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Background: Positive correlation between caseload and outcome has previously been validated for several procedures and cancer treatments. However, there is no information linking caseload and outcome of nasopharyngeal carcinoma (NPC) treatment. We used nationwide population-based data to examine the association between physician case volume and survival rates of patients with NPC. Methods: Between 1998 and 2000, a total of 1225 patients were identified from the Taiwan National Health Insurance Research Database. Survival analysis, the Cox proportional hazards model, and propensity score were used to assess the relationship between 10-year survival rates and physician caseloads. Results: As the caseload of individual physicians increased, unadjusted 10-year survival rates increased (p < 0.001). Using a Cox proportional hazard model, patients with NPC treated by high-volume physicians (caseload ≥ 35) had better survival rates (p = 0.001) after adjusting for comorbidities, hospital, and treatment modality. When analyzed by propensity score, the adjusted 10-year survival rate differed significantly between patients treated by high- volume physicians and patients treated by low/medium-volume physicians (75% vs. 61%; p < 0.001). Conclusions: Our data confirm a positive volume-outcome relationship for NPC. After adjusting for differences in the case mix, our analysis found treatment of NPC by high-volume physicians improved 10-year survival rate. Introduction Taiwan has a high incidence of nasopharyngeal carci- The fact that increased caseload is associated with better noma (NPC): the annual incidence rate is 6.17 per patient outcomes has been noted for three decades in 100,000 as compared with < 1 per 100,000 in Western many areas of health care, including acute myocardial countries . Radiotherapy or concurrent chemora- infarction, many types of high-risk surgeries, and cancer diotherapy (CCRT) is the principal treatment because treatment [1,2]. The “practice makes perfect” hypothesis NPC is anatomically inaccessible and highly sensitive to may be valid for certain procedures such as open-heart radiotherapy and chemotherapy . and vascular surgery and “selective referral” may in part Previous volume-outcome studies have shown account for this phenomenon [3,4]. However, such a improved treatment outcome in breast cancer, oral can- positive volume-outcome relationship is not well vali- cer, esophageal cancer, radical prostatectomy, and nephrectomy [5,9-11]. However, there is scant informa- dated for other procedures. Only a few studies have examined the effect of physician caseload on treatment tion on the volume-outcome relationship for NPC. The outcome for head and neck cancers [5,6]. purpose of this study was to examine the relationship between physician caseload and survival rate in NPC using population-based data. * Correspondence: firstname.lastname@example.org; email@example.com Department of Radiation Oncology, Buddhist Dalin Tzu Chi General In most previous studies on the association between Hospital, Chiayi, Taiwan caseload and outcome, a Cox proportional hazards Division of Plastic Surgery, Department of Surgery, Buddhist Dalin Tzu Chi model or logistic regression was routinely used, raising General Hospital, Chiayi, Taiwan Full list of author information is available at the end of the article © 2011 Lee et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Lee et al. Radiation Oncology 2011, 6:92 Page 2 of 7 http://www.ro-journal.com/content/6/1/92 the possibility that selection bias might still exist. There- for cancer patients [15,16]. Patients with NPC were clas- fore,weevaluated theassociation between physician sified into 4 subgroups: EC 1 (civil servants, full-time or caseload and survival rate using population-based data, regular paid personnel with a government affiliation), Cox regression analysis, and propensity score to mini- EC 2 (employees of privately owned institutions), EC 3 mize the effect of selection bias. (self-employed individuals, other employees, and mem- bers of farmers’ or fishermen’s associations), and EC 4 Patients and methods (veterans, low-income families, and substitute service The database contained a registry of contracted medical draftees) . The hospitals were categorized by ownership (public, facilities, a registry of board-certified physicians, and monthly claims summary for all inpatient claims. not-for-profit or for-profit), geographic location (North- Because these were de-identified secondary data, this ern, Central, Southern, and Eastern Taiwan), and