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Robert W. Platt on Muck Rack

Robert W. Platt

Montreal
As seen in: Wiley Online Library, Memory Studies, BMJ Open Diabetes Research & Care, BMJ Paediatrics & Child Health, Journal of Epidemiology and Community Health, SAGE Publishing, The BMJ, JAMA (The Journal of the American Medical Association), JAMA Psychiatry, Canadian Medical Association Journal and
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Articles

Challenges in Small Sample Size Cluster Trials for Medication Adherence: Insights From TAKE‐IT‐TOO

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By Robert W. Platt, Mary Amanda Dew, Spencer W. Riddell, Annette DeVito Dabbs
| Wiley Online Library Verified
Conflicts of Interest The authors declare no conflicts of interest. Data Availability Statement The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions. References 1, , and , “Cluster Randomised Controlled Trials,” Journal of Evaluation in Clinical Practice 11, no. 5 (2005): 479–483. 2, “Cluster Randomised Trials,” Medical Journal of Australia 210, no. 4 (2019): 154–156.e1.
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Comparing IPCW Models to Adjust for Informative Censoring During COVID‐19 Using Data From the Clinical Practice Research Datalink

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By Gwen Aubrac, Université de Bordeaux, Michael Webster-Clark, Robert W. Platt
| Wiley Online Library Verified
As-treated (AT) analyses estimate effects of starting and adhering to treatment but are vulnerable to selection bias from informative censoring. Informative censoring occurs when outcome predictors are associated with censoring. Inverse probability of censoring weights (IPCW) reweight uncensored patients to resemble censored patients and can be applied either before censoring occurs (using a non-lagged model) or after censoring has already occurred (using a lagged model).
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Demystifying Clone‐Censor‐Weighting to Studying Treatment Initiation Windows: An Example Using Publicly Available Synthetic Medicare Claims Data

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By Michael Webster-Clark, Yi Li, Sophie Dell'Aniello, Robert W. Platt
| Wiley Online Library Verified
Clone-censor-weighting (CCW) can compare the effects of different treatment initiation windows. The causal contrast estimated by CCW when studying initiation windows varies depending on when the population naturally initiates, making information on initiation timings essential context for estimates. We provide straightforward analytic code for a CCW analysis within a cohort constructed from publicly available synthetic Medicare claims data.
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