ISSN:0162-1459    

DOI:10.1080/01621459.1999.10474173

出版商:Taylor & Francis Group    

年代:1999

数据来源: Taylor

摘要:

Retrospectively ascertained data are common in many areas, including demography, epidemiology, and actuarial science. The main objective of this article is to study the effect of retrospective ascertainment on inference regarding recurrent events of time to pregnancy (TTP) data. For the particular TTP dataset that we consider, couples are included retrospectively based on their first pregnancy and then followed prospectively to a second pregnancy or to end of study. We consider a conditional model for the recurrent events data where the second TTP is included only if it is observed and a full model where the nonobserved second TTPs are included as suitably right censored. We furthermore consider two different approaches to modeling the dependencies of the recurrent events. A traditional frailty model, where the frailty enters the model as an unobserved covariate, and a marginal frailty model are applied. We find that efficiency is gained from including the second TTPs, with the full model being the most efficient. Further, the marginal frailty model is preferred over the traditional frailty model because estimates of covariate effects are easier to interpret and are more robust to changes in the frailty distribution.

ISSN:0162-1459    

DOI:10.1080/01621459.1999.10474174

出版商:Taylor & Francis Group    

年代:1999

数据来源: Taylor

摘要:

The polymerase chain reaction (PCR) is a procedure by which the DNA in a single cell can be made to replicate many times in a test tube. By amplifying the DNA from individual sperm cells and typing the results, estimates of male recombination fractions can be made, which are valuable for creating genetic maps and locating regions of unusually intense crossover activity on the human genome. Because PCR typing results are subject to random error, stochastic models must be constructed to obtain accurate results. In practice, to obtain enough information to accurately estimate small recombination fractions, it is necessary to combine data from several PCR experiments. Stochastic models in common use assume that PCR error rates are constant across experiments. We show by analysis of a dataset that PCR error rates can vary considerably from experiment to experiment, and that models that fail to take this heterogeneity into account can produce biased estimators. We present two new estimators and show with simulation studies that they perform better than conventional methods under realistic conditions. These estimators may be appropriate whenever PCR data from several experiments are combined.

ISSN:0162-1459    

DOI:10.1080/01621459.1999.10474175

出版商:Taylor & Francis Group    

年代:1999

数据来源: Taylor

摘要:

The primary goal of this article is to determine benchmark doses based on the ethylene glycol study, which comprises data from a developmental toxicity study in mice. Because the data involve a vector of malformation indicators, a flexible model for multivariate clustered data is required. An exponential family model is considered and pseudolikelihood-based inferential tools are proposed, hence avoiding excessive computational requirements.

ISSN:0162-1459    

DOI:10.1080/01621459.1999.10474176

出版商:Taylor & Francis Group    

年代:1999

数据来源: Taylor

摘要:

Hormones serve as regulating signals for many biological processes. In recent years, it was determined that many hormones are secreted in a pulsatile manner and that the pulsatile secretion pattern, in addition to the absolute concentration level, is important in regulating biological processes. Consequently, it is necessary to characterize the latent secretion patterns from measurements of concentration levels. The characterization is complicated by the presence of a biological circadian rhythm. When hormone concentrations are plotted over time, the resultant time series usually exhibits occasional short rises superimposed on a slowly changing baseline. This is a result of a mixture of pulsatile secretions and a circadian rhythm. In this article we present a signal extraction approach to model simultaneously a slowly changing component and a pulsatile component of a time series. A smoothing spline is used to model the baseline, and a multiprocess dynamic linear model is used to model the pulsatile component. An additive structure is assumed, and both components are estimated simultaneously using a multiprocess Kalman filter. The unknown parameters are estimated by approximate maximum likelihood. The locations and amplitudes of the pulses are also estimated as posterior means via the multiprocess Kalman filter. Bayesian confidence intervals can be constructed for the baseline. This approach is found to be robust in simulated data and effective in modeling hormone time series.

ISSN:0162-1459    

DOI:10.1080/01621459.1999.10474177

出版商:Taylor & Francis Group    

年代:1999

数据来源: Taylor

摘要:

This article presents an autoregressive random coefficient model with overdispersed negative multinomial marginal distributions for the analysis of heterogeneity and serial dependencies in multivariate longitudinal count data. The model structure consists of four components that take into account (a) individual difference effects, (b) random time effects, (c) multiple event categories, and (d) autodependencies. The last component is based on a stochastic integer-valued autoregressive process proposed by McKenzie. The model is applied to analyze count data from a panel diary study about the relationship between personality factors and emotion experiences. It is shown that there are large and stable individual personality differences in the incidence and duration of self-reported emotional experiences. Theoretical and clinical implications of this result are discussed.

ISSN:0162-1459    

DOI:10.1080/01621459.1999.10474178

出版商:Taylor & Francis Group    

年代:1999

数据来源: Taylor

摘要:

Social scientists are commonly interested in relating a latent trait (e.g., criminal tendency) to measurable individual covariates (e.g., poor parenting) to understand what defines or perhaps causes the latent trait. In this article we develop an efficient and convenient method for answering such questions. The basic model presumes that two types of variables have been measured: Response variables (possibly longitudinal) that partially determine the latent class membership, and covariates or risk factors that we wish to relate to these latent class variables. The model assumes that these observable variables are conditionally independent, given the latent class variable. We use a mixture model for the joint distribution of the observables. We apply this model to a longitudinal dataset assembled as part of the Cambridge Study of Delinquent Development to test a fundamental theory of criminal development. This theory holds that crime is committed by two distinct groups within the population: Adolescent-limited offenders and life-course-persistent offenders. As these labels suggest, the two groups are distinguished by the longevity of their offending careers. The theory also predicts that life-course-persistent offenders are disproportionately comprised of individuals born with neurological deficits and reared by caregivers without the skills and resources to effectively socialize a difficult child.

ISSN:0162-1459    

DOI:10.1080/01621459.1999.10474179

出版商:Taylor & Francis Group    

年代:1999

数据来源: Taylor

摘要:

A hierarchical Bayesian approach is proposed for variable selection and function estimation in additive nonparametric Gaussian regression models and additive nonparametric binary regression models. The prior for each component function is an integrated Wiener process resulting in a posterior mean estimate that is a cubic smoothing spline. Each of the explanatory variables is allowed to be in or out of the model, and the regression functions are estimated by model averaging. To allow variable selection and model averaging, data-based priors are used for the smoothing parameter and the slope at 0 of each component function. A two-step Markov chain Monte Carlo method is used to efficiently obtain the data-based prior and to carry out variable selection and function estimation. It is shown by simulation that significant improvements in the function estimators can be obtained over an approach that estimates all the unknown functions simultaneously. The methodology is illustrated for a binary regression using heart attack data.

ISSN:0162-1459    

DOI:10.1080/01621459.1999.10474180

出版商:Taylor & Francis Group    

年代:1999

数据来源: Taylor

ISSN:0162-1459    

DOI:10.1080/01621459.1999.10474181

出版商:Taylor & Francis Group    

年代:1999

数据来源: Taylor

ISSN:0162-1459    

DOI:10.1080/01621459.1999.10474182

出版商:Taylor & Francis Group    

年代:1999

数据来源: Taylor