hospi- study was exempt from full review by the internal tal type (medical center, regional hospital, and district review board. hospital). Statistical analysis Patients and study design The SAS statistical package (version 9.2; SAS Institute, We used data for the years 1998 to 2008 from the Inc., Cary, N.C.) and SPSS (version 15, SPSS Inc., Chi- National Health Insurance (NHI) Research Database, cago, IL, USA) were used for data analysis. A two-sided which contains data on all covered medical benefit value of p < 0.05 was used to determine statistical claims for over 23 million people in Taiwan (approxi- significance. mately 97 percent of the island’s population). The cumulative 10-year survival rates and the survival All patients with NPC (International Classification of curves of each group were compared by the log-rank Disease, Ninth Revision, Clinical Modification codes test. Survival was measured from the time of NPC diag- 147.0-147.9) who received curative treatment by radiother- nosis to the time of death. Cox proportional regression apy or chemoradiotherapy between the years 1998 and model and survival analysis with propensity score strati- 2000 were included. Patients with unclear treatment mod- fication were used to compare outcomes between differ- ality and incomplete physician data or treated by physi- ent caseload size groups. cians with a very small caseload (less than 4 cases within 3 (1) Cox proportional hazards model The Cox propor- years) were excluded. Finally, 1225 patients treated by 98 tional regression model was used to evaluate the effect radiation oncologist during this period were included. of caseload on survival rate after adjusting for hospital Physicians were further sorted by their total patient type, surgeon characteristics, and patient demographics. volume using the unique physician identifiers in this (2) Propensity score Propensity analysis was used to database and by their caseload of NPC patients. The reduce the effect of selection bias on our hypothesis as volume category cutoff points (high, medium, and low) described by Rosenbaum and Rubin [18-20]. Propensity were determined by sorting the 1225 patients into 3 score stratification replaces the many confounding fac- groups of approximately equal size (4-16 cases [low], tors that may be present in an observational study with 17-34 cases [medium], and ≧35 cases [high]) as pre- a variable of these factors. To calculate the propensity viously described [5,12,13]. score, patient characteristics in this study were entered These NPC patients were then linked to the death into a logistic regression model predicting selection for data extracted from the records covering the years 1998 high-volume surgeons. These characteristics included to 2008. year in which the patient was diagnosed, age, gender, Measurements Charlson Comorbidity Index score, geographic area of The key dependent variable of interest was the 10-year residence, enrollee category, and treatment modality. survival rate. The key independent variables were the The study population was then divided into five discrete NPC caseloads (low, medium, or high). Other physician strata on the basis of propensity score. The effect of characteristics included age (≦40, 41-50, ≧51 years) and caseload assignment on 10-year survival rate was ana- gender. Patient characteristics included age, gender, geo- lyzed within each quintile. The Mantel-Haenszel odds graphic location, treatment modality, severity of disease, ratio was calculated in addition to the Cochran-Mantel- and enrollee category (EC). The disease severity in each Haenszel c statistic. patient was assessed using the modified Charlson Comorbidity Index score, which has been widely used in Results recent years for risk adjustment in administrative claims A total of 423 patients (35%) died out of 1225 patients data sets . who underwent curative treatment between 1998 and This studyusedECasaproxymeasureofsocioeco- 2000. A total of 98 radiation oncologists were included. nomic status, which is an important prognostic factor The characteristics of the physicians and patients are Lee et al. Radiation Oncology 2011, 6:92 Page 3 of 7 http://www.ro-journal.com/content/6/1/92 summarized in Tables 1 and 2. The majority of the Table 2 Physician Characteristics (n = 98) patients were male (72%). Patients in the high-volume Physician caseload group physician group were more likely to undergo radiother- Variable Low Medium High p (4-16) (17-34) (35-152) apy, reside in Northern Taiwan, have lower comorbidity score, and better enrollee category than their counter- Total