Statistical Science

Multivariate Bayesian Logistic Regression for Analysis of Clinical Study Safety Issues

William DuMouchel

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Abstract

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.

Article information

Source
Statist. Sci. Volume 27, Number 3 (2012), 319-339.

Dates
First available in Project Euclid: 5 September 2012

Permanent link to this document
http://projecteuclid.org/euclid.ss/1346849939

Digital Object Identifier
doi:10.1214/11-STS381

Mathematical Reviews number (MathSciNet)
MR3012426

Zentralblatt MATH identifier
1331.62416

Keywords
Adverse drug reactions Bayesian shrinkage drug safety data granularity hierarchical Bayesian model parallel logistic regressions sparse data variance component estimation

Citation

DuMouchel, William. Multivariate Bayesian Logistic Regression for Analysis of Clinical Study Safety Issues. Statist. Sci. 27 (2012), no. 3, 319--339. doi:10.1214/11-STS381. http://projecteuclid.org/euclid.ss/1346849939.


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References

  • 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.
    Mathematical Reviews (MathSciNet): MR2066276
    Digital Object Identifier: doi:10.1111/j.0006-341X.2004.00186.x
  • Carlin, B. P. and Louis, T. A. (2000). Bayes and Empirical Bayes Methods for Data Analysis, 2nd ed. Chapman & Hall/CRC, Boca Raton, FL.
    Mathematical Reviews (MathSciNet): MR1427749
  • Dean, B. (2003). Adverse drug events: What’s the truth? Qual. Saf. Health Care 12 165.
  • 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.
    Mathematical Reviews (MathSciNet): MR2655663
    Zentralblatt MATH: 1156.62017
    Digital Object Identifier: doi:10.1214/08-AOAS191
    Project Euclid: euclid.aoas/1231424214
  • Lee, Y. and Nelder, J. A. (1996). Hierarchical generalized linear models. J. Roy. Statist. Soc. Ser. B 58 619–678.
    Mathematical Reviews (MathSciNet): MR1410182
  • 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.
    Mathematical Reviews (MathSciNet): MR2259540
    Zentralblatt MATH: 1110.62092
  • Meng, X.-L. (2009). Decoding the H-likelihood. Statist. Sci. 24 280–293.
    Mathematical Reviews (MathSciNet): MR2757430
    Digital Object Identifier: doi:10.1214/09-STS277C
    Project Euclid: euclid.ss/1270041255
  • Searle, S. R., Casella, G. and McCulloch, C. E. (1992). Variance Components. Wiley, New York.
    Mathematical Reviews (MathSciNet): MR1190470

See also

  • Discussion of: "Multivariate Bayesian Logistic Regression for Analysis of Clinical Trial Safety Issues" by W. DuMouchel.
    Digital Object Identifier: doi:10.1214/12-STS381A
  • Discussion of: "Multivariate Bayesian Logistic Regression for Analysis of Clinical Trial Safety Issues" by W. DuMouchel.
    Digital Object Identifier: doi:10.1214/12-STS381B
  • Discussion of: An Answer to Multiple Problems with Analysis of Data on Harms?.
    Digital Object Identifier: doi:10.1214/12-STS381C
  • Rejoinder: Multivariate Bayesian Logistic Regression for Analysis of Clinical Study Safety Issues.
    Digital Object Identifier: doi:10.1214/12-STS381REJ

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