## Multivariate Bayesian logistic regression for analysis of clinical study safety issues.(English)Zbl 1331.62416

Summary: This paper describes a method for a model-based analysis of clinical safety data called multivariate Bayesian logistic regression (MBLR). Parallel logistic regression models are fit to a set of medically related issues, or response variables, and MBLR allows information from the different issues to “borrow strength” from each other. The method is especially suited to sparse response data, as often occurs when fine-grained adverse events are collected from subjects in studies sized more for efficacy than for safety investigations. A combined analysis of data from multiple studies can be performed and the method enables a search for vulnerable subgroups based on the covariates in the regression model. An example involving 10 medically related issues from a pool of 8 studies is presented, as well as simulations showing distributional properties of the method.

### MSC:

 62P10 Applications of statistics to biology and medical sciences; meta analysis 62F15 Bayesian inference 62-07 Data analysis (statistics) (MSC2010) 62J02 General nonlinear regression 62J07 Ridge regression; shrinkage estimators (Lasso)
Full Text:

### References:

 [1] Berry, S. M. and Berry, D. A. (2004). Accounting for multiplicities in assessing drug safety: A three-level hierarchical mixture model. Biometrics 60 418-426. · Zbl 1125.62118 [2] Carlin, B. P. and Louis, T. A. (2000). Bayes and Empirical Bayes Methods for Data Analysis , 2nd ed. Chapman & Hall/CRC, Boca Raton, FL. · Zbl 1017.62005 [3] Dean, B. (2003). Adverse drug events: What’s the truth? Qual. Saf. Health Care 12 165. [4] Gelman, A., Jakulin, A., Pittau, M. G. and Su, Y.-S. (2008). A weakly informative default prior distribution for logistic and other regression models. Ann. Appl. Stat. 2 1360-1383. · Zbl 1156.62017 [5] Lee, Y. and Nelder, J. A. (1996). Hierarchical generalized linear models. J. Roy. Statist. Soc. Ser. B 58 619-678. · Zbl 0880.62076 [6] Lee, Y., Nelder, J. A. and Pawitan, Y. (2006). Generalized Linear Models with Random Effects. Unified Analysis via $$H$$-Likelihood. Monographs on Statistics and Applied Probability 106 . Chapman & Hall/CRC, Boca Raton, FL. · Zbl 1110.62092 [7] Meng, X.-L. (2009). Decoding the H-likelihood. Statist. Sci. 24 280-293. · Zbl 1329.62340 [8] Searle, S. R., Casella, G. and McCulloch, C. E. (1992). Variance Components . Wiley, New York. · Zbl 0850.62007
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.