Computational Genome Analysis: An Introduction

the difficulty of assessing utilities, “a formal decision model can. synthesize current best evidence and clinical judgment. and via its utility component, link this evidence to clinical decisions. Decision makers are. reluctant to delegate [decisions]. to model-based formalisms [but are]. increasingly. relying on structured approaches to make more informed decisions” (p. 69). The “Decision Making” chapter continues with a presentation of the main mathematical details of maximizing SEU that includes as an example a costeffectiveness analysis of stroke interventions combining QUALYs and the Markov model developed in earlier sections. The chapter concludes with a presentation of hypothesis testing and a brief mention of sample size selection. The final chapter of the “Methods” section, “Simulation,” introduces Markov chain Monte Carlo and includes a section on Monte Carlo estimation of expected utility. The second section, “Case Studies,” has chapters titled “Meta-Analysis,” “Decision Trees,” and “Chronic Disease Modeling. ” In the first of these, hierarchical Bayesian meta-analysis of exchangeable studies is developed in some detail, including WinBUGS-style directed graphs and code. Topics include combination of continuous and dichotomous endpoints using latent variables and sensitivity to prior specification. The chapter summary (p. 124) suggests that the resulting posterior distributions of effect magnitudes, “can. become components of a formal decision analysis [or]. of a comprehensive decision model. ” However, no such examples are presented, and this chapter, while an excellent, practical introduction to Bayesian meta-analysis, is not well integrated with the main topic of the book. The “Decision Trees” chapter is organized around a case study of the decision between axillary lymph node dissection or not followed by the choice of none or one of three adjuvant therapies in the treatment of early-stage breast cancer. It is a good, clear presentation of backward induction and uses most of the machinery set up in the methods chapters: posterior distributions, predictive distributions, QUALYs, and SEU maximization. The final case study, “Chronic Disease Modeling,” deals with optimizing the frequency of radiological screening for breast cancer. The core models are a continuous-time four-state Markov model (healthy, preclinical, clinical, and dead) for which the inferential component is the estimation of age-dependent transition densities (through sojourn-time distributions), a sensitivity model that may depend on age and tumor size, a model of the number of auxiliary lymph nodes involved that depends on age and tumor size, and a survival model that depends on node involvement and a vector of other prognostic variables. The decision to be made is the screening schedule as a function of age, and in theory, the trade-off is between the cost of screening and the expected gain in QUALYs. Needless to say, this case study is only sketched, but the sketch is sufficiently detailed to give an idea of the modeling strategies and how the backward induction was implemented. I have one small quibble: regarding the statement on page 33: “to speak of a probability distribution for [the parameter] β we need to imagine a metapopulation, or a universe of possible populations, each with a different [value of the parameter],” I believe that this is not at all what Bayesians mean by probability. Rather, Bayesian inference treats probabilities as epistemic—referring to states of uncertainty based on incomplete information, not to alternate universes. To say, for example, that the speed of light, C, has a distribution is to say that I am uncertain about the exact value of C in this universe and that my uncertainty is described by a probability distribution. Speaking as a statistician, Modeling in Medical Decision Making: A Bayesian Approach would be good to use as one component in a graduate course on decision making following an introductory Bayesian course. It assumes an understanding of calculus, or at minimum, calculus notation. For established statisticians and biostatisticians, the book is a good way to get up to speed on Bayesian decision analysis in health care and could serve as an entry point into the large published literature alluded to at the beginning of this review.