no. physicians 74 17 7 parts in other groups. There were 74 radiation oncolo- Age(year) 0.507 gists (76%) in the low-volume group, 17 physicians Mean ± SD 39 ± 13 39 ± 11 45 ± 13 (17%) in the medium-volume group, and 7 (7%) physi- Gender 0.832 cians in the high-volume group. The mean age of all Male 65(88) 14(82) 6(86) physicians was 40 ± 12 years. There was no significant Female 9(12) 3(18) 1(14) difference in age between these three caseload groups (p Caseload < 0.001 = 0.507). Mean ± SD 6 ± 5 24 ± 6 62 ± 45 Values are given as number (percentage). Analysis using a Cox proportional hazards model Abbreviations: SD = standard deviation. The 10-year survival rate, by physician caseload group, is shown in Figure 1. The 10-year survival rates were physicians had better survival rates (hazard ratio [HR] = 75%, 61%, and 60% for low-, medium-, and high-volume 0.6; 95% confidence interval [CI], 0.45-0.78; p < 0.001) surgeons, respectively (p <0.001). Table3showsthe after adjust other factors. adjusted hazard ratios calculated using the Cox propor- tional hazards regression model after adjusting for Analysis using propensity scores patient comorbidities, hospital type, and treatment mod- Patients were stratified by propensity score and the ality. The positive association between survival and phy- effect of physician caseload on survival was assessed. sician caseload remained statistically significant in The population was stratified into propensity quintiles multivariate analysis. Patients treated by high-volume Table 1 Patient Characteristics in Different Caseload Groups (n = 1225) NPC caseload group Variable Low Medium High p (4-16) (17-34) (35-152) (n = 424) (n = 394) (n = 407) Age 0.037 35-44 years 136(32) 90(23) 103(25) 45-54 years 118(28) 143(36) 145(36) 55-64 years 93(22) 100(25) 99(24) 65-74 years 59(14) 51(13) 48(12) ≧ 75 years 18(4) 10(3) 12(3) Gender 0.389 Male 316(75) 285(72) 286(70) Female 108(25) 109(28) 121(30) Charlson Comorbidity Index score < 0.001 < 4 216(51) 229(58) 274(67) ≧4 208(49) 165(42) 133(33) Treatment modality < 0.001 Radiotherapy 278(66) 271(69) 322(79) Chemoradiotherapy 146(34) 123(31) 85(21) Geographic location < 0.001 North 266(63) 240(61) 317(78) Central 93(22) 61(15) 43(11) Southern and Eastern 65(15) 93(24) 47(11) Enrollee category 0.008 EC 1-2 168(40) 133(34) 183(45) EC 3 181(43) 172(44) 164(40) EC 4 75(18) 89(23) 60(15) Values are given as number (percentage). Lee et al. Radiation Oncology 2011, 6:92 Page 4 of 7 http://www.ro-journal.com/content/6/1/92 Table 3 Nasopharyngeal Carcinoma Survival Rate and Adjusted Hazard Ratios by Physician Caseload Groups and the Characteristics of the Patients and Providers (n = 1225) Variable Adjusted hazard 95% CI p ratio Physician characteristics Physician volume Low (3-17) 1 Medium (17-53) 0.884 0.70-1.16 0.884 High (54-130) 0.60 0.45-0.78 < 0.001 Physician age ≦40 years 1 41-50 years 1.22 0.97-1.52 0.086 ≥51 years 0.78 0.59-1.02 0.073 Hospital characteristics Hospital ownership Public 1 Non-for-profit 1.11 0.87-1.42 0.414 Figure 1 Nasopharyngeal carcinoma survival rates by physician For-profit 0.94 0.65-1.36 0.746 caseload. Hospital level Medical center 1 Regional hospital 0.88 0.68-1.16 0.368 as previously described. Table 4 shows survival rates for District hospital 1.25 0.77-2.03 0.376 both caseload groups after stratification. The percentage Patient characteristics of patients treated by low/medium-volume physicians Patient gender decreased from the first propensity quintile to the fifth Female 1 as predicted by the propensity model. In each of the five Male 0.93 0.75-1.15 0.509 strata, patients treated by high-volume physicians had a Patient age higher 10-year survival rate. The p value for the 35-44 years 1 Cochran-Mantel-Haenszel statistic for the difference in 45-54 years 1.15 0.89-1.49 0.277 survival between patients treated by low/medium- and 55-64 years 1.10 0.83-1.45 0.507 high-volume physicians, while controlling for propensity 65-74 years 1.12 0.81-1.56 0.488 score, was < 0.001, with fewer patients dying who were ≧ 75 years 0.88 0.48-1.51 0.675 treated by high-volume physicians (adjusted odds ratio Charlson Comorbidity = 0.54, 95% CI, 0.41-0.7). The adjusted 10-year survival Index score rates for low/medium- and high-volume physicians were <4 1 61% and 75% (p < 0.001). ≧4 1.28 1.04-1.56 0.018 In summary, NPC patients treated by high-volume Treatment modality physicians had better survival. The robustness of this Radiotherapy 1 result was demonstrated by two different multivariate Chemoradiotherapy 1.03 0.82-1.29 0.784 analyses, the Cox proportional regression