Skip to main content
Erschienen in: Lifetime Data Analysis 4/2014

01.10.2014

Semicompeting risks in aging research: methods, issues and needs

verfasst von: Ravi Varadhan, Qian-Li Xue, Karen Bandeen-Roche

Erschienen in: Lifetime Data Analysis | Ausgabe 4/2014

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

A semicompeting risks problem involves two-types of events: a nonterminal and a terminal event (death). Typically, the nonterminal event is the focus of the study, but the terminal event can preclude the occurrence of the nonterminal event. Semicompeting risks are ubiquitous in studies of aging. Examples of semicompeting risk dyads include: dementia and death, frailty syndrome and death, disability and death, and nursing home placement and death. Semicompeting risk models can be divided into two broad classes: models based only on observables quantities (class \(\mathcal {O}\)) and those based on potential (latent) failure times (class \(\mathcal {L}\)). The classical illness-death model belongs to class \(\mathcal {O}\). This model is a special case of the multistate models, which has been an active area of methodology development. During the past decade and a half, there has also been a flurry of methodological activity on semicompeting risks based on latent failure times (\(\mathcal {L}\) models). These advances notwithstanding, the semicompeting risks methodology has not penetrated biomedical research, in general, and gerontological research, in particular. Some possible reasons for this lack of uptake are: the methods are relatively new and sophisticated, conceptual problems associated with potential failure time models are difficult to overcome, paucity of expository articles aimed at educating practitioners, and non-availability of readily usable software. The main goals of this review article are: (i) to describe the major types of semicompeting risks problems arising in aging research, (ii) to provide a brief survey of the semicompeting risks methods, (iii) to suggest appropriate methods for addressing the problems in aging research, (iv) to highlight areas where more work is needed, and (v) to suggest ways to facilitate the uptake of the semicompeting risks methodology by the broader biomedical research community.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Literatur
Zurück zum Zitat Aalen OO (1994) Effects of frailty in survival analysis. Stat Methods Med Res 3:227–243CrossRef Aalen OO (1994) Effects of frailty in survival analysis. Stat Methods Med Res 3:227–243CrossRef
Zurück zum Zitat Bandeen-Roche K, Liang KY (2002) Modelling multivariate failure time associations in the presence of a competing risk. Biometrika 89:299–314 Bandeen-Roche K, Liang KY (2002) Modelling multivariate failure time associations in the presence of a competing risk. Biometrika 89:299–314
Zurück zum Zitat Boyd CM, Fortin M (2010) Future of multimorbidity research: how should understanding of multimorbidity inform health system design? Public Health Rev 32:451–474 Boyd CM, Fortin M (2010) Future of multimorbidity research: how should understanding of multimorbidity inform health system design? Public Health Rev 32:451–474
Zurück zum Zitat Cappola AR, O’Meara ES, Guo W, Bartz TM, Fried LP, Newman AB (2009) Trajectories of dehydroepiandrosterone sulfate predict mortality in older adults: the cardiovascular health study. J Gerontol Med Sci 64:1268–1274CrossRef Cappola AR, O’Meara ES, Guo W, Bartz TM, Fried LP, Newman AB (2009) Trajectories of dehydroepiandrosterone sulfate predict mortality in older adults: the cardiovascular health study. J Gerontol Med Sci 64:1268–1274CrossRef
Zurück zum Zitat Chen Y-H (2012) Maximum likelihood analysis of semicompeting risks data with semiparametric regression models. Lifetime Data Anal 18:36–57MathSciNetCrossRef Chen Y-H (2012) Maximum likelihood analysis of semicompeting risks data with semiparametric regression models. Lifetime Data Anal 18:36–57MathSciNetCrossRef
Zurück zum Zitat Cheng Y, Fine JP (2008) Nonparametric estimation of cause-specific cross hazard ratio with bivariate competing risks data. Biometrika 95:233–40. Cheng Y, Fine JP (2008) Nonparametric estimation of cause-specific cross hazard ratio with bivariate competing risks data. Biometrika 95:233–40.
