ALBERT

Description:    In many randomized clinical trials, the primary response variable, for example, the survival time, is not observed directly after the patients enroll in the study but rather observed after some period of time (lag time). It is often the case that such a response variable is missing for some patients due to censoring that occurs when the study ends before the patient’s response is observed or when the patients drop out of the study. It is often assumed that censoring occurs at random which is referred to as noninformative censoring; however, in many cases such an assumption may not be reasonable. If the missing data are not analyzed properly, the estimator or test for the treatment effect may be biased. In this paper, we use semiparametric theory to derive a class of consistent and asymptotically normal estimators for the treatment effect parameter which are applicable when the response variable is right censored. The baseline auxiliary covariates and post-treatment auxiliary covariates, which may be time-dependent, are also considered in our semiparametric model. These auxiliary covariates are used to derive estimators that both account for informative censoring and are more efficient then the estimators which do not consider the auxiliary covariates. Content Type Journal Article Pages 1-28 DOI 10.1007/s10985-011-9199-8 Authors Xiaomin Lu, Department of Biostatistics, College of Medicine and College of Public Health and health Professions, University of Florida, Gainesville, FL 32611, USA Anastasios A. Tsiatis, Department of Statistics, North Carolina State University, Raleigh, NC 27695-8203, USA Journal Lifetime Data Analysis Online ISSN 1572-9249 Print ISSN 1380-7870

Description:    This paper considers the analysis of multivariate survival data where the marginal distributions are specified by semiparametric transformation models, a general class including the Cox model and the proportional odds model as special cases. First, consideration is given to the situation where the joint distribution of all failure times within the same cluster is specified by the Clayton–Oakes model (Clayton, Biometrika 65:141–151, l978 ; Oakes, J R Stat Soc B 44:412–422, 1982 ). A two-stage estimation procedure is adopted by first estimating the marginal parameters under the independence working assumption, and then the association parameter is estimated from the maximization of the full likelihood function with the estimators of the marginal parameters plugged in. The asymptotic properties of all estimators in the semiparametric model are derived. For the second situation, the third and higher order dependency structures are left unspecified, and interest focuses on the pairwise correlation between any two failure times. Thus, the pairwise association estimate can be obtained in the second stage by maximizing the pairwise likelihood function. Large sample properties for the pairwise association are also derived. Simulation studies show that the proposed approach is appropriate for practical use. To illustrate, a subset of the data from the Diabetic Retinopathy Study is used. Content Type Journal Article Pages 1-22 DOI 10.1007/s10985-011-9205-1 Authors Chyong-Mei Chen, Department of Statistics and Informatics Science, Providence University, Taichung, Taiwan, ROC Chang-Yung Yu, Department of Financial and Computational Mathematics, Providence University, Taichung, Taiwan, ROC Journal Lifetime Data Analysis Online ISSN 1572-9249 Print ISSN 1380-7870

Description:    Life tables used in life insurance determine the age of death distribution only at integer ages. Therefore, actuaries make fractional age assumptions to interpolate between integer age values when they have to value payments that are not restricted to integer ages. Traditional fractional age assumptions as well as the fractional independence assumption are easy to apply but result in a non-intuitive overall shape of the force of mortality. Other approaches proposed either require expensive optimization procedures or produce many discontinuities. We suggest a new, computationally inexpensive algorithm to select the parameters within the LFM-family introduced by Jones and Mereu (Insur Math Econ 27:261–276, 2000 ). In contrast to previously suggested methods, our algorithm enforces a monotone force of mortality between integer ages if the mortality rates are monotone and keeps the number of discontinuities small. Content Type Journal Article Pages 1-13 DOI 10.1007/s10985-011-9211-3 Authors Christiane Barz, Anderson School of Management, University of California at Los Angeles, 110 Westwood Plaza, Los Angeles, CA 90095, USA Alfred Müller, Department Mathematik, Universität Siegen, 57072 Siegen, Germany Journal Lifetime Data Analysis Online ISSN 1572-9249 Print ISSN 1380-7870

