Statistical Analysis of Mixed Recurrent-Event Data with Application to Cancer Survivor Study

Dr. Liang Zhu, University of Texas Health Science Center, Houston
Monday, October 1, 2018 - 4:00pm to 5:00pm
310 Middlebush Hall

Abstract:  Event history studies occur in many fields including economics, medical studies and social science. In such studies concerning some recurrent events, two types of data have been extensively discussed in the literature. One is recurrent event data and the other is panel count data. There are two other important data types, panel binary data and panel ordinal data. While these 4 data types are all generated from recurrent-event processes, they have different endpoints. Recurrent event data record the occurring time points of each event, panel count data record the number of events since the last observation, panel ordinal data record the number of events categorically, and panel binary data record if any event has happened since the last observation. Among the 4 data types, recurrent event data offer the greatest amount of relevant information, followed by panel count, ordinal, and binary data. In real life, sometimes we have to deal with a mixed data as different endpoints may be collected for the same variable in multiple observations. The statistical literature on mixed recurrent-event data is sparse though examples of these data types exist abundantly in cancer and non-cancer studies. We developed several methods to analyze these mixed data flexibly and efficiently. The methods we developed were applied to the motivating example from the Childhood Cancer Survivor Study (CCSS).