Zurück zum Zitat Cheng Y, Fine JP, Kosorok MR (2007) Nonparametric association analysis of bivariate competing-risks data. J Am Stat Assoc 102:1407–1415 Cheng Y, Fine JP, Kosorok MR (2007) Nonparametric association analysis of bivariate competing-risks data. J Am Stat Assoc 102:1407–1415
Zurück zum Zitat Day R, Bryant J, Lefkopoulou M (1997) Adaptation of bivariate frailty models for prediction, with application to biological markers as prognostic indicators. Biometrika 84:45–56MathSciNetCrossRefMATH Day R, Bryant J, Lefkopoulou M (1997) Adaptation of bivariate frailty models for prediction, with application to biological markers as prognostic indicators. Biometrika 84:45–56MathSciNetCrossRefMATH
Zurück zum Zitat Ding AA, Shi G, Wang W, Hsieh J-J (2009) Marginal regression analysis for semi-competing risks data under dependent censoring. Scand J Stat 36:481–500MathSciNetCrossRefMATH Ding AA, Shi G, Wang W, Hsieh J-J (2009) Marginal regression analysis for semi-competing risks data under dependent censoring. Scand J Stat 36:481–500MathSciNetCrossRefMATH
Zurück zum Zitat Elashoff RM, Li G, Li N (2007) An approach to joint analysis of longitudinal measurements and competing risks failure time data. Stat Med 26:2813–2835MathSciNetCrossRef Elashoff RM, Li G, Li N (2007) An approach to joint analysis of longitudinal measurements and competing risks failure time data. Stat Med 26:2813–2835MathSciNetCrossRef
Zurück zum Zitat Elashoff RM, Li G, Li N (2008) A joint model for longitudinal measurements and survival data in the presence of multiple failure types. Biometrics 64:762–771MathSciNetCrossRefMATH Elashoff RM, Li G, Li N (2008) A joint model for longitudinal measurements and survival data in the presence of multiple failure types. Biometrics 64:762–771MathSciNetCrossRefMATH
Zurück zum Zitat Fine JP, Gray RJ (1999) A proportional hazards model for the subdistribution of a competing risk. J Am Stat Assoc 94:496–509 Fine JP, Gray RJ (1999) A proportional hazards model for the subdistribution of a competing risk. J Am Stat Assoc 94:496–509
Zurück zum Zitat Fine JP, Jiang H, Chappell R (2001) On semi-competing risks data. Biometrika 88:907–919 Fine JP, Jiang H, Chappell R (2001) On semi-competing risks data. Biometrika 88:907–919
Zurück zum Zitat Fix E, Neyman J (1951) A simple stochastic model to recovery, relapse, death and loss of patients. Hum Biol 23:204–241 Fix E, Neyman J (1951) A simple stochastic model to recovery, relapse, death and loss of patients. Hum Biol 23:204–241
Zurück zum Zitat Frangakis CE, Rubin DB, An M-W, Mackenzie E (2007) Principal stratification designs to estimate input data missing due to death. Biometrics 63:641–662MathSciNetCrossRefMATH Frangakis CE, Rubin DB, An M-W, Mackenzie E (2007) Principal stratification designs to estimate input data missing due to death. Biometrics 63:641–662MathSciNetCrossRefMATH
Zurück zum Zitat Fries JF (1980) Aging, natural death, and the compression of morbidity. N Engl J Med 303:1369–1370CrossRef Fries JF (1980) Aging, natural death, and the compression of morbidity. N Engl J Med 303:1369–1370CrossRef
Zurück zum Zitat Gulati R (2011) what if i don’t treat my psa-detected prostate cancer? Answers from three natural history models. Cancer Epidemiol Biomark Prev 55:3–24 Gulati R (2011) what if i don’t treat my psa-detected prostate cancer? Answers from three natural history models. Cancer Epidemiol Biomark Prev 55:3–24
