1. 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

2. 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

3. 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

4. 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

5. 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.