Description:    Multiple biomarkers are frequently observed or collected for detecting or understanding a disease. The research interest of this article is to extend tools of receiver operating characteristic (ROC) analysis from univariate marker setting to multivariate marker setting for evaluating predictive accuracy of biomarkers using a tree-based classification rule. Using an arbitrarily combined and-or classifier, an ROC function together with a weighted ROC function (WROC) and their conjugate counterparts are introduced for examining the performance of multivariate markers. Specific features of the ROC and WROC functions and other related statistics are discussed in comparison with those familiar properties for univariate marker. Nonparametric methods are developed for estimating the ROC and WROC functions, and area under curve and concordance probability. With emphasis on population average performance of markers, the proposed procedures and inferential results are useful for evaluating marker predictability based on multivariate marker measurements with different choices of markers, and for evaluating different and-or combinations in classifiers. Content Type Journal Article Pages 1-21 DOI 10.1007/s10985-012-9233-5 Authors Mei-Cheng Wang, Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe Street, Baltimore, MD 21205, USA Shanshan Li, Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe Street, Baltimore, MD 21205, USA Journal Lifetime Data Analysis Online ISSN 1572-9249 Print ISSN 1380-7870

Description:    In many clinical research applications the time to occurrence of one event of interest, that may be obscured by another—so called competing—event, is investigated. Specific interventions can only have an effect on the endpoint they address or research questions might focus on risk factors for a certain outcome. Different approaches for the analysis of time-to-event data in the presence of competing risks were introduced in the last decades including some new methodologies, which are not yet frequently used in the analysis of competing risks data. Cause-specific hazard regression, subdistribution hazard regression, mixture models, vertical modelling and the analysis of time-to-event data based on pseudo-observations are described in this article and are applied to a dataset of a cohort study intended to establish risk stratification for cardiac death after myocardial infarction. Data analysts are encouraged to use the appropriate methods for their specific research questions by comparing different regression approaches in the competing risks setting regarding assumptions, methodology and interpretation of the results. Notes on application of the mentioned methods using the statistical software R are presented and extensions to the presented standard methods proposed in statistical literature are mentioned. Content Type Journal Article Pages 1-26 DOI 10.1007/s10985-012-9230-8 Authors Bernhard Haller, Institut für Medizinische Statistik und Epidemiologie der Technischen Universität München, Ismaninger Straße 22, 81675 Munich, Germany Georg Schmidt, 1. Medizinische Klinik und Poliklinik der Technischen Universität München, Munich, Germany Kurt Ulm, Institut für Medizinische Statistik und Epidemiologie der Technischen Universität München, Ismaninger Straße 22, 81675 Munich, Germany Journal Lifetime Data Analysis Online ISSN 1572-9249 Print ISSN 1380-7870

Description:    Competing risks data are routinely encountered in various medical applications due to the fact that patients may die from different causes. Recently, several models have been proposed for fitting such survival data. In this paper, we develop a fully specified subdistribution model for survival data in the presence of competing risks via a subdistribution model for the primary cause of death and conditional distributions for other causes of death. Various properties of this fully specified subdistribution model have been examined. An efficient Gibbs sampling algorithm via latent variables is developed to carry out posterior computations. Deviance information criterion (DIC) and logarithm of the pseudomarginal likelihood (LPML) are used for model comparison. An extensive simulation study is carried out to examine the performance of DIC and LPML in comparing the cause-specific hazards model, the mixture model, and the fully specified subdistribution model. The proposed methodology is applied to analyze a real dataset from a prostate cancer study in detail. Content Type Journal Article Pages 1-25 DOI 10.1007/s10985-012-9221-9 Authors Miaomiao Ge, Clinical Bio Statistics, Boehringer Ingelheim Pharmaceuticals, Inc., 900 Ridgefield Road, Ridgefield, CT 06877, USA Ming-Hui Chen, Department of Statistics, University of Connecticut, 215 Glenbrook Road, U-4120, Storrs, CT 06269, USA Journal Lifetime Data Analysis Online ISSN 1572-9249 Print ISSN 1380-7870

Description:    We propose an evidence synthesis approach through a degradation model to estimate causal influences of physiological factors on myocardial infarction (MI) and coronary heart disease (CHD). For instance several studies give incidences of MI and CHD for different age strata, other studies give relative or absolute risks for strata of main risk factors of MI or CHD. Evidence synthesis of several studies allows incorporating these disparate pieces of information into a single model. For doing this we need to develop a sufficiently general dynamical model; we also need to estimate the distribution of explanatory factors in the population. We develop a degradation model for both MI and CHD using a Brownian motion with drift, and the drift is modeled as a function of indicators of obesity, lipid profile, inflammation and blood pressure. Conditionally on these factors the times to MI or CHD have inverse Gaussian ( IG ) distributions. The results we want to fit are generally not conditional on all the factors and thus we need marginal distributions of the time of occurrence of MI and CHD; this leads us to manipulate the inverse Gaussian normal distribution ( IGN ) (an IG whose drift parameter has a normal distribution). Another possible model arises if a factor modifies the threshold. This led us to define an extension of IGN obtained when both drift and threshold parameters have normal distributions. We applied the model to results published in five important studies of MI and CHD and their risk factors. The fit of the model using the evidence synthesis approach was satisfactory and the effects of the four risk factors were highly significant. Content Type Journal Article Pages 1-18 DOI 10.1007/s10985-012-9227-3 Authors Daniel Commenges, INSERM, ISPED, Centre INSERM U-897-Epidmilogie-Biostatistique, Bordeaux, 33000 France Boris P. Hejblum, INSERM, ISPED, Centre INSERM U-897-Epidmilogie-Biostatistique, Bordeaux, 33000 France Journal Lifetime Data Analysis Online ISSN 1572-9249 Print ISSN 1380-7870