model and Geographic location stratification by propensity score. North 1 Central 1.18 0.90-1.55 0.242 Discussion Southern and 1.30 1.00-1.70 0.051 Eastern Using a Cox proportional hazards model and propensity Enrollee category score, the relative benefit of treatment by high-volume EC 1-2 1 physicians over low/medium-volume physicians was EC 3 1.35 0.71-2.55 0.358 evaluated in NPC. After controlling for patient charac- EC 4 1.04 0.86-1.26 0.698 teristics and other variables in the Cox proportional regression model, the adjusted hazard ratio was 0.6 for 95% CI, 95% confidence interval. Lee et al. Radiation Oncology 2011, 6:92 Page 5 of 7 http://www.ro-journal.com/content/6/1/92 Table 4 10-year survival of NPC patients in different propensity score strata; low/medium-volume vs. high-volume physicians Propensity score stratum Low/medium-volume physician group High-volume physician group p No. % of stratum Survival rate (%) No. % of stratum Survival rate (%) 1 193 79 56 51 21 75 0.004 2 191 78 59 52 22 74 0.029 3 173 70 57 74 30 75 0.013 4 145 58 64 104 42 76 0.021 5 116 48 69 126 52 76 0.28 Total 818 61 407 33 75 < 0.001 a. Stratum 1 had the strongest propensity for low/medium physicians; stratum 5, for high-volume physicians. b. Conchran-Mantel-Haenszel statistics; adjusted odds ratio = 0.54, 95% confidence interval = 0.41-0.70. high-volume physicians, indicating that patients with eradication of disease by radiotherapy. In order to NPC treated by high-volume physicians had a lower risk explore the caseload effect of radiotherapy on NPC sur- of death and were more likely to live longer. When ana- vival, we calculated the caseload volume of radiation lyzed by propensity score, the adjusted 10-year survival oncologists. In agreement with previous volume-out- rate was 75% for patients treated by high-volume physi- come studies, our results indicated that increased case- cians and 61% for patients treated by low/medium- load of radiation oncologists is associated with improved volume physicians. Moreover, fewer patients treated by outcomes after other factors. high-volume physicians died. The results of both forms Several hypotheses relating to the volume-outcome of analyses led to the conclusion that the 10-year survi- relationship have been proposed. The “practice makes val rates for patients with NPC treated by high-volume perfect” concept suggests that increased caseload may physicians were significantly better. help physicians or hospital staff improve the execution Previous studies have evaluated the benefits of high of treatment procedures, such as planning the radiation hospital and physician volume on the outcomes of can- field and manipulation of the radioactive source of tele- cer treatment. In head and neck cancer, Lin et al. therapy units. The role of surgery in the treatment of reported that physician volume (not hospital volume) NPC is limited, and carefully defining the planning tar- get volume with the aid of CT or MRI images is impor- was associated with oral cancer survival rates . In our tant for radiotherapy or concurrent chemoradiotherapy series, we also found a better 10-year survival rate asso- ciated with treatment by high-volume physicians. in NPC. A high-volume team may be more adept at The quality of the risk-adjustment technique in ana- administering a radiation dose, with or without a boos- lyzing administrative information is an important issue. ter dose, that balances the benefit of successful loco- In the first part of this study, a Cox proportional hazard regional control against the risk of radiation toxicity. model was used to compare the effects of high volume Previous study reported that high-volume physicians versus low/medium volume on survival rate. We found use effective treatment and strategies more often than treatment by high-volume physicians was significantly do low-volume physicians . In breast cancer series, associated with lower adjusted hazard ratio for death. high-volume surgeons adopted a multi-disciplinary Patients treated by high-volume physicians were found approach whereas low-volume surgeons were less likely to havea40% lowerriskofdeath after adjusting for to interact with oncologists or attend multi-disciplinary comorbidities and other confounding factors. However, meetings . Use of multidisciplinary approaches may there was some difference in age and clinical condition account for the better outcomes achieved by high- between caseload groups. In the second part of our ser- volume physicians. Possibly, low-volume physicians do ies, propensity score was used to stratify patients into not