Fan Li
0000-0001-6183-1893
Duke University
27 papers found
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Group sequential two‐stage preference designs
Adherence to key recommendations for design and analysis of stepped-wedge cluster randomized trials: A review of trials published 2016–2022
A mixed model approach to estimate the survivor average causal effect in cluster‐randomized trials
Designing multicenter individually randomized group treatment trials
Impact of Marital Stress on 1‐Year Health Outcomes Among Young Adults With Acute Myocardial Infarction
Estimating the causal effects of multiple intermittent treatments with application to COVID-19
Maximin optimal cluster randomized designs for assessing treatment effect heterogeneity
Sample size considerations for assessing treatment effect heterogeneity in randomized trials with heterogeneous intracluster correlations and variances
Leveraging baseline covariates to analyze small cluster‐randomized trials with a rare binary outcome
A Scoping Review described diversity in methods of randomization and reporting of baseline balance in Stepped-Wedge Cluster Randomized Trials
Estimands in cluster-randomized trials: choosing analyses that answer the right question
Sample size calculators for planning stepped-wedge cluster randomized trials: a review and comparison
Stepped Wedge Cluster Randomized Trials: A Methodological Overview
Blood pressure response to commonly administered antihypertensives for severe inpatient hypertension
Severe inpatient hypertension prevalence and blood pressure response to antihypertensive treatment
Sample size calculation in hierarchical 2×2 factorial trials with unequal cluster sizes
swdpwr: A SAS macro and an R package for power calculations in stepped wedge cluster randomized trials
Accounting for unequal cluster sizes in designing cluster randomized trials to detect treatment effect heterogeneity
Impact of complex, partially nested clustering in a three-arm individually randomized group treatment trial: A case study with the wHOPE trial
Estimating heterogeneous survival treatment effect in observational data using machine learning
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