Purpose Measuring prostate cancer patient HRQOL in routine clinical practice is hindered by lack of instruments enabling efficient real-time, point-of-care scoring of multiple HRQOL domains. We sought to develop an instrument for this purpose. Materials and Methods The EPIC for Clinical Practice (EPIC-CP) is a one-page, 16-item questionnaire to measure urinary incontinence, urinary irritation, bowel, sexual, and hormonal HRQOL domains that we constructed by eliminating conceptually overlapping items from the 3 page EPIC-26, and revising the questionnaire format to mirror the AUA Symptom Index, thereby enabling practitioners to calculate HRQOL scores at point of care. We administered EPIC-CP to a new cohort of PCa patients in community-based and academic oncology, radiation, and urology practices to evaluate the instrument’s validity and ease of use for clinical practice. Results 175 treated and 132 untreated PCa subjects completed EPIC-CP (N = 307). EPIC-CP domain scores correlated highly with respective domain scores from longer versions of EPIC (r ≥ 0.93 for all domains). EPIC-CP showed high internal consistency (Cronbach’s α = 0.64-0.84) and sensitivity to PCa treatment-related effects (p < 0.05 in each of 5 HRQOL domains). Patients completed EPIC-CP efficiently (96% in <10 minutes, and 11% missing items). It was deemed ‘very convenient’ by clinicians in 87% of routine clinical encounters, and clinicians accurately scored completed questionnaires 94% of the time. Conclusions EPIC-CP is a valid instrument that enables patient-reported HRQOL to be measured efficiently and accurately at the point of care, and can thereby facilitate improved emphasis and management of patient-reported outcomes.

Background Data continues to emerge on the relative merits of different treatment modalities for prostate cancer. The purpose of this study is to compare patient-reported quality-of-life outcomes (QOL) after proton therapy (PT) and intensity-modulated radiation therapy (IMRT) for prostate cancer. Methods A comparison was performed of prospectively collected QOL data using the expanded prostate cancer index (EPIC) questionnaire. QOL data was collected during the first 2 years following treatment for men treated with PT and IMRT. PT was delivered to 1,243 men at a single center to 76-82Gy. IMRT was delivered to 204 men included in the Prostate Cancer Quality Assurance Study (PROSTQA) in doses of 75.6-79.4Gy.The Wilcoxon rank sum test was used to compare EPIC outcomes by modality using baseline-adjusted scores at different time points. Individual questions were assessed by converting to binary outcomes and testing with generalized estimating equations. Results No differences in changes in summary scores for bowel, urinary incontinence, urinary irritative/obstructive, and sexual domains were seen between the two cohorts. However, more men treated with IMRT reported moderate/big problems with rectal urgency (p=0.02) and frequent bowel movements (p=0.05) than men treated with PT. Conclusions There were no differences in QOL summary scores between the IMRT and PT cohorts during early follow-up up to 2-years. Response to individual questions suggests possible differences in specific bowel symptoms between the two cohorts. These outcomes highlight the need for further comparative studies of PT and IMRT.

