Biomedical subjects

David B Wolfson

Publications and source records attributed to David B Wolfson.

3 recordsLinked to original sources

PubMed2006

A formal test for the stationarity of the incidence rate using data from a prevalent cohort study with follow-up.

In a prevalent cohort study with follow-up, the incidence process is not directly observed since only the onset times of prevalent cases can be ascertained. Assessing the "stationarity" of the underlying incidence process can be important for at least three reasons, including an improvement in efficiency when estimating the survivor function. We propose, for the first time, a formal test for stationarity using data from a prevalent cohort study with follow-up. The test makes use of a characterization of stationarity, an extension of this characterization developed in this paper, and of a test for matched pairs of right censored data. We report the results from a power study assuming varying degrees of departure from the null hypothesis of stationarity. The test is also applied to data obtained as part of the Canadian Study of Health and Aging (CSHA) to verify whether the incidence rate of dementia amongst the elderly in Canada has remained constant.

Aged↗

PubMed2006

Checking stationarity of the incidence rate using prevalent cohort survival data.

When survival data are collected as part of a prevalent cohort study with follow-up, the recruited cases have already experienced their initiating event, say onset of a disease, and consequently the incidence process is only partially observed. Nevertheless, there are good reasons for interest in certain features of the underlying incidence process, for example whether or not it is stationary. Indeed, the well known relationship between incidence and prevalence, often used by epidemiologists, requires stationarity of the incidence rate for its validity. Also, the statistician can exploit stationarity of the incidence process by improving the efficiency of estimators in a prevalent cohort survival analysis. In addition, whether the incident rate is stationary is often in itself of central importance to medical and other researchers. We present here a necessary and sufficient condition for stationarity of the underlying incidence process, which uses only survival observations, possibly right censored, from a prevalent cohort study with follow-up. This leads to a simple graphical means of checking for the stationarity of the underlying incidence times by comparing the plots of two Kaplan-Meier estimates that are based on partially observed incidence times and follow-up survival data. We use our method to discuss the incidence rate of dementia in Canada between 1971 and 1991.

Age of Onset↗

PubMed2003

A Bayesian A-optimal and model robust design criterion.

Suppose that the true model underlying a set of data is one of a finite set of candidate models, and that parameter estimation for this model is of primary interest. With this goal, optimal design must depend on a loss function across all possible models. A common method that accounts for model uncertainty is to average the loss over all models; this is the basis of what is known as Läuter's criterion. We generalize Läuter's criterion and show that it can be placed in a Bayesian decision theoretic framework, by extending the definition of Bayesian A-optimality. We use this generalized A-optimality to find optimal design points in an environmental safety setting. In estimating the smallest detectable trace limit in a water contamination problem, we obtain optimal designs that are quite different from those suggested by standard A-optimality.

Bayes Theorem↗