Competing-Risks Mortality After Radiotherapy vs. Observation for Localized Prostate Cancer: A Population-based Study

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Radiation Oncology biology

physics

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Clinical Investigation: Genitourinary Cancer

Competing-Risks Mortality After Radiotherapy vs. Observation for Localized Prostate Cancer: A Population-based Study Firas Abdollah, M.D.,*,z,a Maxine Sun, B.Sc.,*,a Jan Schmitges, M.D.,*,x Rodolphe Thuret, M.D.,*,{ Zhe Tian, B.Sc.,* Shahrokh F. Shariat, M.D.,k Alberto Briganti, M.D.,z Claudio Jeldres, M.D.,*,y Paul Perrotte, M.D.,*,y Francesco Montorsi, M.D.,z and Pierre I. Karakiewicz, M.D.*,y *Cancer Prognostics and Health Outcomes Unit and yDepartment of Urology, University of Montreal Health Centre, Montreal, Canada; zDepartment of Urology, Vita Salute San Raffaele University, Milan, Italy; xMartini-Clinic, Prostate Cancer Center Hamburg-Eppendorf, Hamburg, Germany; {Department of Urology, University of Montpellier Health Centre, Montpellier, France; and kDepartment of Urology, Weill Medical College of Cornell University, New York, NY Received Dec 28, 2010, and in revised form Nov 10, 2011. Accepted for publication Nov 13, 2011

Summary Radiotherapy substantially improves cancer-specific mortality in patients with high-risk prostate cancer, with little or no benefit in patients with low-/intermediate-risk tumors, relative to observation. These findings must be interpreted within the context of the limitations of observational data.

Purpose: Contemporary patients with localized prostate cancer (PCa) are more frequently treated with radiotherapy. However, there are limited data on the effect of this treatment on cancer-specific mortality (CSM). Our objective was to test the relationship between radiotherapy and survival in men with localized PCa and compare it with those treated with observation. Methods: A population-based cohort identified 68,797 men with cT1eT2 PCa treated with radiotherapy or observation between the years 1992 and 2005. Propensity-score matching was used to minimize potential bias related to treatment assignment. Competing-risks analyses tested the effect of treatment type (radiotherapy vs. observation) on CSM, after accounting to othercause mortality. All analyses were carried out within PCa risk, baseline comorbidity status, and age groups. Results: Radiotherapy was associated with more favorable 10-year CSM rates than observation in patients with high-risk PCa (8.8 vs. 14.4%, hazard ratio [HR]: 0.59, 95% confidence interval [CI]: 0.50e0.68). Conversely, the beneficial effect of radiotherapy on CSM was not evident in patients with low-intermediate risk PCa (3.7 vs. 4.1%, HR: 0.91, 95% CI: 0.80e1.04). Radiotherapy was beneficial in elderly patients (5.6 vs. 7.3%, HR: 0.70, 95% CI: 0.59e0.80). Moreover, it was associated with improved CSM rates among patients with no comorbidities (5.7 vs. 6.5%, HR: 0.81, 95% CI: 0.67e0.98), one comorbidity (4.6 vs. 6.0%, HR: 0.87, 95% CI: 0.75e0.99), and more than two comorbidities (4.2 vs. 5.0%, HR: 0.79, 95% CI: 0.65e0.96). Conclusions: Radiotherapy substantially improves CSM in patients with high-risk PCa, with little or no benefit in patients with low-/intermediate-risk PCa relative to observation. These

Reprint requests to: Firas Abdollah, M.D., Cancer Prognostics and Health Outcomes Unit, University of Montreal Health Center, Montreal, Quebec, H2X 3J4, Canada. Tel: (514) 890-8000 ext. 353361; Fax: (514) 227-5103; E-mail: [email protected] Conflicts of interest: none. a Equal contribution. Int J Radiation Oncol Biol Phys, Vol. 84, No. 1, pp. 95e103, 2012 0360-3016/$ - see front matter Ó 2012 Elsevier Inc. All rights reserved. doi:10.1016/j.ijrobp.2011.11.034

AcknowledgmentsdPierre I. Karakiewicz is partially supported by the University of Montreal Health Centre Urology Specialists, Fonds de la Recherche en Sante du Quebec, the University of Montreal Department of Surgery and the University of Montreal Health Centre (CHUM) Foundation.

