How best to improve the early detection of autism spectrum disorder (ASD) is the subject of significant controversy. Some argue that universal ASD screeners are highly accurate, whereas others argue that evidence for this claim is insufficient. Relatedly, there is no clear consensus as to the optimal role of screening for making referral decisions for evaluation and treatment. Published screening research can meaningfully inform these questions-but only through careful consideration of children who do not complete diagnostic follow-up. We developed two simulation models that re-analyze the results of a large-scale validation study of the M-CHAT-R/F by Robins et al. (2014, Pediatrics, 133, 37). Model #1 re-analyzes screener accuracy across six scenarios, each reflecting different assumptions regarding loss to follow-up. Model #2 builds on this by closely examining differential attrition at each point of the multi-step detection process. Estimates of sensitivity ranged from 40% to 94% across scenarios, demonstrating that estimates of accuracy depend on assumptions regarding the diagnostic status of children who were lost to follow-up. Across a range of plausible assumptions, data also suggest that children with undiagnosed ASD may be more likely to complete follow-up than children without ASD, highlighting the role of clinicians and caregivers in the detection process. Using simulation modeling as a quantitative method to examine potential bias in screening studies, analyses suggest that ASD screening tools may be less accurate than is often reported. Models also demonstrate the critical importance of every step in a detection process-including steps that determine whether children should complete an additional evaluation. We conclude that parent and clinician decision-making regarding follow-up may contribute more to detection than is widely assumed.