So sánh phương pháp

	Phân tích rủi ro cạnh tranh Bayes	Mô hình sống còn đa trạng thái
Lĩnh vực	Dịch tễ học	Phân tích sống còn
Họ	Process / pipeline	Survival analysis
Năm ra đời	1980s–2000s (classical CR: 1970s; Bayesian extension: 1990s–2000s)	1978
Người khởi xướng	Various; Bayesian formulation advanced by Gelfand, Dey, Larson, and Dinse among others	Andersen, P.K. & Keiding, N. (foundational framework); popularised by Putter, Fiocco & Geskus (2007)
Loại	Bayesian survival/time-to-event model	Semi-parametric hazard model
Công trình gốc	Larson, M. G., & Dinse, G. E. (1985). A mixture model for the regression analysis of competing risks data. Applied Statistics, 34(3), 201–211.	Putter, H., Fiocco, M. & Geskus, R.B. (2007). Tutorial in Biostatistics: Competing Risks and Multi-State Models. Statistics in Medicine, 26(11), 2389–2430.
Tên gọi khác	Bayesian cause-specific hazard model, Bayesian subdistribution hazard model, BCRA, Bayesian cumulative incidence analysis	illness-death model, multi-state transition model, Çok Durumlu Model (Multi-State / Illness-Death)
Liên quan	3	4
Tóm tắt	Bayesian competing risks analysis is a time-to-event method for settings where subjects can fail from more than one mutually exclusive cause — such as death from cancer versus death from cardiovascular disease — and prior knowledge or small-sample uncertainty makes a Bayesian framework advantageous. It extends classical competing risks models (cause-specific hazards and cumulative incidence functions) by placing probability distributions over unknown parameters and updating those distributions with observed data, yielding full posterior inference for each failure type.	The multi-state model is a generalised survival framework, formalised in the work of Andersen and Keiding and brought to wide biostatistical practice by Putter, Fiocco and Geskus (2007), that models individuals moving through multiple distinct health states — for example, healthy, ill and dead — over time. A separate hazard function is estimated for each possible transition, and transition probabilities are recovered via the product-integral of the cumulative transition intensities.