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Simple Power and Sample Size Estimation for Non-Randomized Longitudinal Difference in Differences Studies.

Yirui Hu1, D R Hoover2

  • 1Biomedical and Translational Informatics, Geisinger, Danville, 17821, USA.

Journal of Biometrics & Biostatistics
|July 31, 2019
PubMed
Summary
This summary is machine-generated.

New power and sample size formulas for non-randomized Difference-in-Differences (DD) studies are available. These methods incorporate repeated measures and simplify planning for intervention effect estimation.

Keywords:
Compound symmetry covariance matrixNon-randomized longitudinal studyOptimal pre-post intervention allocationRepeated measure

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Area of Science:

  • Biostatistics
  • Longitudinal Data Analysis
  • Interventional Study Design

Background:

  • Difference-in-Differences (DD) analysis is commonly used for estimating intervention effects in longitudinal studies.
  • Randomized designs are ideal, but non-randomized designs are often necessitated by practical constraints.
  • Existing power and sample size estimation methods for non-randomized DD designs lack incorporation of repeated measures' correlation structure.

Purpose of the Study:

  • To derive power and sample size estimation methods for non-randomized Difference-in-Differences (DD) studies with continuous longitudinal outcomes.
  • To incorporate the correlation structure of repeated measures into these estimation methods.
  • To provide practical tools for study planning in interventional research.

Main Methods:

  • Generalized Least Squares (GLS) variance estimate of the intervention effect was derived.
  • Formulas were simplified for the compound symmetry (CS) correlation structure, assuming a constant correlation coefficient (ρ).
  • Extensions for cluster designs and time-invariant covariates were developed.

Main Results:

  • Simple, implementable power and sample size estimation formulas were developed for non-randomized DD studies under CS correlation.
  • Maximizing power requires an equal number of pre- and post-intervention timepoints (b=k) for a fixed total number of timepoints (T).
  • For studies with T≤7, the power difference between randomized and non-randomized DD designs is minimal when correlation (ρ) is high (ρ≥0.6 for b≥2, or ρ≥0.8 for b=1).

Conclusions:

  • The developed GLS-based formulas provide a practical approach for power and sample size estimation in non-randomized longitudinal interventional studies.
  • Study design choices, such as balancing pre- and post-intervention timepoints, significantly impact statistical power.
  • The findings offer guidance for optimizing study planning, especially when randomization is not feasible.