विधियों की तुलना करें

	बायेसियन प्रतिस्पर्धी जोखिम विश्लेषण	बहु-राज्य उत्तरजीविता मॉडल
क्षेत्र	महामारी विज्ञान	उत्तरजीविता
परिवार	Process / pipeline	Survival analysis
उद्भव वर्ष	1980s–2000s (classical CR: 1970s; Bayesian extension: 1990s–2000s)	1978
प्रवर्तक	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)
प्रकार	Bayesian survival/time-to-event model	Semi-parametric hazard model
मौलिक स्रोत	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.
उपनाम	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)
संबंधित	3	4
सारांश	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.