8 Matching Annotations
  1. Aug 2019
    1. so that instead of predicting the time of event, we are predicting the probability that an event happens at a particular time .
  2. Jul 2019
    1. In practice, we found that it is not appropriate to use Aalen’s additive hazardsmodel for all datasets, because when we estimate cumulativeregression functionsB(t),they are restricted to the time interval where X (X has been defined in Chapter 3) is offull rank, that meansX0Xis invertible. Sometimes we found that X is not of full rank,which was not a problem with the Cox model.
    2. An overall conclusion is that the two models give different pieces of informationand should not be viewed as alternatives to each other, but ascomplementary methodsthat may be used together to give a fuller and more comprehensive understanding ofdata
    3. The effect ofthe covariates on survival is to act multiplicatively on some unknown baseline hazardrate, which makes it difficult to model covariate effects that change over time. Secondly,if covariates are deleted from a model or measured with a different level of precision, theproportional hazards assumption is no longer valid. These weaknesses in the Cox modelhave generated interest in alternative models. One such alternative model is Aalen’s(1989) additive model. This model assumes that covariates act in an additive manneron an unknown baseline hazard rate. The unknown risk coefficients are allowed to befunctions of time, so that the effect of a covariate may vary over time.
    1. Note that, three often used transformations can be specified using the argument fun: “log”: log transformation of the survivor function, “event”: plots cumulative events (f(y) = 1-y). It’s also known as the cumulative incidence, “cumhaz” plots the cumulative hazard function (f(y) = -log(y))
    2. Note that, the confidence limits are wide at the tail of the curves, making meaningful interpretations difficult. This can be explained by the fact that, in practice, there are usually patients who are lost to follow-up or alive at the end of follow-up. Thus, it may be sensible to shorten plots before the end of follow-up on the x-axis (Pocock et al, 2002).
    1. RF is now a standard to effectively analyze a large number of variables, of many different types, with no previous variable selection process. It is not parametric, and in particular for survival target it does not assume the proportional risks assumption.
    1. Thesurvival function gives,for every time,the probability of surviving(or not experiencing the event) up to that time.The hazard function gives the potential that the event will occur, per time unit, given that an individual has survived up to the specified time.