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Background Patient exposure to antibiotics promotes the emergence of drug-resistant pathogens. The aim of this study was to identify whether the temporal dynamics of resistance emergence at the individual-patient level were predictable for specific pathogen-drug classes. Methods Following a systematic review, a novel robust error meta-regression (REMR) method for dose-response meta-analysis (DRMA) was used to estimate the odds ratio (OR) for carrying resistant bacteria during and following treatment compared to baseline. Probability density functions fitted to the resulting dose-response curves were then used to optimize the period during and/or after treatment when resistant pathogens were most likely to be identified. Results Studies of Streptococcus pneumoniae treatment with β-lactam antibiotics demonstrated a peak in resistance prevalence among patients four days after completing treatment with a 3.32-fold increase in odds (95%CI 1.71 - 6.46). Resistance waned more gradually than it emerged, returning to pre-exposure levels one month after treatment (OR 0.98, 95%CI 0.55 - 1.75). Patient isolation during the peak dose-response period would be expected to reduce the risk that a transmitted pathogen is resistant equivalently to a 50% longer isolation window timed from the first day of treatment. Conclusions Predictable temporal dynamics of resistance levels have implications both for surveillance and control.