77 citations · 82 across the 5 of their papers we have counts for
5 papers
Sequential monitoring using the Second Generation P-Value with Type I error controlled by monitoring frequency
Jonathan J. Chipman, Robert A. Greevy, Lindsay Mayberry +1
Many adaptive monitoring schemes adjust the required evidence toward a hypothesis to control Type I error. This shifts focus away from determining scientific relevance with an unco…
Rematching on-the-fly: sequential matched randomization and a case for covariate-adjusted randomization
Jonathan J. Chipman, Lindsay Mayberry, Robert A. Greevy
Covariate-adjusted randomization (CAR) can reduce the risk of covariate imbalance and, when accounted for in analysis, increase the power of a trial. Despite CAR advances, stratifi…
Contextualizing E-values for Interpretable Sensitivity to Unmeasured Confounding Analyses
Lucy D'Agostino McGowan, Robert A. Greevy
The strength of evidence provided by epidemiological and observational studies is inherently limited by the potential for unmeasured confounding. Researchers should present a quant…
Bounding the local average treatment effect in an instrumental variable analysis of engagement with a mobile intervention
Andrew J. Spieker, Robert A. Greevy, Lyndsay A. Nelson +1
Estimation of local average treatment effects in randomized trials typically requires an assumption known as the exclusion restriction in cases where we are unwilling to rule out u…
Second-generation p-values: improved rigor, reproducibility, & transparency in statistical analyses
Jeffrey D. Blume, Lucy DAgostino McGowan, William D. Dupont +1
Verifying that a statistically significant result is scientifically meaningful is not only good scientific practice, it is a natural way to control the Type I error rate. Here we i…