Baseline Hazard (baseline + hazard)

Distribution by Scientific Domains

Terms modified by Baseline Hazard

  • baseline hazard function

  • Selected Abstracts


    Aspects of the Armitage,Doll gamma frailty model for cancer incidence data

    ENVIRONMETRICS, Issue 3 2004
    Shizue Izumi
    Abstract Using solid cancer incidence data from atomic bomb survivors in Japan, we examine some aspects of the Armitage,Doll gamma frailty (ADF) model. We consider the following two interpretations for lack of fit of the Armitage,Doll multistage (AD) model found with cancer data: the AD type individual hazards are heterogeneous or the individual hazards increase more slowly with age than the AD type hazards. In order to examine these interpretations, we applied the ADF model and the modified AD model to radiation-related cancer incidence rates. We assessed the magnitude of frailty by a frailty parameter at the ADF model and departures from the AD-type baseline hazard by a shape increment parameter at the modified AD model. Akaike's information criterion (AIC) was used to examine the goodness of fit of the models. The modified AD model provided as good a fit as the ADF model. Our results support both interpretations and imply that these interpretations may be practically unidentifiable in univariate failure time data. Thus, results from the frailty model for univariate failure time data should be interpreted carefully. Copyright © 2004 John Wiley & Sons, Ltd. [source]


    Bayesian semiparametric estimation of discrete duration models: an application of the dirichlet process prior

    JOURNAL OF APPLIED ECONOMETRICS, Issue 1 2001
    Michele Campolieti
    This paper proposes a Bayesian estimator for a discrete time duration model which incorporates a non-parametric specification of the unobserved heterogeneity distribution, through the use of a Dirichlet process prior. This estimator offers distinct advantages over the Nonparametric Maximum Likelihood estimator of this model. First, it allows for exact finite sample inference. Second, it is easily estimated and mixed with flexible specifications of the baseline hazard. An application of the model to employment duration data from the Canadian province of New Brunswick is provided. Copyright © 2001 John Wiley & Sons, Ltd. [source]


    Some alternatives in the statistical analysis of sickness absence

    AMERICAN JOURNAL OF INDUSTRIAL MEDICINE, Issue 10 2009
    Albert Navarro
    Abstract Purpose Sickness absence (SA) is a commonly used outcome in occupational health cohort studies. Without the use of statistical techniques that take into account that SA is a recurrent event, the probability of obtaining biased estimates of the effects related to SA is very high. The objective of this article is to examine the application of marginal models, comparing them to count-based models, when the outcome of interest is SA. Methods By re-sampling the data of a reference study, 1,000 samples of 1,200 individuals were generated. In each of these samples, the coefficients of two factors were estimated by fitting various models: Poisson, Negative Binomial, standard Cox model for a first occurrence, Andersen,Gill and Prentice,Williams,Peterson. Results In general, differences among the models are observed in the estimates of variances and coefficients, as well as in their distribution. Specifically, the Poisson model estimates the greatest effect for both coefficients (IRR,=,1.17 and IRR,=,1.60), and the Prentice,Williams,Peterson the least effect (HR,=,1.01 and HR,=,1.26). Conclusions Whenever possible, the instantaneous form of analysis should be used for occurrences of a recurrent event. Collection of study data should be organized in order to permit recording of the most complete information possible, particularly regarding event occurrences. This should allow the presence of within-individual heterogeneity and/or occurrence dependency to be studied, and would further permit the most appropriate model to be chosen. When there is occurrence dependence, the choice of a model using the specific baseline hazard seems to be appropriate. Am. J. Ind. Med. 52:811,816, 2009. © 2009 Wiley-Liss, Inc. [source]


    Fitting Semiparametric Additive Hazards Models using Standard Statistical Software

    BIOMETRICAL JOURNAL, Issue 5 2007
    Douglas E. Schaubel
    Abstract The Cox proportional hazards model has become the standard in biomedical studies, particularly for settings in which the estimation covariate effects (as opposed to prediction) is the primary objective. In spite of the obvious flexibility of this approach and its wide applicability, the model is not usually chosen for its fit to the data, but by convention and for reasons of convenience. It is quite possible that the covariates add to, rather than multiply the baseline hazard, making an additive hazards model a more suitable choice. Typically, proportionality is assumed, with the potential for additive covariate effects not evaluated or even seriously considered. Contributing to this phenomenon is the fact that many popular software packages (e.g., SAS, S-PLUS/R) have standard procedures to fit the Cox model (e.g., proc phreg, coxph), but as of yet no analogous procedures to fit its additive analog, the Lin and Ying (1994) semiparametric additive hazards model. In this article, we establish the connections between the Lin and Ying (1994) model and both Cox and least squares regression. We demonstrate how SAS's phreg and reg procedures may be used to fit the additive hazards model, after some straightforward data manipulations. We then apply the additive hazards model to examine the relationship between Model for End-stage Liver Disease (MELD) score and mortality among patients wait-listed for liver transplantation. (© 2007 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source]


