Bayesian crossover designs for generalized linear models

Satya Prakash Singh and Siuli Mukhopadhyay

Computational Statistics & Data Analysis, 2016, vol. 104, issue C, 35-50

Abstract: This article discusses optimal Bayesian crossover designs for generalized linear models. Crossover trials with t treatments and p periods, for t<=p, are considered. The designs proposed in this paper minimize the log determinant of the variance of the estimated treatment effects over all possible allocation of the n subjects to the treatment sequences. It is assumed that the p observations from each subject are mutually correlated while the observations from different subjects are uncorrelated. Since main interest is in estimating the treatment effects, the subject effect is assumed to be nuisance, and generalized estimating equations are used to estimate the marginal means. To address the issue of parameter dependence a Bayesian approach is employed. Prior distributions are assumed on the model parameters which are then incorporated into the DA-optimal design criterion by integrating it over the prior distribution. Three case studies, one with binary outcomes in a 4×4 crossover trial, second one based on count data for a 2×2 trial and a third one with Gamma responses in a 3×2 crossover trial are used to illustrate the proposed method. The effect of the choice of prior distributions on the designs is also studied. A general equivalence theorem is stated to verify the optimality of designs obtained.

Keywords: Bayesian designs; Count data; Efficiency; Gamma response; Generalized estimating equations; Logistic regression (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167947316301396
Full text for ScienceDirect subscribers only.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:104:y:2016:i:c:p:35-50

DOI: 10.1016/j.csda.2016.06.002

Access Statistics for this article

Computational Statistics & Data Analysis is currently edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().