Beyond intention to treat: What is the right question?
Bibliographic record
Abstract
BACKGROUND: Most methodologists recommend intention-to-treat (ITT) analysis in order to minimize bias. Although ITT analysis provides an unbiased estimate for the effect of treatment assignment on the outcome, the estimate is biased for the actual effect of receiving treatment (active treatment) compared to some comparison group (control). Other common analyses include measuring effects in (1) participants who follow their assigned treatment (Per Protocol), (2) participants according to treatment received (As Treated), and (3) those who would comply with recommended treatment (Complier Average Causal Effect (CACE) as estimated by Principal Stratification or Instrumental Variable Analyses). As each of these analyses compares different study subpopulations, they address different research questions. PURPOSE: For each type of analysis, we review and explain (1) the terminology being used, (2) the main underlying concepts, (3) the questions that are answered and whether the method provides valid causal estimates, and (4) the situations when the analysis should be conducted. METHODS: We first review the major concepts in relation to four nuances of the clinical question, 'Does treatment improve health?' After reviewing these concepts, we compare the results of the different analyses using data from two published randomized controlled trials (RCTs). Each analysis has particular underlying assumptions and all require dichotomizing adherence into Yes or No. We apply sensitivity analyses so that intermediate adherence is considered (1) as adherence and (2) as non-adherence. RESULTS: The ITT approach provides an unbiased estimate for how active treatment will improve (1) health in the population if a policy or program is enacted or (2) health of patients if a clinician changes treatment practice. The CACE approach generally provides an unbiased estimate of the effect of active treatment on health of patients who would follow the clinician's advice to take active treatment. Unfortunately, there is no current analysis for clinicians and patients who want to know whether active treatment will improve the patient's health if taken, which is different from the effect in patients who would follow the clinician's advice to take active treatment. Sensitivity analysis for the CACE using two published data sets suggests that the underlying assumptions appeared to be violated. LIMITATIONS: There are several methods within each analytical approach we describe. Our analyses are based on a subset of these approaches. CONCLUSIONS: Although adherence-based analyses may provide meaningful information, the analytical method should match the clinical question, and investigators should clearly outline why they believe assumptions hold and should provide empirical tests of the assumptions where possible.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.631 | 0.821 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.008 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.004 | 0.033 |
| Scholarly communication | 0.015 | 0.021 |
| Open science | 0.009 | 0.004 |
| Research integrity | 0.022 | 0.036 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".