Time-to-event Data (time-to-event + data)

Distribution by Scientific Domains


Selected Abstracts


On Estimating the Relationship between Longitudinal Measurements and Time-to-Event Data Using a Simple Two-Stage Procedure

BIOMETRICS, Issue 3 2010
Paul S. Albert
SummaryYe, Lin, and Taylor (2008,,Biometrics,64, 1238,1246) proposed a joint model for longitudinal measurements and time-to-event data in which the longitudinal measurements are modeled with a semiparametric mixed model to allow for the complex patterns in longitudinal biomarker data. They proposed a two-stage regression calibration approach that is simpler to implement than a joint modeling approach. In the first stage of their approach, the mixed model is fit without regard to the time-to-event data. In the second stage, the posterior expectation of an individual's random effects from the mixed-model are included as covariates in a Cox model. Although Ye et al. (2008) acknowledged that their regression calibration approach may cause a bias due to the problem of informative dropout and measurement error, they argued that the bias is small relative to alternative methods. In this article, we show that this bias may be substantial. We show how to alleviate much of this bias with an alternative regression calibration approach that can be applied for both discrete and continuous time-to-event data. Through simulations, the proposed approach is shown to have substantially less bias than the regression calibration approach proposed by Ye et al. (2008). In agreement with the methodology proposed by Ye et al. (2008), an advantage of our proposed approach over joint modeling is that it can be implemented with standard statistical software and does not require complex estimation techniques. [source]


Applied Survival Analysis: Regression Modeling of Time-to-Event Data, 2nd edition by HOSMER, D. W., LEMESHOW, S., and MAY, S.

BIOMETRICS, Issue 2 2009
Article first published online: 28 MAY 200
No abstract is available for this article. [source]


Flowgraph Models for Multistate Time-to-Event Data

BIOMETRICS, Issue 3 2006
Per Kragh Andersen
No abstract is available for this article. [source]


Inference in Spline-Based Models for Multiple Time-to-Event Data, with Applications to a Breast Cancer Prevention Trial

BIOMETRICS, Issue 4 2003
Kiros Berhane
Summary. As part of the National Surgical Adjuvant Breast and Bowel Project, a controlled clinical trial known as the Breast Cancer Prevention Trial (BCPT) was conducted to assess the effectiveness of tamoxifen as a preventive agent for breast cancer. In addition to the incidence of breast cancer, data were collected on several other, possibly adverse, outcomes, such as invasive endometrial cancer, ischemic heart disease, transient ischemic attack, deep vein thrombosis and/or pulmonary embolism. In this article, we present results from an illustrative analysis of the BCPT data, based on a new modeling technique, to assess the effectiveness of the drug tamoxifen as a preventive agent for breast cancer. We extended the flexible model of Gray (1994, Spline-based test in survival analysis, Biometrics50, 640,652) to allow inference on multiple time-to-event outcomes in the style of the marginal modeling setup of Wei, Lin, and Weissfeld (1989, Regression analysis of multivariate incomplete failure time data by modeling marginal distributions, Journal of the American Statistical Association84, 1065,1073). This proposed model makes inference possible for multiple time-to-event data while allowing for greater flexibility in modeling the effects of prognostic factors with nonlinear exposure-response relationships. Results from simulation studies on the small-sample properties of the asymptotic tests will also be presented. [source]


Risk factors and impact of recurrent lupus nephritis in patients with systemic lupus erythematosus undergoing renal transplantation: Data from a single US institution

ARTHRITIS & RHEUMATISM, Issue 9 2009
Paula I. Burgos
Objective To determine the risk factors for recurrent lupus nephritis, allograft loss, and survival among patients with systemic lupus erythematosus (SLE) undergoing kidney transplantation. Methods The archival records of all kidney transplant recipients with a prior diagnosis of SLE (according to the American College of Rheumatology criteria) from June 1977 to June 2007 were reviewed. Patients who had died or lost the allograft within 90 days of engraftment were excluded. Time-to-event data were examined by univariable and multivariable Cox proportional hazards regression analyses. Results Two hundred twenty of nearly 7,000 renal transplantations were performed in 202 SLE patients during the 30-year interval. Of the 177 patients who met the criteria for study entry, the majority were women (80%) and African American (65%), the mean age was 35.6 years, and the mean disease duration was 11.2 years. Recurrent lupus nephritis was noted in 20 patients (11%), allograft loss in 69 patients (39%), and death in 36 patients (20%). African American ethnicity was found to be associated with a shorter time-to-event for recurrent lupus nephritis (hazard ratio [HR] 4.63, 95% confidence interval [95% CI] 1.29,16.65) and death (HR 2.47, 95% CI 0.91,6.71), although, with the latter, the association was not statistically significant. Recurrent lupus nephritis and chronic rejection of the kidney transplant were found to be risk factors for allograft loss (HR 2.48, 95% CI 1.09,5.60 and HR 2.72, 95% CI 1.55,4.78, respectively). In patients with recurrent lupus nephritis, the lesion in the engrafted kidney was predominantly mesangial, compared with a predominance of proliferative or membranous lesions in the native kidneys. Conclusion African American ethnicity was independently associated with recurrent lupus nephritis. Allograft loss was associated with chronic transplant rejection and recurrence of lupus nephritis. Recurrent lupus nephritis is infrequent and relatively benign, without influence on a patient's survival. [source]