always follow the international guidelines for NPC five strata with similar propensity score in order to treatment. reduce the effect of selection bias on caseload groups The “selective referral hypothesis” postulates that heal- [19-21]. Patients treated by high-volume physicians were thier patients or patients with early-stage disease tend to found to have a 14% relative improvement in adjusted be referred to high-volume physicians. The referral sys- 10-year survival rate (p < 0.001). tem in Taiwan is weakly enforced, and people are free Although NPC patients may be followed up in a team to choose any physician. Because official performance information to help consumers select healthcare provi- consisting of otolaryngologist, radiation oncologists, ders is not available, patients choose physicians with hematology oncologists, and radiologists, the corner- stone of treatment of NPC relied on the successful better reputations or more successful physicians after Lee et al. Radiation Oncology 2011, 6:92 Page 6 of 7 http://www.ro-journal.com/content/6/1/92 Oncology, Buddhist Dalin Tzu Chi General Hospital, Chiayi, Taiwan. consulting with their relatives and friends . Selective Department of Hematology Oncology, Buddhist Dalin Tzu Chi General referral bias may also result from the referral of more Hospital, Chiayi, Taiwan. Division of Plastic Surgery, Department of Surgery, curable patients to high-volume physicians. Patients not Buddhist Dalin Tzu Chi General Hospital, Chiayi, Taiwan. School of Medicine, Tzu Chi University, Hualien, Taiwan. Community Medicine Research Center seeking curative treatment or for whom curative treat- and Institute of Public Health, National Yang-Ming University, Taipei, Taiwan. ment is not possible may continue to receive their care from low-volume physicians. Authors’ contributions LCC, CSH and HSK developed the ideas for these studies, performed much Our study revealed some issues that may be useful for of the work, and drafted the manuscript. CSH, CP, LCC, HTT and HSK revised policy makers. Research is needed to identify the differ- the manuscript. LMS, SYC, CP, CWY and LHY designed the study, managed ences in care and treatment strategy between low-, med- and interpreted the data. LCC performed the statistical analysis. All authors read and approved the final manuscript. ium-, and high-volume physicians. In our study, nearly 33% of patients were treated by 7 high-volume radiation Received: 27 February 2011 Accepted: 11 August 2011 oncologists. The viewpoints of high-volume physicians Published: 11 August 2011 may influence the development of effective protocols References and practice guidelines for the majority of clinical situa- 1. Luft HS, Bunker JP, Enthoven AC: Should operations be regionalized? The tions. The treatment strategies of high-volume physi- empirical relation between surgical volume and mortality. N Engl J Med cians should be analyzed and adopted throughout the 1979, 301:1364-1369. 2. Halm EA, Lee C, Chassin MR: Is volume related to outcome in health country to improve survival rates. care? A systematic review and methodologic critique of the literature. Our study has several limitations. First, we could not Ann Intern Med 2002, 137:511-520. assess the relationship of caseload to NPC stage because 3. Luft Harold S, Hunt Sandra S, Maerki SC: The volume-outcome relationship: practice-makes-perfect or selective-referral patterns? Health this information was not available from the database. Serv Res 1987, 22:157-582. However, Begg et al., using a SEER-Medicare linked 4. Cheng SH, Song HY: Physician performance information and consumer database, reported that cancer stage and patient age choice: a survey of subjects with the freedom to choose between doctors. Qual Saf Health Care 2004, 13:98-101. were independent of caseload volume . Instead of 5. Lin CC, Lin HC: Effects of surgeon and hospital volume on 5-year survival cancer-specific survival rates, overall survival rate was rates following oral cancer resections: the experience of an Asian used, because it was not possible to determine cause- country. Surgery 2008, 143:343-351. 