Survival Analysis Commons^™

Crude Cumulative Incidence In The Form Of A Horvitz-Thompson Like And Kaplan-Meier Like Estimator, Laura Antolini, Elia Mario Biganzoli, Patrizia Boracchi

COBRA Preprint Series

The link between the nonparametric estimator of the crude cumulative incidence of a competing risk and the Kaplan-Meier estimator is exploited. The equivalence of the nonparametric crude cumulative incidence to an inverse-probability-of-censoring weighted average of the sub-distribution function is proved. The link between the estimation of crude cumulative incidence curves and Gray's family of nonparametric tests is considered. The crude cumulative incidence is proved to be a Kaplan-Meier like estimator based on the sub-distribution hazard, i.e. the quantity on which Gray's family of tests is based. A standard probabilistic formalism is adopted to have a note accessible to applied statisticians.

Spatial Cluster Detection For Censored Outcome Data, Andrea J. Cook, Diane Gold, Yi Li

Harvard University Biostatistics Working Paper Series

No abstract provided.

Structural Inference In Transition Measurement Error Models For Longitudinal Data, Wenqin Pan, Xihong Lin, Donglin Zeng

Harvard University Biostatistics Working Paper Series

No abstract provided.

Estimation In Semiparametric Transition Measurement Error Models For Longitudinal Data, Wenqin Pan, Donglin Zeng, Xihong Lin

Harvard University Biostatistics Working Paper Series

No abstract provided.

Nonparametric Regression Using Local Kernel Estimating Equations For Correlated Failure Time Data, Zhangsheng Yu, Xihong Lin

Harvard University Biostatistics Working Paper Series

No abstract provided.

Causal Inference In Hybrid Intervention Trials Involving Treatment Choice, Qi Long, Rod Little, Xihong Lin

Harvard University Biostatistics Working Paper Series

No abstract provided.

A Comparison Of Methods For Estimating The Causal Effect Of A Treatment In Randomized Clinical Trials Subject To Noncompliance, Rod Little, Qi Long, Xihong Lin

Harvard University Biostatistics Working Paper Series

No abstract provided.

Hierarchical Lévy Frailty Models And A Frailty Analysis Of Data On Infant Mortality In Norwegian Siblings, Tron Anders Moger, Odd O. Aalen

UW Biostatistics Working Paper Series

Distributions determined by non-negative Lévy processes, which include the power variance function (PVF) distributions among others, are commonly used as frailty distributions to model dependent survival times in family data. We present a hierarchical frailty model constructed by randomizing scale parameters, corresponding to time parameters of Lévy processes, in the Lévy frailty distributions. In its simplest form, this yields a two-model with heterogeneity the individual and family level. The family level frailty is shared within families, creating dependence. In the more complex models, it is extended to allow for several levels of dependence. This yields models with nested dependence structures …

Doubly Robust Censoring Unbiased Transformations, Daniel Rubin, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

We consider random design nonparametric regression when the response variable is subject to right censoring. Following the work of Fan and Gijbels (1994), a common approach to this problem is to apply what has been termed a censoring unbiased transformation to the data to obtain surrogate responses, and then enter these surrogate responses with covariate data into standard smoothing algorithms. Existing censoring unbiased transformations generally depend on either the conditional survival function of the response of interest, or that of the censoring variable. We show that a mapping introduced in another statistical context is in fact a censoring unbiased transformation …

Using Profile Likelihood For Semiparametric Model Selection With Application To Proportional Hazards Mixed Models, Ronghui Xu, Anthony Gamst, Michael Donohue, Florin Vaida, David P. Harrington

Harvard University Biostatistics Working Paper Series

No abstract provided.

