Hostname: page-component-78c5997874-xbtfd Total loading time: 0 Render date: 2024-11-10T19:26:11.416Z Has data issue: false hasContentIssue false

A Bayesian framework for the ratio of two Poisson rates in thecontext of vaccine efficacy trials

Published online by Cambridge University Press:  03 September 2012

Stéphane Laurent
Affiliation:
Institut de Statistique, Biostatistique et Sciences Actuarielles (ISBA), Université catholique de Louvain, Voie du Roman Pays, 20, 1348 Louvain la Neuve, Belgium. stephane.laurent@uclouvain.be; catherine.legrand@uclouvain.be
Catherine Legrand
Affiliation:
Institut de Statistique, Biostatistique et Sciences Actuarielles (ISBA), Université catholique de Louvain, Voie du Roman Pays, 20, 1348 Louvain la Neuve, Belgium. stephane.laurent@uclouvain.be; catherine.legrand@uclouvain.be
Get access

Abstract

In many applications, we assume that two random observations x andy are generated according to independent Poisson distributions\hbox{$\PPP(\lambdaS)$}𝒫(λS)and \hbox{$\PPP(\muT)$}𝒫(μT)and we are interested in performing statistical inference on the ratioφ = λ / μ of the twoincidence rates. In vaccine efficacy trials, x and y aretypically the numbers of cases in the vaccine and the control groups respectively,φ is called the relative risk and the statistical model is called‘partial immunity model’. In this paper we start by defining a natural semi-conjugatefamily of prior distributions for this model, allowing straightforward computation of theposterior inference. Following theory on reference priors, we define the reference priorfor the partial immunity model when φ is the parameter of interest. Wealso define a family of reference priors with partial information on μwhile remaining uninformative about φ. We notice that these priors belongto the semi-conjugate family. We then demonstrate using numerical examples that Bayesiancredible intervals for φ enjoy attractive frequentist properties whenusing reference priors, a typical property of reference priors.

