Background: Clone-censor-weighting (CCW) can compare treatment regimens that are initially indistinguishable (such as starting treatment within specific time windows) without using landmarks or creating immortal time. The causal contrasts estimated in these cases and the analyses themselves can become quite complex, however. Objective: Provide a tutorial on CCW for comparing initiation windows and illustrate the causal contrasts underlying such comparisons. Methods: We identified patients with myocardial infarctions without past aspirin or clopidogrel use in Medicare’s synthetic public data files. We assigned “clones” to three regimens: 1) initiation within 30 days; 2) initiation within 90 days; or 3) initiation from 30-90 days. Clones were censored when deviating from their assigned regimen by failing to initiate treatment in time or by initiating treatment too early. We addressed informative censoring using inverse probability of censoring weights (IPCW), calculated weighted 180-day risks of re-hospitalization or death using Kaplan-Meier methods, and visualized the portion of the population exposed during the first 90 days to compare exposure distributions underlying regimens. Results: We identified 1,589 patients experiencing myocardial infarction with no past medication use. 15% initiated within 30 days and 26% initiated between 30 and 90 days. After IPCW, the 180-day outcome risk was 40.2% in the 30-day regimen, 35.7% in the 90-day regimen, and 35.2% in the 30-to-90 day regimen. Conclusions: Though CCW can be complex to implement and the effects it estimates can vary substantially across study populations that initiate treatments at different times, it is a useful tool for contrasting initiation windows.

Purpose: While much has been written about how distributed networks address internal validity, external validity is rarely discussed. We aimed to define key terms related to external validity, discuss how they relate to distributed networks, and identify how three networks (the US Food and Drug Administration’s Sentinel System, the Canadian Network for Observational Drug Effect Studies [CNODES], and PCORnet, the National Patient Centered Clinical Research Network, initiated and supported by the Patient-Centered Outcomes Research Institute. Methods: We define external validity, target populations, target validity, generalizability, and transportability and describe how each relates to distributed networks. We then describe Sentinel, CNODES, and PCORnet and how each approaches these concepts. Results: Each network approaches external validity differently Sentinel answers regulatory questions in the general US population using data from commercial health plans and Medicare fee-for-service beneficiaries and considers external validity when exploring outliers or performing subgroup analyses to examine potential heterogeneity of treatment effects. CNODES focuses on a Canadian target population but includes UK and US data and thus has to make decisions about which partners can be included in each analysis. PCORnet supports a wider array of studies including randomized trials and often assesses whether a given study will be representative of the wider US population. Conclusions: There is no one-size-fits-all approach to external validity within distributed networks. With these networks and comparisons between their findings becoming a key part of pharmacoepidemiology, there is a need to adapt tools for improving external validity to the distributed network setting.