Jenna Sykes1, Sanja Stanojevic2, Christopher H Goss3, Bradley S Quon4, Bruce C Marshall5, Kristofer Petren5, Josh Ostrenga5, Aliza Fink5, Alexander Elbert5, Anne L Stephenson6. 1. Department of Respirology, St. Michael's Hospital, 30 Bond Street, 6th Floor, Bond Wing, Toronto, Ontario, Canada M5B 1W8. Electronic address: sykesj@smh.ca. 2. Division of Respiratory Medicine, Department of Pediatrics, The Hospital for Sick Children, 555 University Avenue, Toronto, Ontario, Canada M5G 1X8; Institute of Health Policy, Management and Evaluation, University of Toronto, 155 College Street, Toronto, Ontario, Canada M5T 3M6. 3. Division of Pulmonary and Critical Care Medicine, Department of Medicine and Pediatrics, University of Washington Medical Center, 1959 N.E. Pacific, Seattle, WA, USA 98195-6522. 4. Department of Medicine, University of British Columbia, 2775 Laurel Street, Vancouver, British Columbia, Canada V5Z 1M9. 5. Cystic Fibrosis Foundation, 6931 Arlington Road, Bethesda, MD, USA 20814. 6. Department of Respirology, St. Michael's Hospital, 30 Bond Street, 6th Floor, Bond Wing, Toronto, Ontario, Canada M5B 1W8; Keenan Research Centre, Li Ka Shing Knowledge Institute of St Michael's Hospital, 209 Victoria Street, Toronto, Ontario, Canada M5B 1T8.
Abstract
OBJECTIVES: Our objective was to quantify the effect of different statistical techniques, inclusion/exclusion criteria, and missing data on the predicted median survival age. STUDY DESIGN AND SETTING: Using the Canadian cystic fibrosis registry (CCFR), the median age of survival was calculated using both the Cox proportional hazards (PH) and the life-table methods. Through simulations, we examined how the median age of survival would change when: (1) patients were excluded, (2) death dates were inaccurate, (3) patients were lost to follow-up, (4) entire years with no clinic visits were excluded even if the patient had a visit in subsequent years, and (5) censoring patients at their date of transplant. Simulations were run assuming 5-35% of data were affected by each scenario. RESULTS: Over the period 2009-2013, there were 4,666 individuals in the CCFR with 240 deaths. The observed median age of survival calculated by the Cox PH method was 50.9 [95% confidence interval (CI): 47.4, 54.3] and 50.5 from the life-table method (95% CI: 47.5, 53.5). Censoring patients at their transplant date overestimated the median age of survival by 7.2 years (58.1; 95% CI: 53.3, 64.7). Simulations determined that by missing just 15% of deaths, the median age of survival can be overestimated by 3.5 years (54.4; 95% CI: 54.2, 56.1), and having 25% of patients lost to follow-up can underestimate the median age of survival by 3.3 years (47.6; 95% CI: 46.8, 47.7). CONCLUSION: We present several recommendations to assist national cystic fibrosis registries in calculating and reporting the median age of survival in a standardized fashion. It is imperative to state the statistical method used as well as the proportion lost to follow-up and the treatment of missing data and transplanted patients. Registries must be diligent in their data collection as incomplete data can lead to overestimation and underestimation of survival.
OBJECTIVES: Our objective was to quantify the effect of different statistical techniques, inclusion/exclusion criteria, and missing data on the predicted median survival age. STUDY DESIGN AND SETTING: Using the Canadian cystic fibrosis registry (CCFR), the median age of survival was calculated using both the Cox proportional hazards (PH) and the life-table methods. Through simulations, we examined how the median age of survival would change when: (1) patients were excluded, (2) death dates were inaccurate, (3) patients were lost to follow-up, (4) entire years with no clinic visits were excluded even if the patient had a visit in subsequent years, and (5) censoring patients at their date of transplant. Simulations were run assuming 5-35% of data were affected by each scenario. RESULTS: Over the period 2009-2013, there were 4,666 individuals in the CCFR with 240 deaths. The observed median age of survival calculated by the Cox PH method was 50.9 [95% confidence interval (CI): 47.4, 54.3] and 50.5 from the life-table method (95% CI: 47.5, 53.5). Censoring patients at their transplant date overestimated the median age of survival by 7.2 years (58.1; 95% CI: 53.3, 64.7). Simulations determined that by missing just 15% of deaths, the median age of survival can be overestimated by 3.5 years (54.4; 95% CI: 54.2, 56.1), and having 25% of patients lost to follow-up can underestimate the median age of survival by 3.3 years (47.6; 95% CI: 46.8, 47.7). CONCLUSION: We present several recommendations to assist national cystic fibrosis registries in calculating and reporting the median age of survival in a standardized fashion. It is imperative to state the statistical method used as well as the proportion lost to follow-up and the treatment of missing data and transplanted patients. Registries must be diligent in their data collection as incomplete data can lead to overestimation and underestimation of survival.
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Authors: Bradley S Quon; Jenna Sykes; Sanja Stanojevic; Bruce C Marshall; Kristofer Petren; Josh Ostrenga; Aliza Fink; Alexander Elbert; Albert Faro; Christopher H Goss; Anne L Stephenson Journal: Clin Transplant Date: 2018-02-11 Impact factor: 2.863
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Authors: Anne L Stephenson; Jenna Sykes; Sanja Stanojevic; Bradley S Quon; Bruce C Marshall; Kristofer Petren; Josh Ostrenga; Aliza K Fink; Alexander Elbert; Christopher H Goss Journal: Ann Intern Med Date: 2017-03-14 Impact factor: 25.391
Authors: Christopher H Goss; Jenna Sykes; Sanja Stanojevic; Bruce Marshall; Kristofer Petren; Josh Ostrenga; Aliza Fink; Alexander Elbert; Bradley S Quon; Anne L Stephenson Journal: Am J Respir Crit Care Med Date: 2018-03-15 Impact factor: 30.528
Authors: Anne L Stephenson; Kathleen J Ramos; Jenna Sykes; Xiayi Ma; Sanja Stanojevic; Bradley S Quon; Bruce C Marshall; Kristofer Petren; Joshua S Ostrenga; Aliza K Fink; Albert Faro; Alexander Elbert; Cecilia Chaparro; Christopher H Goss Journal: J Heart Lung Transplant Date: 2020-12-07 Impact factor: 10.247