Type
Research Article
Copyright
© EDP Sciences, SMAI, 2012

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Références

N. Balakrishnan, N.L. Johnson and S. Kotz, Continuous Univariate Distributions, 2nd edition. John Wiley, New York 1 (1995).
Bayarri, M.J. and Berger, J., The interplay of Bayesian and frequentist analysis. Stat. Sci. 19 (2004) 5880. Google Scholar
J.O. Berger and J.M. Bernardo, Ordered Group Reference Priors With Applications to Multinomial and Variance Component Problems. Technical Report Dept. of Statistics, Purdue University (1989).
Berger, J.O. and Bernardo, J.M., Estimating a product of means : Bayesian analysis with reference priors. J. Amer. Statist. Assoc. 84 (1989) 200207. Google Scholar
Berger, J.O. and Bernardo, J.M., Ordered group reference priors, with applications to multinomial problems. Biometrika 79 (1992) 2537. Google Scholar
J.O. Berger and J.M. Bernardo, On the development of reference priors, edited by J.M. Bernardo, J.O. Berger, A.P. Dawid and A.F.M. Smith, Bayesian Statistics. University Press, Oxford (with discussion) 4 (1992) 35–60.
Berger, J.O. and Sun, D., Reference priors with partial information. Biometrika 85 (1998) 5571. Google Scholar
J.O. Berger and R. Yang, A catalog of noninformative priors. ISDS Discussion Paper, Duke Univ. (1997) 97–42.
Berger, J.O., Bernardo, J.M. and Sun, D., The formal definition of reference priors. Ann. Stat. 37 (2009). Google Scholar
Bernardo, J.M., Reference posterior distributions for Bayesian inference (with discussion). J. R. Stat. Soc. B 41 (1979) 113148. Google Scholar
Bernardo, J.M., Noninformative priors do not exist : a discussion. (with discussion) J. Stat. Plann. Inference 65 (1997) 159189. Google Scholar
J.M. Bernardo, Reference Analysis, edited by D.K. Dey and C.R. Rao. Handbook of Stat. 25 (2005) 17–90.
Bernardo, J.M., Intrinsic credible regions : an objective Bayesian approach to interval estimation (with discussion). Test 14 (2005) 317384. Google Scholar
Bernardo, J.M. and Ramon, J.M., An introduction to Bayesian reference analysis : inference on the ratio of multinomial parameters. J. R. Stat. Soc. D 47 (1998) 101135. Google Scholar
J.M. Bernardo and A.F.M. Smith, Bayesian Theory. Wiley, Chichester (1994).
Berry, D.A., Wolff, M.C. and Sack, D., Decision making during a phase III randomized controlled trial. Control. Clin. Trials 15 (1994) 360378. Google ScholarPubMed
Brown, L.D., Cai, T.T. and DasGupta, A., Interval estimation for a binomial proportion (with discussion). Stat. Sci. 16 (2001) 101133. Google Scholar
Brown, L.D., Cai, T.T. and DasGupta, A., Confidence intervals for a binomial proportion and edgeworth expansions. Ann. Stat. 30 (2002) 160201. Google Scholar
Chu, H. and Halloran, M.E., Bayesian estimation of vaccine efficacy. Clin. Trials 1 (2004) 306314. Google ScholarPubMed
Cousins, R.D., Improved central confidence intervals for the ratio of Poisson means. Nucl. Instrum. Methods Phys. Res. A 417 (1998) 391399. CrossRefGoogle Scholar
G.S. Datta and R. Mukerjee, Probability Matching Priors : Higher Order Asymptotics. Springer, New-York (2004).
Ewell, M., Comparing methods for calculating confidence intervals for vaccine efficacy. Stat. Med. 15 (1996) 23792392. Google ScholarPubMed
Halloran, M.E., Longini, I.M.Jr. and Struchiner, C.J., Design and interpretation of vaccine field studies. Epidemiol. Rev. 21 (1999) 7388. Google ScholarPubMed
N.L. Johnson, A.W. Kemp and S. Kotz, Univariate Discrete Distributions, 3rd edition. John Wiley, New York (2005).
Kass, R.E. and Wasserman, L., The selection of prior distributions by formal rules. J. Am. Statist. Assoc. 91 (1996) 13431370. Google Scholar
C. Kleiber and S. Kotz, Statistical Size Distributions in Economics and Actuarial Sciences, Wiley (2003).
Krishnamoorthy, K. and Lee, M., Inference for functions of parameters in discrete distributions based on fiducial approach : Binomial and Poisson cases. J. Statist. Plann. Inference 140 (2009) 11821192. Google Scholar
Krishnamoorthy, K. and Thomson, J., A more powerful test for comparing two Poisson means. J. Statist. Plann. Inference 119 (2004) 2335. Google Scholar
Lecoutre, B., And if you were a Bayesian without knowing it? Bayesian inference and maximum entropy methods in science and engineering. AIP Conf. Proc. 872 (2006) 1522. Google Scholar
E.L. Lehmann and J.P. Romano, Testing Statistical Hypotheses, 3rd edition. Springer, New York (2005).
B. Liseo, Elimination of Nuisance Parameters with Reference Noninformative Priors. Technical Report #90-58C, Purdue University, Department of Statistics (1990).
Price, R.M. and Bonett, D.G., Estimating the ratio of two Poisson rates. Comput. Stat. Data Anal. 34 (2000) 345356. Google Scholar
C. Robert, The Bayesian Choice : From Decision-Theoretic Foundations to Computational Implementation, 2nd edition. Springer Texts in Statistics (2001).
Robins, J. and Wasserman, L., Conditioning, likelihood and coherence : A review of some foundational concepts. J. Amer. Statist. Assoc. 95 (2000) 13401346. Google Scholar
Sahai, H. and Khurshid, A., Confidence intervals for the ratio of two Poisson means. Math. Sci. 18 (1993) 4350. Google Scholar
Stamey, J.D., Young, D.M., Bratcher, T.L., Bayesian sample-size determination for one and two Poisson rate parameters with applications to quality control. J. Appl. Stat. 33 (2006) 583594. Google Scholar