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Informing power and sample size calculations when using inverse probability of treatment weighting using the propensity score.

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.

SUBMITTER: Austin PC

PROVIDER: S-EPMC9293235 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND:It is important to estimate the treatment effect of interest accurately and precisely within the analysis of randomised controlled trials. One way to increase precision in the estimate and thus improve the power for randomised trials with continuous outcomes is through adjustment for pre-specified prognostic baseline covariates. Typically covariate adjustment is conducted using regression analysis, however recently, Inverse Probability of Treatment Weighting (IPTW) using the propensity score has been proposed as an alternative method. For a continuous outcome it has been shown that the IPTW estimator has the same large sample statistical properties as that obtained via analysis of covariance. However the performance of IPTW has not been explored for smaller population trials (<?100 participants), where precise estimation of the treatment effect has potential for greater impact than in larger samples. METHODS:In this paper we explore the performance of the baseline adjusted treatment effect estimated using IPTW in smaller population trial settings. To do so we present a simulation study including a number of different trial scenarios with sample sizes ranging from 40 to 200 and adjustment for up to 6 covariates. We also re-analyse a paediatric eczema trial that includes 60 children. RESULTS:In the simulation study the performance of the IPTW variance estimator was sub-optimal with smaller sample sizes. The coverage of 95% CI's was marginally below 95% for sample sizes <?150 and???100. For sample sizes <?100 the coverage of 95% CI's was always significantly below 95% for all covariate settings. The minimum coverage obtained with IPTW was 89% with n?=?40. In comparison, regression adjustment always resulted in 95% coverage. The analysis of the eczema trial confirmed discrepancies between the IPTW and regression estimators in a real life small population setting. CONCLUSIONS:The IPTW variance estimator does not perform so well with small samples. Thus we caution against the use of IPTW in small sample settings when the sample size is less than 150 and particularly when sample size <?100.

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Informing power and sample size calculations when using inverse probability of treatment weighting using the propensity score.

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