Quantifying the Predictive Performance of Prognostic Models for Censored Survival Data with Time‐Dependent Covariates

Schoop, R.; Graf, Erika; Schumacher, Martin

doi:10.1111/j.1541-0420.2007.00889.x

Cited by 61 publications

(67 citation statements)

References 38 publications

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“…We have exemplified and illustrated the some of the most popular and currently available approaches by means of the data of the epo study (Henke et al, 2006) where we have studied epo treatment success and local disease free survival as two different status response variables. For the latter it would also be possible to include time-dependent information in the risk prediction model; for a recent concept of quantifying the prediction performance in such a situation we refer to Schoop et al (2008). We have shown that various performance measures will fit into this general framework the most prominent ones being the Brier score and ROC-based quantities.…”

Section: Summary and Discussionmentioning

confidence: 98%

The Performance of Risk Prediction Models

Gerds¹,

Cai²,

2008

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SummaryFor medical decision making and patient information, predictions of future status variables play an important role. Risk prediction models can be derived with many different statistical approaches. To compare them, measures of predictive performance are derived from ROC methodology and from probability forecasting theory. These tools can be applied to assess single markers, multivariable regression models and complex model selection algorithms. This article provides a systematic review of the modern way of assessing risk prediction models. Particular attention is put on proper benchmarks and resampling techniques that are important for the interpretation of measured performance. All methods are illustrated with data from a clinical study in head and neck cancer patients.

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Section: Summary and Discussionmentioning

confidence: 98%

The Performance of Risk Prediction Models

Gerds¹,

Cai²,

2008

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“…A corresponding adaptation of our estimator is suggested in Schoop (2008, chapter 5) using the methodology presented in Schoop et al (2008). Acknowledgment…”

Section: Discussionmentioning

confidence: 99%

Quantifying the predictive accuracy of time-to-event models in the presence of competing risks

et al. 2011

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The development of prognostic time to event models is an area of active research, notably in the fields of cardiology, oncology and intensive care medicine. These models try to find a link between patient covariates and an event at a later time point, and are used for example for therapy assignment, risk stratification or interhospital quality assurance. Increasingly, interest is not only in prognostic models with one possible type of event (e.g. death), but in models that distinguish between several competing endpoints. However, research into methods for the evaluation of the prognostic potential of these models is still needed, as most proposed methods measure either discrimination or calibration of models, but do not examine both simultaneously. We adapt the prediction error proposal of Graf et al. (1999) and Gerds and Schumacher (2006) to handle models with more than one possible event type and introduce a consistent estimator. A simulation study investigating the behaviour of the estimator in small sample size situations and for different levels of censoring together with a real data application follows, highlighting the usefulness of the proposed approach for quantifying effects of model misspecification in summary models for a competing risks setting.

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“…A more general approach like this one is called "dynamic prediction" with CS constituting its simplest form. Dynamic predictions can be derived from multistate models and/or joint models for longitudinal and time-to-event data (4); statistical methods for development and assessment are available (16), but are beyond this contribution.…”

Section: Discussionmentioning

confidence: 99%

Conditional Survival: A Useful Concept to Provide Information on How Prognosis Evolves over Time

Hieke

Kleber

König

et al. 2015

Clinical Cancer Research

174

177

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Conditional survival (CS) is defined as the probability of surviving further t years, given that a patient has already survived s years after the diagnosis of a chronic disease. It is the simplest form of a dynamic prediction in which other events in the course of the disease or biomarker values measured up to time s can be incorporated. CS has attracted attention in recent years either in an absolute or relative form where the latter is based on a comparison with an age-adjusted normal population being highly relevant from a public health perspective. In its absolute form, CS constitutes the quantity of major interest in a clinical context. Given a clinical cohort of patients with a particular type of cancer, absolute CS can be estimated by conditional Kaplan-Meier estimates in strata defined, for example, by age and disease stage or by a conditional version of the Cox and other regression models for timeto-event data. CS can be displayed as a function of the prediction time s in parametric as well as nonparametric fashion. We illustrate the use of absolute CS in a large clinical cohort of patients with multiple myeloma. For investigating CS, it is necessary to ensure almost complete long-term follow-up of the patients enrolled in the clinical cohort and to consider potential age-stage migration as well as changing treatment modalities over time. CS provides valuable and relevant information on how prognosis develops over time. It also serves as a starting point for identifying factors related to long-term survival.

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Quantifying the Predictive Performance of Prognostic Models for Censored Survival Data with Time‐Dependent Covariates

Cited by 61 publications

References 38 publications

The Performance of Risk Prediction Models

The Performance of Risk Prediction Models

Quantifying the predictive accuracy of time-to-event models in the presence of competing risks

Conditional Survival: A Useful Concept to Provide Information on How Prognosis Evolves over Time

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