![risk probability xbeta risk probability xbeta](https://keisan.casio.jp/keisan/lib/real/system/2006/1161228837/BetafPQ.gif)
5.4B takes the sample dataset, HFBKBGYR, as an example to depict the survival function (probability) of participants with heart failure (blue line) and those without heart failure (black line) from all-cause mortality at the end of follow-up. Using data from a real setting, however, what we observed is survival curves are not smooth curves because of the data from sampling studies, which have sampling errors, censored data, and unmeasured errors. 7 Theoretically, when time (T) goes from 0 up to infinity, the survivor function is graphed as a decreasing smooth curve, which begins at S(t) = 1 at t = 0, and S(t) monotonically decreases to zero as t increases toward infinity ( Fig. It can happen that an individual does not experience the event of interest at the end of follow-up, or an individual who is lost to follow up during the study period, or an individual has to stop or withdraw from the study (such as due to an adverse drug reaction or other competing events).Īlthough censoring is a problem in survival analysis, to estimate the survival probability at a given time, we make use of the risk set at that point to include the information we have on a censored individual up to the date of censorship, rather than simply throwing away all the information on a censored individual. Specifically, in most observational prospective studies, a censoring occurs although we know an individual’s follow-up time, we are not aware of the survival time exactly. 5.3 depicts the types of survival data.Ĭensoring: We do not know the time exactly (i.e., the exact survival or experienced time of an event). Time: It can be days, weeks, months, or years from the beginning of follow-up of an individual until an event occurs or at the end of follow-up.Įvent: It can be incidence of disease, readmission, mortality, or becoming a different stage of disease (such as from heart failure stages A to C or D), or change to a different treatment approach (such as heart transplantation), any designed event of a study. Īble to compare survival function between two or more groups.An outcome with binary classification (i.e., an event either occurs or does not)