Zurück zum Zitat Henderson R, Diggle P, Dobson A (2000) Joint modelling of longitudinal measurements and event time data. Biostatistics 1:465–480CrossRefMATH Henderson R, Diggle P, Dobson A (2000) Joint modelling of longitudinal measurements and event time data. Biostatistics 1:465–480CrossRefMATH
Zurück zum Zitat Hernan MA, Alonso A, Logroscino G (2008) Cigarette smoking and dementia: potential selection bias in the elderly. Epidemiology 19:448–450CrossRef Hernan MA, Alonso A, Logroscino G (2008) Cigarette smoking and dementia: potential selection bias in the elderly. Epidemiology 19:448–450CrossRef
Zurück zum Zitat Hernan MA (2010) The hazards of hazard ratios. Epidemiology 21:13–15CrossRef Hernan MA (2010) The hazards of hazard ratios. Epidemiology 21:13–15CrossRef
Zurück zum Zitat Hougaard P (1999) Multi-state models: a review. Lifetime Data Anal 5:239–264 Hougaard P (1999) Multi-state models: a review. Lifetime Data Anal 5:239–264
Zurück zum Zitat Hsieh J-J, Huang Y-T (2012) Regression analysis based on conditional likelihood approach under semi-competing risks data. Lifetime Data Anal 103:302320MathSciNet Hsieh J-J, Huang Y-T (2012) Regression analysis based on conditional likelihood approach under semi-competing risks data. Lifetime Data Anal 103:302320MathSciNet
Zurück zum Zitat Hu W, Li G, Li N (2009) A bayesian approach to joint analysis of longitudinal measurements and competing risks failure time data. Stat Med 28:1601–1619MathSciNetCrossRef Hu W, Li G, Li N (2009) A bayesian approach to joint analysis of longitudinal measurements and competing risks failure time data. Stat Med 28:1601–1619MathSciNetCrossRef
Zurück zum Zitat Kaplan GA, Haan MN, Wallace RB (1999) Understanding changing risk factor associations with increasing age in adults. Annu Rev Public Health 20:89–108CrossRef Kaplan GA, Haan MN, Wallace RB (1999) Understanding changing risk factor associations with increasing age in adults. Annu Rev Public Health 20:89–108CrossRef
Zurück zum Zitat Lakhal L, Rivest LP, Abdous B (2008) Estimating survival and association in a semicompeting risks model. Biometrics 64:180–188MathSciNetCrossRefMATH Lakhal L, Rivest LP, Abdous B (2008) Estimating survival and association in a semicompeting risks model. Biometrics 64:180–188MathSciNetCrossRefMATH
Zurück zum Zitat Larson MG, Dinse GE (1985) A mixture model for the regression-analysis of competing risks data. Appl Stat J R Stat Soc Ser C 34:201–211MathSciNet Larson MG, Dinse GE (1985) A mixture model for the regression-analysis of competing risks data. Appl Stat J R Stat Soc Ser C 34:201–211MathSciNet
Zurück zum Zitat Li N, Elashoff RM, Li G, Saver J (2010) Joint modeling of longitudinal ordinal data and competing risks survival times and analysis of the ninds rt-pa stroke trial. Stat Med 29:546–557MathSciNet Li N, Elashoff RM, Li G, Saver J (2010) Joint modeling of longitudinal ordinal data and competing risks survival times and analysis of the ninds rt-pa stroke trial. Stat Med 29:546–557MathSciNet
Zurück zum Zitat Lin DY, Robins JM, Wei LJ (1996) Comparing two failure time distributions in the presence of dependent censoring. Biometrika 83:381–393MathSciNetCrossRefMATH Lin DY, Robins JM, Wei LJ (1996) Comparing two failure time distributions in the presence of dependent censoring. Biometrika 83:381–393MathSciNetCrossRefMATH
