nach oben

Lifetime Data Analysis

06.05.2024

Competing risks and multivariate outcomes in epidemiological and clinical trial research

verfasst von: R. L. Prentice

Erschienen in: Lifetime Data Analysis

Einloggen

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

search-config

KI-gestützte Suche

Aus

Abstract

Data analysis methods for the study of treatments or exposures in relation to a clinical outcome in the presence of competing risks have a long history, often with inference targets that are hypothetical, thereby requiring strong assumptions for identifiability with available data. Here data analysis methods are considered that are based on single and higher dimensional marginal hazard rates, quantities that are identifiable under standard independent censoring assumptions. These lead naturally to joint survival function estimators for outcomes of interest, including competing risk outcomes, and provide the basis for addressing a variety of data analysis questions. These methods will be illustrated using simulations and Women’s Health Initiative cohort and clinical trial data sets, and additional research needs will be described.

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!

Jetzt informieren

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!

Jetzt informieren

Andersen PK, Gill RD (1982) Cox’s regression model for counting processes: a large sample study. Ann Stat 10(4):1100–1120MathSciNetCrossRef

Anderson G, Cummings S, Freedman L, Furberg C, Henderson M, Johnson S, Kuller L, Manson J, Oberman A, Prentice R (1998) Design of the Women’s Health Initiative clinical trial and observational study. Control Clin Trials 19(1):61–109CrossRef

Cheng Y, Fine JP, Kosorok MR (2007) Nonparametric association analysis of bivariate competing-risks data. J Am Stat Assoc 102(480):1407–1415. http://www.jstor.org/stable/27639990

Clayton DG (1978) Model for association in bivariate life tables and its application in epidemiological studies of familial tendency in chronic disease incidence. Biometrika 65:141–151MathSciNetCrossRef

Cook RJ, Lawless JF (1997) Marginal analysis of recurrent events and a terminating event. Stat Med 16(8):911–924CrossRef

Cook RJ, Lawless JF (2007) The statistical analysis of recurrent events. Springer, New York

Cox DR (1972) Regression models and life-tables (with discussion). J R Stat Soc Ser B (Methodol) 34(2):187–220CrossRef

Fine JP, Gray RJ (1999) A proportional hazards model for the subdistribution of a competing risk. J Am Stat Assoc 94(446):496–509MathSciNetCrossRef

Fine JP, Jiang H, Chappell R (2001) On semi-competing risks data. Biometrika 88(4):907–919MathSciNetCrossRef

Freedman AN, Graubard BI, Rao SR, McCaskill-Stevens W, Ballard-Barbash R, Gail MH (2003) Estimates of the number of US women who could benefit from tamoxifen for breast cancer chemoprevention. J Natl Cancer Inst 95(7):526–532CrossRef

Gail M (1975) A review and critique of some models used in competing risk analysis. Biometrics 31:209–222MathSciNetCrossRef

Gail MH, Costantino JP (2001) Validating and improving models for projecting the absolute risk of breast cancer. J Natl Cancer Inst 93(5):334–335CrossRef

Gail MH, Brinton LA, Byar DP, Corle DK, Green SB, Schairer C, Mulvihill JJ (1989) Projecting individualized probabilities of developing breast cancer for white females who are being examined annually. J Natl Cancer Inst 81(24):1879–1886CrossRef

Ghosh D, Lin DY (2002) Marginal regression models for recurrent and terminal events. Stat Sin 12:663–688MathSciNet

Gill RD, van der Laan MJ, Wellner JA (1995) Inefficient estimators of the bivariate survival function for three models. Annales de l’IHP Probabilités et Statistiques 31(3):545–597MathSciNet

Kalbfleisch JD, Prentice RL (2002) The statistical analysis of failure time data, 2nd edn. Wiley and Sons, New YorkCrossRef

Latouche A, Boisson V, Chevret S, Porcher R (2007) Misspecified regression model for the subdistribution hazard of a competing risk. Stat Med 26(5):965–974MathSciNetCrossRef

Lin D, Wei L, Yang I, Ying Z (2000) Semiparametric regression for the mean and rate functions of recurrent events. J R Stat Soc Ser B (Stat Methodol) 62(4):711–730MathSciNetCrossRef

Oakes D (1986) Semiparametric inference in a model for association in bivariate survival data. Biometrika 73(2):353–361MathSciNet

Pepe MS, Cai J (1993) Some graphical displays and marginal regression analyses for recurrent failure times and time dependent covariates. J Am Stat Assoc 88(423):811–820CrossRef

Pfeiffer RM, Gail MH (2017) Absolute risk: methods and applications in clinical management and public health. CRC Press, Boca RatonCrossRef

Prentice RL, Zhao S (2019) The statistical analysis of multivariate failure time data: a marginal modeling approach. CRC Press, Boca RatonCrossRef

Prentice RL, Zhao S (2021) Regression models and multivariate life tables. J Am Stat Assoc 116:1330–1345MathSciNetCrossRef

Prentice RL, Kalbfleisch JD, Peterson Jr AV, Flournoy N, Farewell V, Breslow N (1978) The analysis of failure times in the presence of competing risks. Biometrics 34:541–554CrossRef

Prentice RL, Aragaki AK, Chlebowski RT, Zhao S, Anderson GL, Rossouw JE, Wallace R, Banack H, Shadyab AH, Qi L et al (2020) Dual-outcome intention-to-treat analyses in the Women’s Health Initiative randomized controlled hormone therapy trials. Am J Epidemiol 189(9):972–981CrossRef

Rossouw J, Anderson G, Prentice R, LaCroix A, Kooperberg C, Stefanick M, Jackson R, Beresford S, Howard B, Johnson K, Kotchen J, Ockene J, Writing Group for the Women’s Health Initiative Investigators (2002) Risks and benefits of estrogen plus progestin in healthy postmenopausal women: principal results from the Women’s Health Initiative randomized controlled trial. J Am Med Assoc 288:321–333CrossRef

Spiekerman CF, Lin D (1998) Marginal regression models for multivariate failure time data. J Am Stat Assoc 93(443):1164–1175MathSciNetCrossRef

Tsiatis A (1975) A nonidentifiability aspect of the problem of competing risks. Proc Natl Acad Sci 72(1):20–22MathSciNetCrossRef

Titel: Competing risks and multivariate outcomes in epidemiological and clinical trial research
verfasst von: R. L. Prentice
Publikationsdatum: 06.05.2024
Verlag: Springer US
Erschienen in: Lifetime Data Analysis
Print ISSN: 1380-7870
Elektronische ISSN: 1572-9249
DOI: https://doi.org/10.1007/s10985-024-09629-8