Paper

{
  "raw_xml": "<entry>\n    <id>http://arxiv.org/abs/2603.19051v1</id>\n    <title>Optimal Sample Size Calculation in Cost-Effectiveness Longitudinal Cluster Randomized Trials</title>\n    <updated>2026-03-19T15:46:19Z</updated>\n    <link href='https://arxiv.org/abs/2603.19051v1' rel='alternate' type='text/html'/>\n    <link href='https://arxiv.org/pdf/2603.19051v1' rel='related' title='pdf' type='application/pdf'/>\n    <summary>Longitudinal cluster randomized trials (L-CRTs) are increasingly used to evaluate the cost-effectiveness of healthcare interventions across multiple assessment periods, yet design methods for powering these trials remain underdeveloped. Existing methods for cost-effectiveness analyses in cluster settings are limited to simple parallel-arm cluster randomized trials with a single follow-up assessment period. These methods cannot accommodate the complex correlation structures in L-CRTs conducted over multiple periods, which require differentiation between within-period and between-period correlations for both clinical and cost outcomes, as well as between-outcome correlations. Moreover, while substantial methodological advances have been made for the design of L-CRTs with univariate outcomes, none specifically address cost-effectiveness objectives where clinical and cost outcomes must be jointly modeled. We provide a design-stage framework for powering cost-effectiveness L-CRTs across three design variants: parallel-arm, crossover, and stepped wedge designs. We derive closed-form variance expressions for the generalized least squares estimator of the average incremental net monetary benefit under a bivariate linear mixed model. We propose a standardized ceiling ratio that adjusts willingness-to-pay for relative outcome variability to inform optimal design. We then develop local optimal designs that maximize statistical power under known correlation parameters and MaxiMin designs that ensure robust performance across parameter uncertainty for all three design variants. Through a real stepped wedge trial data example, we demonstrate the sample size calculation for testing intervention cost-effectiveness under local optimal and MaxiMin designs.</summary>\n    <category scheme='http://arxiv.org/schemas/atom' term='stat.ME'/>\n    <published>2026-03-19T15:46:19Z</published>\n    <arxiv:primary_category term='stat.ME'/>\n    <author>\n      <name>Hao Wang</name>\n    </author>\n    <author>\n      <name>Jingxia Liu</name>\n    </author>\n    <author>\n      <name>Drew B. Cameron</name>\n    </author>\n    <author>\n      <name>Jiaqi Tong</name>\n    </author>\n    <author>\n      <name>Donna Spiegelman</name>\n    </author>\n    <author>\n      <name>Daniella Meeker</name>\n    </author>\n    <author>\n      <name>Fan Li</name>\n    </author>\n  </entry>"
}