Search results for "proPO"

Opportunities and challenges of combined effect measures based on prioritized outcomes

2013

Many authors have proposed different approaches to combine multiple endpoints in a univariate outcome measure in the literature. In case of binary or time-to-event variables, composite endpoints, which combine several event types within a single event or time-to-first-event analysis are often used to assess the overall treatment effect. A main drawback of this approach is that the interpretation of the composite effect can be difficult as a negative effect in one component can be masked by a positive effect in another. Recently, some authors proposed more general approaches based on a priority ranking of outcomes, which moreover allow to combine outcome variables of different scale levels. …

Sample size planning for survival prediction with focus on high-dimensional data

2011

Sample size planning should reflect the primary objective of a trial. If the primary objective is prediction, the sample size determination should focus on prediction accuracy instead of power. We present formulas for the determination of training set sample size for survival prediction. Sample size is chosen to control the difference between optimal and expected prediction error. Prediction is carried out by Cox proportional hazards models. The general approach considers censoring as well as low-dimensional and high-dimensional explanatory variables. For dimension reduction in the high-dimensional setting, a variable selection step is inserted. If not all informative variables are included…

Bayesian regularization for flexible baseline hazard functions in Cox survival models.

2019

Fully Bayesian methods for Cox models specify a model for the baseline hazard function. Parametric approaches generally provide monotone estimations. Semi-parametric choices allow for more flexible patterns but they can suffer from overfitting and instability. Regularization methods through prior distributions with correlated structures usually give reasonable answers to these types of situations. We discuss Bayesian regularization for Cox survival models defined via flexible baseline hazards specified by a mixture of piecewise constant functions and by a cubic B-spline function. For those "semi-parametric" proposals, different prior scenarios ranging from prior independence to particular c…

Bayesian joint ordinal and survival modeling for breast cancer risk assessment

2016

We propose a joint model to analyze the structure and intensity of the association between longitudinal measurements of an ordinal marker and time to a relevant event. The longitudinal process is defined in terms of a proportional-odds cumulative logit model. Time-to-event is modeled through a left-truncated proportionalhazards model, which incorporates information of the longitudinal marker as well as baseline covariates. Both longitudinal and survival processes are connected by means of a common vector of random effects. General inferences are discussed under the Bayesian approach and include the posterior distribution of the probabilities associated to each longitudinal category and the …

Generating survival times to simulate Cox proportional hazards models by Ralf Bender, Thomas Augustin and Maria Blettner,Statistics in Medicine 2005;…

2006

A weighted combined effect measure for the analysis of a composite time-to-first-event endpoint with components of different clinical relevance

2018

Composite endpoints combine several events within a single variable, which increases the number of expected events and is thereby meant to increase the power. However, the interpretation of results can be difficult as the observed effect for the composite does not necessarily reflect the effects for the components, which may be of different magnitude or even point in adverse directions. Moreover, in clinical applications, the event types are often of different clinical relevance, which also complicates the interpretation of the composite effect. The common effect measure for composite endpoints is the all-cause hazard ratio, which gives equal weight to all events irrespective of their type …

Generating survival times to simulate Cox proportional hazards models

2005

Simulation studies present an important statistical tool to investigate the performance, properties and adequacy of statistical models in pre-specified situations. One of the most important statistical models in medical research is the proportional hazards model of Cox. In this paper, techniques to generate survival times for simulation studies regarding Cox proportional hazards models are presented. A general formula describing the relation between the hazard and the corresponding survival time of the Cox model is derived, which is useful in simulation studies. It is shown how the exponential, the Weibull and the Gompertz distribution can be applied to generate appropriate survival times f…

Updating input–output matrices: assessing alternatives through simulation

2009

A problem that frequently arises in economics, demography, statistics, transportation planning and stochastic modelling is how to adjust the entries of a matrix to fulfil row and column aggregation constraints. Biproportional methods in general and the so-called RAS algorithm in particular, have been used for decades to find solutions to this type of problem. Although alternatives exist, the RAS algorithm and its extensions are still the most popular. Apart from some interesting empirical and theoretical properties, tradition, simplicity and very low computational costs are among the reasons behind the great success of RAS. Nowadays computer hardware and software have made alternative proce…

Sparse kernel methods for high-dimensional survival data

2008

Abstract Sparse kernel methods like support vector machines (SVM) have been applied with great success to classification and (standard) regression settings. Existing support vector classification and regression techniques however are not suitable for partly censored survival data, which are typically analysed using Cox's proportional hazards model. As the partial likelihood of the proportional hazards model only depends on the covariates through inner products, it can be ‘kernelized’. The kernelized proportional hazards model however yields a solution that is dense, i.e. the solution depends on all observations. One of the key features of an SVM is that it yields a sparse solution, dependin…

Coupled variable selection for regression modeling of complex treatment patterns in a clinical cancer registry.

2013

For determining a manageable set of covariates potentially influential with respect to a time-to-event endpoint, Cox proportional hazards models can be combined with variable selection techniques, such as stepwise forward selection or backward elimination based on p-values, or regularized regression techniques such as component-wise boosting. Cox regression models have also been adapted for dealing with more complex event patterns, for example, for competing risks settings with separate, cause-specific hazard models for each event type, or for determining the prognostic effect pattern of a variable over different landmark times, with one conditional survival model for each landmark. Motivat…