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findings must be interpreted within the context of the limitations of observational data. Ó 2012 Elsevier Inc. Keywords: Observation, Prostatic neoplasms/mortality, Prostatic neoplasms/therapy, Radiotherapy/statistics and numerical data, SEER program

Introduction Prostate cancer (PCa) is the most common noncutaneous malignancy in North America men (1). Most patients have clinically localized tumors at diagnosis (2). Radical prostatectomy (RP), radiotherapy, and observation represent the main management options for those individuals (3). A recent report showed a significant increase in the utilization rate of radiotherapy in comparison to other treatment modalities (4). However, no randomized data that test the benefit of radiotherapy on cancer control outcomes are available. In the absence of randomized-controlled trial data, observational studies can provide insight into important clinical issues. Under this premise, Wong et al. (5) used a large population-based cohort to test the effect of active treatment, namely RP or radiotherapy, versus observation on overall mortality. Although overall mortality represents the endpoint of choice in cancer epidemiology, it is also important to quantify the effect of treatment on cancerspecific mortality (CSM). Given the “indolent” and protracted natural history of PCa, a great majority of PCa patients succumb to causes other than cancer (4, 6). In consequence, the quantification of overall mortality may not be an adequate representation of the benefits of treatment. Instead, it may merely be a reflection of the baseline morbidity status of those receiving active treatment. To address this important shortcoming, we performed an analysis of CSM, with adjustment for other-cause mortality (OCM) according to competing-risks methodology (7). In as well, because baseline PCa stage and grade, comorbidity, and age of patients represent a wide continuum, our analyses were carried out according to these variables. Our intent was to provide PCa stageand grade-, comorbidity-, and age-specific estimates of radiotherapy benefit on CSM relative to observation.

Methods Data source We used the Surveillance, Epidemiology, and End Results (SEER) registrieseMedicare insurance program linked database to identify patients with PCa. This database is 98% complete for case ascertainment. The SEER registries covered approximately 14% of the US population before 2000, and 26% thereafter. Medicare insurance includes approximately 97% of Americans aged 65 years. Linkage to the SEER database is complete for approximately 93% of cases (8).

disease between 1992 and 2005. These had both Medicare Part A and Part B claims available and were not enrolled in a health maintenance organization throughout the duration of the study. Patients were not included if their original or current reason for Medicare entitlement was listed as disability or Medicare status code including disability. Similarly, patients were not included if PCa was diagnosed at autopsy or using death certificate only. Patients treated surgically or with initial hormonal therapy were not included. Patients were also excluded if they harbored T3/T4 tumors (n Z 8,556), had anaplastic or unknown grade (n Z 5,657), had unknown stage (n Z 227), were age >80 years at diagnosis (n Z 18,880), or had missing socioeconomic data (n Z 971). Of the remaining patients, 68,797 were treated with either radiotherapy or observation; these individuals represented the focus of the current study.

Variable definition Patient age was obtained from the Medicare file. Patient race (white vs. black vs. others), marital status (married vs. unmarried), population density (metropolitan vs. non-metropolitan), and SEER registry were obtained from SEER data. The percentage of the 2000 census tract with a 4-year college education and the median income per census tract were used as proxies for socioeconomic status. Charlson comorbidity index (CCI) was derived from Medicare claims using a validated algorithm (9). SEER data were also used to abstract tumor grade (Gleason score: 2e4 vs. 5e7 vs. 8e10), and tumor stage that was derived from SEER tumor extension data (5). Treatment type was identified by searching Medicare files, which included the inpatient claims (Part A), the carrier or physician file (Part B), and the outpatient claims file, for the appropriate International Classification of Disease (ICD), 9th revision, and Healthcare Common Procedure Coding System codes during the 6 months after the date of diagnosis, as described by Wong et al. (5).

Outcomes Cause of death is determined by the SEER data. Patients who succumbed to PCa (ICD-9 185.9 or ICD-10 C619) were classified as CSM. Patients who succumbed to other causes were classified as OCM. Data on cause-specific mortality were available through the end of 2007.

Statistical analyses Study population We identified 141,155 individuals aged 65 years or older who were diagnosed with nonmetastatic PCa as their first malignant

Descriptive statistics focused on frequencies and proportions for categorical variables. The chi-square test compared the statistical significance of differences in proportions.

