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Dealing with Competing Risks in survival analysis

Banff International Research Station for Mathematical Innovation and Discovery

In survival analysis, end of follow-up can be caused by the occurrence of the event of primary interest, by the occurrence of a competing event that prevents the event of primary interest from happening, or by `genuine right-censoring' (such as loss to follow-up or end of follow-up). When the ambition is to estimate probabilities (`risks') for the event of primary interest, it is crucial to distinguish between occurrence of a competing event and right-censoring.

Nevertheless, in numerous applied medical articles, competing risks are treated as if it were genuine right-censoring, e.g., by using one minus the Kaplan-Meier estimator as a risk estimator (thereby overestimating the risk) and in many medical disciplines, including cardiology, hepatology, oncology, and epidemiology, papers explaining these difficulties in a supposedly easy-to-read manner have appeared.

For these reasons, competing risks will be a crucial topic for the STRATOS TG8 working with analysis of survival data.

However, dealing with competing risks is also important for other STRATOS topic groups. This includes, among others:

TG1 (missing data) When doing multiple imputation with the response variable included in the imputation model, inclusion of a (possibly right-censored) competing risks response is not trivial.

TG2 (variable selection and functional forms of a dose-response relationship) Special regression models are typically used for survival analysis (possibly with competing risks) though working with models with a linear predictor is quite similar for different types of outcome variable.

TG6 (evaluating diagnostic tests and prediction models) When assessing predictive accuracy, special care is needed for right-censored outcomes, including situations with competing risks.

TG7 (causal inference) Both when using IPTW and when using the g-formula, special techniques are needed in the presence of competing risks.

In the talk, we will discuss both problems in connection with reporting results in the medical literature when competing risks are present and how these points play a role in the work of other STRATOS topic groups. The possible role of so-called pseudo-observations will also be discussed.

Mathematics; Statistics; Computer science

video/mp4

Author affiliation: University of Copenhagen

2017-02-01

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/60150

Unreviewed

http://creativecommons.org/licenses/by-nc-nd/4.0/