Recurrent Event Models In The Presence Of A Terminal Event: Comparison, Inference And Data Analysis, Xianghua Luo, Mei-Cheng Wang

Johns Hopkins University, Dept. of Biostatistics Working Papers

This article focuses on statistical implications of proportional rate models for recurrent event data in the presence of a terminal event. In such circumstances, various definitions of the recurrent rate function have been adopted in the proportional rate models. Although these rate functions have quite different interpretations, recognition of the differences has been lacking theoretically and practically. We compare three types of rate functions from both conceptual and quantitative perspectives; conclude that the inappropriate choice of a rate function may lead to misleading scientific conclusions. Simulations are conducted for comparisons of the focused models. Analysis of data from an AIDS …

Survival Analysis With Change Point Hazard Functions, Melody S. Goodman, Yi Li, Ram C. Tiwari

Harvard University Biostatistics Working Paper Series

No abstract provided.

Censored Data Regression In High-Dimension And Low-Sample Size Settings For Genomic Applications, Hongzhe Li

UPenn Biostatistics Working Papers

New high-throughput technologies are generating various types of high-dimensional genomic and proteomic data and meta-data (e.g., networks and pathways) in order to obtain a systems-level understanding of various complex diseases such as human cancers and cardiovascular diseases. As the amount and complexity of the data increase and as the questions being addressed become more sophisticated, we face the great challenge of how to model such data in order to draw valid statistical and biological conclusions. One important problem in genomic research is to relate these high-throughput genomic data to various clinical outcomes, including possibly censored survival outcomes such as age …

The Two-Sample Problem For Failure Rates Depending On A Continuous Mark: An Application To Vaccine Efficacy, Peter B. Gilbert, Ian W. Mckeague, Yanqing Sun

UW Biostatistics Working Paper Series

The efficacy of an HIV vaccine to prevent infection is likely to depend on the genetic variation of the exposing virus. This paper addresses the problem of using data on the HIV sequences that infect vaccine efficacy trial participants to 1) test for vaccine efficacy more powerfully than procedures that ignore the sequence data; and 2) evaluate the dependence of vaccine efficacy on the divergence of infecting HIV strains from the HIV strain that is contained in the vaccine. Because hundreds of amino acid sites in each HIV genome are sequenced, it is natural to treat the divergence (defined in …

Evaluating Prediction Rules For T-Year Survivors With Censored Regression Models, Hajime Uno, Tianxi Cai, Lu Tian, L.J. Wei

Harvard University Biostatistics Working Paper Series

Suppose that we are interested in establishing simple, but reliable rules for predicting future t-year survivors via censored regression models. In this article, we present inference procedures for evaluating such binary classification rules based on various prediction precision measures quantified by the overall misclassification rate, sensitivity and specificity, and positive and negative predictive values. Specifically, under various working models we derive consistent estimators for the above measures via substitution and cross validation estimation procedures. Furthermore, we provide large sample approximations to the distributions of these nonsmooth estimators without assuming that the working model is correctly specified. Confidence intervals, for example, …

Survival Analysis Methods In Genetic Epidemiology, Hongzhe Li

UPenn Biostatistics Working Papers

Mapping genes for complex human diseases is a challenging problem due to the fact that many such diseases are due to both genetic and enviromental risk factors and many also exhibit phenotypic heterogeneity, such as variable age of onset. Information on variable age of disease onset is often a good indicator for disease heterogeneity and incorporation of such information together with enviromental risk factors into genetic analysis should lead to more powerful tests for genetic analysis. Due to the problem of censoring, survival analysis methods have proved to be very useful for genetic analysis. In this paper, I review some …

Case-Cohort Methods For Survival Data On Families From Routine Registers, Tron Anders Moger, Yudi Pawitan, Ørnulf Borgan

UW Biostatistics Working Paper Series

In the Nordic countries, there exist several registers containing information on diseases and risk factors for millions of individuals. This information can be linked into families by use of personal identification numbers, and represent a great opportunity for studying diseases that show familial aggregation. Due to the size of the registers, it is difficult to analyze the data by using traditional methods for multivariate survival analysis, such as frailty or copula models. Since the size of the cohort is known, case-cohort methods based on pseudo-likelihoods are suitable for analyzing the data. We present methods for sampling control families both with …

Semiparametric Approaches For Joint Modeling Of Longitudinal And Survival Data With Time Varying Coefficients, Xiao Song, C.Y. Wang

