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1.  On shrinkage and model extrapolation in the evaluation of clinical center performance 
Biostatistics (Oxford, England)  2014;15(4):651-664.
We consider statistical methods for benchmarking clinical centers based on a dichotomous outcome indicator. Borrowing ideas from the causal inference literature, we aim to reveal how the entire study population would have fared under the current care level of each center. To this end, we evaluate direct standardization based on fixed versus random center effects outcome models that incorporate patient-specific baseline covariates to adjust for differential case-mix. We explore fixed effects (FE) regression with Firth correction and normal mixed effects (ME) regression to maintain convergence in the presence of very small centers. Moreover, we study doubly robust FE regression to avoid outcome model extrapolation. Simulation studies show that shrinkage following standard ME modeling can result in substantial power loss relative to the considered alternatives, especially for small centers. Results are consistent with findings in the analysis of 30-day mortality risk following acute stroke across 90 centers in the Swedish Stroke Register.
PMCID: PMC4173104  PMID: 24812420
Causal inference; Double robustness; Firth correction; Profiling center performance; Propensity score; Quality of care; Random and fixed effects
2.  Design and testing for clinical trials faced with misclassified causes of death 
Biostatistics (Oxford, England)  2010;11(3):546-558.
With clinical trials under pressure to produce more convincing results faster, we reexamine relative efficiencies for the semiparametric comparison of cause-specific rather than all-cause mortality events, observing that in many settings misclassification of cause of failure is not negligible. By incorporating known misclassification rates, we derive an adapted logrank test that optimizes power when the alternative treatment effect is confined to the cause-specific hazard. We derive sample size calculations for this test as well as for the corresponding all-cause mortality and naive cause-specific logrank test which ignores the misclassification. This may lead to new options at the design stage which we discuss. We reexamine a recently closed vaccine trial in this light and find the sample size needed for the new test to be 32% smaller than for the equivalent all-cause analysis, leading to a reduction of 41 224 participants.
PMCID: PMC2883300  PMID: 20212319
Cause-specific analysis; Clinical trials; Competing risks; Misclassification; Sample size; Survival analysis; Verbal autopsy
3.  A structural mean model to allow for noncompliance in a randomized trial comparing 2 active treatments 
Biostatistics (Oxford, England)  2010;12(2):247-257.
We propose a structural mean modeling approach to obtain compliance-adjusted estimates for treatment effects in a randomized-controlled trial comparing 2 active treatments. The model relates an individual's observed outcome to his or her counterfactual untreated outcome through the observed receipt of active treatments. Our proposed estimation procedure exploits baseline covariates that predict compliance levels on each arm. We give a closed-form estimator which allows for differential and unexplained selectivity (i.e. noncausal compliance-outcome association due to unobserved confounding) as well as a nonparametric error distribution. In a simple linear model for a 2-arm trial, we show that the distinct causal parameters are identified unless covariate-specific expected compliance levels are proportional on both treatment arms. In the latter case, only a linear contrast between the 2 treatment effects is estimable and may well be of key interest. We demonstrate the method in a clinical trial comparing 2 antidepressants.
PMCID: PMC3062146  PMID: 20805286
Causal inference; Randomized-controlled trials; Structural mean models

Results 1-3 (3)