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Semi‐parametric and non‐parametric methods for the analysis of repeated measurements with applications to clinical trials

1991; Wiley; Volume: 10; Issue: 12 Linguagem: Inglês

10.1002/sim.4780101210

1097-0258

Charles S. Davis,

Statistical Methods and Bayesian Inference

Abstract Techniques applicable for the analysis of longitudinal data when the response variable is non‐normal are not nearly as comprehensive as for normally‐distributed outcomes. However, there have been several recent developments. Semi‐parametric and non‐parametric methodology for the analysis of repeated measurements is reviewed. The commonly encountered design in which, for each subject, one assesses a univariate response variable at multiple fixed time points, is considered. The types of outcomes considered include binary, ordered categorical, and continuous (but extremely non‐normal) response variables. All of the methods considered allow for incomplete data due to the occurrence of missing observations. In addition, discrete and/or continuous covariates, which may be time‐dependent, are accommodated by some of the approaches. The methods are demonstrated using data from three clinical trials.