Statistical Methods for Correlated Data in Health Research

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The general goal of my research program is to develop statistical methods that can have a real impact on statistical practice. Specific objectives for the next five years are as follows: 1) Development of methods for complex inter-observer and reliability studies with binary outcomes. In this part of my research, I will develop methods for concurrent assessment of both inter-observer agreement and intra-observer reliability. The finite sample performance of asymptotic results will be compared to the bootstrap. These results will be used to analyze several ongoing large studies for which I am a co-investigator. 2) Development of methods for community intervention trials with time-to-event outcomes. A defining feature of these trials is that they often involve a small number of large clusters. However, methods that can be safely applied such trials are not well developed. I will propose small sample adjustments to asymptotic methods. I will also develop computationally intensive approaches such as the bootstrap. 3) Development of confidence interval procedures for the intraclass correlation coefficients in cluster randomization trials with binary outcomes. Investigators frequently use point estimates from previous literature without fully appreciating the inherent lack of precision associated with these estimates. A confidence interval procedure that is applicable for trials with small number of large clusters would have high practical value. I will develop new procedures based on my previous results developed for large number of small clusters. Confidence interval procedure for new dependence parameters for binary outcomes arising from cluster randomization trials will also be developed. 4) Development of methods for causal mediation analysis of cluster randomization trials with binary outcomes. Although mediation analysis is a natural tool for illuminating how an intervention works, virtually no research work has been done in the context of cluster randomization trials. I will develop relevant statistical methods using the counterfactual framework.