Zurück zum Zitat Lin OS, Kozarek RA, Schembre DB et al (2006) Screening colnoscopy in very elderly patients: prevalence of neoplasia and estimated impact on life expectancy. JAMA 295:2357–2365CrossRef Lin OS, Kozarek RA, Schembre DB et al (2006) Screening colnoscopy in very elderly patients: prevalence of neoplasia and estimated impact on life expectancy. JAMA 295:2357–2365CrossRef
Zurück zum Zitat Marengoni A, Angleman S, Melis R et al (2011) Aging with multimorbidity: a systematic review of the literature. Aging Res Rev 10:430–439CrossRef Marengoni A, Angleman S, Melis R et al (2011) Aging with multimorbidity: a systematic review of the literature. Aging Res Rev 10:430–439CrossRef
Zurück zum Zitat Ng SK, McLachlan GJ (2003) An em-based semi-parametric mixture model approach to the regression analysis of competing-risks data. Stat Med 22:1097–1111CrossRef Ng SK, McLachlan GJ (2003) An em-based semi-parametric mixture model approach to the regression analysis of competing-risks data. Stat Med 22:1097–1111CrossRef
Zurück zum Zitat Peng L, Fine JP (2006) Rank estimation of accelerated lifetime models with dependent censoring. J Am Stat Assoc 101:1085–1093 Peng L, Fine JP (2006) Rank estimation of accelerated lifetime models with dependent censoring. J Am Stat Assoc 101:1085–1093
Zurück zum Zitat Peterson AV Jr (1976) Bounds for a joint distribution function with fixed sub-distribution functions: application to competing risks. Proc Natl Acad Sci 73:11–13 Peterson AV Jr (1976) Bounds for a joint distribution function with fixed sub-distribution functions: application to competing risks. Proc Natl Acad Sci 73:11–13
Zurück zum Zitat Peterson B, Harrell FE (1990) Partial proportional odds models for ordinal response variables. Appl Stat J R Stat Soc Ser C 39:205–217MATH Peterson B, Harrell FE (1990) Partial proportional odds models for ordinal response variables. Appl Stat J R Stat Soc Ser C 39:205–217MATH
Zurück zum Zitat Prentice RL, Kalbfleisch JD, Peterson AV Jr, Flournoy N, Farewell VT, Breslow NE (1978) The analysis of failure times in the presence of competing risks. Biometrics 34:541–554CrossRefMATH Prentice RL, Kalbfleisch JD, Peterson AV Jr, Flournoy N, Farewell VT, Breslow NE (1978) The analysis of failure times in the presence of competing risks. Biometrics 34:541–554CrossRefMATH
Zurück zum Zitat Putter H (2011) Special issue about competing risks and multi-state models. J Stat Softw 38:1–4 Putter H (2011) Special issue about competing risks and multi-state models. J Stat Softw 38:1–4
Zurück zum Zitat Rantanen T, Guralnik JM, Foley D, Masaki K, Leveille S, Curb JD, White L (1999) Midlife hand grip strength as a predictor of old age disability. JAMA 281:558–560CrossRef Rantanen T, Guralnik JM, Foley D, Masaki K, Leveille S, Curb JD, White L (1999) Midlife hand grip strength as a predictor of old age disability. JAMA 281:558–560CrossRef
Zurück zum Zitat Robins JM (1987) A new approach to causal inference in mortality studies with sustained exposure periods—application to control of the healthy worker survivor effect. Comput Mathl Appl 14:923–945MathSciNetCrossRefMATH Robins JM (1987) A new approach to causal inference in mortality studies with sustained exposure periods—application to control of the healthy worker survivor effect. Comput Mathl Appl 14:923–945MathSciNetCrossRefMATH
