Causal Inference Methods for Secondary Analysis of Randomized Screening Trials
Bibliographic record
Abstract
The primary objective of randomized trials is usually pre-specified in the protocol and typically adheres to the intention-to-treat (ITT) principle, allowing for simple comparisons between intervention arms. However, trials often collect high-quality data that can be utilized for secondary analysis. This thesis is focused on randomized screening trials where asymptomatic individuals are assigned to receive a series of screening examinations or standard care and subsequently followed for a pre-specified period. While the primary analysis in randomized screening trials estimates the effect of intention-to-screen (ITS) on cancer-specific mortality, among the screening-detectable subgroup we might also be interested in the causal effect of early (screening-induced) treatments compared to delayed treatments in the absence of screening. The first objective of this thesis is to develop estimators for the effect of early versus delayed cancer treatments among the screening-detectable subgroup. Using the framework of Rubin’s causal model, we consider two alternative measures, proportional and absolute mortality reductions in the subgroup. We propose estimators for these using the instrumental variable principle as well as outline their identifying assumptions. These estimators generalize existing IV estimators to allow for time-dependent exposure/latent subgroup. The existing models for screening trials, primarily proposed for planning future trials with adequate power for the ITS analysis, are unnecessarily complex for defining and estimating the causal effect of screening-induced early treatments. To address this, we formulate a simplified structural multi-state model, in which the causal effect of early treatments is summarized using a time-invariant, cause-specific structural hazard ratio. For estimating the hazard ratio, we propose two methods, based on an estimating equation and a likelihood expression. Finally, with the aim to generalize the IV methods outside of the trial setting and to allow for covariate-dependent censoring, we introduce covariate adjustment into the estimation. We consider both parametric and non-parametric covariate adjustment using hazard regression models and machine learning algorithms. For the latter, we propose a sub-sampling approach to avoid large-counting process datasets. The performance of all the proposed estimators in this thesis are illustrated through simulation studies and real data examples.
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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 | 0.250 | 0.480 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.009 | 0.005 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".