Evaluation of efficient designs for observational epidemiologic studies

Recent research in the design of observational epidemiologic studies has been focused on determining which design is most efficient for controlling a potential confounding factor in the analysis of the disease-exposure relationship. Typically, only two candidate designs have been considered, the matched design and the random sample design. These are merely two of an infinite variety of potential designs in which the distributions of the confounder in the comparison groups and the ratio of group sample sizes are arbitrarily chosen. Only in special cases will either of the standard designs be the most efficient or "optimal" design. We construct optimal designs by minimizing the variance of the desired estimate of effect with respect to the controllable design parameters. Construction of an optimal design depends on unknown parameters, so that in practice only an approximately optimal design, perhaps constructed sequentially, is possible. We evaluate the potential usefulness of optimal designs by identifying circumstances in which an optimal design results in large efficiency gains relative to both the matched and random sample designs. We find that there can be substantial efficiency gains in follow-up studies when both the exposure and confounder are strong risk factors. Practical issues in the implementation of these designs are discussed.