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Activity Number: 502 - Propensity Score Methods to Conduct Observational Studies Using Complex Survey Data
Type: Topic Contributed
Date/Time: Wednesday, July 31, 2019 : 10:30 AM to 12:20 PM
Sponsor: Survey Research Methods Section
Abstract #303013 Presentation
Title: Assessing the Causal Effect of Cumulative Load for Recurrent Injury Events in Professional Tennis Using a Flexible Cox Marginal Structural Model
Author(s): Stephanie Kovalchik*
Companies: Tennis Australia/Victoria University
Keywords: Causal inference; Time-dependent; Cumulative exposure; Sports

Injury prevention is critical to the achievement of peak performance in elite sport. For professional tennis players, the topic of injury prevention has gained even greater importance in recent years as multiple of the best male players have been sidelined or forced to retire owing to injury. Understanding the risk of injury in tennis is complicated by a number of factors including the complexity of the risk mechanism and confounding factors, which each involve time-dependent cumulative variables. To address these challenges, this paper applies the Cox marginal structural model with weighted cumulative exposure proposed by Xiao and colleagues. In this application, I use 10 years of competition schedules of top tennis players to model the hazard of an absence of play. The treatment of interest is the cumulative time spent in competition ("competition load"), which is flexibly incorporated into the Cox framework using cubic splines. The time-dependent confounders of age, player ability, and previous absences from the play are controlled for using inverse probability of treatment weights. I discuss the study implications for scheduling and injury prevention in elite tennis.

Authors who are presenting talks have a * after their name.

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