Adding unblind_for_evidence_synthesis field to `cm_diagnostics_summ…

…ary` table. The `cm_diagnostics_summary` table now also contains negative controls. Fixes #153 and #154.

NEWS.md

@@ -22,6 +22,10 @@ Changes:
 7. Improve speed of covariate balance computation.
 
 8. Adding one-sided (calibrated) p-values to results summary and results model.
+
+9. Adding `unblind_for_evidence_synthesis` field to `cm_diagnostics_summary` table.
+
+10. The `cm_diagnostics_summary` table now also contains negative controls. 
 
 
 Bugfixes:

R/Export.R

@@ -58,7 +58,7 @@ createCmDiagnosticThresholds <- function(mdrrThreshold = 10,
   checkmate::reportAssertions(collection = errorMessages)
   if (!is.null(attritionFractionThreshold)) {
     warning("The attritionFractionThreshold argument is deprecated and will be ignored. ",
-    "See generalizabilitySdmThreshold instead.")
+            "See generalizabilitySdmThreshold instead.")
   }
   thresholds <- list()
   for (name in names(formals(createCmDiagnosticThresholds))) {
@@ -594,8 +594,8 @@ exportCmInteractionResults <- function(outputFolder,
 }
 
 exportLikelihoodProfiles <- function(outputFolder,
-                                    exportFolder,
-                                    databaseId) {
+                                     exportFolder,
+                                     databaseId) {
   message("- likelihood_profile table")
   reference <- getFileReference(outputFolder)
   fileName <- file.path(exportFolder, "cm_likelihood_profile.csv")
@@ -764,7 +764,7 @@ tidyBalance <- function(balance, minCellCount) {
                round(.data$targetStdDiff, 3) == 0 &
                round(.data$comparatorStdDiff, 3) == 0 &
                round(.data$targetComparatorStdDiff, 3) == 0)
-           ) %>%
+    ) %>%
     enforceMinCellValue("targetMeanBefore",
                         minCellCount / inferredTargetBeforeSize,
                         silent = TRUE
@@ -1200,7 +1200,6 @@ exportDiagnosticsSummary <- function(outputFolder,
     pull()
   equipoise <- bind_rows(lapply(sharedPsFiles, getEquipoise))
   results <- reference %>%
-    filter(.data$outcomeOfInterest) %>%
     inner_join(
       resultsSummary,
       by = join_by("analysisId", "targetId", "comparatorId", "outcomeId")) %>%
@@ -1227,6 +1226,7 @@ exportDiagnosticsSummary <- function(outputFolder,
 
   # Apply diagnostics thresholds:
   results <- results %>%
+    mutate(databaseId = !!databaseId) %>%
     mutate(balanceDiagnostic = case_when(
       is.na(.data$maxSdm) ~ "NOT EVALUATED",
       .data$maxSdm < cmDiagnosticThresholds$sdmThreshold ~ "PASS",
@@ -1262,8 +1262,12 @@ exportDiagnosticsSummary <- function(outputFolder,
                               .data$easeDiagnostic != "FAIL" &
                               .data$equipoiseDiagnostic != "FAIL" &
                               .data$balanceDiagnostic != "FAIL" &
-                              .data$sharedBalanceDiagnostic != "FAIL", 1, 0),
-           databaseId = !!databaseId)
+                              .data$sharedBalanceDiagnostic != "FAIL", 1, 0)) %>%
+    mutate(unblindForEvidenceSynthesis = ifelse(.data$generalizabilityDiagnostic != "FAIL" &
+                                                  .data$easeDiagnostic != "FAIL" &
+                                                  .data$equipoiseDiagnostic != "FAIL" &
+                                                  .data$balanceDiagnostic != "FAIL" &
+                                                  .data$sharedBalanceDiagnostic != "FAIL", 1, 0))
 
   # Add deprecated fields:
   results <- results %>%

inst/csv/resultsDataModelSpecification.csv

@@ -131,6 +131,7 @@ cm_diagnostics_summary,attrition_diagnostic,varchar(20),No,No,No,Yes,DEPRECATED:
 cm_diagnostics_summary,generalizability_diagnostic,varchar(20),Yes,No,No,No,Pass / warning / fail classification of the generalizability diagnostic.
 cm_diagnostics_summary,ease_diagnostic,varchar(20),Yes,No,No,No,Pass / warning / fail classification of the EASE diagnostic.
 cm_diagnostics_summary,unblind,int,Yes,No,No,No,"Is unblinding the result recommended? (1 = yes, 0 = no)"
+cm_diagnostics_summary,unblind_for_evidence_synthesis,int,Yes,No,No,No,"Is unblinding the result for inclusion in evidence synthesis recommended? This ignores the MDRR diagnostic. (1 = yes, 0 = no)"
 cm_target_comparator_outcome,outcome_id,int,Yes,Yes,No,No,The identifier for the outcome cohort.
 cm_target_comparator_outcome,outcome_of_interest,int,Yes,No,No,No,"Is the outcome of interest (1 = yes, 0 = no)"
 cm_target_comparator_outcome,true_effect_size,float,No,No,No,No,The true effect size for the target-comparator-outcome. For negatitive controls this equals 1.

inst/sql/sql_server/migrations/Migration_1-v5_2_0.sql

@@ -14,8 +14,9 @@
 --    Add:
 --   - generalizability_max_sdm
 --   - generalizability_diagnostic
+--   - unblind_for_evidence_synthesis
 ALTER TABLE @database_schema.@table_prefixcm_result ADD target_estimator VARCHAR(3), one_sided_p FLOAT, calibrated_one_sided_p FLOAT;
 ALTER TABLE @database_schema.@table_prefixcm_interaction_result ADD target_estimator VARCHAR(3);
 ALTER TABLE @database_schema.@table_prefixcm_covariate_balance ADD mean_before FLOAT, mean_after FLOAT, target_std_diff FLOAT, comparator_std_diff FLOAT, target_comparator_std_diff FLOAT;
 ALTER TABLE @database_schema.@table_prefixcm_shared_covariate_balance ADD mean_before FLOAT, mean_after FLOAT, target_std_diff FLOAT, comparator_std_diff FLOAT, target_comparator_std_diff FLOAT;
-ALTER TABLE @database_schema.@table_prefixcm_diagnostics_summary ADD generalizability_max_sdm FLOAT, generalizability_diagnostic VARCHAR(20);
+ALTER TABLE @database_schema.@table_prefixcm_diagnostics_summary ADD generalizability_max_sdm FLOAT, generalizability_diagnostic VARCHAR(20), unblind_for_evidence_synthesis INT;

tests/testthat/test-eunomia.R

@@ -218,6 +218,12 @@ test_that("Multiple analyses", {
   targetComparatorOutcome <- readr::read_csv(file.path(outputFolder, "export", "cm_target_comparator_outcome.csv"), show_col_types = FALSE)
   expect_true(is.numeric(targetComparatorOutcome$outcome_of_interest))
 
+  # Verify negative controls have diagnostics:
+  ncDiagnostics <- diagnosticsSummary %>%
+    inner_join(targetComparatorOutcome) %>%
+    filter(.data$outcome_of_interest == 0)
+  expect_gt(nrow(ncDiagnostics), 0)
+
   cohorts <- data.frame(
     cohortId = c(1, 2, 998, 999, 3, 4),
     cohortName = c("e1", "e2", "e3", "e4", "o1", "o2")