手法を比較

	前向き競合リスク解析	前向き生存時間分析
分野	疫学	疫学
系統	Process / pipeline	Process / pipeline
提唱年	1978–1999 (foundational frameworks; prospective application standard by 2000s)	1958–1972 (foundational methods); prospective design emphasis formalized by 1980s
提唱者	Fine & Gray (subdistribution hazard model, 1999); Prentice, Kalbfleisch et al. (cause-specific hazard, 1978)	Kaplan & Meier (estimator, 1958); Cox (proportional hazards model, 1972); prospective design formalised in modern clinical epidemiology
種類	Observational analytic study with event-time statistical analysis	Longitudinal observational or experimental study design with time-to-event analysis
原典	Fine, J. P., & Gray, R. J. (1999). A proportional hazards model for the subdistribution of a competing risk. Journal of the American Statistical Association, 94(446), 496–509.	Kleinbaum, D. G., & Klein, M. (2012). Survival Analysis: A Self-Learning Text (3rd ed.). Springer. ISBN: 978-1441966452
別名	prospective CRA, prospective subdistribution hazard analysis, prospective cause-specific hazard analysis, forward-looking competing events analysis	prospective time-to-event analysis, prospective failure-time analysis, forward-looking survival study, prospective event-time study
関連	4	5
概要	Prospective competing risks analysis is an observational study design that follows participants forward in time from a well-defined starting point, recording all events — including those that prevent the primary event from occurring — and then estimates cause-specific incidence while correctly accounting for competing outcomes. It combines the temporal clarity of prospective cohort follow-up with the statistical rigor of competing risks methodology to avoid the overestimation inherent in standard Kaplan-Meier curves when multiple event types are present.	Prospective survival analysis is a longitudinal study design in which participants are enrolled before the event of interest occurs, followed forward in time under standardised conditions, and analysed using survival-analytic methods to estimate the time until a defined clinical endpoint — such as death, disease recurrence, or treatment failure. Because data are collected prospectively, exposure and covariate information are recorded before outcomes are known, substantially reducing recall and selection bias relative to retrospective approaches.