Optimal multi-wave sampling for regression modelling in two-phase designs
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Two-phase designs involve measuring extra variables on a subset of the cohort where some variables are already measured. The goal of two-phase designs is to choose a subsample of individuals from the cohort and analyse that subsample efficiently. It is of interest to obtain an optimal design that gives the most efficient estimates of regression parameters. In this paper, we propose a multi-wave sampling design to approximate the optimal design for design-based estimators. Influences functions are used to compute the optimal sampling allocations. We propose to use informative priors on regression parameters to derive the wave-1 sampling probabilities because any pre-specified sampling probabilities may be far from optimal and decrease efficiency. Generalised raking is used in statistical analysis. We show that a two-wave sampling with reasonable informative priors will end up with higher precision for the parameter of interest and be close to the underlying optimal design.
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