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Biomedical subjects

G A Whitmore

Publications and source records attributed to G A Whitmore.

7 recordsLinked to original sources
PubMed1998

Failure inference from a marker process based on a bivariate Wiener model.

Many models have been proposed that relate failure times and stochastic time-varying covariates. In some of these models, failure occurs when a particular observable marker crosses a threshold level. We are interested in the more difficult, and often more realistic, situation where failure is not related deterministically to an observable marker. In this case, joint models for marker evolution and failure tend to lead to complicated calculations for characteristics such as the marginal distribution of failure time or the joint distribution of failure time and marker value at failure. This paper presents a model based on a bivariate Wiener process in which one component represents the marker and the second, which is latent (unobservable), determines the failure time. In particular, failure occurs when the latent component crosses a threshold level. The model yields reasonably simple expressions for the characteristics mentioned above and is easy to fit to commonly occurring data that involve the marker value at the censoring time for surviving cases and the marker value and failure time for failing cases. Parametric and predictive inference are discussed, as well as model checking. An extension of the model permits the construction of a composite marker from several candidate markers that may be available. The methodology is demonstrated by a simulated example and a case application.

Biometry↗
PubMed1997

Modelling accelerated degradation data using Wiener diffusion with a time scale transformation.

Engineering degradation tests allow industry to assess the potential life span of long-life products that do not fail readily under accelerated conditions in life tests. A general statistical model is presented here for performance degradation of an item of equipment. The degradation process in the model is taken to be a Wiener diffusion process with a time scale transformation. The model incorporates Arrhenius extrapolation for high stress testing. The lifetime of an item is defined as the time until performance deteriorates to a specified failure threshold. The model can be used to predict the lifetime of an item or the extent of degradation of an item at a specified future time. Inference methods for the model parameters, based on accelerated degradation test data, are presented. The model and inference methods are illustrated with a case application involving self-regulating heating cables. The paper also discusses a number of practical issues encountered in applications.

Equipment Failure↗
PubMed1997

Analysis of overdispersed count data by mixtures of Poisson variables and Poisson processes.

Count data often show overdispersion compared to the Poisson distribution. Overdispersion is typically modeled by a random effect for the mean, based on the gamma distribution, leading to the negative binomial distribution for the count. This paper considers a larger family of mixture distributions, including the inverse Gaussian mixture distribution. It is demonstrated that it gives a significantly better fit for a data set on the frequency of epileptic seizures. The same approach can be used to generate counting processes from Poisson processes, where the rate or the time is random. A random rate corresponds to variation between patients, whereas a random time corresponds to variation within patients.

Anticonvulsants↗
PubMed1995

Estimating degradation by a Wiener diffusion process subject to measurement error.

Most materials and components degrade physically before they fail. Engineering degradation tests are designed to measure these degradation processes. Measurements in the tests reflect the inherent randomness of degradation itself as well as measurement errors created by imperfect instruments, procedures and environments. This paper describes a statistical model for measured degradation data that takes both sources of variation into account. The degradation process in the model is taken to be a Wiener diffusion process. The measurement errors are assumed to be independent normal random outcomes that are independent of the degradation process. The paper describes inference procedures for the model and discusses some practical issues that must be considered in dealing with the statistical problem. A case study is presented.

Engineering↗
PubMed1976

The mortality component of health status indexes.

The mortality component of contemporary health indexes is discussed. Since these indexes reduce to mortality indexes when only life and death states enter the analysis, they share the conceptual weaknesses of mortality indexes. Also, they do not incorporate consumption variables explicity and therefore provide no structure for relating health status and living standard. Some attention is devoted to methodological problems of assessing survival probabilities, either from survey or experimental data or from beliefs of experts or individuals who are affected directly. The final section deals with individual preferences for survival lotteries. Conceptual weaknesses of common indexes are discussed, several canonical models for survival preferences are presented, the interdependence of individual utilities is discussed, and methods for eliciting individual survival preferences are considered, along with some illustrative empirical results.

Choice Behavior↗
PubMed1975

The inverse Gaussian distribution as a model of hospital stay.

Properties of the inverse gaussian distribution are presented with comments on fitting the distribution to lentgh-of-stay data. A conceptual framework for the hospitalization process is described; it suggests that the inverse gaussian distribution has considerable potential as both a descriptive and prescriptive model of length of stay, especially in the setting of psychiatric hospitals.

Humans↗