Abstract
Summary
Failure to account for competing mortality gave higher estimates of 10-year fracture probability than if appropriate adjustment is made for competing mortality, particularly among subgroups with higher mortality. A modified Kaplan–Meier method is easy to implement and provides an alternative approach to existing methods for competing mortality risk adjustment.
Introduction
A unique feature of FRAX® is that 10-year fracture probability accounts for mortality as a competing risk. We compared the effect of competing mortality adjustment on nonparametric and parametric methods of fracture probability estimation.
Methods
The Manitoba Bone Mineral Density (BMD) database was used to identify men and women age ≥50 years with FRAX probabilities calculated using femoral neck BMD (N = 39,063). Fractures were assessed from administrative data (N = 2,543 with a major osteoporotic fracture, N = 549 with a hip fracture during mean 5.3 years follow-up).
Results
The following subgroups with higher mortality were identified: men, age >80 years, high fracture probability, and presence of diabetes. Failure to account for competing mortality in these subgroups overestimated fracture probability by 16–56 % with the standard nonparametric (Kaplan–Meier) method and 15–29 % with the standard parametric (Cox) model. When the outcome was hip fractures, failure to account for competing mortality overestimated hip fracture probability by 18–36 % and 17–35 %, respectively. A simple modified Kaplan–Meier method showed very close agreement with methods that adjusted for competing mortality (within 2 %).
Conclusions
Failure to account for competing mortality risk gives considerably higher estimates of 10-year fracture probability than if adjustment is made for this competing risk.
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References
Kanis JA, Oden A, Johnell O et al (2007) The use of clinical risk factors enhances the performance of BMD in the prediction of hip and osteoporotic fractures in men and women. Osteoporos Int 18:1033–1046
Kanis JA, Oden A, Johansson H et al (2009) FRAX and its applications to clinical practice. Bone 44:734–743
Aus G, Pileblad E, Hugosson J (1995) Impact of competing mortality on the cancer-related mortality in localized prostate cancer. Urology 46:672–675
Goldie SJ, O’Shea M, Campos NG et al (2008) Health and economic outcomes of HPV 16,18 vaccination in 72 GAVI-eligible countries. Vaccine 26:4080–4093
Kim WR, Poterucha JJ, Benson JT et al (2004) The impact of competing risks on the observed rate of chronic hepatitis C progression. Gastroenterology 127:749–755
Mell LK, Jeong JH, Nichols MA et al (2010) Predictors of competing mortality in early breast cancer. Cancer 116:5365–5373
Mell LK, Dignam JJ, Salama JK et al (2010) Predictors of competing mortality in advanced head and neck cancer. J Clin Oncol 28:15–20
Simard EP, Pfeiffer RM, Engels EA (2011) Cumulative incidence of cancer among individuals with acquired immunodeficiency syndrome in the United States. Cancer 117:1089–1096
Nguyen ND, Frost SA, Center JR et al (2007) Development of a nomogram for individualizing hip fracture risk in men and women. Osteoporos Int 18:1109–1117
Nguyen ND, Frost SA, Center JR et al (2008) Development of prognostic nomograms for individualizing 5-year and 10-year fracture risks. Osteoporos Int 19:1431–1444
Henry MJ, Pasco JA, Sanders KM et al (2006) Fracture risk (FRISK) score: Geelong osteoporosis study. Radiology 241:190–196
Pluijm SM, Koes B, de Laet C et al (2009) A simple risk score for the assessment of absolute fracture risk in general practice based on two longitudinal studies. J Bone Miner Res 24:768–774
Bolland MJ, Siu AT, Mason BH et al (2011) Evaluation of the FRAX and Garvan fracture risk calculators in older women. J Bone Miner Res 26:420–427
Henry MJ, Pasco JA, Merriman EN et al (2011) Fracture risk score and absolute risk of fracture. Radiology 259:495–501
Satagopan JM, Ben-Porat L, Berwick M et al (2004) A note on competing risks in survival data analysis. Br J Cancer 91:1229–1235
Scrucca L, Santucci A, Aversa F (2007) Competing risk analysis using R: an easy guide for clinicians. Bone Marrow Transpl 40:381–387
Leslie WD, MacWilliam L, Lix L et al (2005) A population-based study of osteoporosis testing and treatment following introduction of a new bone densitometry service. Osteoporos Int 16:773–782
Leslie WD, Caetano PA, MacWilliam LR et al (2005) Construction and validation of a population-based bone densitometry database. J Clin Densitom 8:25–30
Binkley N, Kiebzak GM, Lewiecki EM et al (2005) Recalculation of the NHANES database SD improves T-score agreement and reduces osteoporosis prevalence. J Bone Miner Res 20:195–201
Kanis JA, McCloskey EV, Johansson H et al (2008) A reference standard for the description of osteoporosis. Bone 42:467–475
Leslie WD (2006) The importance of spectrum bias on bone density monitoring in clinical practice. Bone 39:361–368
Leslie WD, Lix LM, Johansson H et al (2010) Independent clinical validation of a Canadian FRAX® tool: fracture prediction and model calibration. J Bone Miner Res 25(11):2350–2358
Leslie WD, Berger C, Langsetmo L et al (2010) Construction and validation of a simplified fracture risk assessment tool for Canadian women and men: results from the CaMos and Manitoba cohorts. Osteoporos Int 22(6):1873–1883
Fraser LA, Langsetmo L, Berger C et al (2011) Fracture prediction and calibration of a Canadian FRAX® tool: a population-based report from CaMos. Osteoporos Int 22(3):829–837
Papaioannou A, Morin S, Cheung AM et al (2010) 2010 clinical practice guidelines for the diagnosis and management of osteoporosis in Canada: summary. CMAJ 182:1864–1873
Leslie WD, Tsang JF, Caetano PA et al (2007) Effectiveness of bone density measurement for predicting osteoporotic fractures in clinical practice. J Clin Endocrinol Metab 92:77–81
Roos LL, Sharp SM, Wajda A (1989) Assessing data quality: a computerized approach. Soc Sci Med 28:175–182
Allison P (2005) Survival analysis using the SAS® system: a practical guide. SAS Institute Inc
Klein JP, Gerster M, Andersen PK et al (2008) SAS and R functions to compute pseudo-values for censored data regression. Comput Methods Programs Biomed 89:289–300
Acknowledgments
The authors are indebted to Manitoba Health for the provision of data (HIPC file no. 2007/2008-49). The results and conclusions are those of the authors, and no official endorsement by Manitoba Health is intended or should be inferred. This article has been reviewed and approved by the members of the Manitoba Bone Density Program Committee. We would like to thank Ms. Helena Johansson and Dr. John Kanis for generating the FRAX results for the Manitoba cohort. LML is supported by a Centennial Research Chair at the University of Saskatchewan.
Conflicts of interest
William D. Leslie received speaker fees and unrestricted research grants from Merck Frosst Canada Ltd; unrestricted research grants from Sanofi-Aventis, Procter & Gamble Pharmaceuticals, Novartis, Amgen Pharmaceuticals, Genzyme; and advisory boards for Genzyme, Novartis, and Amgen Pharmaceuticals. Lisa M. Lix received unrestricted research grants from Amgen Pharmaceuticals.
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Leslie, W.D., Lix, L.M., Wu, X. et al. Competing mortality and fracture risk assessment. Osteoporos Int 24, 681–688 (2013). https://doi.org/10.1007/s00198-012-2051-5
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DOI: https://doi.org/10.1007/s00198-012-2051-5