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Efficient Bayesian Sample Size Calculation for Designing a Clinical Trial with Multi‐Cluster Outcome Data
Author(s) -
Zou Kelly H.,
Resnic Frederic S.,
Gogate Adheet S.,
OndateguiParra Silvia,
OhnoMachado Lucila
Publication year - 2003
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.200390052
Subject(s) - bayesian probability , sample size determination , statistics , beta binomial distribution , bayesian hierarchical modeling , event (particle physics) , bayesian inference , outcome (game theory) , negative binomial distribution , sample (material) , mathematics , econometrics , computer science , data mining , poisson distribution , physics , chemistry , mathematical economics , chromatography , quantum mechanics
Health care utilization and outcome studies call for hierarchical approaches. The objectives were to predict major complications following percutaneous coronary interventions by health providers, and to compare Bayesian and non‐Bayesian sample size calculation methods. The hierarchical data structure consisted of: (1) Strata: PGY4, PGY7, and physician assistant as providers with varied experiences; (2) Clusters: k s providers per stratum; (3) Individuals: n s patients reviewed by each provider. The main outcome event illustrated was mortality modeled by a Bayesian beta‐binomial model. Pilot information and assumptions were utilized to elicit beta prior distributions. Sample size calculations were based on the approximated average length, fixed at 1%, of 95% posterior intervals of the mean event rate parameter. Necessary sample sizes by both non‐Bayesian and Bayesian methods were compared. We demonstrated that the developed Bayesian methods can be efficient and may require fewer subjects to satisfy the same length criterion.