Directly parameterized regression conditioning on being alive: analysis of longitudinal data truncated by deaths

For observational longitudinal studies of geriatric populations, outcomes such as disability or cognitive functioning are often censored by death. Statistical analysis of such data may explicitly condition on either vital status or survival time when summarizing the longitudinal response. For example a pattern-mixture model characterizes the mean response at time t conditional on death at time S = s (for s > t), and thus uses future status as a predictor for the time t response. As an alternative, we define regression conditioning on being alive as a regression model that conditions on survival status, rather than a specific survival time. Such models may be referred to as partly conditional since the mean at time t is specified conditional on being alive (S > t), rather than using finer stratification (S = s for s > t). We show that naive use of standard likelihood-based longitudinal methods and generalized estimating equations with non-independence weights may lead to biased estimation of the partly conditional mean model. We develop a taxonomy for accommodation of both dropout and death, and describe estimation for binary longitudinal data that applies selection weights to estimating equations with independence working correlation. Simulation studies and an analysis of monthly disability status illustrate potential bias in regression methods that do not explicitly condition on survival.     

Regression analysis of doubly censored failure time data with frailty

In doubly censored failure time data, the survival time of interest is defined as the elapsed time between an initial event and a subsequent event, and the occurrences of both events cannot be observed exactly. Instead, only right- or interval-censored observations on the occurrence times are available. For the analysis of such data, a number of methods have been proposed under the assumption that the survival time of interest is independent of the occurrence time of the initial event. This article investigates a different situation where the independence may not be true with the focus on regression analysis of doubly censored data. Cox frailty models are applied to describe the effects of covariates and an EM algorithm is developed for estimation. Simulation studies are performed to investigate finite sample properties of the proposed method and an illustrative example from an acquired immune deficiency syndrome (AIDS) cohort study is provided.     

Clustering threshold gradient descent regularization: with applications to microarray studies

MOTIVATION: An important goal of microarray studies is to discover genes that are associated with clinical outcomes, such as disease status and patient survival. While a typical experiment surveys gene expressions on a global scale, there may be only a small number of genes that have significant influence on a clinical outcome. Moreover, expression data have cluster structures and the genes within a cluster have correlated expressions and coordinated functions, but the effects of individual genes in the same cluster may be different. Accordingly, we seek to build statistical models with the following properties. First, the model is sparse in the sense that only a subset of the parameter vector is non-zero. Second, the cluster structures of gene expressions are properly accounted for. RESULTS: For gene expression data without pathway information, we divide genes into clusters using commonly used methods, such as K-means or hierarchical approaches. The optimal number of clusters is determined using the Gap statistic. We propose a clustering threshold gradient descent regularization (CTGDR) method, for simultaneous cluster selection and within cluster gene selection. We apply this method to binary classification and censored survival analysis. Compared to the standard TGDR and other regularization methods, the CTGDR takes into account the cluster structure and carries out feature selection at both the cluster level and within-cluster gene level. We demonstrate the CTGDR on two studies of cancer classification and two studies correlating survival of lymphoma patients with microarray expressions. AVAILABILITY: R code is available upon request. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

Bayesian latent variable models for mixed discrete outcomes

In studies of complex health conditions, mixtures of discrete outcomes (event time, count, binary, ordered categorical) are commonly collected. For example, studies of skin tumorigenesis record latency time prior to the first tumor, increases in the number of tumors at each week, and the occurrence of internal tumors at the time of death. Motivated by this application, we propose a general underlying Poisson variable framework for mixed discrete outcomes, accommodating dependency through an additive gamma frailty model for the Poisson means. The model has log-linear, complementary log-log, and proportional hazards forms for count, binary and discrete event time outcomes, respectively. Simple closed form expressions can be derived for the marginal expectations, variances, and correlations. Following a Bayesian approach to inference, conditionally-conjugate prior distributions are chosen that facilitate posterior computation via an MCMC algorithm. The methods are illustrated using data from a Tg.AC mouse bioassay study.     

