1,720,984 research outputs found
A covariance-based test for shared frailty in multivariate lifetime data
Full text linkWe decompose the score statistic for testing for shared finite variance frailty in multivariate lifetime data into marginal and covariance-based terms. The null properties of the covariance-based statistic are derived in the context of parametric lifetime models. Its non-null properties are estimated using simulation and compared with those of the score test and two likelihood ratio tests when the underlying lifetime distribution is Weibull. Some examples are used to illustrate the covariance-based test. A case is made for using the covariance-based statistic as a simple diagnostic procedure for shared frailty in a parametric exploratory analysis of multivariate lifetime data and a link to the bivariate Clayton-Oakes copula model is shown
Model robust designs for survival trials
Full text linkThe exponential-based proportional hazards model is often assumed in time-to-event experiments but may only approximately hold. Deviations in different neighbourhoods of this model are considered that include other widely used parametric proportional hazards models and the data are assumed to be subject to censoring. Minimax designs are then found explicitly, based on criteria corresponding to classical c- and D-optimality. Analytical characterisations of optimal designs are provided which, unlike optimal designs for related problems in the literature, have finite support and thus avoid the issues of implementing a density-based design in practice. Finally, the proposed designs are compared with the balanced design that is traditionally used in practice, and recommendations for practitioners are given
Motivating dataset for Impact of factors associated with short-term transplant outcomes
No full textThis dataset is the motivating dataset for the analyses in chapter 4 and 5 of the thesis titled 'Impact of factors associated with short-term transplant outcomes'. This data relates to 227 controlled DCD organ donors from January 2013-April 2015.</span
Proportional hazards models with discrete frailty
No full textWe extend proportional hazards frailty models for lifetime data to allow a negative bi-nomial, Poisson, Geometric or other discrete distribution of the frailty variable. This might represent, for example, the unknown number of flaws in an item under test. Zero frailty corresponds to a limited failure model containing a proportion of units that never fail (long- term survivors). Ways of modifying the model to avoid this are discussed. The models areapplied to a previously published set of data on failures of printed circuit boards and to new data on breaking strengths of samples of cord
Optimal designs for two-parameter nonlinear models with application to survival models
Full text linkCensoring may occur in many industrial or biomedical time to event experiments. Efficient designs for such experiments are needed but finding such designs can be problematic since the statistical models involved will usually be nonlinear, making the optimal choice of design parameter dependent. We provide analytical characterisations of locally D- and c-optimal designs for a large class of models. Our results are illustrated using the natural proportional hazards parameterisation of the exponential regression model, thus reducing the numerical effort for design search substantially. We also determine designs based on standardised optimality criteria when a range of parameter values is provided by the experimenter. Different censoring mechanisms are incorporated and the robustness of designs to parameter misspecification is assessed. We demonstrate that, unlike traditional designs, the designs found perform well across a broad range of scenarios<br/
Sensitivity analysis for informative censoring in parametric survival models: an evaluation of the method
No full textIn a paper by Siannis, Copas and Lu in Biostatistics, the authors proposed and studied a sensitivity analysis for informative censoring in parametric survival analysis. More specifically, they introduced a parametric model that allows for dependence between the failure and censoring processes in terms of a parameter delta which can be thought of as measuring the size of the dependence between the two processes, and a bias function that measures the pattern of this dependence. Based on this model, for small values of delta, they also derived simplified closed form expressions (approximations) for the sensitivity analysis of the associated parameters of the model. Since then, some extensions of this approach have also appeared in the literature. In this paper, some theoretical issues concerning the above approach are discussed. Then the results of an extensive simulation study are reported, which indicate some shortcomings of the proposed sensitivity analysis, particularly in the presence of nuisance parameters
Proportional hazards models with discrete frailty
Full text linkWe extend proportional hazards frailty models for lifetime data to allow a negative binomial, Poisson, Geometric or other discrete distribution of the frailty variable. This might represent, for example, the unknown number of flaws in an item under test. Zero frailty corresponds to a limited failure model containing a proportion of units that never fail (long-term survivors). Ways of modifying the model to avoid this are discussed. The models are illustrated on a previously published set of data on failures of printed circuit boards and on new data on breaking strengths of samples of cord
Optimal designs for full and partial likelihood information - with application to survival models
Full text linkTime-to-event data are often modelled through Cox's proportional hazards model for which inference is based on the partial likelihood function. We derive a general expression for the asymptotic covariance matrix of Cox's partial likelihood estimator for the covariate coefficients. Our approach is illustrated through an application to the special case of only one covariate, for which we construct minimum variance designs for different censoring mechanisms and both binary and interval design spaces. We compare these designs with the corresponding ones found using the full likelihood approach and demonstrate that the latter designs are highly efficient also for partial likelihood estimation
Informative censoring in piecewise exponential survival models
No full textThere are often reasons to suppose that there is dependence between the time to event and time to censoring, or informative censoring, for survival data, particularly when considering medical data. This is because the decision to treat or not is often made according to prognosis, usually with the most ill patients being prioritised. Due to identifiability issues, sensitivity analyses are often used to assess whether non-informative censoring can lead to misleading results. In this paper, a sensitivity analysis method for piecewise exponential survival models is presented. This method assesses the sensitivity of the results of standard survival models to small amounts of dependence between the time to failure and time to censoring variables. It uses the same assumption about the dependence between the time to failure and time to censoring as previous sensitivity analyses for both standard parametric survival models and the Cox model. However, the method presented in this paper allows the use of more flexible models for the marginal distributions whilst remaining computationally simple. A simulation study is used to assess the accuracy of the sensitivity analysis method and identify the situations in which it is suitable to use this method. The study found that the sensitivity analysis performs well in many situations, but not when the data has a high proportion of censoring
Multiple Imputation of Composite Covariates in Survival Studies
No full textMissing covariate values are a common problem in survival studies, and the method of choice when handling such incomplete data is often multiple imputation. However, it is not obvious how this can be used most effectively when an incomplete covariate is a function of other covariates. For example, body mass index (BMI) is the ratio of weight and height-squared. In this situation, the following question arises: Should a composite covariate such as BMI be imputed directly, or is it advantageous to impute its constituents, weight and height, first and to construct BMI afterwards? We address this question through a carefully designed simulation study that compares various approaches to multiple imputation of composite covariates in a survival context. We discuss advantages and limitations of these approaches for various types of missingness and imputation models. Our results are a first step towards providing much needed guidance to practitioners for analysing their incomplete survival data effectively
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