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Table 1 Descriptive characteristics of 68,797 patients treated with radiotherapy vs. observation for prostate cancer between 1992 and 2005 within the Surveillance, Epidemiology, and End Results (SEER)-Medicare linked database, and 41,972 treatment type-propensity score-matched patients Entire cohort (n Z 68,797)

Characteristics Age (y) 65e69 70e74 75e80 Race White Black Other Marital status Married Unmarried Annual median income ($) 36,724.00 36,724.01e48,609.00 48,609.01e65,376.00 65,376.01 College education (%) 14.53 14.54e24.81 24.82e41.35 41.36 Charlson comorbidity index 0 1 2 Population density Metropolitan Nonmetropolitan Clinical stage T1 T2a/b T2c Tumor grade Gleason score 2e5 Gleason score 6e7 Gleason score 8e10 Year of diagnosis 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Propensity score-matched cohort (n Z 41,972)

Standardized difference

Observation n Z 20,986 (50%)

Radiotherapy n Z 20,986 (50%)

Standardized difference

11,209 (24.1) 19,279 (41.4) 16,033 (34.5)

e 15.0 20.0

4,671 (22.3) 7,300 (34.8) 9,015 (43.0)

4,909 (23.4) 7,728 (36.8) 8,349 (39.8)

e 2.0 3.1

18,335 (82.3) 2,440 (11.0) 1,501 (6.7)

40,437 (86.9) 3,716 (8.0) 2,368 (5.1)

e 10.9 7.5

17,366 (82.8) 2,261 (10.8) 1,359 (6.5)

17,404 (82.9) 2,202 (10.5) 1,380 (6.6)

e 0.2 0.1

14,686 (65.9) 7,590 (34.1)

35,103 (75.5) 11,418 (24.5)

e 22.0

14,059 (67.0) 6,927 (33.0)

14,314 (68.2) 6,672 (31.8)

e 1.2

Observation n Z 22,276 (32.3%)

Radiotherapy n Z 46,521 (67.6%)

4,866 (21.8) 7,563 (34.0) 9,847 (44.2)

6,301 5,705 5,250 5,020

(28.3) (25.6) (23.6) (22.5)

10,900 11,498 11,944 12,179

(23.4) (24.7) (25.7) (26.2)

e 2.1 4.8 8.3

5,821 5,369 4,969 4,827

(27.7) (25.6) (23.7) (23.0)

5,598 5,501 5,234 4,653

(26.7) (26.2) (24.9) (22.2)

e 0.6 1.2 0.8

6,025 5,675 5,228 5348

(27.0) (25.5) (23.5) (24.0)

11,193 11,527 11,951 11,850

(24.1) (24.8) (25.7) (25.5)

e 1.6 5.1 3.4

5,636 5,337 4,944 5,069

(26.9) (25.4) (23.6) (24.2)

5,552 5,345 5,294 4,795

(26.5) (25.5) (25.2) (22.8)

e 0 1.6 1.3 e

9,584 (43.0) 5,832 (26.2) 6,860 (30.8)

20,100 (43.2) 13,835 (29.7) 12,586 (27.1)

e 7.8 12.4

9,089 (43.3) 5,588 (26.6) 6,309 (30.1)

8,671 (41.3) 5,957 (28.4) 6,358 (30.3)

1.7 0.6 0.1

18,841 (84.6) 3,435 (15.4)

40,078 (86.2) 6,443 (13.8)

e 4.5

17,806 (84.8) 3,180 (15.2)

17,898 (85.3) 3,088 (14.7)

e 0.4

11,542 (51.8) 9,222 (41.4) 1,512 (6.8)

18,946 (40.7) 22,127 (47.6) 5,448 (11.7)

e 12.3 15.3

10,471 (49.9) 9,009 (42.9) 1,506 (7.2)

9,343 (44.5) 9,416 (44.9) 2,227 (10.6)

e 1.9 3.4

3,906 (17.5) 15,067 (67.6) 3,303 (14.8)

2,555 (5.5) 31,544 (67.8) 12,422 (26.7)

e 0.4 26.8

2,716 (12.9) 14967 (71.3) 3,303 (15.7)

2,289 (10.9) 14,051 (67.0) 4,646 (22.1)

e 4.3 6.4

e 10.3 10.4 13.2 8.5 5.3 2.5 0.3 6.4 6.5 5.7 5.6 6.5 3.4

1,305 1,274 1,173 1,158 1,106 1,106 1,011 1,051 1,848 1,971 2,142 1,965 1,904 1,972

