Accounting for expected attrition in the planning of cluster randomized trials for assessing treatment effect heterogeneity

BMC Medical Research Methodology

Research

Michael O. Harhay¹  Jiaqi Tong²  Fan Li³  Guangyu Tong³ 

[1] Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA;Department of Biostatistics, Yale School of Public Health, 135 College Street, 06510, New Haven, CT, USA;Department of Biostatistics, Yale School of Public Health, 135 College Street, 06510, New Haven, CT, USA;Center for Methods in Implementation and Prevention Science, Yale School of Public Health, New Haven, CT, USA;

关键词: Heterogeneity of treatment effect;    Missing data;    Missing at random;    Missing completely at random;    Power calculation;    Intracluster correlation coefficient;    Cluster randomized trial;   

DOI  :  10.1186/s12874-023-01887-8

 received in 2022-12-18, accepted in 2023-03-10,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundDetecting treatment effect heterogeneity is an important objective in cluster randomized trials and implementation research. While sample size procedures for testing the average treatment effect accounting for participant attrition assuming missing completely at random or missing at random have been previously developed, the impact of attrition on the power for detecting heterogeneous treatment effects in cluster randomized trials remains unknown.MethodsWe provide a sample size formula for testing for a heterogeneous treatment effect assuming the outcome is missing completely at random. We also propose an efficient Monte Carlo sample size procedure for assessing heterogeneous treatment effect assuming covariate-dependent outcome missingness (missing at random). We compare our sample size methods with the direct inflation method that divides the estimated sample size by the mean follow-up rate. We also evaluate our methods through simulation studies and illustrate them with a real-world example.ResultsSimulation results show that our proposed sample size methods under both missing completely at random and missing at random provide sufficient power for assessing heterogeneous treatment effect. The proposed sample size methods lead to more accurate sample size estimates than the direct inflation method when the missingness rate is high (e.g., ≥ 30%). Moreover, sample size estimation under both missing completely at random and missing at random is sensitive to the missingness rate, but not sensitive to the intracluster correlation coefficient among the missingness indicators.ConclusionOur new sample size methods can assist in planning cluster randomized trials that plan to assess a heterogeneous treatment effect and participant attrition is expected to occur.

【 授权许可】

CC BY   
© The Author(s) 2023. corrected publication 2023

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