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Risks Data (risk + data)

Distribution by Scientific Domains

Mathematics and Statistics	87%

Selected Abstracts

Nonparametric Association Analysis of Exchangeable Clustered Competing Risks Data

BIOMETRICS, Issue 2 2009
Yu Cheng
Summary The work is motivated by the Cache County Study of Aging, a population-based study in Utah, in which sibship associations in dementia onset are of interest. Complications arise because only a fraction of the population ever develops dementia, with the majority dying without dementia. The application of standard dependence analyses for independently right-censored data may not be appropriate with such multivariate competing risks data, where death may violate the independent censoring assumption. Nonparametric estimators of the bivariate cumulative hazard function and the bivariate cumulative incidence function are adapted from the simple nonexchangeable bivariate setup to exchangeable clustered data, as needed with the large sibships in the Cache County Study. Time-dependent association measures are evaluated using these estimators. Large sample inferences are studied rigorously using empirical process techniques. The practical utility of the methodology is demonstrated with realistic samples both via simulations and via an application to the Cache County Study, where dementia onset clustering among siblings varies strongly by age. [source]

Method for moderation: measuring lifetime risk of alcohol-attributable mortality as a basis for drinking guidelines

INTERNATIONAL JOURNAL OF METHODS IN PSYCHIATRIC RESEARCH, Issue 3 2008
Jürgen Rehm
Abstract The objective of this paper was to determine separately the lifetime risk of drinking alcohol for chronic disease and acute injury outcomes as a basis for setting general population drinking guidelines for Australia. Relative risk data for different levels of average consumption of alcohol were combined with age, sex, and disease-specific risks of dying from an alcohol-attributable chronic disease. For injury, combinations of the number of drinks per occasion and frequency of drinking occasions were combined to model lifetime risk of death for different drinking pattern scenarios. A lifetime risk of injury death of 1 in 100 is reached for consumption levels of about three drinks daily per week for women, and three drinks five times a week for men. For chronic disease death, lifetime risk increases by about 10% with each 10-gram (one drink) increase in daily average alcohol consumption, although risks are higher for women than men, particularly at higher average consumption levels. Lifetime risks for injury and chronic disease combine to overall risk of alcohol-attributable mortality. In terms of guidelines, if a lifetime risk standard of 1 in 100 is set, then the implications of the analysis presented here are that both men and women should not exceed a volume of two drinks a day for chronic disease mortality, and for occasional drinking three or four drinks seem tolerable. Copyright © 2008 John Wiley & Sons, Ltd. [source]

Regression analysis based on semicompeting risks data

JOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES B (STATISTICAL METHODOLOGY), Issue 1 2008
Jin-Jian Hsieh
Summary., Semicompeting risks data are commonly seen in biomedical applications in which a terminal event censors a non-terminal event. Possible dependent censoring complicates statistical analysis. We consider regression analysis based on a non-terminal event, say disease progression, which is subject to censoring by death. The methodology proposed is developed for discrete covariates under two types of assumption. First, separate copula models are assumed for each covariate group and then a flexible regression model is imposed on the progression time which is of major interest. Model checking procedures are also proposed to help to choose a best-fitted model. Under a two-sample setting, Lin and co-workers proposed a competing method which requires an additional marginal assumption on the terminal event and implicitly assumes that the dependence structures in the two groups are the same. Using simulations, we compare the two approaches on the basis of their finite sample performances and robustness properties under model misspecification. The method proposed is applied to a bone marrow transplant data set. [source]

Robust Joint Modeling of Longitudinal Measurements and Competing Risks Failure Time Data

BIOMETRICAL JOURNAL, Issue 1 2009
Ning Li
Abstract Existing methods for joint modeling of longitudinal measurements and survival data can be highly influenced by outliers in the longitudinal outcome. We propose a joint model for analysis of longitudinal measurements and competing risks failure time data which is robust in the presence of outlying longitudinal observations during follow-up. Our model consists of a linear mixed effects sub-model for the longitudinal outcome and a proportional cause-specific hazards frailty sub-model for the competing risks data, linked together by latent random effects. Instead of the usual normality assumption for measurement errors in the linear mixed effects sub-model, we adopt a t -distribution which has a longer tail and thus is more robust to outliers. We derive an EM algorithm for the maximum likelihood estimates of the parameters and estimate their standard errors using a profile likelihood method. The proposed method is evaluated by simulation studies and is applied to a scleroderma lung study (© 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source]

Nonparametric Association Analysis of Exchangeable Clustered Competing Risks Data

On Assessing Surrogacy in a Single Trial Setting Using a Semicompeting Risks Paradigm

BIOMETRICS, Issue 2 2009
Debashis Ghosh
Summary There has been a recent emphasis on the identification of biomarkers and other biologic measures that may be potentially used as surrogate endpoints in clinical trials. We focus on the setting of data from a single clinical trial. In this article, we consider a framework in which the surrogate must occur before the true endpoint. This suggests viewing the surrogate and true endpoints as semicompeting risks data; this approach is new to the literature on surrogate endpoints and leads to an asymmetrical treatment of the surrogate and true endpoints. However, such a data structure also conceptually complicates many of the previously considered measures of surrogacy in the literature. We propose novel estimation and inferential procedures for the relative effect and adjusted association quantities proposed by Buyse and Molenberghs (1998, Biometrics54, 1014,1029). The proposed methodology is illustrated with application to simulated data, as well as to data from a leukemia study. [source]

Estimation of Competing Risks with General Missing Pattern in Failure Types

BIOMETRICS, Issue 4 2003
Anup Dewanji
Summary. In competing risks data, missing failure types (causes) is a very common phenomenon. In this work, we consider a general missing pattern in which, if a failure type is not observed, one observes a set of possible types containing the true type, along with the failure time. We first consider maximum likelihood estimation with missing-at-random assumption via the expectation maximization (EM) algorithm. We then propose a Nelson-Aalen type estimator for situations when certain information on the conditional probability of the true type given a set of possible failure types is available from the experimentalists. This is based on a least-squares type method using the relationships between hazards for different types and hazards for different combinations of missing types. We conduct a simulation study to investigate the performance of this method, which indicates that bias may be small, even for high proportion of missing data, for sufficiently large number of observations. The estimates are somewhat sensitive to misspecification of the conditional probabilities of the true types when the missing proportion is high. We also consider an example from an animal experiment to illustrate our methodology. [source]