1,411 1,487 1,347 1,390 1,254 1,210 1,094 1,094 1,899 2,008 2,177 1,989 1,924 1,992

(6.3) (6.7) (6.0) (6.2) (5.6) (5.4) (4.9) (4.9) (8.5) (9.0) (9.8) (8.9) (8.6) (8.9)

2,673 2,102 1,867 1,738 1,850 2,024 2,042 2,264 4,882 5,141 5,397 4,956 4,951 4,634

(5.7) (4.5) (4.0) (3.7) (4.0) (4.4) (4.4) (4.9) (10.5) (11.1) (11.6) (10.7) (10.6) (10.0)

(6.2) (6.1) (5.6) (5.5) (5.3) (5.3) (4.8) (5.0) (8.8) (9.4) (10.2) (9.4) (9.1) (9.4)

1,261 1,165 1,099 1,086 1,029 1,063 971 988 2,002 1,952 2,182 2,106 2,064 2,018

(6.0) (5.6) (5.2) (5.2) (4.9) (5.1) (4.6) (4.7) (9.5) (9.3) (10.4) (10.0) (9.8) (9.6)

e 0.3 0.3 0.3 0.2 0.1 0.3 0.7 0.0 0.1 0.6 0.7 0.2 0.0

(continued on next page)

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Table 1 (continued ) Entire cohort (n Z 68,797)

Characteristics SEER registry San Francesco Connecticut Detroit Hawaii Iowa New Mexico Seattle Utah Atlanta San Jose Los Angeles Rural Georgia Greater California Kentucky Louisiana New Jersey

Observation n Z 22,276 (32.3%) 1,319 2,054 3,090 343 1,910 973 1,947 1,219 743 743 2,351 71 2,225 821 771 1,696

(5.9) (9.2) (13.9) (1.5) (8.6) (4.4) (8.7) (5.5) (3.3) (3.3) (10.6) (0.3) (10.0) (3.7) (3.5) (7.6)

Radiotherapy n Z 46,521 (67.6%) 1,923 5,023 6,533 1,095 3,892 1,352 3,343 1,784 2,139 1,162 3,334 137 4,510 2,293 1,963 6,038

(4.1) (10.8) (14.0) (2.4) (8.4) (2.9) (7.2) (3.8) (4.6) (2.5) (7.2) (0.3) (9.7) (4.9) (4.2) (13.0)

Our statistical analyses consisted of two steps. In the first step, we attempted to adjust for the selection bias inherent in observational data by relying on propensity-score matching to balance the observed covariates between the radiotherapy and observation groups (10). Propensity scores attempt to statistically reproduce randomized trials by balancing the characteristics of different treatment groups (11). The propensity to undergo radiotherapy was calculated using a multivariable logistic regression model that adjusted to age at diagnosis, race, marital status, annual median income quartiles, percentage of 4-year college education quartiles, CCI, population density, clinical stage, tumor grade, year of diagnosis, and SEER registry. We used the nearest neighbor matching with a caliper width of 0.2 of the standard deviation of the logit to match cases. This optimizes the matching with minimal residual bias and highest precision (12). Subsequently, the standardized difference in means was measured to assess how well the controls (observation patients) match to the cases (radiotherapy patients) (13e15). In the second step of our analyses, which was based exclusively on the matched-cohort, patients were categorized according to tumor characteristics (we used a PCa modifiedrisk classification that consisted of two categories: (1) highrisk group, which consisted of patients with tumor stage T2c or Gleason score 8e10 vs. (2) low-intermediate risk group, which consisted of all the other patients), CCI (0 vs. 1 vs. 2), and age (65e69 vs. 70e74 vs. 75e80). For each category, cumulative incidence plots were used to graphically depict CSM and OCM rates. The Gray test was used to assess the statistical significance of differences in CSM and OCM rates (16). The number needed to treat (NNT) and their respective 95% confidence intervals (CI) was also calculated. The latter was defined as the reciprocal of the absolute mortality rate reduction. A 95% CI for the NNT was constructed by simply inverting and exchanging the limits of

Propensity score-matched cohort (n Z 41,972)

