Abstract not found

In a long term clinical trial to evaluate a new treatment, quite often each study subject may experience a number of ‘failures ’ that correspond to repeated occurrences of the same type of event or events of entirely different natures during his/her follow-up period. To obtain efficient inference procedures for the therapeutic effect over time, it is desirable to utilize those multiple event times in the analysis. In this article, we review some useful procedures for analysing different kinds of multivariate failure time data. Specifically, we discuss the two-sample problems and the general regression problems with various survival models. We also give some recommendations of appropriate procedures for each type of multiple event data structure for

The present paper deals with the estimation of a frailty model of multivariate failure times. The failure times are modeled by an Accelerated Failure Time Model including observed covariates and an unobservable frailty component. The frailty is assumed random and differs across elementary units, but is constant across the spells of a unit or a group. We develop an estimator (of the regression parameters) that combines the GEE approach (Liang and Zeger, 1986) with the Buckley-James estimator for censored data. This estimator is robust against violations of the correlation structure and the distributional assumptions. Some simulation studies are conducted in order to study the empirical performance of the estimator. Finally, the methods are applied to data of repeated appearances of malign ventricular arrhythmias at patients with implanted defibrillator.

Imperfect repair models are a class of stochastic models that deal with recurrent phenomena. This article focuses on the Block, Borges, and Savits (1985) age-dependent minimal repair model (the BBS model) in which a system that fails at time t undergoes one of two types of repair: with probability p(t), a perfect repair is performed, or with probability 1; p(t), a minimal repair is performed. The goodness-of- t problem of interest concerns the initial distribution of the failure ages. In particular, interest is on testing the null hypothesis that the hazard rate function of the time-to-first event occurrence, (), is equal to a prespeci ed hazard rate function 0 (). This paper extends the class of hazard-based smooth goodness-of-fit tests introduced in Peña (1998a) to the case where data accrual is from a BBS model. The goodness-of-fit tests are score tests derived by reformulating Neyman's idea of smooth tests in terms of hazard functions. Omnibus as well as directional tests are developed and simulation results are presented to illustrate the sensitivities of the proposed tests for certain types of alternatives.

This paper examines the value of sell-side analysts to firms by evaluating the long-term consequences of losing all analyst coverage for periods of at least one year. Our findings are consistent with the hypothesis that analysts add value to a firm by maintaining investor recognition for that firm‟s stock. In particular, we find that, in the years after the loss of coverage, sample firms experience a decrease in trading volume, stock liquidity, and institutional ownership, while their operating prospects are similar to their covered peers. Analysis of delisting rates indicates that sample firms are significantly more likely to delist than their covered peers, which are control firms matched on the propensity for bankruptcy and the potential for generating brokerage revenue. We find similar results when we examine a subsample of firms that lose all analyst coverage following an exogenous shock. Our results provide insight into the reasons why firms place so much

In this manuscript, we study the nonparametric k-sample test problem with panel count data. The asymptotic normality of a smooth functional of the nonparametric maximum pseudo-likelihood estimator (Wellner and Zhang, 2000) is established under some mild conditions. We construct a class of easy-to-implement nonparametric tests for comparing mean functions of k populations based on this asymptotic normality. We conduct various simulations to validate and compare the tests. The simulations show that the tests perform quite well and generally have a good power to detect difference of the mean functions. The method is illustrated with two real data examples.

Statistical methodology is presented for the regression analysis of multiple events in the presence of random effects and measurement error. Omitted covariates are modeled as random effects. Our approach to parameter estimation and significance testing is to start with a naive model of semi-parametric Poisson process regression, and then to adjust for random effects and any possible covariate measurement error. We illustrate the techniques with data from a randomized clinical trial for the prevention of recurrent skin tumors. KEY WORDS: Consistency; Cox model; Estimating equations; Frailty; Measurement error; Omitted covariates; Point process; Poisson regression; Proportional intensities; Robust estimator; Selenium; Skin cancer; Specification analysis; Unobserved heterogeneity; Validation data. 1.

Procedures for estimating the parameters of the general class of semiparametric models for recurrent events proposed by Peña and Hollander (2004) are developed. This class of models incorporates an effective age function which encodes the changes that occur after each event occurrence such as the impact of an intervention, it allows for the modeling of the impact of accumulating event occurrences on the unit, it admits a link function in which the effect of possibly time-dependent covariates are incorporated, and it allows the incorporation of unobservable frailty components which induce dependencies among the inter-event times for each unit. The estimation procedures are semiparametric in that a baseline hazard function is nonparametrically specified. The sampling distribution properties of the estimators are examined through a simulation study, and the consequences of mis-specifying the model are analyzed. The results indicate that the flexibility of this general class of models provides a safeguard for analyzing recurrent event data, even data possibly arising from a frailty-less mechanism. The estimation procedures are applied to real data sets arising in the biomedical and public health settings, as well as from reliability and engineering situations. In particular, the procedures are applied to a data set pertaining to times to recurrence of bladder cancer and the results of the analysis are compared to those obtained using three methods of analyzing recurrent event data.

Recurrent event analysis has been a topic that has generated much attention in the last few years. The analysis of recurrent event data can be a complex task, with many different relationships and dependencies that need to be specified. One aspect of recurrent events that is difficult to specify, and even more complex to justify, is the relationship a subject’s rate of failure will have as events continue to occur. Marginal modeling is a method that estimates parameters by considering the marginal distributions of the data. In the thinking that the dependence between the events is not an interesting aspect of the analysis, marginal modeling ignores the dependence on event number for estimation of parameters and corrects for this dependence in the variance. The fundamental issues of marginal modeling in recurrent event analysis will be discussed, specifically when used in Cox proportional hazards regression. 1

This research is concerned with developing methods for summarizing the fertility experiences of a cohort of women. A general model for fertility evaluation based on survival techniques is presented. The proposed piece-wise survival function has a multiplicative exponential hazard rate whereby the relationship between birth predictor covariates ·e and the reproductive experience of the women under study can be examined. Here the event of interest is a live birth. The first approach to maximum likelihood estimation of the regression coefficients is through construction of the full likelihood function. Race by parity by age by calendar year specific U.S. birth rates (National Center for Health Statistics, 1976) estimate the underlying fertility hazard rate for a woman in the study cohort with similar characteristics during the same year interval. The crucial assumption of conditional independence of a woman's yearly contribution