4. Bayesian data analysis (2nd edn). Andrew Gelman, John B. Carlin, Hal S. Stern and Donald B. Rubin (eds), Chapman & Hall/CRC, Boca Raton, 2003. No. of pages: xxv + 668. Price: $59.95. ISBN 1‐58488‐388‐X

3401 regression structure may be the primary focus. The marginalized latent variable models allow a exible choice between modelling the marginal means or the conditional means. The marginalized transition models separate the dependence on the exposure variables from the dependence on previous response values. Orthogonality properties between the mean and the dependence parameters in a marginalized model secure robustness for the marginal means. Marginalized models further allow for simple procedures to determine a suitable dependence model for the data. Chapter 12 on time-dependent covariates is also new. The temporal order between key exposure and response events is emphasized and exogenous and endogenous covariates are formally deÿned. When covariates are endogenous, then meaningful targets for inference need to be formulated as well as valid methods of estimation. A longitudinal study on maternal stress, child illness and maternal employment illustrates concepts. The scientiÿc questions include (i) Is there an association between maternal employment and stress? (ii) Is there an association between maternal employment and child illness? (iii) Do the data provide evidence that maternal stress causes child illness? Since stress may be in the causal pathway that leads from employment to illness no adjustment is made for the daily stress indicators when evaluating the dependence of illness on employment. Similarly no adjustment is made for illness in the analysis of employment and stress. Question (iii) raises issues such as 'does illness at day t depend on prior stress measured at day (t − k)' and 'does illness on day (t − k) predict stress on day t'. A covariate which is both a predictor for the response and is predicted by earlier responses is endogenous. No standard regression methods are available to obtain causal statements when dealing with endogenous covari-ates. Targets for inference are discussed in terms of counterfactual outcomes. Causal eeects refer to interventions in the entire population rather than among possibly select, observed subgroups. Focus is on an average response after assignment of the covariate value rather than the average response in subgroups after simply observing the covariate status. The g-computation algorithm of Robins is presented as well as estimation using inverse probability of treatment weighting (IPTW). Chapter 13 discusses approaches to dealing with incomplete data in longitudinal studies, with emphasis on random and informative missing data mechanism. Likelihood inference and generalized estimating equations when data are missing at random are dealt with. Selection models and pattern mixture models are …