Response-dependent two-phase designs are used increasingly often in epidemiological studies to ensure sampling strategies offer good statistical efficiency while working within resource constraints. By using a two-phase sampling approach where only a small sub-sample give complete information, it is possible to obtain precise estimates at a greatly reduced cost. Efficiency gains are realized by determining the optimal sub-sample to completely observe. Optimal response-dependent two-phase designs are difficult to implement, however, since they require specification of unknown parameters.
This talk will examine adaptive two-phase designs which exploit information from an internal pilot study to approximate the optimal sampling scheme for an analysis based on mean score estimating equations. Application to a motivating biomarker study illustrates how available data can be exploited mid-study to ensure the final sample provides the best value for money.
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