Standardized difference e 5.1 0.5 5.4 0.8 8.7 6.0 8.5 6.0 5.4 13.1 0.4 1.0 5.7 3.8 16.0

Observation n Z 20,986 (50%) 1,185 1,965 2,966 340 1,814 825 1,793 1,073 727 687 2,108 66 2,171 812 762 1,692

(5.6) (9.4) (14.1) (1.6) (8.6) (3.9) (8.5) (5.1) (3.5) (3.3) (10.0) (0.3) (10.3) (3.9) (3.6) (8.1)

Radiotherapy n Z 20,986 (50%) 1,063 1,958 2,992 479 1,734 811 1,701 1,023 849 634 1,983 61 2,050 902 859 1,887

(5.1) (9.3) (14.3) (2.3) (8.3) (3.9) (8.1) (4.9) (4.0) (3.0) (9.4) (0.3) (9.8) (4.3) (4.1) (9.0)

Standardized difference e 0.3 0.1 0.6 0.3 0.0 0.4 0.2 0.5 0.2 0.6 0.0 0.5 0.4 0.4 0.9

a 95% CI for the absolute risk reduction, as in previously defined methodology (17). Finally, multivariable stratified competing-risks regression analyses tested the effect of treatment type on CSM, with adjustment for OCM (7, 18). Covariates consisted of age at diagnosis, race, marital status, annual median income quartiles, percentage of 4-year college education quartiles, CCI, population density, clinical stage, tumor grade, year of diagnosis, and SEER registry. We also performed a sensitivity analysis to measure the potential effect of an unmeasured confounder on the relationship between treatment type and CSM (19, 20). All statistical analyses were performed using R statistical package system (R Foundation for Statistical Computing, Vienna, Austria). The cmprsk library (Robert J. Gray, Dana-Farber Cancer Institute, Boston, MA) was used to conduct competing-risks regression analyses. Two-sided significance level was set at p < 0.05.

Results Between 1992 and 2005, 46,521 (67.6%) patients were treated with radiotherapy vs. 22,276 (32.3%) patients with observation (Table 1). Patients treated with radiotherapy were younger (age 75e80 years: 34.5 vs. 44.2%, p < 0.001), and more frequently white (86.9 vs. 82.3%, p < 0.001) and married (75.5 vs. 65.9%, p < 0.001) than patients treated with observation. Radiotherapy patients had higher annual median income (4th quartile: 26.2 vs. 22.5%, p < 0.001), had higher education level (4th quartile: 25.5 vs. 24.0%, p < 0.001), and more often resided in metropolitan areas (86.2 vs. 84.6%, p Z 0.003). Radiotherapy patients also had lower comorbidity (CCI 2: 27.1 vs. 30.8%, p < 0.001), but harbored higher clinical stage (cT2c: 11.7 vs. 6.8%) and higher tumor grade (Gleason score 8e10: 26.7 vs. 14.8%, p < 0.001) relative to observation patients. In the most contemporary year of

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respectively, 3.7 vs. 4.1% (p Z 0.1). For patients with high-risk PCa (Fig. 1B), the 10-year CSM rates were 8.8 vs. 14.4% (p < 0.001), for the same groups, respectively. The corresponding NNT were respectively 250 (95% CI: 191e362) and 18 (95% CI: 10e104). In the multivariable analyses, radiotherapy was not an independent predictor of CSM in patients with low-/intermediate-risk PCa. Conversely, radiotherapy was an independent predictor of more favorable CSM rates in patients with high-risk PCa (Table 2). For patients with a CCI of 0, 1, and 2 (Fig. 2AeC), the 10-year CSM rates were 5.7 vs. 6.5% (p Z 0.05), 4.6 vs. 6.0% (p Z 0.01), and 4.2 vs. 5.0% (p Z 0.01) for those treated with radiotherapy vs. observation, respectively. The corresponding NNT were 125 (95% CI: 103e160), 71 (95% CI: 61e86), and 125 (95% CI: 103e160), respectively. In the multivariable analyses, radiotherapy was an independent predictor of more favorable CSM rates in patients with a CCI of 0, 1, and 2 (Table 2). For patients ages 65e69, 70e74, and 75e80 years (Fig. 3AeC) the 10-year CSM rates were, respectively, 4.1 vs. 4.5% (p Z 0.7), 4.8 vs. 5.1% (p Z 0.6), and 5.6 vs. 7.3% (p < 0.001), for patients treated with radiotherapy vs. observation, respectively. The corresponding NNT were 250 (95% CI: 191e362), 333 (95% CI: 246e519), and 59 (95% CI: 51e69), respectively. In the multivariable analyses, radiotherapy was not an independent predictor of CSM in patients ages 65e59 or those ages 70e74 years. Conversely, radiotherapy was an independent predictor of more favorable CSM rates in patients aged 75e80 years (Table 2). Our sensitivity analysis (Table 3) showed that the observed relationship between treatment type and CSM would no longer be detectable if the prevalence of an unknown confounder with a HR of 1.5, 2.0, and 3.0 was at least 30%, 20%, and 10% higher, respectively, in one group of patients vs. another.

