A tree-based method of analysis for prospective studies.
Prospective studies often involve rare events as study outcomes, and a primary concern is to identify risk factors and risk groups associated with the outcomes. We discuss practical solutions to risk factor analyses in prospective studies and address strategies to determine tree structures, to estimate relative risks, and to manage missing data in connection with some important epidemiologic problems. Some of the basic ideas for our strategies follow from work of Breiman, Friedman, Olshen, and Stone, although we propose extensions to their methods to resolve some practical problems that arise in implementation of these methods in epidemiologic studies. To illustrate these ideas, we analyse low birthweight associated risk factors with use of a data set from the Yale Pregnancy Outcome Study.
Zhang
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Department of Epidemiology and Public Health, Yale University School of Medicine, New Haven, CT 06520-8034, USA.
Holford
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Bracken
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MB
D015982:Bias; D003627:Data Interpretation, Statistical; D003663:Decision Trees; D004812:Epidemiologic Methods; D005260:Female; D006801:Humans; D007230:Infant, Low Birth Weight; D007231:Infant, Newborn; D016014:Linear Models; D011247:Pregnancy; D011256:Pregnancy Outcome; D011446:Prospective Studies; D015203:Reproducibility of Results; D012306:Risk; D012307:Risk Factors; D018709:Statistics, Nonparametric