Bootstrap hypothesis testing in generalized additive models for comparing curves of treatments in longitudinal studies

NORES, MARÍA LAURA

JOURNAL OF APPLIED STATISTICS

ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD

Año: 2016 vol. 43 p. 810 - 810

0266-4763

he study of the effect of a treatment may involve the evaluation of a variable at a number of moments. When assuming a smooth curve for the mean response along time, estimation can be afforded by spline regression, in the context of generalized additive models. The novelty of our work lies in the construction of hypothesis tests to compare two curves of treatments in any interval of time for several types of response variables. The within-subject correlation is not modeled but is considered to obtain valid inferences by the use of bootstrap. We propose both semiparametric and nonparametric bootstrap approaches, based on resampling vectors of residuals or responses, respectively. Simulation studies revealed a good performance of the tests, considering, for the outcome, different distribution functions in the exponential family and varying the correlation between observations along time. We show that the sizes of bootstrap tests are close to the nominal value, with tests based on a stan