0000000951 00000 n In Stata, the stcrreg function permits estimation of subdistribution hazard regression models. In this case, 2 different types of hazard functions are of interest: the cause-specific hazard function and the subdistribution hazard function. CIF indicates cumulative incidence function; and KM, Kaplan–Meier. Thus, one may have a study with 3 types of events: diagnosis of heart disease, diagnosis of cancer, and death. The Enhanced Feedback for Effective Cardiac Treatment (EFFECT) data used in the study was funded by a CIHR Team Grant in Cardiovascular Outcomes Research. E-mail. The latter allows one to estimate the effect of covariates on the absolute risk of the outcome over time. This echoes the previous distinction made between interpretation of the incidence rate and the hazard rate. The Kaplan-Meier method for estimating survival functions and the Cox proportional hazards model for estimating the effects of covariates on the hazard of the occurrence of the event are commonly used statistical methods for the analysis of survival data. We summarized continuous variables by using medians and the 25th and 75th percentiles, whereas dichotomous variables were summarized by using frequencies and percentages. This article is structured as follows. The covariates have a relative effect on the hazard function because of the use of the logarithmic transformation. This illustrates how the apparent reduction in the absolute risk of cardiac death from cancer may be explained via the effect of cancer on noncardiac death.17. A figure similar to Figure 1 should be presented to estimate cumulative incidence in the presence of competing risk.13. Table 1.
In survival analysis, we use information on event status and follow up time to estimate a survival function. In contrast to this, clinical prediction models and risk-scoring systems are interested in estimating the absolute incidence of the event of interest. 0000005255 00000 n In examining the estimated hazard ratios for the different outcomes and different types of hazard models, one notes that some variables have a qualitatively similar effect on the incidence of cardiac death as on the incidence of noncardiac death. 142, Issue Suppl_4, November 17, 2020: Vol. The objective of this tutorial is to introduce readers to statistical methods for the analysis of survival data that account for competing risks.
As the subdistribution hazard model allows one to model directly the effect of covariates on the incidence of the primary event after accounting for competing events, it lends itself naturally to risk prediction. They are used in ways similar to the hazard function and the survival function. First, it may violate the assumption of noninformative censoring: it may be unreasonable to assume that subjects who died of noncardiovascular causes (and were thus treated as censored) can be represented by those subjects who remained alive and had not yet died of any cause.
Furthermore, the subdistribution hazard may be of greater interest if one is interested in the overall impact of covariates on the incidence of the outcome of interest, even when predictions of incidence are not of direct interest. Survival analysis is a branch of statistics for analyzing the expected duration of time until one or more events happen, such as death in biological organisms and failure in mechanical systems. Although the cumulative incidence of cardiovascular death exceeded that of noncardiovascular death at each point in time, the incidence of noncardiovascular death was not negligible in this population. The analysis of survival data plays a key role in cardiovascular research. Hazard Ratios (and 95% Confidence Intervals) From Cause-Specific and Subdistribution Hazard Models for Cardiac and Noncardiac Death. Competing risks are prevalent in much of cardiovascular research. Competing risk regression models for epidemiologic data. Others refer to such data as time-to-event data or event history data. Computing Cumulative Incidence Functions with the etmCIF Function, with a view Towards Pregnancy Applications Arthur Allignol 1 Introduction This paper documents the use of the etmCIF function to compute the cumulative incidence function (CIF) in pregnancy data. Failure to account correctly for competing events can result in adverse consequences, including overestimation of the probability of the occurrence of the event and mis-estimation of the magnitude of relative effects of covariates on the incidence of the outcome. Cancer was associated with a substantial decrease in the incidence of cardiac death (subdistribution hazard ratio, 0.82), whereas it had no association with the rate of cardiac death in subjects who were still alive (cause-specific hazard ratio, 0.96). Furthermore, age had a more pronounced effect on the cause-specific hazard of a given outcome than it did on the incidence of the same outcome. Cardiovascular research often focuses on outcomes that are defined as the time to the occurrence of an outcome of interest. Unlike the survival function in the absence of competing risks, CIFk(t) will not necessarily approach unity as time becomes large, because of the occurrence of competing events that preclude the occurrence of events of type k. In the case study that follows, when using the CIF, the estimated incidence of cardiovascular death within 5 years of hospital admission was 36.8%. We refer the interested reader elsewhere for further background on these methods and others for the analysis of survival data.1–7. h�bbd``b`� BH0� �3@Bo��:�R�D�qw�X� �:�5H�d0012��La`� ��@� �s+ If the event of interest is death, then the time of the event is censored for those subjects who are still alive at the end of the study. 142, Issue 16_suppl_2, Basic, Translational, and Clinical Research. R code for estimating the CIFs, the subdistribution hazard models and the cause-specific hazard models is described in Appendix A in the online-only Data Supplement. For example, if a subject develops 1 form of heart disease, can he or she subsequently develop a second form of heart disease, or are the 2 conditions mutually exclusive, thus precluding the later second disease?
As an work-around, Cumulative Incidence Function (CIF) was proposed to solve this particular issue by estimating the marginal probability of a certain event as a function of its cause-specific probability and overall survival probability. 1-800-242-8721
By continuing to browse this site you are agreeing to our use of cookies. 7272 Greenville Ave. This example serves as a constructive warning about the use of inappropriate statistical methods when estimating patient prognosis. We refer the interested reader to introductions and reviews of differing levels of statistical depth.8–13,15,17–21 We summarize our recommendations in Table 3. Given the availability of software, analyses of the cumulative incidence function have become increasingly popular and widely reported in recent years. Such an observation has been made by Lau et al9 previously. No endorsement by ICES or the Ontario MOHLTC is intended or should be inferred.
Although the regression coefficients from the Cox model describe the relative effect of the covariates on the hazard of the occurrence of the outcome, the following relationship also holds in the absence of competing risks: , where S(t) denotes the survival function for an individual whose set of covariates is equal to X, and S0(t)denotes the baseline survival function (ie, the survival function for a subject whose covariates are all equal to zero).