    A Version of the EM Algorithm for Proportional Hazard Model with Random Effects

    BIOMETRICAL JOURNAL, Issue 6 2005
    José Cortiñas Abrahantes
    Abstract Proportional hazard models with multivariate random effects (frailties) acting multiplicatively on the baseline hazard have recently become a topic of an intensive research. One of the main practical problems related to the models is the estimation of parameters. To this aim, several approaches based on the EM algorithm have been proposed. The major difference between these approaches is the method of the computation of conditional expectations required at the E-step. In this paper an alternative implementation of the EM algorithm is proposed, in which the expected values are computed with the use of the Laplace approximation. The method is computationally less demanding than the approaches developed previously. Its performance is assessed based on a simulation study and compared to a non-EM based estimation approach proposed by Ripatti and Palmgren (2000). (© 2005 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source]


    Bayesian Case Influence Diagnostics for Survival Models

    BIOMETRICS, Issue 1 2009
    Hyunsoon Cho
    Summary We propose Bayesian case influence diagnostics for complex survival models. We develop case deletion influence diagnostics for both the joint and marginal posterior distributions based on the Kullback,Leibler divergence (K,L divergence). We present a simplified expression for computing the K,L divergence between the posterior with the full data and the posterior based on single case deletion, as well as investigate its relationships to the conditional predictive ordinate. All the computations for the proposed diagnostic measures can be easily done using Markov chain Monte Carlo samples from the full data posterior distribution. We consider the Cox model with a gamma process prior on the cumulative baseline hazard. We also present a theoretical relationship between our case-deletion diagnostics and diagnostics based on Cox's partial likelihood. A simulated data example and two real data examples are given to demonstrate the methodology. [source]


    Joint Analysis of Time-to-Event and Multiple Binary Indicators of Latent Classes

    BIOMETRICS, Issue 1 2004
    Klaus Larsen
    Summary. Multiple categorical variables are commonly used in medical and epidemiological research to measure specific aspects of human health and functioning. To analyze such data, models have been developed considering these categorical variables as imperfect indicators of an individual's "true" status of health or functioning. In this article, the latent class regression model is used to model the relationship between covariates, a latent class variable (the unobserved status of health or functioning), and the observed indicators (e.g., variables from a questionnaire). The Cox model is extended to encompass a latent class variable as predictor of time-to-event, while using information about latent class membership available from multiple categorical indicators. The expectation-maximization (EM) algorithm is employed to obtain maximum likelihood estimates, and standard errors are calculated based on the profile likelihood, treating the nonparametric baseline hazard as a nuisance parameter. A sampling-based method for model checking is proposed. It allows for graphical investigation of the assumption of proportional hazards across latent classes. It may also be used for checking other model assumptions, such as no additional effect of the observed indicators given latent class. The usefulness of the model framework and the proposed techniques are illustrated in an analysis of data from the Women's Health and Aging Study concerning the effect of severe mobility disability on time-to-death for elderly women. [source]


    Mortgage Terminations, Heterogeneity and the Exercise of Mortgage Options

    ECONOMETRICA, Issue 2 2000
    Yongheng Deng
    As applied to the behavior of homeowners with mortgages, option theory predicts that mortgage prepayment or default will be exercised if the call or put option is ,in the money' by some specific amount. Our analysis: tests the extent to which the option approach can explain default and prepayment behavior; evaluates the practical importance of modeling both options simultaneously; and models the unobserved heterogeneity of borrowers in the home mortgage market. The paper presents a unified model of the competing risks of mortgage termination by prepayment and default, considering the two hazards as dependent competing risks that are estimated jointly. It also accounts for the unobserved heterogeneity among borrowers, and estimates the unobserved heterogeneity simultaneously with the parameters and baseline hazards associated with prepayment and default functions. Our results show that the option model, in its most straightforward version, does a good job of explaining default and prepayment, but it is not enough by itself. The simultaneity of the options is very important empirically in explaining behavior. The results also show that there exists significant heterogeneity among mortgage borrowers. Ignoring this heterogeneity results in serious errors in estimating the prepayment behavior of homeowners. [source]