Clinical decision models often rely upon survival models predicated on disease-specific hazard functions combined with baseline hazard functions obtained from standard life tables. Two biases may arise in such a modeling process. First, life expectancy estimates may be biased even if estimates of survival probabilities are unbiased (misestimation bias). In sim ulation studies, the authors discovered that the magnitude of misestimation bias is larger as life expectancy increases, sample size decreases, and censoring percentage increases. In the context of a simple decision analysis, they found that imbalances in the sample sizes for the data used to estimate the parameters among different strategies resulted in non- optimal decisions in the long run. The second bias stems from misspecification of the survival model itself (misspecification bias). Using a simple cost-effectiveness model, the authors found that life expectancies and incremental cost-effectiveness ratios differed depending on whether an excess-mortality or a proportional-hazards model was specified. In addition, a predictable pattern was observed for these two survival models when extrapolated to other age and gender groups. Key words: Markov models; Markov-cycle tree; decision making; excess-mortality model; proportional-hazards model; life expectancy. (Med Decis Making 1995;15:158-169).