Discussion Fig. 1. Competing-risks plots depicting cancer-specific (CSM) and other causes mortality (OCM), in patients with lowintermediate risk (A), and high risk (B) prostate cancer, treated with radiotherapy vs. observation. NNT Z number needed to treat. the study (2005), patients were more frequently treated with radiotherapy in comparison to observation (10.0 vs. 8.9%, p < 0.001). Propensity-score matching resulted in a cohort of 41,972 patients (Table 1). The standardized difference in all the characteristics of matched patients was less than 10%, which indicates a high degree of similarity in the distribution of prognostic variables (13e15). Of these 41,972 patients, 22.8%, 35.8%, and 41.4% were ages 65e69, 70e74, and 75e80 years, respectively. CCI was 0 vs. 1 vs. 2 in 42.3 vs. 27.5 vs. 30.2%, respectively. The rate of high-risk PCa was 24.9%, and it was 24.0, 24.1, and 26.1% (p < 0.001) in patients ages 65e69, 70e74, and 75e80 years, respectively. The rate of high-risk PCa was 24.8%, 24.4%, and 25.3% (p Z 0.2) in patients with a CCI of respectively 0, 1, and 2. For patients with low-intermediate risk PCa (Fig. 1A) treated with radiotherapy vs. observation, the 10-year CSM rates were,

Recent studies reported an important increase in the utilization rate of radiotherapy in comparison to other treatment modalities for localized PCa (4, 21). However, to date, there are no randomized data that show a beneficial effect of radiotherapy vs. observation on cancer control outcomes. To address this lack of data, we decided to test the effect of radiotherapy relative to observation on CSM, after accounting for OCM. Additionally, we opted to provide estimates for specific PCa modified-risk groups, baseline comorbidity status, and age categories. The rationale for focusing on CSM stems from the protracted natural history of PCa. This means that many individuals die of causes other than that of cancer (4, 6). Under this premise, when treatment efficacy is assessed, CSM may represent a better metric than overall mortality, which was recently examined as an endpoint in a comparison between active treatment and observation (5). The current study aims to circumvent the limitation related to the use of overall mortality, and provide a comparison between radiotherapy and observation using a more specific metric, namely CSM. The rationale for stratification of CSM rates according to PCa modified-risk groups, baseline comorbidity status, and age is welldocumented. All three characteristics may significantly affect CSM. Further adjustment for OCM provides the most valid

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Table 2 Multivariable competing risks regression models for prediction of cancer-specific mortality (after accounting for noncancerrelated mortality) and other-cause mortality (after accounting for cancer-specific mortality) in 41,972 patients with organ-confined prostate disease, treated with radiotherapy vs. observation, between 1992 and 2005, within the Surveillance, Epidemiology, and End Results (SEER)-Medicare-linked database Cancer-specific mortality Patient categories Low-intermediate risk prostate cancer* High-risk prostate cancer* Charlson comorbidity index of 0y Charlson comorbidity index of 1y Charlson comorbidity index of 2 or morey Patient age 65e69 yearsz Patient age 70e74 yearsz Patient age 75e80 yearsz

