Derives new analytical sample size and power formulas for marginal hazard ratios in causal inference with time-to-event outcomes, applicable to randomized trials and observational studies via IPW estimators.
American Journal of Epidemiology , volume=
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Loss-weighted targeting in TMLE introduces more systematic bias than clever-covariate-scaled targeting under positivity stress, while a proposed Lepski-type adaptive truncation with brake improves stability over fixed rules like c/(sqrt(n) log n) with c=5 or 6.
Emulating stepped-wedge cluster randomized trials in the target trial emulation framework provides a conceptual structure for evaluating health policies with staggered adoption in observational and quasi-experimental studies.
The paper proposes TrialCalibre, a multi-agent framework to automate RCT benchmarking and calibration of observational trial emulations for causal effect estimation.
citing papers explorer
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Sample size and power calculations for causal inference with time-to-event outcomes
Derives new analytical sample size and power formulas for marginal hazard ratios in causal inference with time-to-event outcomes, applicable to randomized trials and observational studies via IPW estimators.
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Investigating Targeting Strategies and Truncation in TMLE for the Average Treatment Effect under Practical Positivity Violations
Loss-weighted targeting in TMLE introduces more systematic bias than clever-covariate-scaled targeting under positivity stress, while a proposed Lepski-type adaptive truncation with brake improves stability over fixed rules like c/(sqrt(n) log n) with c=5 or 6.
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Emulating Stepped-Wedge Cluster Randomized Trials to Evaluate Health Policies and Interventions
Emulating stepped-wedge cluster randomized trials in the target trial emulation framework provides a conceptual structure for evaluating health policies with staggered adoption in observational and quasi-experimental studies.
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TrialCalibre: A Fully Automated Causal Engine for RCT Benchmarking and Observational Trial Calibration
The paper proposes TrialCalibre, a multi-agent framework to automate RCT benchmarking and calibration of observational trial emulations for causal effect estimation.