Adjustment of selection bias for clinical trials: a simulation study
Bibliographic record
Abstract
Clinical trial selection bias is a common issue, as patients are typically not selected randomly from a target population. Various statistical approaches have been proposed to adjust for this bias, including IPW (inverse probability weights), SPS (subclassification with propensity scores), and EVB (external validity bias). However, there has been very little statistical research to compare the performance of these methods in clinical trials. To bridge this gap, we conducted a simulation study using a patient population with seven covariates and a true treatment effect size of 0.5 (Cohen’s d). Next, we assessed the efficacy of the three statistical methods on nonrandom clinical trial samples with varying sizes and covariates. Based on our simulation results, EVB is the most effective method for adjusting clinical trial selection bias when there are seven covariates. SPS is the most effective method for adjusting clinical trial selection bias when there are three and five covariates. However, we observed that IPW's performance was inadequate, indicating that it may not be a suitable option for selection bias adjustment in clinical trials. In summary, our study sheds light on the effectiveness of various statistical methods in mitigating selection bias in clinical trials.
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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 | 0.110 | 0.293 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".