313 research outputs found
Analyzing Competing Risk Data Using the R timereg Package
No full textIn this paper we describe flexible competing risks regression models using the comp.risk() function available in the timereg package for R based on Scheike et al. (2008). Regression models are specified for the transition probabilities, that is the cumulative incidence in the competing risks setting. The model contains the Fine and Gray (1999) model as a special case. This can be used to do goodness-of-fit test for the subdistribution hazardsâ proportionality assumption (Scheike and Zhang 2008). The program can also construct confidence bands for predicted cumulative incidence curves. We apply the methods to data on follicular cell lymphoma from Pintilie (2007), where the competing risks are disease relapse and death without relapse. There is important non-proportionality present in the data, and it is demonstrated how one can analyze these data using the flexible regression models.
Penalized estimation for competing risks regression with applications to high-dimensional covariates
No full textHigh-dimensional regression has become an increasingly important topic for many research fields. For example, biomedical research generates an increasing amount of data to characterize patients' bio-profiles (e.g. from a genomic high-throughput assay). The increasing complexity in the characterization of patients' bio-profiles is added to the complexity related to the prolonged follow-up of patients with the registration of the occurrence of possible adverse events. This information may offer useful insight into disease dynamics and in identifying subset of patients with worse prognosis and better response to the therapy. Although in the last years the number of contributions for coping with high and ultra-high-dimensional data in standard survival analysis have increased (Witten and Tibshirani, 2010. Survival analysis with high-dimensional covariates. Statistical Methods in Medical Research 19(1), 29-51), the research regarding competing risks is less developed (Binder and others, 2009. Boosting for high-dimensional time-to-event data with competing risks. Bioinformatics 25(7), 890-896). The aim of this work is to consider how to do penalized regression in the presence of competing events. The direct binomial regression model of Scheike and others (2008. Predicting cumulative incidence probability by direct binomial regression. Biometrika 95(1), 205-220) is reformulated in a penalized framework to possibly fit a sparse regression model. The developed approach is easily implementable using existing high-performance software to do penalized regression. Results from simulation studies are presented together with an application to genomic data when the endpoint is progression-free survival. An R function is provided to perform regularized competing risks regression according to the binomial model in the package timereg (Scheike and Martinussen, 2006. Dynamic Regression models for survival data. New York: Springer), available through CRAN
Regression models for the restricted residual mean life for right-censored and left-truncated data.
No full textEfficient estimation of the marginal mean of recurrent events.
No full textRecurrent events are often encountered in clinical and epidemiological studies where a terminal event is also observed. With recurrent events data it is of great interest to estimate the marginal mean of the cumulative number of recurrent events experienced prior to the terminal event. The standard nonparametric estimator was suggested in Cook and Lawless and further developed in Ghosh and Lin. We here investigate the efficiency of this estimator that, surprisingly, has not been studied before. We rewrite the standard estimator as an inverse probability of censoring weighted estimator. From this representation, we derive an efficient augmented estimator using efficient estimation theory for right-censored data. We show that the standard estimator is efficient in settings with no heterogeneity. In other settings with different sources of heterogeneity, we show theoretically and by simulations that the efficiency can be greatly improved when an efficient augmented estimator based on dynamic predictions is employed, at no extra cost to robustness. The estimators are applied and compared to study the mean number of catheter-related bloodstream infections in heterogeneous patients with chronic intestinal failure who can possibly die, and the efficiency gain is highlighted in the resulting point-wise confidence intervals
Regression models for the restricted residual mean time for right-censored and left-truncated data.
No full textAnalyzing Competing Risk Data Using the R timereg Package
No full textIn this paper we describe flexible competing risks regression models using the comp.risk() function available in the timereg package for R based on Scheike et al. (2008). Regression models are specified for the transition probabilities, that is the cumulative incidence in the competing risks setting. The model contains the Fine and Gray (1999) model as a special case. This can be used to do goodness-of-fit test for the subdistribution hazards’ proportionality assumption (Scheike and Zhang 2008). The program can also construct confidence bands for predicted cumulative incidence curves
Flexible survival regression modelling
No full textRegression 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
Editorial to: Six papers on Dynamic Statistical Models:Special issue of Scandinavian Journal of Statistics, 41(1), 22, 2014
No full textThe following six papers are based on invited lectures at the satellite meeting held at the University of Copenhagen before the 58th World Statistics Congress of the International Statistical Institute in Dublin in 2011. At the invitation of the Bernoulli Society, the satellite meeting was organized around the theme “Dynamic Statistical Models” as a part of the Program of Excellence at the University of Copenhagen on “Statistical methods for complex and high dimensional models” (http://statistics.ku.dk/). The Excellence Program in Statistics was a research project to develop and investigate statistical methodology and theory for large and complex data sets that included biostatisticians and mathematical statisticians from three faculties at the University of Copenhagen.The satellite meeting took place August 17–19, 2011. Its purpose was to bring together researchers in statistics and related areas working with frontier research topics in statistics for dynamic models. This issue of SJS contains a quite diverse collection of six papers from the conference:Spectral Estimation of Covolatility from Noisy Observations Using Local WeightsMarkus Bibinger and Markus ReißOne-Way Anova for Functional Data via Globalizing the Pointwise F-testJin-Ting Zhang and Xuehua LiangWeakly Decomposable Regularization Penalties and Structured SparsitySara van de GeerIntegrative Analysis of Cancer Diagnosis Studies with Composite PenalizationJin Liu, Shuangge Ma and Jian HuangInference in Targeted Group-Sequential Covariate-Adjusted Randomized Clinical TrialsAntoine Chambaz and Mark J. van der LaanEstimation of Causal Odds of Concordance using the Aalen Additive ModelTorben Martinussen and Christian Bressen PipperWe would like to acknowledge the financial support from the University of Copenhagen Program of Excellence and Elsevier. We would also like to thank the authors for contributing interesting papers, the referees for their helpful reports, and the present and previous editors of SJS for their support of the publication of the papers from the satellite meeting
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