Abstract
Matched case-control designs are used in ecology and wildlife management to estimate resource selection functions, which provide insight into habitat use by animals. Recent suggestions to incorporate random effects into these models have little statistical justification because they incorrectly assume unconstrained sampling of study sites, ignoring matching in the study design. Matched case-control designs have been used extensively in epidemiology, where conditional likelihood functions are used to account for constrained sampling. Here, we illustrate the discrepancies between the constrained and unconstrained models, and evaluate the bias of parameter estimates using simulation. We evaluated the conditional logistic model, which produces consistent estimates, and compared results with estimates from prospective logistic models, stratified case-control models, and marginal logistic models. Conditional logistic models had the lowest bias across a wide range of sampling schemes and parameter values. In contrast, marginal logistic models tended to have greater bias and poor confidence interval coverage rates.