เปรียบเทียบวิธี

	การวิเคราะห์ความเสี่ยงคู่แข่งแบบเบย์ (Bayesian Competing Risks Analysis)	แบบจำลองการรอดชีพแบบหลายสถานะ
สาขาวิชา	ระบาดวิทยา	การวิเคราะห์การอยู่รอด
ตระกูล	Process / pipeline	Survival analysis
ปีกำเนิด	1980s–2000s (classical CR: 1970s; Bayesian extension: 1990s–2000s)	1978
ผู้ริเริ่ม	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)
ประเภท	Bayesian survival/time-to-event model	Semi-parametric hazard model
แหล่งต้นตำรับ	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.
ชื่อเรียกอื่น	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)
ที่เกี่ยวข้อง	3	4
สรุป	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.