kathoffman/lmtp-tutorial
Corresponding code guide to the tutorial paper "Introducing longitudinal modified treatment policies: a unified framework for studying complex exposures" (Hoffman et al., 2023)
This project helps researchers and statisticians analyze how the timing of medical interventions, like delaying intubation, affects patient outcomes such as mortality. It takes electronic health records or similar longitudinal data with time-varying treatments and confounders. It produces clear estimates of treatment effects and visual summaries to help understand complex causal relationships over time. Medical researchers and public health analysts would use this.
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Use this if you need to determine the causal effect of time-varying treatments on outcomes in complex observational studies, accounting for many changing factors and practical challenges like positivity violations or competing risks.
Not ideal if your study involves a simple, one-time treatment and a single outcome without time-varying confounders, as simpler causal inference methods might be more straightforward.
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May 11, 2023
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