Packaged software has the advantages of requiring less programming and less statistical sophistication.īased on the current state of knowledge, we recommend using power methods developed for multivariate models to calculate sample size for studies using common mixed models for data analysis. We recommend using appropriate software that has been tested and validated whenever it is available. One possible method for finding reliable power or sample size when no power formulas are available is to conduct a computer simulation study. In some cases, the planned data analysis has no published power analysis methods aligned with the data analysis. In addition, most of these methods are based on approximations, and make simple assumptions about the study design. However, validated power and sample size methods exist only for a limited class of mixed models. In practice, mixed models have become the most popular method for analyzing repeated measures and longitudinal data. Misalignment between the design used for sample size calculations and the design used for data analysis can lead to a sample size that is either too large or too small, contributing to inconclusive findings. In this case, a sample size calculation based on a two-group t-test would be inappropriate, since the planned data analysis is not a t-test. The planned data analysis is an analysis of covariance (ANCOVA), with age as the covariate. The researcher plans to control for both gender and age. As an example, consider a study in which a researcher plans to test whether veterans and non-veterans respond similarly to a drug. One of the first steps in computing a sample size is to select a power analysis method that adequately aligns with the data analysis method. In turn, we also assume that an appropriate analysis plan has been selected, which sets the stage for sample size selection. We assume the iterative process of choosing and refining the research goals, the primary outcomes, and the sampling plan has succeeded. Although statistical consulting will have value at any stage of research, the earlier stages of planning a study profit most from consulting. We also illustrate the process of sample size selection by working through an example with repeated measurements of pain memory, using the web-based power and sample size program GLIMMPSE.įor the sake of brevity, we will not elaborate on the fundamental question of choosing a data analysis method. In the present article, we describe methods for gathering the information required for selecting a sample size for studies with repeated measurements of normally distributed continuous responses. In addition, some current software may require programming skills that are beyond the resources available to many researchers. As discussed later in the paper, oversimplified assumptions can give investigators false confidence in the chosen sample size. Some current software packages used for sample size calculations are based on oversimplified assumptions about correlation patterns. Unlike studies with independent observations, repeated measurements taken from the same participant are correlated, and the correlations must be accounted for in calculating the appropriate sample size. In spite of the advantages over cross-sectional designs, repeated measures designs complicate the crucial process of selecting a sample size. Moreover, collecting repeated measurements can simultaneously increase statistical power for detecting changes while reducing the costs of conducting a study. For instance, collecting repeated measurements of key variables can provide a more definitive evaluation of within-person change across time. Repeated measures designs are widely used because they have advantages over cross-sectional designs. Choosing the right sample size increases the chance of detecting an effect, and ensures that the study is both ethical and cost-effective. On the other hand, a study with an excessive sample size wastes resources and may unnecessarily expose study participants to potential harm. A study with an insufficient sample size may not have sufficient statistical power to detect meaningful effects and may produce unreliable answers to important research questions. Selecting an appropriate sample size is a crucial step in designing a successful study.
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