IMPORTANCE Before 2016, safety concerns limited metformin use in patients with kidney disease; however, the effectiveness of metformin on clinical outcomes in patients with reduced kidney function remains unknown. OBJECTIVE To compare major adverse cardiovascular events (MACE) among patients with diabetes and reduced kidney function who continued treatment with metformin or a sulfonylurea. DESIGN, SETTING, AND PARTICIPANTS Retrospective cohort study of US veterans receiving care within the national Veterans Health Administration, with data supplemented by linkage to Medicare, Medicaid, and National Death Index data from 2001 through 2016. There were 174 882 persistent new users of metformin and sulfonylureas who reached a reduced kidney function threshold (estimated glomerular filtration rate <60 mL/min/1.73 m 2 or creatinine Ն1.4 mg/dL for women or Ն1.5 mg/dL for men). Patients were followed up from reduced kidney function threshold until MACE, treatment change, loss to follow-up, death, or study end (December 2016). EXPOSURES New users of metformin or sulfonylurea monotherapy who continued treatment with their glucose-lowering medication after reaching reduced kidney function. MAIN OUTCOMES AND MEASURES MACE included hospitalization for acute myocardial infarction, stroke, transient ischemic attack, or cardiovascular death. The analyses used propensity score weighting to compare the cause-specific hazard of MACE between treatments and estimate cumulative risk accounting for the competing risks of changing therapy or noncardiovascular death. RESULTS There were 67 749 metformin and 28 976 sulfonylurea persistent monotherapy users; the weighted cohort included 24 679 metformin and 24 799 sulfonylurea users (median age, 70 years [interquartile range {IQR}, 62.8-77.8]; 48 497 men [98%]; and 40 476 white individuals [82%], with median estimated glomerular filtration rate of 55.8 mL/min/1.73 m 2 [IQR, 51.6-58.2] and hemoglobin A 1c level of 6.6% [IQR, 6.1%-7.2%] at cohort entry). During follow-up (median, 1.0 year for metformin vs 1.2 years for sulfonylurea), there were 1048 MACE outcomes (23.0 per 1000 person-years) among metformin users and 1394 events (29.2 per 1000 person-years) among sulfonylurea users. The cause-specific adjusted hazard ratio of MACE for metformin was 0.80 (95% CI, 0.75-0.86) compared with sulfonylureas, yielding an adjusted rate difference of 5.8 (95% CI, 4.1-7.3) fewer events per 1000 person-years of metformin use compared with sulfonylurea use. CONCLUSIONS AND RELEVANCE Among patients with diabetes and reduced kidney function persisting with monotherapy, treatment with metformin, compared with a sulfonylurea, was associated with a lower risk of MACE.

Purpose To expand the clinical usefulness of the Expanded Prostate Cancer Index Composite for Clinical Practice (EPIC-CP) by evaluating its responsiveness to health related quality of life (HRQOL) changes, defining the minimally important differences (MID) for an individual patient's change in each domain, and applying it to a sexual outcome prediction model. Methods In 1,201 subjects from a previously described multi-center, longitudinal cohort, we modeled each treatment group's EPIC-CP domain scores at pre-treatment, short-term, and long-term follow-up. We considered post-treatment domain score changes from pre-treatment ≥ 0.5 standard deviations (SD) clinically significant and with a p-value ≤ 0.01 as statistically significant. We determined domain MIDs using the 0.5 pooled SD of the 2, 6, 12, and 24 month post-treatment changes from pre-treatment. We recalibrated an EPIC-CP-based nomogram model predicting 2-year post-prostatectomy functional erections from that developed using EPIC-26. Results For every HRQOL domain, EPIC-CP was sensitive to similar post-treatment HRQOL changes over time as had been observed using EPIC-26. The EPIC-CP MIDs for changes in the urinary incontinence, urinary irritation/obstruction, bowel, sexual, and vitality/hormonal domains are 1.0, 1.3, 1.2, 1.6, and 1.0, respectively. The EPIC-CP-based sexual prediction model performs well (AUC=0.76) and shows robust agreement with its EPIC-26-based counterpart, with predicted probability differences between models of ≤10% for 95% of individuals and a mean difference of 0.0 (SD=0.05) across all individuals. Conclusions EPIC-CP is responsive to HRQOL changes during convalescence, and can be used to predict 2-year post-prostatectomy sexual outcomes. Its use can facilitate shared medical decision-making and patient-centered care.