Bayesian cure rate models for malignant melanoma: a case-study of Eastern Cooperative Oncology Group trial E1690

JOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES C (APPLIED STATISTICS), Issue 2 2002
Ming-Hui Chen
We propose several Bayesian models for modelling time-to-event data. We consider a piecewise exponential model, a fully parametric cure rate model and a semiparametric cure rate model. For each model, we derive the likelihood function and examine some of its properties for carrying out Bayesian inference with non-informative priors. We also examine model identifiability issues and give conditions which guarantee identifiability. Also, for each model, we construct a class of informative prior distributions based on historical data, i.e. data from similar previous studies. These priors, called power priors, prove to be quite useful in this context. We examine the properties of the power priors for Bayesian inference and, in particular, we study their effect on the current analysis. Tools for model comparison and model assessment are also proposed. A detailed case-study of a recently completed melanoma clinical trial conducted by the Eastern Cooperative Oncology Group is presented and the methodology proposed is demonstrated in detail. [source]


Sample size estimation for non-inferiority trials of time-to-event data

PHARMACEUTICAL STATISTICS: THE JOURNAL OF APPLIED STATISTICS IN THE PHARMACEUTICAL INDUSTRY, Issue 4 2008
Adam Crisp
Abstract We consider the problem of sample size calculation for non-inferiority based on the hazard ratio in time-to-event trials where overall study duration is fixed and subject enrolment is staggered with variable follow-up. An adaptation of previously developed formulae for the superiority framework is presented that specifically allows for effect reversal under the non-inferiority setting, and its consequent effect on variance. Empirical performance is assessed through a small simulation study, and an example based on an ongoing trial is presented. The formulae are straightforward to program and may prove a useful tool in planning trials of this type. Copyright 2007 John Wiley & Sons, Ltd. [source]


Joint Modelling of Repeated Transitions in Follow-up Data , A Case Study on Breast Cancer Data

BIOMETRICAL JOURNAL, Issue 3 2005
B. Genser
Abstract In longitudinal studies where time to a final event is the ultimate outcome often information is available about intermediate events the individuals may experience during the observation period. Even though many extensions of the Cox proportional hazards model have been proposed to model such multivariate time-to-event data these approaches are still very rarely applied to real datasets. The aim of this paper is to illustrate the application of extended Cox models for multiple time-to-event data and to show their implementation in popular statistical software packages. We demonstrate a systematic way of jointly modelling similar or repeated transitions in follow-up data by analysing an event-history dataset consisting of 270 breast cancer patients, that were followed-up for different clinical events during treatment in metastatic disease. First, we show how this methodology can also be applied to non Markovian stochastic processes by representing these processes as "conditional" Markov processes. Secondly, we compare the application of different Cox-related approaches to the breast cancer data by varying their key model components (i.e. analysis time scale, risk set and baseline hazard function). Our study showed that extended Cox models are a powerful tool for analysing complex event history datasets since the approach can address many dynamic data features such as multiple time scales, dynamic risk sets, time-varying covariates, transition by covariate interactions, autoregressive dependence or intra-subject correlation. ( 2005 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source]


On Estimating the Relationship between Longitudinal Measurements and Time-to-Event Data Using a Simple Two-Stage Procedure

BIOMETRICS, Issue 3 2010
Paul S. Albert
SummaryYe, Lin, and Taylor (2008,,Biometrics,64, 1238,1246) proposed a joint model for longitudinal measurements and time-to-event data in which the longitudinal measurements are modeled with a semiparametric mixed model to allow for the complex patterns in longitudinal biomarker data. They proposed a two-stage regression calibration approach that is simpler to implement than a joint modeling approach. In the first stage of their approach, the mixed model is fit without regard to the time-to-event data. In the second stage, the posterior expectation of an individual's random effects from the mixed-model are included as covariates in a Cox model. Although Ye et al. (2008) acknowledged that their regression calibration approach may cause a bias due to the problem of informative dropout and measurement error, they argued that the bias is small relative to alternative methods. In this article, we show that this bias may be substantial. We show how to alleviate much of this bias with an alternative regression calibration approach that can be applied for both discrete and continuous time-to-event data. Through simulations, the proposed approach is shown to have substantially less bias than the regression calibration approach proposed by Ye et al. (2008). In agreement with the methodology proposed by Ye et al. (2008), an advantage of our proposed approach over joint modeling is that it can be implemented with standard statistical software and does not require complex estimation techniques. [source]


