Evaluating Normal Values Methods for Longitudinal Data Defining Expected Quality of Life After Prostate Cancer Treatment Jeff Slezak, MS July 30, 2018 Goals Clinical: Provide personalized “normal values” for each patient for up to two years after treatment Better inform patients on treatment options Surgery, Radiation, Hormonal Therapy, Watchful Waiting, and combinations Set expectations and monitor progress; may be useful for both patients and providers Statistical: Determine the best methods for estimating percentiles for longitudinal, repeated-measures data Department of Research and Evaluation

Patient-reported Quality of Life Data 5727 men were mailed surveys at diagnosis and up to 2 years after treatment, with about 7700 completed responses Five domains Sexual function, Bowel function, Hormonal, Urinary continence, Urinary bother or irritation Each domain is based on several questions, standardized to a scale of 0 (worst) to 100 (best). Patient demographics Tumor characteristics (stage, grade, PSA, etc.) Treatments received

Normal Values Methods Methods Evaluated Linear regression with parametric z-score percentile estimation (O’Brien & Dyck) Quantile Regression Baysian estimation, estimating percentiles from posterior distribution Baysian quantile regression With and without adjustment for repeated measures The same predictors were used in all models 10-fold cross validation was used to assess model calibration

Comparing Percentile Estimates Example: Estimated percentiles of QOL score over time for 50 year-old man with good function before electing surgery. Quantile regression Parametric z-score Baysian Gray includes estimated 25th to 75th percentiles Red extends to 10th percentile, green to 90th percentile

Conclusions No method was clearly superior Parametric methods using linear regression and z-score percentile estimation, incorporating adjustment for repeated measures generally demonstrated the best overall calibration. Quantile regression notably underperformed in the case of strong ceiling effects (>25% of responses at top score) The impact of adjusting for repeated measures is small, but it does slightly improve percentile estimation Thanks to my collaborators, Drs. Steven Jacobsen, Gary Chein, and Stephanie Reading.