ArticleStatistics in medicine2021
Informing power and sample size calculations when using inverse probability of treatment weighting using the propensity score.
Article in Statistics in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06945510 (A Multi-center Study to Evaluate the Economic and Clinical Impact of PCT Guided Antibiotics Use in Critically Ill Adult Patients in China.), which is not on this map. Cited by 25 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
A Multi-center Study to Evaluate the Economic and Clinical Impact of PCT Guided Antibiotics Use in Critically Ill Adult Patients in China.
Who cites it
25 citing papers in PubMed.
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- Preexisting hemodialysis and survival outcome in out-of-hospital cardiac arrest patients: Ulsan, South Korea.Frontiers in medicine · 2025Article
- First Impressions of the New da Vinci 5 Robotic Platform and Experience in Performing Robot-assisted Radical Prostatectomy.European urology open science · 2024Article
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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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