Biomedical subjects

H K Iyer

Publications and source records attributed to H K Iyer.

4 recordsLinked to original sources

PubMed2001

Evaluating subject-treatment interaction when comparing two treatments.

Clinical and other studies that evaluate the effect of a treatment relative to a control often focus on estimating a mean treatment effect; however, the mean treatment effect may be misleading when the effect of the treatment varies widely across subjects. Methods are proposed to evaluate individual treatment heterogeneity (i.e., subject-treatment interaction) and its consequences in clinical experiments. The method of maximum likelihood is used to derive estimators and their properties. A bootstrap procedure that requires fewer assumptions is also presented as a small sample alternative to the maximum likelihood approach. It is shown that estimators for subject-treatment interaction are sensitive to an inestimable correlation parameter. This sensitivity is illustrated using some example data sets and using graphical plots. The practical consequence of subject-treatment interaction is that a proportion of the population may be not be responding to the treatment as indicated by the average treatment effect. Results obtained from the methods reported here can alert the practitioner to the possibility that individual treatment effects vary widely in the population and help to assess the potential consequences of this variation. Applications of the proposed procedures to clinical decision making, pharmacogenetic studies, and other contexts are discussed.

Algorithms↗

PubMed2000

Unit-treatment interaction and its practical consequences.

Most statistical characterizations of a treatment effect focus on the average effect of the treatment over an entire population. However, average effects may provide inadequate information, sometimes misleading information, when a substantial unit-treatment interaction is present in the population. It is even possible that a nonnegligible proportion of the individuals in the population experience an unfavorable treatment effect even though the treatment might appear to be beneficial when considering population averages. This paper examines the extent to which information about unit-treatment interaction can be extracted using observed data from a two-treatment completely randomized experiment. A method for utilizing the information from an available covariate is proposed. Although unit-treatment interaction is a nonidentifiable quantity, we show that mathematical bounds for it can be estimated from observed data. These bounds lead to estimated bounds for the probability of an unfavorable treatment effect. Maximum likelihood estimators of the bounds and their corresponding large-sample distributions are given. The use of the estimated bounds is illustrated in a clinical trials data example.

Analysis of Variance↗

PubMed1997

Tolerance intervals for assessing individual bioequivalence.

Evaluation of equivalence of two formulations of a drug typically entails the comparison of average bioavailabilities. Recently, however, authors have become aware that this may be insufficient to assess individual bioequivalence, that is, interchangeability of formulations on an individual basis. This paper outlines a tolerance interval procedure to assess individual bioequivalence based on a model that includes a subject by formulation interaction. We give methods for several higher-order cross-over designs along with examples.

Chemistry, Pharmaceutical↗

PubMed1997

Human visual sensitivity to plumes with a Gaussian luminance distribution: experiments to develop an empirical probability of detection model.

This paper discusses results of a research project designed to develop an empirical model that could be used as a tool to predict human visual sensitivity to plumes. The resultant probability of detection algorithm (PROBDET) allows one to estimate the probability of a plume of known size, shape and contrast being detected visually. As a basis for the algorithm, a series of laboratory experiments using a high threshold signal detection procedure and computer generated images of plumes with Gaussian luminance distributions was conducted to measure human visual sensitivity to plumes. Results of the laboratory experiments are compared with results of contrast sensitivity experiments that examined visual sensitivity to stimuli with square and sine wave luminance distributions. An example of the PROBDET algorithm is presented to demonstrate its potential usefulness for assessing how probability of detection estimates change as plume size and contrast parameters vary. Since this research was designed to build on existing knowledge, a discussion of that knowledge and how it relates to the research conducted is also presented. The focus of this discussion is on the human visual system (HVS) and on how visual sensitivity is affected by factors such as the luminance of the stimulus and the surround, the luminance distribution of the stimulus, the size of the surround, and the size and spatial frequency characteristics of the stimulus.

Adult↗