Jennifer Wilcock
University of Auckland
Alan Lee
University of Auckland
In two-stage case-control studies, outcome status and one or more inexpensive covariates are observed for a large sample but additional, more expensive covariates are collected for a subsample only, selected by random sampling from the strata defined at the first stage. Large efficiency and/or cost gains are possible using two-stage rather than one-stage studies of comparable cost or power. Here we demonstrate a method for designing two-stage studies to obtain the best possible precision under specified cost constraints, by applying an efficient semi- parametric maximum likelihood approach due to Scott and Wild (University of Auckland) which has been developed for the analysis of a class of generalised case-control designs.
As with all model-based approaches, the ‘optimal’ design found is sensitive to the values of the model parameters used for deriving the design. If the design parameters are particularly inaccurate this may result in an ‘optimal’ design which is less efficient than that which would have been derived using a more robust design approach. The efficiency of the design depends on the sampling fractions within each stratum, and here a method will be presented for comparing designs with ‘optimal’ to those with balanced second stage sample sizes, under specified cost constraints.
Independent component analysis and statistical parametric mapping of the relationship between personality and brain blood flow in normal males
Session 3d, Medical Statistics: 16:20 — 16:40, Room 445