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OBJECTIVES To investigate patient uncertainty and perception of danger regarding prospects for clinical prostate cancer control. To determine the impact of these factors on satisfaction with overall prostate cancer treatment outcome. PATIENTS AND METHODS Men who had undergone primary treatment for early stage prostate cancer and who were participants in the Prostate Cancer Outcomes and Satisfaction with Treatment Quality Assessment (PROSTQA) prospective cohort study of prostate cancer outcomes (the parent study) were offered the opportunity to participate in the present study. Centralized phone interviews were conducted to determine patient-reported uncertainty regarding cancer status (measured by the Mishel Uncertainty in Illness Scale-Community Form), perception of danger (measured by Folkman and Lazarus’ Appraisal Scale) and satisfaction with treatment outcome (measured by the Service Satisfaction Scale for Cancer Care). The study used the same centralized telephone interview centre as was used in the parent study. Data were collected at 48, 60 or 72 months after the completion of prostate cancer treatment. Relationships among measures were characterized by Spearman rank correlation coefficients (r). RESULTS A total of 338 agreed to participate, representing 76% of those who were invited. Younger patients experienced less uncertainty (r = 0.20, P < 0.001), yet reported greater perception of danger (r = −0.12; P = 0.03) concerning their previously treated prostate cancer. African-American patients showed greater uncertainty than other ethnic groups (P = 0.005) but did not have a greater perception of danger (P = 0.36). Education played a major role in uncertainty; patients with lower levels of education tended to report higher degrees of uncertainty (r = −0.25; P < 0.001). There was a mild to moderate general association between the three outcomes. A greater sense of uncertainty was associated with a greater perception of danger (r = 0.34, P < 0.001), and as danger and uncertainty increased, satisfaction with treatment outcome tended to decrease (r was between −0.30 and −0.34, P < 0.001). CONCLUSIONS Results suggest that possible disparities related to patient racial background and education may exist in the perception of cancer-related uncertainty. Racial and educational disparities, coupled with a mild to moderate association of uncertainty or danger perception and overall outcome satisfaction, suggest an unmet need for healthcare and nursing services for men undergoing treatment for prostate cancer.

Background Randomization is the foundation of any clinical trial involving treatment comparison. It helps mitigate selection bias, promotes similarity of treatment groups with respect to important known and unknown confounders, and contributes to the validity of statistical tests. Various restricted randomization procedures with different probabilistic structures and different statistical properties are available. The goal of this paper is to present a systematic roadmap for the choice and application of a restricted randomization procedure in a clinical trial. Methods We survey available restricted randomization procedures for sequential allocation of subjects in a randomized, comparative, parallel group clinical trial with equal (1:1) allocation. We explore statistical properties of these procedures, including balance/randomness tradeoff, type I error rate and power. We perform head-to-head comparisons of different procedures through simulation under various experimental scenarios, including cases when common model assumptions are violated. We also provide some real-life clinical trial examples to illustrate the thinking process for selecting a randomization procedure for implementation in practice. Results Restricted randomization procedures targeting 1:1 allocation vary in the degree of balance/randomness they induce, and more importantly, they vary in terms of validity and efficiency of statistical inference when common model assumptions are violated (e.g. when outcomes are affected by a linear time trend; measurement error distribution is misspecified; or selection bias is introduced in the experiment). Some procedures are more robust than others. Covariate-adjusted analysis may be essential to ensure validity of the results. Special considerations are required when selecting a randomization procedure for a clinical trial with very small sample size. Conclusions The choice of randomization design, data analytic technique (parametric or nonparametric), and analysis strategy (randomization-based or population model-based) are all very important considerations. Randomization-based tests are robust and valid alternatives to likelihood-based tests and should be considered more frequently by clinical investigators.

The Integrated Discrimination Improvement (IDI) is commonly used to compare two risk prediction models; it summarizes the extent a new model increases risk in events and decreases risk in non-events. The IDI averages risks across events and non-events and is therefore susceptible to Simpson's Paradox. In some settings, adding a predictive covariate to a well calibrated model results in an overall negative (positive) IDI. However, if stratified by that same covariate, the strata-specific IDIs are positive (negative). Meanwhile, the calibration (observed to expected ratio, O/E, and Hosmer-Lemeshow Goodness of Fit Test, GOF), area under the receiver-operating-characteristic curve (AUC), and Brier score improve overall and by stratum. We ran extensive simulations to investigate the impact of an imbalanced covariate upon metrics (IDI, AUC, Brier score, and R2), provide an analytic explanation for the paradox in the IDI, and use an investigative metric, a Weighted IDI, to better understand the paradox. In simulations, all instances of the paradox occurred under stratum-specific mis-calibration, yet there were mis-calibrated settings in which the paradox did not occur. The paradox is illustrated on Cancer Genomics Network data by calculating predictions based on two versions of BRCAPRO, a Mendelian risk prediction model for breast and ovarian cancer. In both simulations and the CGN data, overall model calibration did not guarantee stratum-level calibration. We conclude that the IDI should only assess model performance among a clinically relevant subset when stratum-level calibration is strictly met and recommend calculating additional metrics to confirm the direction and conclusions of the IDI.