UW Biostatistics Working Paper Series

We study joint modeling of survival and longitudinal data. There are two regression models of interest. The primary model is for survival outcomes, which are assumed to follow a time varying coefficient proportional hazards model. The second model is for longitudinal data, which are assumed to follow a random effects model. Based on the trajectory of a subject's longitudinal data, some covariates in the survival model are functions of the unobserved random effects. Estimated random effects are generally different from the unobserved random effects and hence this leads to covariate measurement error. To deal with covariate measurement error, we propose …

Nonparametric Estimation Of Bivariate Failure Time Associations In The Presence Of A Competing Risk, Karen Bandeen-Roche, Jing Ning

Johns Hopkins University, Dept. of Biostatistics Working Papers

There has been much research on the study of associations among paired failure times. Most has either assumed time invariance of association or been based on complex measures or estimators. Little has accommodated failures arising amid competing risks. This paper targets the conditional cause specific hazard ratio, a recent modification of the conditional hazard ratio to accommodate competing risks data. Estimation is accomplished by an intuitive, nonparametric method that localizes Kendall’s tau. Time variance is accommodated through a partitioning of space into “bins” between which the strength of association may differ. Inferential procedures are researched, small sample performance evaluated, and …

Correspondences Between Regression Models For Complex Binary Outcomes And Those For Structured Multivariate Survival Analyses, Nicholas P. Jewell

U.C. Berkeley Division of Biostatistics Working Paper Series

Doksum and Gasko [5] described a one-to-one correspondence between regression models for binary outcomes and those for continuous time survival analyses. This correspondence has been exploited heavily in the analysis of current status data (Jewell and van der Laan [11], Shiboski [18]). Here, we explore similar correspondences for complex survival models and categorical regression models for polytomous data. We include discussion of competing risks and progressive multi-state survival random variables.

Analyzing Panel Count Data With Informative Observation Times, Chiung-Yu Huang, Mei-Cheng Wang, Ying Zhang

Johns Hopkins University, Dept. of Biostatistics Working Papers

In this paper, we study panel count data with informative observation times. We assume nonparametric and semiparametric proportional rate models for the underlying recurrent event process, where the form of the baseline rate function is left unspecified and a subject-specific frailty variable inflates or deflates the rate function multiplicatively. The proposed models allow the recurrent event processes and observation times to be correlated through their connections with the unobserved frailty; moreover, the distributions of both the frailty variable and observation times are considered as nuisance parameters. The baseline rate function and the regression parameters are estimated by maximizing a conditional …

Designed Extension Of Survival Studies: Application To Clinical Trials With Unrecognized Heterogeneity, Yi Li, Mei-Chiung Shih, Rebecca A. Betensky

Harvard University Biostatistics Working Paper Series

It is well known that unrecognized heterogeneity among patients, such as is conferred by genetic subtype, can undermine the power of randomized trial, designed under the assumption of homogeneity, to detect a truly beneficial treatment. We consider the conditional power approach to allow for recovery of power under unexplained heterogeneity. While Proschan and Hunsberger (1995) confined the application of conditional power design to normally distributed observations, we consider more general and difficult settings in which the data are in the framework of continuous time and are subject to censoring. In particular, we derive a procedure appropriate for the analysis of …

Cross-Validated Bagged Prediction Of Survival, Sandra E. Sinisi, Romain Neugebauer, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

In this article, we show how to apply our previously proposed Deletion/Substitution/Addition algorithm in the context of right-censoring for the prediction of survival. Furthermore, we introduce how to incorporate bagging into the algorithm to obtain a cross-validated bagged estimator. The method is used for predicting the survival time of patients with diffuse large B-cell lymphoma based on gene expression variables.