Statistical Methods for Analyzing Right-Censored Length-Biased Data under Cox Model

BIOMETRICS, Issue 2 2010
Jing Qin
Summary Length-biased time-to-event data are commonly encountered in applications ranging from epidemiological cohort studies or cancer prevention trials to studies of labor economy. A longstanding statistical problem is how to assess the association of risk factors with survival in the target population given the observed length-biased data. In this article, we demonstrate how to estimate these effects under the semiparametric Cox proportional hazards model. The structure of the Cox model is changed under length-biased sampling in general. Although the existing partial likelihood approach for left-truncated data can be used to estimate covariate effects, it may not be efficient for analyzing length-biased data. We propose two estimating equation approaches for estimating the covariate coefficients under the Cox model. We use the modern stochastic process and martingale theory to develop the asymptotic properties of the estimators. We evaluate the empirical performance and efficiency of the two methods through extensive simulation studies. We use data from a dementia study to illustrate the proposed methodology, and demonstrate the computational algorithms for point estimates, which can be directly linked to the existing functions in S-PLUS or R. [source]


Multiple-Imputation-Based Residuals and Diagnostic Plots for Joint Models of Longitudinal and Survival Outcomes

BIOMETRICS, Issue 1 2010
Dimitris Rizopoulos
Summary The majority of the statistical literature for the joint modeling of longitudinal and time-to-event data has focused on the development of models that aim at capturing specific aspects of the motivating case studies. However, little attention has been given to the development of diagnostic and model-assessment tools. The main difficulty in using standard model diagnostics in joint models is the nonrandom dropout in the longitudinal outcome caused by the occurrence of events. In particular, the reference distribution of statistics, such as the residuals, in missing data settings is not directly available and complex calculations are required to derive it. In this article, we propose a multiple-imputation-based approach for creating multiple versions of the completed data set under the assumed joint model. Residuals and diagnostic plots for the complete data model can then be calculated based on these imputed data sets. Our proposals are exemplified using two real data sets. [source]


Improved Logrank-Type Tests for Survival Data Using Adaptive Weights

BIOMETRICS, Issue 1 2010
Song Yang
Summary For testing for treatment effects with time-to-event data, the logrank test is the most popular choice and has some optimality properties under proportional hazards alternatives. It may also be combined with other tests when a range of nonproportional alternatives are entertained. We introduce some versatile tests that use adaptively weighted logrank statistics. The adaptive weights utilize the hazard ratio obtained by fitting the model of Yang and Prentice (2005,,Biometrika,92, 1,17). Extensive numerical studies have been performed under proportional and nonproportional alternatives, with a wide range of hazard ratios patterns. These studies show that these new tests typically improve the tests they are designed to modify. In particular, the adaptively weighted logrank test maintains optimality at the proportional alternatives, while improving the power over a wide range of nonproportional alternatives. The new tests are illustrated in several real data examples. [source]


Inference in Spline-Based Models for Multiple Time-to-Event Data, with Applications to a Breast Cancer Prevention Trial

BIOMETRICS, Issue 4 2003
Kiros Berhane
Summary. As part of the National Surgical Adjuvant Breast and Bowel Project, a controlled clinical trial known as the Breast Cancer Prevention Trial (BCPT) was conducted to assess the effectiveness of tamoxifen as a preventive agent for breast cancer. In addition to the incidence of breast cancer, data were collected on several other, possibly adverse, outcomes, such as invasive endometrial cancer, ischemic heart disease, transient ischemic attack, deep vein thrombosis and/or pulmonary embolism. In this article, we present results from an illustrative analysis of the BCPT data, based on a new modeling technique, to assess the effectiveness of the drug tamoxifen as a preventive agent for breast cancer. We extended the flexible model of Gray (1994, Spline-based test in survival analysis, Biometrics50, 640,652) to allow inference on multiple time-to-event outcomes in the style of the marginal modeling setup of Wei, Lin, and Weissfeld (1989, Regression analysis of multivariate incomplete failure time data by modeling marginal distributions, Journal of the American Statistical Association84, 1065,1073). This proposed model makes inference possible for multiple time-to-event data while allowing for greater flexibility in modeling the effects of prognostic factors with nonlinear exposure-response relationships. Results from simulation studies on the small-sample properties of the asymptotic tests will also be presented. [source]