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We compare the performance of five well-known truncation heuristics for mitigating the effects of initialization bias in the output analysis of steady-state simulations. Two of these rules are variants of the MSER heuristic studied by White (1997); the remaining rules are adaptations of bias-detection tests based on the seminal work of Schruben (1982). Each heuristic was tested in each of a 168 different experiments. Each experiment comprised multiple tests on different realizations of the sample path of a second-order autoregressive process with known (deterministic) bias function. Different experiments employed alternative process parameters, generating a range of damped and underdamped stochastic responses. These were combined with alternative damped, underdamped, and mean shift bias functions. The performance of each rule was evaluated based on the ability of the rule to remove bias from the mean estimator for the steady-state process. Results confirmed that four of the five rules were effective and reliable, consistently yielding truncated sequences with reduced bias. In general, the MSER heuristics outperformed the three rules based on bias detection, with Spratt's (1998) MSER-5 the most effective and robust choice for a general-purpose method.