![]() ![]() Simulation can also estimate the expected impact of deviations from optimal study implementation, such as item non-response and participant drop out. The method is universally applicable to a broad range of outcomes and designs, and it easily accommodates complex design features such as different follow-up plans, multiple treatment interventions, or different site-specific cluster effects. Here, we provide an overview of a general method to estimate study power for randomized trials based on a simulation technique that arises naturally from the underlying data model typically assumed by power and sample size equations. In these situations, simulation techniques offer a flexible alternative that is easy to implement in modern statistical software. multiple treatment interventions, where one treatment is deployed at the group level and a second at the individual level). However, in our applied research we have routinely encountered study designs that do not conform to conventional power equations (e.g. For this reason, power equations are used to inform most study designs. The advantage of using an equation to estimate power for study designs is that the approach is fast and easy to implement using existing software. Statisticians have also derived equations to estimate power for more complex designs, such as designs with two levels of correlation or designs with two levels of correlation, multiple treatments and attrition. There exist analytic (closed-form) power equations for simple designs such as parallel randomized trials with treatment assigned at the individual level or cluster (group) level. The approach we have described is universally applicable for evaluating study designs used in epidemiologic and social science research.Įstimating the sample size and statistical power for a study is an integral part of study design and has profound consequences for the cost and statistical precision of a study. Simulation methods offer a flexible option to estimate statistical power for standard and non-traditional study designs and parameters of interest. ![]() Finally, we discuss extensions to the examples in the article, and provide computer code to efficiently run the example simulations in both R and Stata. We then demonstrate how to extend the simulation approach to more complex designs. We first show how simulation reproduces conventional power estimates for simple randomized designs over a broad range of sample scenarios to familiarize the reader with the approach. We illustrate the method using two examples (one simple, one complex) based on sanitation and nutritional interventions to improve child growth. The method is universally applicable to a broad range of designs and outcomes, and we present the material in a way that is approachable for quantitative, applied researchers. This flexible approach arises naturally from the model used to derive conventional power equations, but extends those methods to accommodate arbitrarily complex designs. We review an approach to estimate study power for individual- or cluster-randomized designs using computer simulation. This article aims to address this knowledge gap. ![]() Although this approach is well known among statisticians, in our experience many epidemiologists and social scientists are unfamiliar with the technique. For such complex study designs, computer simulation is a useful alternative for estimating study power. For standard designs, power equations provide an efficient solution to the problem, but they are unavailable for many complex study designs that arise in practice. Estimating the required sample size and statistical power for a study is an integral part of study design. ![]()
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