Quality of Life after Prostate Cancer Treatment: Trajectories of Recovery using Piecewise Latent Growth Curve Analyses Michael A. Diefenbach, Ph.D., Icahn School of Medicine at Mount Sinai, NYC, Dep of Urology William N. Dudley, Ph.D., Jeffrey D. Labban, Ph.D., Robert A. Henson, Ph.D., University of North Carolina, Greensboro, NC

2013 Estimated US Cancer Cases* *Excludes basal and squamous cell skin cancers and in situ carcinomas except urinary bladder. Source: American Cancer Society, Men 854,790 Women 805,500 29%Breast 14%Lung and bronchus 9% Colon and rectum 6%Uterine corpus 6% Thyroid 4%Non-Hodgkin lymphoma 4%Melanoma of skin 3% Ovary 3%Pancreas 2%Urinary bladder 20%All Other Sites Prostate28% Lung and bronchus14% Colon and rectum9% Urinary bladder6% Melanoma of skin5% Kidney5% Non-Hodgkin4% lymphoma Leukemia3% Oral Cavity3% Pancreas3% All Other Sites20%

Treatment Options SurgeryRadiation Active Surveillance Functional quality of life impacted by treatment Urinary, Sexual, & Bowel functioning Urinary, Sexual, & Bowel functioning

QOL Sexual QOL decreased for all treatments Factors associated w/ decreased sex fx: Factors associated w/ decreased sex fx: Older age, large prostate, high PSA at Dx Distress among partners about decline of sex fx: 44% after prostatectomy; 22% after XRT; 13% after Brachytherapy. Urinary Incontinence: Urinary Incontinence: Lowest 2 mo post prostatectomy Factors assoc w/ incontinence: Older age; black race; high PSA Older age; black race; high PSA Sanda et al., NEJM, 2008; 358:

How can we prepare Pts for recovery?

Physical Wellbeing among Random Sample of 20 patients Time in Months Grand Mean Physical Wellbeing Score

Current Study Uses growth curve analysis to determine the change over time in patient outcomes. Uses growth curve analysis to determine the change over time in patient outcomes. Piecewise growth curve modeling Raudenbush & Bryk, 2002;

ANVOA vs. Latent Growth Model Capabilities CapabilitiesANOVALatent Growth Curve Missing DataOften List wiseFull Information Ability to model missing data patterns Unequal spacing of measurements Typically time is ordinalTime is treated as numerical data Individually varying times points NoYes individuals can vary in spacing and number of measurement occasions Robust estimatorsPerhapsYes - MLRE Distributional assumptions Typically assume normality MPLUS allows several distribution (ZIP, censored normal) Level of MeasurementContinuousMPLUS allows categorical and continuous

Isolation of segments of the trajectory of change Allows for the testing of subsets of predictors on specific portions of the trajectory EBR reference group

Sample Characteristics N = 915 Mean age: years old (6.9) Employment: Employment: Employed: 367 (40%) Employed: 367 (40%) Retired: 548 (60%) Retired: 548 (60%)Education: High School or less: 475 (52%) High School or less: 475 (52%) Some College to post grad: 439 (48%) Some College to post grad: 439 (48%)Race: Caucasians: n = 819 (91%) Caucasians: n = 819 (91%) African Americans and others: n = 78 (9%) African Americans and others: n = 78 (9%)

Sexual Dysfunction

Results: Sexual Dysfunction ModelBaselineResponseRecovery InterceptSlope1Slope2Quad Sexual Dysfunction CFI = 0.985, RMSEA =0.032 EBR Surgery vs EBR 1.632***-0.373***0.030*** Seeds vs EBR 0.240*** Age0.05*** Marital Status Education-0.192** Employment Gleason PSA Health-0.338*** T1CESD T1 POMS T1 Social Well Being T2 CESD T2 POMS Distress.174*-.034* T2 Social Well Being.042

Urinary Dysfunction

Results: Urinary Dysfunction Urinary Dysfunction CFI = RMSEA = Intercepts Surgery vs EBR Seeds vs EBR 0.676***-0.198***0.020* Age0.011** Marital Status Education Employment Gleason PSA0.006 T1 Health-0.106** T1 CESD0.454*** T1 POMS T2 CESD T2 POMS T2 Social Well Being

Summary Baseline differences in age, education and self-rated health were not associated with recovery neither for sex fx or urinary fx. Only higher levels of distress was related to slower recovery of erectile dysfunction

Conclusions Piecewise growth curve analysis is a suitable method to examine predictors of functional change across various time segments. Piecewise growth curve analysis is a suitable method to examine predictors of functional change across various time segments. Useful to visualize recovery trajectory in detail for patient education & decision making Vigilant for signs of distress

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