Nonparametric estimation in a Markov "illness-death" process from interval censored observations with missing intermediate transition status

Summary .  In many clinical trials patients are intermittently assessed for the transition to an intermediate state, such as occurrence of a disease-related nonfatal event, and death. Estimation of the distribution of nonfatal event free survival time, that is, the time to the first occurrence of the nonfatal event or death, is the primary focus of the data analysis. The difficulty with this estimation is that the intermittent assessment of patients results in two forms of incompleteness: the times of occurrence of nonfatal events are interval censored and, when a nonfatal event does not occur by the time of the last assessment, a patient's nonfatal event status is not known from the time of the last assessment until the end of follow-up for death. We consider both forms of incompleteness within the framework of an "illness–death" model. We develop nonparametric maximum likelihood (ML) estimation in an "illness–death" model from interval-censored observations with missing status of intermediate transition. We show that the ML estimators are self-consistent and propose an algorithm for obtaining them. This work thus provides new methodology for the analysis of incomplete data that arise from clinical trials. We apply this methodology to the data from a recently reported cancer clinical trial ( Bonner et al., 2006 , New England Journal of Medicine 354, 567–578) and compare our estimation results with those obtained using a Food and Drug Administration recommended convention.     

Dimension reduction methods for microarrays with application to censored survival data

MOTIVATION: Recent research has shown that gene expression profiles can potentially be used for predicting various clinical phenotypes, such as tumor class, drug response and survival time. While there has been extensive studies on tumor classification, there has been less emphasis on other phenotypic features, in particular, patient survival time or time to cancer recurrence, which are subject to right censoring. We consider in this paper an analysis of censored survival time based on microarray gene expression profiles. RESULTS: We propose a dimension reduction strategy, which combines principal components analysis and sliced inverse regression, to identify linear combinations of genes, that both account for the variability in the gene expression levels and preserve the phenotypic information. The extracted gene combinations are then employed as covariates in a predictive survival model formulation. We apply the proposed method to a large diffuse large-B-cell lymphoma dataset, which consists of 240 patients and 7399 genes, and build a Cox proportional hazards model based on the derived gene expression components. The proposed method is shown to provide a good predictive performance for patient survival, as demonstrated by both the significant survival difference between the predicted risk groups and the receiver operator characteristics analysis. AVAILABILITY: R programs are available upon request from the authors. SUPPLEMENTARY INFORMATION: http://dna.ucdavis.edu/~hli/bioinfo-surv-supp.pdf.     

The possible role of BAX and BI-1 genes in chilling-induced cell death in cucumber fruit

The aim of the present study was to investigate the possible role of BAX and BI-1 genes in chilling injury of cucumber fruit. BAX and BI-1 gene expressions were assayed under 2 ± 1 °C. Meanwhile, cell death, cellular integrity, specific chromatin fragmentation and nucleus morphology in cucumber (Cucumis sativus L. cv. Zhexiu-1) fruits were determined. Results indicated that BAX and BI-1 genes were activated by low temperature and the expression level of the BAX was much higher than BI-1. At the same time, electrolyte leakage and cell death were increased coupled with nuclear envelope disassembly and DNA fragmentation during the occurrence of chilling injury. In addition, characteristic features of programmed cell death were induced as well as the initiation of chilling injury. The interaction of BAX and BI-1 might predetermine the cell life or death in response to cold stimulus.     

Adenoviral-induced islet cell cytotoxicity is not counteracted by Bcl-2 overexpression

BACKGROUND: The ability to transfer immunoregulatory, cytoprotective, or anti-apoptotic genes into pancreatic islet cells may allow enhanced resistance against the autoimmune destruction of these cells in type 1 diabetes. We describe here an inducible transduction system for expression of the anti-apoptotic bcl-2 gene in insulin-producing cells as a potential tool for protecting against beta-cell death. MATERIALS AND METHODS: Isolated pancreatic rat islet cells or rat insulinoma (RINm5F) cells were transduced using a progesterone antagonist (RU 486) inducible adenoviral vector system, expressing the bcl-2 gene. Bcl-2 overexpression was measured by Western blot assays and flow cytometry analysis. Following exposure to cytokines or to the mitochondrial uncoupler FCCP, cell survival was determined using fluorescence and electron microscopy, and a colorimetric assay (2,3-bis[2-methoxy-4-nitro-5-sulfophenyl]- 2H-tetrazolium-5-carboxanilide [XTT]-based) for cell viability. The mitochondrial membrane potential ((m)) was assessed using the lipophilic cationic membrane potential-sensitive dye JC-1. RESULTS: The adenoviral gene transfer system induced Bcl-2 expression in more than 70% of beta-cells and the protein expression levels were successfully regulated in response to varying concentrations of progesterone antagonist RU 486. Exposure of islet cells to proinflammatory cytokines IL-1beta, TNF-alpha, and IFN-gamma, or to the mitochondrial uncoupler FCCP resulted in disruption of the mitochondrial membrane potential ((m)) and beta-cell death. Bcl-2 overexpression stabilized (m) and prevented cell death in RINm5F cells but not in islet cells. In addition, prolonged in vitro culture revealed adenoviral-induced islet cell necrosis. CONCLUSIONS: The RU 486-regulated adenoviral system can achieve an efficient control of gene transfer at relatively low doses of the adenoviral vector. However, Bcl-2 overexpression in islet cells did not prevent adenoviral- or cytokine-induced toxicity, suggesting that the specific death pathway involved in adenoviral toxicity in beta-cells may bypass the mitochondrial permeability transition event.     

