Concordance probability and discriminatory power in proportional hazards regression

Gönen, Mithat; Heller, Glenn

doi:10.1093/biomet/92.4.965

Cited by 566 publications

(436 citation statements)

References 12 publications

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“…We used the method of Gonen and Heller, which estimates the concordance index, to assess the discriminatory power of the multivariable model. 28 The concordance index has the same interpretation as Harrell's C statistic for survival data. 29 We used a two-sided p value of 0.05 to denote statistical significance.…”

Section: Statistical Analysesmentioning

confidence: 99%

Self-Reported Health Status Predicts Other-Cause Mortality in Men with Localized Prostate Cancer: Results from the Prostate Cancer Outcomes Study

et al. 2015

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BACKGROUND: Guidelines recommend against treating localized prostate cancer (PCa) in men with a greater than10-year life expectancy. However, physicians have difficulty accurately estimating life expectancy. OBJECTIVE: We used data from a population-based observational study to develop a nomogram to estimate long-term other-cause mortality based on self-reported health status (SRHS), race/ethnicity, and age at diagnosis. DESIGN: This was an observational study. SUBJECTS: Men diagnosed with localized PCa from October 1994 through October 1995 participated in the study. MAIN MEASURES: Initial measures obtained 6 months after diagnosis included sociodemographic and tumor characteristics, treatment, and a single item on the SRHS, with response options ranging from excellent to poor. We used Surveillance, Epidemiology, and End-Results program data to determine date and cause of death through December 2010. We estimated other-cause mortality with proportional hazards survival analyses, accounting for competing risks. KEY RESULTS: We evaluated 2,695 men, of whom 74 % underwent aggressive therapy (surgery or radiotherapy). At the initial survey, 18 % reported excellent (E), 36 % very good (VG), 31 % good (G), and 15 % fair/poor (F/P) health. Healthier men were younger, and more likely to be white, better educated, and to undergo surgery. At follow-up, 44 % of the cohort had died; 78 % of deaths were from causes other than PCa. SRHS predicted other-cause mortality; for men reporting E, VG, G, F/P health, the cumulative incidences of other-cause mortality were 20 %, 29 %, 40 %, and 53 %, respectively, p<0.001. Compared to a reference of excellent SRHS, multivariable hazard ratios (95 % CI) for other-cause mortality for men reporting VG, G, and F/P health were 1.22 (0

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Section: Statistical Analysesmentioning

confidence: 99%

Self-Reported Health Status Predicts Other-Cause Mortality in Men with Localized Prostate Cancer: Results from the Prostate Cancer Outcomes Study

et al. 2015

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“…Adjusted HRs by the multivariate stratified Cox model were also estimated including age, sex, performance status, pathological type, and macroscopic type as covariates. Concordance probability estimates (CPE) were also calculated from the stratified Cox model for each cutoff to investigate how well each cutoff discriminated overall survival [27]. If the patient in the responder group lived longer, then the pair was regarded as concordant.…”

Section: Statistical Considerationmentioning

confidence: 99%

Determination of the optimal cutoff percentage of residual tumors to define the pathological response rate for gastric cancer treated with preoperative therapy (JCOG1004-A)

Nakamura

Kuwata

Shimoda³

et al. 2014

Gastric Cancer

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Background Pathological response rate (pathRR) is a common endpoint used to assess the efficacy of preoperative therapy for gastric cancer. PathRR is estimated based on the percentage of the residual tumor area in the primary tumorous bed. Various cutoff definitions used in previous trials (e.g., 10, 33, 40, 50, 67 %) often impair the comparability of pathRRs between trials. Methods Individual patient data were used from four JCOG trials evaluating preoperative chemotherapy (JCOG0001, JCOG0002, JCOG0210, JCOG0405). Pathological specimens were evaluated from 173 out of 188 patients (92 %) who underwent surgery. Residual tumor area and primary tumorous beds were traced on a virtual microscopic slide by one pathologist and another confirmed these areas. The hazard ratio (HR) in overall survival was calculated for each cutoff percentage by stratified Cox regression analysis, including the study as a stratification factor, and concordance probability estimates (CPE) were calculated. Results The numbers of patients with 0%, 1-10 %, 11-33 %, 34-50 %, 51-66 %, and 67-100 % residual tumors were 8, 35, 33, 27, 23, and 47, respectively. HRs in 10, 33, 50, and 67 % cutoffs were 1.91, 1.70, 1.55, and 1.71 for the overall population, and CPEs were 0.56, 0.56, 0.55, and 0.55, respectively. In patients with R0 resection, HRs in 10, 33, 50, and 67 % cutoffs were 1.87, 1.54, 1.24, and 1.38, and CPEs were 0.56, 0.55, 0.52, and 0.52. In subgroup analyses, the 10 % cutoff did not predict survival well for type 4 (linitis plastica) tumors. Conclusions The 10 % cutoff should be the global standard cutoff of %residual tumor to determine pathRR. PathRR might not be recommended for clinical trials where the main subjects are type 4 tumors.

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“…Here  denotes the number of the pairs which have The concordance probability (CP) [26], [27] for the Cox PH model is defined as:…”

Section: Empirical Validation a Performance Measuresmentioning

confidence: 99%

Machine Learning Approaches to Survival Analysis: Case Studies in Microarray for Breast Cancer

Liu¹,

Pelckmans²

2014

IJMLC

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Concordance probability and discriminatory power in proportional hazards regression

Cited by 566 publications

References 12 publications

Self-Reported Health Status Predicts Other-Cause Mortality in Men with Localized Prostate Cancer: Results from the Prostate Cancer Outcomes Study

Self-Reported Health Status Predicts Other-Cause Mortality in Men with Localized Prostate Cancer: Results from the Prostate Cancer Outcomes Study

Determination of the optimal cutoff percentage of residual tumors to define the pathological response rate for gastric cancer treated with preoperative therapy (JCOG1004-A)

Machine Learning Approaches to Survival Analysis: Case Studies in Microarray for Breast Cancer

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