HR (95% CI) 0.91 0.59 0.81 0.87 0.79 0.93 0.84 0.70

(0.80e1.04) (0.50e0.68) (0.67e0.98) (0.75e0.99) (0.65e0.96) (0.72e1.19) (0.68e1.03) (0.59e0.80)

p value 0.2 <0.001 0.03 0.04 0.01 0.6 0.08 <0.001

Other-cause mortality HR (95% CI) 0.83 0.78 0.85 0.84 0.79 0.74 0.89 0.88

(0.79e0.86) (0.73e0.85) (0.8e0.91) (0.79e0.9) (0.74e0.83) (0.71e0.78) (0.81e0.98) (0.82e0.93)

p value <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.01 <0.001

Abbreviations: 95% CI Z 95% confidence interval; HR Z hazard ratio. * Multivariable analyses tested the relationship between treatment type and cancer-specific, as well as other-cause mortality, after adjusting to age at diagnosis, race, marital status, annual median income quartiles, percentage of 4-year college education quartiles, Charlson Comorbidity Index, population density, clinical stage, tumor grade, year of diagnosis, and SEER registry. y Multivariable analyses tested the relationship between treatment type and cancer-specific, as well as other-cause mortality, after adjusting to age at diagnosis, race, marital status, annual median income quartiles, percentage of 4-year college education quartiles, population density, clinical stage, tumor grade, year of diagnosis, and SEER registry. z Multivariable analyses tested the relationship between treatment type and cancer-specific, as well as other-cause mortality, after adjusting to race, marital status, annual median income quartiles, percentage of 4-year college education quartiles, Charlson Comorbidity Index, population density, clinical stage, tumor grade, year of diagnosis, and SEER registry.

estimation of treatment efficacy. Stratified and OCM-adjusted estimates show treatment efficacy in a more specific fashion than an average for the entire population, as was previously reported (6, 22). When patients were stratified according to PCa modified-risk groups, defined using stage and grade, the NNT ranged from 18 for patients harboring high-risk PCa to 250 for patients harboring low-/intermediate-risk PCa. When patients were stratified according to baseline comorbidity, the NNT were 125, 71, and 125 for patients with respectively 0, 1, and 2 or more CCI. Finally, when patients were stratified according to age, the NNT were 250, 333, and 59 for patients ages 65e69, 70e74, and 75e80 years, respectively. These results were confirmed in the corresponding multivariable analysis (Table 2). The most substantial benefit of radiotherapy on CSM was recorded in patients with high-risk PCa. In this category, 18 needed to be treated with radiotherapy instead of observation to achieve a benefit in 1 patient. Conversely, the beneficial effect of radiotherapy observed in patients with high-risk PCa was not observed in patients with low-/intermediate-risk PCa. In this category, a very high number of patients (250) needed treatment with radiotherapy instead of observation to achieve very mild benefit on 10-year CSM rate. This mild improvement in CSM rate was not statistically significant. When patients were stratified according to baseline comorbidity, a relatively high number of patients (71e125) needed to be treated with radiotherapy instead of observation to achieve a benefit in 1 patient across all examined CCI categories. In multivariable analyses (Table 2), the protective effect of radiotherapy on CSM was more evident in patients with higher comorbidity. This observation may result from a higher proportion of individuals with high-risk PCa in the high comorbidity category relative to other categories. This finding is consistent with clinical practice, where radiotherapy is more frequently offered to patients with higher comorbidity, when their PCa characteristics indicate

more aggressive disease. Although we adjusted for tumor stage and grade, we were unable to adjust for other indicators of tumor aggressiveness, such as prostate-specific antigen value and the percentage of positive cores, which may have affected treatment decision and outcomes. When patients were stratified according to age categories, those ages 65e69 and 70e74 showed no significant benefit of being treated with radiotherapy instead of observation. Conversely, patients ages 75e80 years had more favorable CSM when they were treated with radiotherapy instead of observation. Again, these results may be considered counterintuitive because the oldest age category appears to benefit the most. Intuitively, older patients have a shorter life expectancy, and given the protracted nature of PCa, a lower benefit may be expected from treatment. However, when the effect of OCM was accounted for in the competing-risks model, the benefit of active treatment on CSM was more apparent in older patients. Given the retrospective nature of our cohort, it may be difficult to interpret this observation. It is noteworthy that previous reports observed a higher CSM rate in older patients, even after adjusting for stage and grade (4, 23). In consequence, it may be hypothesized that older patients usually harbor a more aggressive tumor that may benefit from active treatment. However, the possibility of a residual bias caused by an unmeasured confounder for patient selection cannot be completely excluded. Nonetheless, following the application of a stringent and statistically sound methodology, this should have reduced to the minimum the effect of such a factor (10e12). The clinical implications of our findings are several-fold. First, our results clearly confirm the survival benefit of radiotherapy relative to observation in individuals with high-risk PCa. Second, our results also indicate that no survival benefit exists when individuals with low-/intermediate-risk PCa are treated with radiotherapy instead of observation. Third, our findings show that baseline comorbidity and patients age are

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Fig. 2. Competing-risks plots depicting cancer-specific (CSM) and other causes mortality (OCM), in prostate cancer patients with Charlson comorbidity index (CCI) of 0 (A), CCI of 1 (B), and CCI of 2 (C), treated with radiotherapy vs. observation. NNT Z number needed to treat.