Zurück zum Zitat Robins JM (1995a) An analytic method for randomized trials with informative censoring: part i. Lifetime Data Anal 1:241–254MathSciNetCrossRefMATH Robins JM (1995a) An analytic method for randomized trials with informative censoring: part i. Lifetime Data Anal 1:241–254MathSciNetCrossRefMATH
Zurück zum Zitat Robins JM (1995b) An analytic method for randomized trials with informative censoring: part ii. Lifetime Data Anal 1:417–434MathSciNetCrossRefMATH Robins JM (1995b) An analytic method for randomized trials with informative censoring: part ii. Lifetime Data Anal 1:417–434MathSciNetCrossRefMATH
Zurück zum Zitat Scheike TH, Zhang M-J (2008) Flexible competing risks regression modeling and goodness-of-fit. Lifetime Data Anal 14:464–483MathSciNetCrossRefMATH Scheike TH, Zhang M-J (2008) Flexible competing risks regression modeling and goodness-of-fit. Lifetime Data Anal 14:464–483MathSciNetCrossRefMATH
Zurück zum Zitat Scheike TH, Sun Y, Zhang MJ, Jensen TK (2010) A semiparametric random effects model for multivariate competing risks data. Biometrika 97:133–145MathSciNetCrossRefMATH Scheike TH, Sun Y, Zhang MJ, Jensen TK (2010) A semiparametric random effects model for multivariate competing risks data. Biometrika 97:133–145MathSciNetCrossRefMATH
Zurück zum Zitat Shih JH, Albert PS (2010) Modeling familial association of ages at onset of disease in the presence of competing risk. Biometrics 66:1012–1023MathSciNetCrossRefMATH Shih JH, Albert PS (2010) Modeling familial association of ages at onset of disease in the presence of competing risk. Biometrics 66:1012–1023MathSciNetCrossRefMATH
Zurück zum Zitat Slud EV, Rubinstein LV (1983) Dependent competing risks and summary survival curves. Biometrika 70:643–649 Slud EV, Rubinstein LV (1983) Dependent competing risks and summary survival curves. Biometrika 70:643–649
Zurück zum Zitat Tsiatis A (1975) A nonidentifiability aspect of the problem of competing risks. Proc Natl Acad Sci 72:20–22 Tsiatis A (1975) A nonidentifiability aspect of the problem of competing risks. Proc Natl Acad Sci 72:20–22
Zurück zum Zitat Varadhan R, Weiss CO, Segal JB, Wu AW, Scharfstein D, Boyd C (2010) Evaluating health outcomes in the presence of competing risks: a review of statistical methods and clinical applications. Med Care 48:96–105CrossRef Varadhan R, Weiss CO, Segal JB, Wu AW, Scharfstein D, Boyd C (2010) Evaluating health outcomes in the presence of competing risks: a review of statistical methods and clinical applications. Med Care 48:96–105CrossRef
Zurück zum Zitat Vaupel JW, Yashin AI (1985) Heterogeneitys ruses: some surprising effects of selection on population dynamics. Am Stat 39:176–185MathSciNet Vaupel JW, Yashin AI (1985) Heterogeneitys ruses: some surprising effects of selection on population dynamics. Am Stat 39:176–185MathSciNet
Zurück zum Zitat Walter LC, Bertenthal D, Lindquist K, Konety BR (2006) Psa screening among elderly men with limited life expectancies. J Am Med Assoc 296:2336–2342CrossRef Walter LC, Bertenthal D, Lindquist K, Konety BR (2006) Psa screening among elderly men with limited life expectancies. J Am Med Assoc 296:2336–2342CrossRef
Zurück zum Zitat Walter LC, Covinsky KE (2001) Cancer screening in elderly patients: a framework for individualized decision making. J Am Med Assoc 285:2750–2756CrossRef Walter LC, Covinsky KE (2001) Cancer screening in elderly patients: a framework for individualized decision making. J Am Med Assoc 285:2750–2756CrossRef