Mixture Cure Survival Models With Dependent Censoring, Yi Li, Ram C. Tiwari, Subharup Guha

Harvard University Biostatistics Working Paper Series

A number of authors have studies the mixture survival model to analyze survival data with nonnegligible cure fractions. A key assumption made by these authors is the independence between the survival time and the censoring time. To our knowledge, no one has studies the mixture cure model in the presence of dependent censoring. To account for such dependence, we propose a more general cure model which allows for dependent censoring. In particular, we derive the cure models from the perspective of competing risks and model the dependence between the censoring time and the survival time using a class of Archimedean …

Semiparametric Normal Transformation Models For Spatially Correlated Survival Data, Yi Li, Xihong Lin

Harvard University Biostatistics Working Paper Series

There is an emerging interest in modeling spatially correlated survival data in biomedical and epidemiological studies. In this paper, we propose a new class of semiparametric normal transformation models for right censored spatially correlated survival data. This class of models assumes that survival outcomes marginally follow a Cox proportional hazard model with unspecified baseline hazard, and their joint distribution is obtained by transforming survival outcomes to normal random variables, whose joint distribution is assumed to be multivariate normal with a spatial correlation structure. A key feature of the class of semiparametric normal transformation models is that it provides a rich …

Inference On Survival Data With Covariate Measurement Error - An Imputation-Based Approach, Yi Li, Louise Ryan

Harvard University Biostatistics Working Paper Series

We propose a new method for fitting proportional hazards models with error-prone covariates. Regression coefficients are estimated by solving an estimating equation that is the average of the partial likelihood scores based on imputed true covariates. For the purpose of imputation, a linear spline model is assumed on the baseline hazard. We discuss consistency and asymptotic normality of the resulting estimators, and propose a stochastic approximation scheme to obtain the estimates. The algorithm is easy to implement, and reduces to the ordinary Cox partial likelihood approach when the measurement error has a degenerative distribution. Simulations indicate high efficiency and robustness. …

Survival Point Estimate Prediction In Matched And Non-Matched Case-Control Subsample Designed Studies, Annette M. Molinaro, Mark J. Van Der Laan, Dan H. Moore, Karla Kerlikowske

U.C. Berkeley Division of Biostatistics Working Paper Series

Providing information about the risk of disease and clinical factors that may increase or decrease a patient's risk of disease is standard medical practice. Although case-control studies can provide evidence of strong associations between diseases and risk factors, clinicians need to be able to communicate to patients the age-specific risks of disease over a defined time interval for a set of risk factors.

An estimate of absolute risk cannot be determined from case-control studies because cases are generally chosen from a population whose size is not known (necessary for calculation of absolute risk) and where duration of follow-up is not …

Linear Regression Of Censored Length-Biased Lifetimes, Ying Qing Chen, Yan Wang

UW Biostatistics Working Paper Series

Length-biased lifetimes may be collected in observational studies or sample surveys due to biased sampling scheme. In this article, we use a linear regression model, namely, the accelerated failure time model, for the population lifetime distributions in regression analysis of the length-biased lifetimes. It is discovered that the associated regression parameters are invariant under the length-biased sampling scheme. According to this discovery, we propose the quasi partial score estimating equations to estimate the population regression parameters. The proposed methodologies are evaluated and demonstrated by simulation studies and an application to actual data set.

Attributable Risk Function In The Proportional Hazards Model, Ying Qing Chen, Chengcheng Hu, Yan Wang

UW Biostatistics Working Paper Series

As an epidemiological parameter, the population attributable fraction is an important measure to quantify the public health attributable risk of an exposure to morbidity and mortality. In this article, we extend this parameter to the attributable fraction function in survival analysis of time-to-event outcomes, and further establish its estimation and inference procedures based on the widely used proportional hazards models. Numerical examples and simulations studies are presented to validate and demonstrate the proposed methods.

New Statistical Paradigms Leading To Web-Based Tools For Clinical/Translational Science, Knut M. Wittkowski

COBRA Preprint Series

As the field of functional genetics and genomics is beginning to mature, we become confronted with new challenges. The constant drop in price for sequencing and gene expression profiling as well as the increasing number of genetic and genomic variables that can be measured makes it feasible to address more complex questions. The success with rare diseases caused by single loci or genes has provided us with a proof-of-concept that new therapies can be developed based on functional genomics and genetics.

Common diseases, however, typically involve genetic epistasis, genomic pathways, and proteomic pattern. Moreover, to better understand the underlying biologi-cal …