Strong interest in nest success has led to advancement in the analysis of nest-survival data. New approaches allow researchers greater flexibility in modeling nest-survival data and provide methods for relaxing assumptions and accounting for potentially important sources of variation. The most flexible method uses linear-logistic models with a random-effects framework to both incorporate potential covariate effects and model remaining heterogeneity. With the goal of increasing the use of more flexible methods, we provide additional detail regarding linear-logistic mixed models and their implementation. We use an example dataset to (1) demonstrate data preparation for analysis in PROC NLMIXED of SAS, (2) describe the use of code for evaluating competing models, (3) illustrate implementation of models with and without random effects and that evaluate potential effects of observer visits to nests, and (4) present methods of obtaining estimates of nest-survival rate for various covariate conditions of interest. We also conduct Monte Carlo simulations to evaluate the performance of linear-logistic mixed models of nest-survival data. We present the results of evaluation for one scenario and show that the estimation procedure as implemented in PROC NLMIXED is effective and that simulation can be used to gain insights into the advantages and disadvantages of various study designs. We encourage the development of further advancements that will allow greater flexibility in modeling.