Results. The degree of bias depends on the magnitude of the competing risk (cause 2): if there are no competing risks ( ... Fine-Gray model for cumulative incidence regression to estimate CIF. competing risks. 5 Results in Table 2 and Figure 3 make it clear that when competing events are rare and distributed In Cox regression, you focus on the survivor function, which indicates the probability of surviving beyond a given time. other approach is Fine and Gray’s (1999) extension of the Cox regression that models (the hazards of) the cumulative incidence function. GRAY With explanatory covariates, the standard analysis for competing risks data involves modeling the cause-specific hazard functions via a proportional hazards assumption. Three types of mode of mortality for the under-five children are considered. Examples A proportional hazards model for the subdistribution of a competing risk. The sub-distribution hazard ratio (SHR) and 95% confidence interval (CI) of SHR for selected covariates obtained from Fine and Gray competing risk survival regression model for different causes of under-five child mortality are given in Table 2. Competing Risks Data, Estimators, and Tests. Results of cox proportional hazard model and Fine and Gray’s competing risk model Figure 3. The Annals of Statistics, 16, 1141–1154. 7-9 The risk set contains those subjects who have experienced a competing event. Fundamentals of competing risks have been reviewed extensively elsewhere (1–5,8,16,17).Briefly, in competing risks data, an individual can potentially fail from any of several, say K, event types, but only the time to failure for the earliest (in time) of these (or the last follow-up time if no failure has yet occurred) is observed. 26/37. But it’s necessary in order to get a model that correctly predicts cumulative incidence functions. Three types of mode of mortality for the under-five children are considered. Fine and Gray competing risk regression model. function have been developed (Gray 1988; Fine and Gray 1999). Circulation, 133(6):601–609, 2016. REFERENCES 1. Competing Risk Survival Analysis Using PHREG in SAS 9.4. The subsequent section presents some basic deﬁnitions of quantities of interests in competing-risks Competing risks occur commonly in medical research. Introduction to Competing Risks PROC PHREG { SAS/STAT 13.2 For the Cox model from standard survival analysis we have log( log(S(t jX))) = log(0(t)) + 0X: The sub-distribution version for competing … Competing risks are said to be present when a patient is at risk of more than one mutually exclusive event, such as death from di erent ... Fine & Gray (1999) Sally R. Hinchli e University of Leicester, 2012 20 / 34. bias can be reduced by the using a model which takes competing events into account such as Fine and Gray’s sub-distribution hazard model which has been made available with release of version 9.4 of SAS software. A model-based approach proposed by Gray and Fine (1999) can overcome this problem. Biometrics 67, 39-49. During an average of 8 years follow-up, 1030 (6.35%) incident dementia were identified. The regression coefficients from a Fine‐Gray subdistribution hazard model can be indirectly interpreted as the regression coefficients for a complementary log‐log generalized linear model for the CIF similarly to hazard ratios without competing risks. Geskus RB (2011). Cause-Specific Cumulative Incidence Estimation and the Fine and Gray Model Under Both Left Truncation and Right Censoring. J. P. Fine and R. J. Fine and Gray acknowledge that this is “unnatural” because, in fact, those who experience competing events are no longer actually at risk of the focal event. Although there are different methods for competing risks regression available , there is currently consensus that for prognostic studies, the so-called subdistribution hazards approach proposed by Fine and Gray is the most appropriate method to use. Journal of the American Statistical Association, 94(446):496–509, 1999. For the purpose of analysis, Bangladesh Demographic and Health Survey (BDHS), 2011 data set was used. The cause-specific under-five mortality of Bangladesh has been studied by fitting cumulative incidence function (CIF) based Fine and Gray competing risk regression model (1999). Competing Risks - What, Why, When and How? Competing risks occur commonly in medical research. It is a direct regression modeling of the effect of covariates on the cumulative incidence function for competing risks data. Stata's new stcrreg command implements competing-risks regression based on Fine and Gray's proportional subhazards model. • Fine, J. and Gray, R. (1999), A Proportional Hazards Model for the Subdistribution of a Competing Risk. For example, both treatment-related mortality and disease recurrence are important outcomes of interest and well-known competing risks in cancer research.