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Fig. 3. Competing-risks plots depicting cancer-specific (CSM) and other causes mortality (OCM) in prostate cancer patients aged 65e69 years (A), 70e74 years (B), and 75e80 years (C) treated with radiotherapy vs. observation. NNT Z number needed to treat.

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Table 3

Sensitivity analyses estimating the effect of an unmeasured confounder on the hazard ratio of cancer-specific mortality

Prevalence in radiotherapy patients (%)

Prevalence in observation patients (%)

10

20 30 40 30 40 50 40 50 60

20

30

not as important as PCa characteristics in deciding whether radiotherapy would be beneficial to a patient or not. Indeed, even older patients (ages 75e80) seem to have some benefit when they are treated with radiotherapy instead of observation. However, under these conditions, a relatively high number of patients need to undergo radiotherapy (59) instead of observation to benefit one patient. Whether this number justifies the active treatment or is prohibitive to warrant radiotherapy remains to be debated. The consideration of patient preferences is necessary in such debate. Socioeconomic factors, availability of health care, compliance with observation protocols, and other variables also need to be considered. Although our results cannot consolidate the input form all these sources, our findings provide valuable evidence supporting the benefit of radiotherapy over observation in patients with high-risk PCa. Hopefully, they will contribute to more informed treatment decisions. Our study is not devoid of limitations. All our findings are originating from observational data. As such, they should be interpreted with caution. Lack of randomization between radiotherapy and observation groups according to baseline characteristics may have affected observed outcomes. The propensity-score matching used in our analyses reduces to a minimum the chance that a treatment assignment bias attributes to our results. This was further demonstrated by the robustness of our sensitivity analyses. However, it is possible that some bias remained, especially in the context of the subanalyses. In consequence, despite the pertinence of the current findings, they should be validated through an eventual randomized controlled trial. It is noteworthy that the OCM rates in patients treated with radiotherapy were more favorable across all categories. This may imply that radiotherapy patients were probably “healthier,” and despite the adjustment to CCI in our competing-risks regression analyses, some residual bias may still remain. However, such a bias would rather affect OCM rates and overall survival, but not CSM rate, which represents the main endpoint of our study. It may be also argued that some patients in our cohort were not treated with the contemporary standards of radiotherapy. This limitation should be considered when interpreting our results. Currently, it may not be possible to verify the benefit of contemporary standards of radiotherapy in the community setting, because the available follow-up might not be long enough. This limitation is shared with previous reports (5, 24). Last but not least, prostate-specific antigen was not available for the majority of patients. As a consequence, it was not possible to further stratify patients according to this variable. Nonetheless, we stratified patients

Cancer-specific mortality Hazard ratio 1.50

Hazard ratio 2.0

Hazard ratio 3.0

0.74 0.89 1.05 0.71 0.83 0.95 0.69 0.79 0.89

0.82 1.05 1.28 0.76 0.92 1.09 0.72 0.85 0.98

0.98 1.36 1.75 0.82 1.06 1.29 0.76 0.92 1.09

(0.68e0.81) (0.82e0.98) (0.96e1.15) (0.65e0.78) (0.76e0.91) (0.87e1.04) (0.63e0.76) (0.72e0.87) (0.81e0.98)

(0.75e0.90) (0.96e1.15) (1.17e1.40) (0.69e0.83) (0.84e1.01) (0.99e1.19) (0.66e0.79) (0.78e0.93) (0.89e1.07)

(0.89e1.07) (1.25e1.49) (1.60e1.92) (0.75e0.90) (0.97e1.16) (1.18e1.41) (0.69e0.83) (0.85e1.01) (1.00e1.20)

according to tumor stage and grade, which represent important proxies of the extent of the disease.

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