Informing power and sample size calculations when using inverse probability of treatment weighting using the propensity score
Bibliographic record
Abstract
Propensity score weighting is increasingly being used in observational studies to estimate the effects of treatments. The use of such weights induces a within-person homogeneity in outcomes that must be accounted for when estimating the variance of the estimated treatment effect. Knowledge of the variance inflation factor (VIF), which describes the extent to which the effective sample size has been reduced by weighting, allows for conducting sample size and power calculations for observational studies that use propensity score weighting. However, estimation of the VIF requires knowledge of the weights, which are only known once the study has been conducted. We describe methods to estimate the VIF based on two characteristics of the observational study: the anticipated prevalence of treatment and the anticipated c-statistic of the propensity score model. We considered five different sets of weights: those for estimating the average treatment effect (ATE), the average treated effect in the treated (ATT), and three recently described sets of weights: overlap weights, matching weights, and entropy weights. The VIF was substantially smaller for the latter three sets of weights than for the first two sets of weights. Once the VIF has been estimated during the design phase of the study, sample size and power calculations can be done using calculations appropriate for a randomized controlled trial with similar prevalence of treatment and similar outcome variable, and then multiplying the requisite sample size by the estimated VIF. Implementation of these methods allows for improving the design and reporting of observational studies that use propensity score weighting.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".