Qingyuan Zhao

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  Sensitivity Analysis for IPW Estimators via the Percentile Bootstrap
  Qingyuan Zhao
  In this talk I will introduce a marginal sensitivity model for IPW estimators which is a natural extension of Rosenbaum’s sensitivity model for matched observational studies. The goal is to construct confidence intervals based on inverse probability weighting estimators, such that the intervals have asymptotically nominal coverage of the estimand whenever the data generating distribution is in the collection of marginal sensitivity models. I will use a percentile bootstrap and a generalized minimax/maximin inequality to transform this intractable problem to a linear fractional programming problem, which can be solved very efficiently. I will illustrate our method using a real dataset to estimate the causal effect of fish consumption on blood mercury level.
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  A Bayesian Sequential Design with Adaptive Randomizations
  Qingyuan Zhao
  Bayesian sequential and adaptive randomization designs are gaining popularity in clinical trials thanks to their potentials to reduce the number of required participants and save resources. We propose a Bayesian sequential design with adaptive randomization rates so as to more efficiently attribute newly recruited patients to different treatment arms. In this talk, we consider two-arm clinical trials. Patients are allocated to the two arms with a randomization rate to achieve minimum variance for the test statistic. Alpha spending function is used to control the overall type I error of the hypothesis testing.  Algorithms are presented to calculate the optimal randomization rate, critical values, and power for the proposed design. Sensitivity analysis is implemented to check the influence on design by changing the prior distributions. Simulation studies are applied to compare the proposed method and traditional methods in terms of power and actual sample sizes. Simulations show that, when total sample size is fixed, the proposed design can obtain greater power and/or cost smaller actual sample size than the traditional Bayesian sequential design. Finally, we apply the proposed method to a real data set and compare the results with the Bayesian sequential design without adaptive randomization in terms of sample sizes. The proposed method can further reduce required sample size.