标题：An exploration of childhood antecedents of female adult malaise in two British birth cohorts: Combining Bayesian model averaging and recursive partitioning
摘要：We use information from two prospective British birth cohort studies to explore the antecedents of adult malaise, an indicator of incipient depression. These studies include a wealth of information on childhood circumstances, behaviour, test scores and family background, measured several times during childhood. We are concerned both with incorporating model uncertainty and using a person-centred approach. We explore associations in both cohorts using two separate approaches: Bayesian model averaging (BMA) and recursive trees. The first approach permits us to assess model uncertainty, necessary because many childhood antecedents are highly correlated. BMA also aims to produce more robust results for extrapolation to other data sets through averaging over the range of plausible models. The second approach is concerned with partitioning the sample, through a series of binary splits, into groups of people who are as alike as possible. One advantage is that the approach is person-centred in that it retains real groups of respondents. We compare and contrast the insights obtained from the two approaches and use the results from each to inform the other and thus refine our understanding further. Moreover, we explore the claimed added robustness for extrapolation by using a split-sample for the 1970 cohort. The consistency of results across methods and cohorts is discussed throughout
关键词：well-being, cohort, Bayesian Model Averaging, recursive trees