Description:    Widely recognized in many fields including economics, engineering, epidemiology, health sciences, technology and wildlife management, length-biased sampling generates biased and right-censored data but often provide the best information available for statistical inference. Different from traditional right-censored data, length-biased data have unique aspects resulting from their sampling procedures. We exploit these unique aspects and propose a general imputation-based estimation method for analyzing length-biased data under a class of flexible semiparametric transformation models. We present new computational algorithms that can jointly estimate the regression coefficients and the baseline function semiparametrically. The imputation-based method under the transformation model provides an unbiased estimator regardless whether the censoring is independent or not on the covariates. We establish large-sample properties using the empirical processes method. Simulation studies show that under small to moderate sample sizes, the proposed procedure has smaller mean square errors than two existing estimation procedures. Finally, we demonstrate the estimation procedure by a real data example. Content Type Journal Article Pages 1-34 DOI 10.1007/s10985-012-9225-5 Authors Hao Liu, Division of Biostatistics, Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA Jing Qin, Biostatistics Research Branch, National Institute of Allergy and Infectious Diseases, National Institute of Health Bethesda, Bethesda, MD 20892, USA Yu Shen, Department of Biostatistics, The University of Texas M. D. Anderson Cancer Center, Houston, TX 77030, USA Journal Lifetime Data Analysis Online ISSN 1572-9249 Print ISSN 1380-7870

Description:    Recurrent event data are often encountered in biomedical research, for example, recurrent infections or recurrent hospitalizations for patients after renal transplant. In many studies, there are more than one type of events of interest. Cai and Schaube (Lifetime Data Anal 10:121–138, 2004 ) advocated a proportional marginal rate model for multiple type recurrent event data. In this paper, we propose a general additive marginal rate regression model. Estimating equations approach is used to obtain the estimators of regression coefficients and baseline rate function. We prove the consistency and asymptotic normality of the proposed estimators. The finite sample properties of our estimators are demonstrated by simulations. The proposed methods are applied to the India renal transplant study to examine risk factors for bacterial, fungal and viral infections. Content Type Journal Article Pages 1-24 DOI 10.1007/s10985-012-9226-4 Authors Xiaolin Chen, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190 People’s Republic of China Qihua Wang, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190 People’s Republic of China Jianwen Cai, Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7420, USA Viswanathan Shankar, Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7420, USA Journal Lifetime Data Analysis Online ISSN 1572-9249 Print ISSN 1380-7870

Description:    Time-to-event data in which failures are only assessed at discrete time points are common in many clinical trials. Examples include oncology studies where events are observed through periodic screenings such as radiographic scans. When the survival endpoint is acknowledged to be discrete, common methods for the analysis of observed failure times include the discrete hazard models (e.g., the discrete-time proportional hazards and the continuation ratio model) and the proportional odds model. In this manuscript, we consider estimation of a marginal treatment effect in discrete hazard models where the constant treatment effect assumption is violated. We demonstrate that the estimator resulting from these discrete hazard models is consistent for a parameter that depends on the underlying censoring distribution. An estimator that removes the dependence on the censoring mechanism is proposed and its asymptotic distribution is derived. Basing inference on the proposed estimator allows for statistical inference that is scientifically meaningful and reproducible. Simulation is used to assess the performance of the presented methodology in finite samples. Content Type Journal Article Pages 1-24 DOI 10.1007/s10985-012-9224-6 Authors Vinh Q. Nguyen, Department of Statistics, University of California, Irvine, CA, USA Daniel L. Gillen, Department of Statistics, University of California, Irvine, CA, USA Journal Lifetime Data Analysis Online ISSN 1572-9249 Print ISSN 1380-7870