6. Lin CS, Lee HC, Lin CT, Lin HC: The association between surgeon case specific mortality based on the registry data. Previous volume and hospitalization costs in free flap oral cancer reconstruction study by Roohan et al. showed no significant difference operations. Plast Reconstr Surg 2008, 122:133-139. between survival models for all-cause mortality and 7. Department of Health: The Executive Yuan: Cancer registry annual report. Republic of China 2004. breast cancer mortality . Given the robustness of the 8. Al-Sarraf M, LeBlanc M, Giri PG, Fu KK, Cooper J, Vuong T, Forastiere AA, evidence and statistical analysis in this study, these lim- Adams G, Sakr WA, Schuller DE, Ensley JF: Chemoradiotherapy versus itations are unlikely to compromise our results. radiotherapy in patients with advanced nasopharyngeal cancer: phase III randomized Intergroup study 0099. J Clin Oncol 1998, 16:1310-1317. In summary, our findings support the conclusion that 9. Begg CB, Riedel ER, Bach PB, Kattan MW, Schrag D, Warren JL, Scardino PT: provider volume affects survival outcome in NPC. Ana- Variations in morbidity after radical prostatectomy. N Engl J Med 2002, lysis using a Cox proportional hazard model and pro- 346:1138-1144. 10. Begg CB, Cramer LD, Hoskins WJ, Brennan MF: Impact of hospital volume pensity score found an association between high-volume on operative mortality for major cancer surgery. JAMA 1998, physicians and improved 10-year survival rate in 280:1747-1751. patients with NPC. Analysis of the treatment strategies 11. Sosa JA, Bowman HM, Gordon TA, Bass EB, Yeo CJ, Lillemoe KD, Pitt HA, Tielsch JM, Cameron JL: Importance of hospital volume in the overall adopted by high-volume physicians may improve overall management of pancreatic cancer. Ann Surg 1998, 228:429-438. survival rate. 12. Laks MP, Cohen T, Hack R: Volume of procedures at transplantation centers and mortality after liver transplantation. N Engl J Med 2000, 342:1527. Conflict of interest 13. Goodney PP, Stukel TA, Lucas FL, Finlayson EV, Birkmeyer JD: Hospital The authors declare that they have no competing volume, length of stay, and readmission rates in high-risk surgery. Ann interests. Surg 2003, 238:161-167. 14. Deyo RA, Cherkin DC, Ciol MA: Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol 1992, 45:613-619. Acknowledgements 15. Braaten T, Weiderpass E, Lund E: Socioeconomic differences in cancer This study is based in part on data from the National Health Insurance survival: the Norwegian Women and Cancer Study. BMC Public Health Research Database provided by the Bureau of National Health Insurance, 2009, 9:178. Department of Health and managed by the National Health Research 16. Kwok J, Langevin SM, Argiris A, Grandis JR, Gooding WE, Taioli E: The Institutes (Registry number 99018). The interpretation and conclusions impact of health insurance status on the survival of patients with head contained herein do not represent those of the Bureau of National Health and neck cancer. Cancer 2010, 116:476-485. Insurance, Department of Health, or National Health Research Institutes. 17. Chen CY, Liu CY, Su WC, Huang SL, Lin KM: Factors associated with the diagnosis of neurodevelopmental disorders: a population-based Author details longitudinal study. Pediatrics 2007, 119:435-443. Department of Otolaryngology, Buddhist Dalin Tzu Chi General Hospital, 18. Joffe MM, Rosenbaum PR: Invited commentary: propensity scores. Am J Chiayi, Taiwan. Department of Oral and Maxillofacial Surgery, Buddhist Dalin Epidemiol 1999, 150:327-333. Tzu Chi General Hospital, Chiayi, Taiwan. Department of Radiation Lee et al. Radiation Oncology 2011, 6:92 Page 7 of 7 http://www.ro-journal.com/content/6/1/92 19. Rubin DB: Tasks in statistical inference for studying variation in medicine. Med Care 1993, 31:YS103-110. 20. Rubin DB: Estimating causal effects from large data sets using propensity scores. Ann Intern Med 1997, 127:757-763. 21. D’Agostino RB Jr: Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group. Stat Med 1998, 17:2265-2281. 22. Thiemann DR, Coresh J, Oetgen WJ, Powe NR: The association between hospital volume and survival after acute myocardial infarction in elderly patients. N Engl J Med 1999, 340:1640-1648. 23. Stefoski Mikeljevic J, Haward RA, Johnston C, Sainsbury R, Forman D: Surgeon workload and survival from breast cancer. Br J Cancer 2003, 89:487-491. 24. Begg CB, Cramer LD, Hoskins WJ, Brennan MF: Impact of hospital volume on operative mortality for major cancer surgery. JAMA 1998, 280:1747-1751. 25. Roohan PJ, Bickell NA, Baptiste MS, Therriault GD, Ferrara EP, Siu AL: Hospital volume differences and five-year survival from breast cancer. Am J Public Health 1998, 88:454-457. doi:10.1186/1748-717X-6-92 Cite this article as: Lee et al.: Survival rate in nasopharyngeal carcinoma improved by high caseload volume: a nationwide population-based study in Taiwan. Radiation Oncology 2011 6:92. 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Radiation Oncology – Springer Journals
Published: Aug 11, 2011
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