Biomedical subjects

A M Furey

Publications and source records attributed to A M Furey.

3 recordsLinked to original sources

PubMed1994

GTRACK: a PC program for computing Goldstein's growth constancy index and an alternative measure of tracking.

This paper reviews Goldstein's 'growth constancy index,' Xi, a measure of tracking which can be used to determine whether or not individuals maintain their relative positions in the distribution of a given measurement as that distribution changes over time. We suggest that Xi is an appropriate measure of tracking when the (standardized) measurements arise in the context of a Model I ANOVA, but that the intraclass correlation coefficient, rI, may be preferred when a Model II ANOVA is applicable. We also describe--and make available--a PC program which allows the user to choose between Model I and Model II, and computes the appropriate tracking index and confidence intervals for the corresponding parameter.

Algorithms↗

PubMed1993

PC program extending the two-stage polynomial growth curve model to allow missing data.

A stand-alone, menu-driven PC program, written in GAUSS386i, extending the analysis of one-sample longitudinal data sets satisfying the two-stage polynomial growth curve model (Ten Have et al., Am J Hum Biol, 3 (1991) 269-279) to allow missing data is described, illustrated and made available to interested readers. The method and the program are illustrated using data previously analyzed by the authors (Schneiderman and Kowalski, Am J Phys Anthropol, 67 (1985) 323-333) but with several randomly chosen data points discarded and treated as missing.

Animals↗

PubMed1993

A PC program to aid in the choice of the design matrix in multiple linear regression.

A PC program, DESIGN, which can be used to evaluate and compare alternative choices of the design matrix, X, in the general linear model y = X beta + epsilon is described, illustrated and made available to interested readers. Given X, the program (1) computes various measures of the 'stability' of X and X'X and (2) determines the precisions of estimates of the model parameters, beta, and of predicted values, ŷ, at the given design points. Examples focusing on polynomial regression are given.

Data Interpretation, Statistical↗