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Power and sample-size calculations for trials that compare slopes over time: Introducing the slopepower command.

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|July 21, 2023
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Summary

Planning clinical trials to slow disease progression is simplified with the new slopepower command. This tool calculates sample sizes and statistical power for trials analyzing changes in outcomes over time, using linear mixed models.

Keywords:
parallel-arm trialpowersample-size calculationsslopepowerslopesst0647

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Longitudinal Data Analysis

Background:

  • Clinical trials aiming to slow disease progression often analyze continuous outcomes by comparing changes over time (slopes) between treatment groups.
  • Accurate sample-size calculations for these trials require estimates of between- and within-subject variability, which are frequently unknown.
  • The algebraic complexity of sample-size calculations for slope outcome trials can be a barrier to effective trial planning.

Purpose of the Study:

  • To introduce slopepower, a user-friendly command for sample-size and power calculations in trials comparing slope outcomes.
  • To provide a practical tool based on linear mixed-model methodology for planning clinical trials with longitudinal data.
  • To demonstrate the utility of slopepower in determining necessary sample sizes for various trial designs.

Main Methods:

  • The slopepower command utilizes linear mixed-model methodology, building upon established statistical approaches for longitudinal data.
  • It operates in two stages: first, estimating mean slopes, variances, and covariances from user-supplied data using a linear mixed model.
  • Second, it combines these estimates with user-defined parameters for treatment effectiveness and future trial design to compute sample size or power.

Main Results:

  • The slopepower command offers a streamlined approach to sample-size and power calculations for trials analyzing slope outcomes.
  • It simplifies the complex algebra previously required for such designs, making trial planning more accessible.
  • The tool facilitates the comparison of sample sizes needed across different potential trial designs.

Conclusions:

  • The slopepower command is a valuable tool for researchers designing clinical trials that evaluate disease progression over time.
  • It enhances the efficiency and accuracy of sample-size determination, crucial for resource allocation and study validity.
  • This command aids in optimizing trial design by enabling informed comparisons of different planning scenarios.