Zurück zum Zitat Wang W (2003) Estimating the association parameter for copula models under dependent censoring. J R Stat Soc 65:257–274CrossRefMATH Wang W (2003) Estimating the association parameter for copula models under dependent censoring. J R Stat Soc 65:257–274CrossRefMATH
Zurück zum Zitat Weiss CO, Segal JB, Varadhan R (2012) Assessing the applicability of trial evidence to a target sample in the presence of heterogeneity of treatment effect. Pharmacoepidemiol Drug Saf 21:121–129CrossRef Weiss CO, Segal JB, Varadhan R (2012) Assessing the applicability of trial evidence to a target sample in the presence of heterogeneity of treatment effect. Pharmacoepidemiol Drug Saf 21:121–129CrossRef
Zurück zum Zitat Welch HG, Albertsen PC, Nease RF, Bubolz TA, Wasson JH (1996) Estimating treatment benefits for the elderly: the effect of competing risks. Ann Intern Med 124:577–584CrossRef Welch HG, Albertsen PC, Nease RF, Bubolz TA, Wasson JH (1996) Estimating treatment benefits for the elderly: the effect of competing risks. Ann Intern Med 124:577–584CrossRef
Zurück zum Zitat Xu J, Kalbfeisch JD, Tai B (2010) Statistical analysis of illness-death processes and semicompeting risks data. Biometrics 66:716–725MathSciNetCrossRefMATH Xu J, Kalbfeisch JD, Tai B (2010) Statistical analysis of illness-death processes and semicompeting risks data. Biometrics 66:716–725MathSciNetCrossRefMATH
Zurück zum Zitat Xue QL, Walston JD, Fried LP, Beamer BA (2011) Rate of decline in grip strength predicts the risk of falling, physical disability, and frailty: the womens health and aging study. Arch Intern Med 171:1119–1121CrossRef Xue QL, Walston JD, Fried LP, Beamer BA (2011) Rate of decline in grip strength predicts the risk of falling, physical disability, and frailty: the womens health and aging study. Arch Intern Med 171:1119–1121CrossRef
Zurück zum Zitat Xue QL, Beamer BA, Chaves PHM, Guralnik JM, Fried LP (2010) Heterogeneity in rate of decline in grip, hip, and knee strength and the risk of all-cause mortality: the women’s health and aging study ii. J Am Geriatr Soc 58:2076–2084CrossRef Xue QL, Beamer BA, Chaves PHM, Guralnik JM, Fried LP (2010) Heterogeneity in rate of decline in grip, hip, and knee strength and the risk of all-cause mortality: the women’s health and aging study ii. J Am Geriatr Soc 58:2076–2084CrossRef
Zurück zum Zitat Yu B, Ghosh P (2010) Joint modeling for cognitive trajectory and risk of dementia in the presence of death. Biometrics 66:294–300MathSciNetCrossRefMATH Yu B, Ghosh P (2010) Joint modeling for cognitive trajectory and risk of dementia in the presence of death. Biometrics 66:294–300MathSciNetCrossRefMATH
Zurück zum Zitat Zhang JL, Rubin DB (2003) Estimation of causal effects via principal stratification when some outcomes are truncated by death. J Educ Behav Stat 28:353–368CrossRef Zhang JL, Rubin DB (2003) Estimation of causal effects via principal stratification when some outcomes are truncated by death. J Educ Behav Stat 28:353–368CrossRef
Metadaten
Titel
Semicompeting risks in aging research: methods, issues and needs
verfasst von
Ravi Varadhan
Qian-Li Xue
Karen Bandeen-Roche
Publikationsdatum
01.10.2014
Verlag
Springer US
Erschienen in
Lifetime Data Analysis / Ausgabe 4/2014
Print ISSN: 1380-7870
Elektronische ISSN: 1572-9249
DOI
https://doi.org/10.1007/s10985-014-9295-7

Weitere Artikel der Ausgabe 4/2014

Lifetime Data Analysis 4/2014 Zur Ausgabe