enrollment matching X and/or last enrollment matching Y
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* to get the transitions
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* Instead I need to create shifts of statuses between snapshots, and then remove all of those that did not change.
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*
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* Better yet, just get the last shift to ANR.
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* */
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/* Take each entry and get the status from a lagged snapshot
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* Then select each snapshot moving from previous_state to ANR
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* and filter out everything except the last one.
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* */
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with cte as (
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select
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nct_id
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,lag(current_status, 1) over (partition by nct_id order by earliest_date_observed) as previous_status
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,current_status
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,earliest_date_observed as date_current
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from formatted_data_mat fdm
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), cte2 as (
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select
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nct_id
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,previous_status
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,current_status
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,max(date_current) as date_current_max
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from cte
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where
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previous_status != current_status
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and
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current_status = 'Active, not recruiting'
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group by
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nct_id
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,previous_status
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,current_status
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,date_current
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)
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select *
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from formatted_data_mat fdm
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join cte2
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on cte2.nct_id = fdm.nct_id
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and cte2.date_current_max = fdm.earliest_date_observed
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; --join back into
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