Parametric modelling of prevalent cohort data with uncertainty in the measurement of the initial onset date.

In prevalent cohort studies with follow-up, if disease duration is the focus, the date of onset must be obtained retrospectively. For some diseases, such as Alzheimer's disease, the very notion of a date of onset is unclear, and it can be assumed that the reported date of onset acts only as a proxy for the unknown true date of onset. When adjusting for onset dates reported with error, the features of left-truncation and potential right-censoring of the failure times must be modeled appropriately. Under the assumptions of a classical measurement error model for the onset times and an underlying parametric failure time model, we propose a maximum likelihood estimator for the failure time distribution parameters which requires only the observed backward recurrence times. Costly and time-consuming follow-up may therefore be avoided. We validate the maximum likelihood estimator on simulated datasets under varying parameter combinations and apply the proposed method to the Canadian Study of Health and Aging dataset.

A reevaluation of the duration of survival after the onset of dementia.

BACKGROUND: Dementia shortens life expectancy; estimates of median survival after the onset of dementia have ranged from 5 to 9.3 years. Previous studies of people with existing dementia, however, may have underestimated the deleterious effects of dementia on survival by failing to consider persons with rapidly progressive illness who died before they could be included in a study (referred to as length bias). METHODS: We used data from the Canadian Study of Health and Aging to estimate survival from the onset of symptoms of dementia; the estimate was adjusted for length bias. A random sample of 10,263 subjects 65 years old or older from throughout Canada was screened for cognitive impairment. For those with dementia, we ascertained the date of onset and conducted follow-up for five years. RESULTS: We analyzed data on 821 subjects, of whom 396 had probable Alzheimer's disease, 252 had possible Alzheimer's disease, and 173 had vascular dementia. For the group as a whole, the unadjusted median survival was 6.6 years (95 percent confidence interval, 6.2 to 7.1). After adjustment for length bias, the estimated median survival was 3.3 years (95 percent confidence interval, 2.7 to 4.0). The median survival was 3.1 years for subjects with probable Alzheimer's disease, 3.5 years for subjects with possible Alzheimer's disease, and 3.3 years for subjects with vascular dementia. CONCLUSIONS: Median survival after the onset of dementia is much shorter than has previously been estimated.

Taking account of between-patient variability when modeling decline in Alzheimer's disease.

The pattern of deterioration in patients with Alzheimer's disease is highly variable within a given population. With recent speculation that the apolipoprotein E allele may influence rate of decline and claims that certain drugs may slow the course of the disease, there is a compelling need for sound statistical methodology to address these questions. Current statistical methods for describing decline do not adequately take into account between-patient variability and possible floor and/or ceiling effects in the scale measuring decline, and they fail to allow for uncertainty in disease onset. In this paper, the authors analyze longitudinal Mini-Mental State Examination scores from two groups of Alzheimer's disease subjects from Palo Alto, California, and Minneapolis, Minnesota, in 1981-1993 and 1986-1988, respectively. A Bayesian hierarchical model is introduced as an elegant means of simultaneously overcoming all of the difficulties referred to above.

Change-point analysis of neuron spike train data.

In many medical experiments, data are collected across time, over a number of similar trials, or over a number of experimental units. As is the case of neuron spike train studies, these data may be in the form of counts of events per unit of time. These counts may be correlated within each trial. It is often of interest to know if the introduction of an intervention, such as the application of a stimulus, affects the distribution of the counts over the course of the experiment. In such investigations, each trial generates a sequence of data that may or may not contain a change in distribution at some point in time. Each sequence of integer counts can be viewed as arising from a Poisson process and are therefore independently distributed or as an integer-valued time series that allows for correlations between these counts. The main aim of this paper is to show how the ensemble of sample paths may be used to make inference about the distribution of the instantaneous times of change in a given population. This will be accomplished using a Bayesian hierarchical model for these change-points in time. A bonus of these models is they also allow for inference about the probability of a change in each unit and the magnitude of the effects, if any. The use of such change-point models on integer-valued time series is illustrated on neuron spike train data, although the methods can be applied to other situations where integer-valued processes arise.

The latent period of multiple sclerosis: a critical review.

It is a widely held belief that multiple sclerosis is a disease with a long latent period that is preceded by heightened susceptibility before adolescence. There has, however, been little research focused on either the estimation of the latent period or determination of the susceptibility period. In this article, we present a critical assessment of the relevant literature on migrant studies, cluster studies, the Faroe Islands "epidemic," sibling study, and novel statistical approaches as they pertain to the pre-onset natural history of multiple sclerosis. We also discuss the roles of the latent and susceptibility periods in the design, analysis, and interpretation of epidemiologic studies.

Multiple sclerosis: a comparison of the latent periods of different populations.

While some research in multiple sclerosis has concentrated on the latent period of this disease in single population groups, none has attempted to carry out significance tests to compare the distributions of the latent periods of two distinct groups. Two comparisons are made here, between the populations attending the Multiple Sclerosis Clinic of the Montreal Neurological Institute and Hôpital Neurologique et Neuro-Chirurgical Pierre-Wertheimer, Lyon. We also compared the latent period separately in males and females. There were no significant differences. The significance tests and power studies were based on a nonparametric model that allows for uncertainty in the ages of disease initiation as well as in the ages of onset.

Is multiple sclerosis an infectious disease? Inference about an input process based on the output.

The output process of an infinite-server queue with a Poisson process input is observed starting at time 0 with an empty queue. It is assumed that the service time distribution is known. This article discusses statistical inference about the input intensity. A controversial issue in the study of multiple sclerosis is addressed as a motivation for the model and methods developed.

On the estimation of the distribution of the latent period of multiple sclerosis.

A widely accepted hypothesis is that the initiation of multiple sclerosis occurs many years before the clinical onset of disease. Thus far, attempts to describe the characteristics of the latent period have depended entirely on ad hoc methods relying heavily on the results of migrant studies. Here, by introducing a stochastic model to describe the initiation-onset process, the distribution of the latent period is estimated, and several important consequences for multiple sclerosis discussed.