Flexible survival regression modelling

Cortese, Giuliana; Scheike, Thomas H.; Martinussen, Torben
February 2010
Statistical Methods in Medical Research;Feb2010, Vol. 19 Issue 1, p5
Academic Journal
Regression analysis of survival data, and more generally event history data, is typically based on Cox's regression model. We here review some recent methodology, focusing on the limitations of Cox's regression model. The key limitation is that the model is not well suited to represent time-varying effects. We start by considering classical and also more recent goodness-of-fit procedures for the Cox model that will reveal when the Cox model does not capture important aspects of the data, such as time-varying effects. We present recent regression models that are able to deal with and describe such time-varying effects. The introduced models are all applied to data on breast cancer from the Norwegian cancer registry, and these analyses clearly reveal the shortcomings of Cox's regression model and the need for other supplementary analyses with models such as those we present here.


Related Articles

  • Survival analysis with high-dimensional covariates. Witten, Daniela M.; Tibshirani, Robert // Statistical Methods in Medical Research;Feb2010, Vol. 19 Issue 1, p29 

    In recent years, breakthroughs in biomedical technology have led to a wealth of data in which the number of features (for instance, genes on which expression measurements are available) exceeds the number of observations (e.g. patients). Sometimes survival outcomes are also available for those...

  • Pseudo-observations in survival analysis. Andersen, Per Kragh; Perme, Maja Pohar // Statistical Methods in Medical Research;Feb2010, Vol. 19 Issue 1, p71 

    We review recent work on the application of pseudo-observations in survival and event history analysis. This includes regression models for parameters like the survival function in a single point, the restricted mean survival time and transition or state occupation probabilities in multi-state...

  • Interval censoring. Zhigang Zhang; Jianguo Sun // Statistical Methods in Medical Research;Feb2010, Vol. 19 Issue 1, p53 

    Interval-censored failure time data occur in many medical investigations as well as other studies such as demographical and sociological studies. They include the usual right-censored failure time data as a special case but provide much more complex structure and less relevant information than...

  • Assessing non-inferiority with time-to-event data via the method of non-parametric covariance. Zhang, Xinji; Xu, Jinfang; He, Jia // Statistical Methods in Medical Research;Jun2013, Vol. 22 Issue 3, p346 

    Non-parametric methods have been well recognised as useful tools for time-to-event (survival) data analysis because they provide valid statistical inference with few assumptions. Tangen and Koch have proposed the use of the method of non-parametric covariance for time-to-event data in a...

  • Mental ill-health and second claims for work-related injury. Cherry, N.; Burstyn, I.; Beach, J. // Occupational Medicine;Sep2012, Vol. 62 Issue 6, p462 

    Background There is some evidence that mental ill-health (MIH) is associated with injury at work, but data are sparse. Aims To examine, within a cohort of workers with a first workers’ compensation claim, whether those with a history of MIH had a higher than expected number of second...

  • Comparison of stopped Cox regression with direct methods such as pseudo-values and binomial regression. Houwelingen, Hans; Putter, Hein // Lifetime Data Analysis;Apr2015, Vol. 21 Issue 2, p180 

    By far the most popular model to obtain survival predictions for individual patients is the Cox model. The Cox model does not make any assumptions on the underlying hazard, but it relies heavily on the proportional hazards assumption. The most common ways to circumvent this robustness problem...

  • Frailties in multi-state models: Are they identifiable? Do we need them? Putter, Hein; van Houwelingen, Hans C. // Statistical Methods in Medical Research;Dec2015, Vol. 24 Issue 6, p675 

    The inclusion of latent frailties in survival models can serve two purposes: (1) the modelling of dependence in clustered data, (2) explaining lack of fit of univariate survival models, like deviation from the proportional hazards assumption. Multi-state models are somewhere between univariate...

  • Combining parametric, semi-parametric, and non-parametric survival models with stacked survival models. WEY, ANDREW; CONNETT, JOHN; RUDSER, KYLE // Biostatistics;Jul2015, Vol. 16 Issue 3, p537 

    For estimating conditional survival functions, non-parametric estimators can be preferred to parametric and semi-parametric estimators due to relaxed assumptions that enable robust estimation. Yet, even when misspecified, parametric and semi-parametric estimators can possess better operating...

  • Efficient estimation in additive hazards regression with current status data. Martinussen, Torben; Scheike, Thomas H. // Biometrika;Sep2002, Vol. 89 Issue 3, p649 

    Current status data arise when the exact timing of an event is unobserved, and it is only known at a given point in time whether or not the event has occurred. Recently Lin et al. (1998) studied the additive semiparametric hazards model for current status data. They showed that the analysis of...


Read the Article


Sorry, but this item is not currently available from your library.

Try another library?
Sign out of this library

Other Topics