Activation of the Low Molecular Weight Protein Tyrosine Phosphatase in Keratinocytes Exposed to Hyperosmotic Stress

Herein, we provide new contribution to the mechanisms involved in keratinocytes response to hyperosmotic shock showing, for the first time, the participation of Low Molecular Weight Protein Tyrosine Phosphatase (LMWPTP) activity in this event. We reported that sorbitol-induced osmotic stress mediates alterations in the phosphorylation of pivotal cytoskeletal proteins, particularly Src and cofilin. Furthermore, an increase in the expression of the phosphorylated form of LMWPTP, which was followed by an augment in its catalytic activity, was observed. Of particular importance, these responses occurred in an intracellular milieu characterized by elevated levels of reduced glutathione (GSH) and increased expression of the antioxidant enzymes glutathione peroxidase and glutathione reductase. Altogether, our results suggest that hyperosmostic stress provides a favorable cellular environment to the activation of LMWPTP, which is associated with increased expression of antioxidant enzymes, high levels of GSH and inhibition of Src kinase. Finally, the real contribution of LMWPTP in the hyperosmotic stress response of keratinocytes was demonstrated through analysis of the effects of ACP1 gene knockdown in stressed and non-stressed cells. LMWPTP knockdown attenuates the effects of sorbitol induced-stress in HaCaT cells, mainly in the status of Src kinase, Rac and STAT5 phosphorylation and activity. These results describe for the first time the participation of LMWPTP in the dynamics of cytoskeleton rearrangement during exposure of human keratinocytes to hyperosmotic shock, which may contribute to cell death.     

Principal stratification designs to estimate input data missing due to death

We consider studies of cohorts of individuals after a critical event, such as an injury, with the following characteristics. First, the studies are designed to measure "input" variables, which describe the period before the critical event, and to characterize the distribution of the input variables in the cohort. Second, the studies are designed to measure "output" variables, primarily mortality after the critical event, and to characterize the predictive (conditional) distribution of mortality given the input variables in the cohort. Such studies often possess the complication that the input data are missing for those who die shortly after the critical event because the data collection takes place after the event. Standard methods of dealing with the missing inputs, such as imputation or weighting methods based on an assumption of ignorable missingness, are known to be generally invalid when the missingness of inputs is nonignorable, that is, when the distribution of the inputs is different between those who die and those who live. To address this issue, we propose a novel design that obtains and uses information on an additional key variable-a treatment or externally controlled variable, which if set at its "effective" level, could have prevented the death of those who died. We show that the new design can be used to draw valid inferences for the marginal distribution of inputs in the entire cohort, and for the conditional distribution of mortality given the inputs, also in the entire cohort, even under nonignorable missingness. The crucial framework that we use is principal stratification based on the potential outcomes, here mortality under both levels of treatment. We also show using illustrative preliminary injury data that our approach can reveal results that are more reasonable than the results of standard methods, in relatively dramatic ways. Thus, our approach suggests that the routine collection of data on variables that could be used as possible treatments in such studies of inputs and mortality should become common.     

Applications of Survival Analysis to Aging Research

Many research questions in aging research treat time as the outcome variable. Researchers ask how much time must pass before a specific type of change or event occurs, and whether these times differ by characteristics of the subjects' background, training and treatment. Because of serious technical problems that arise when analyzing duration data, specially derived statistical methods – the methods of survival analysis – are required to answer the questions. In this paper, we introduce the conceptual framework of these new methodologies for analyzing duration data. We begin by identifying the types of research question that might appropriately treat time as an outcome. We then describe the new statistical methods for addressing such questions, provide a broad overview of their application, and identify relevant published sources containing additional background and technical information.