Biomedical subjects

G L Gadbury

Publications and source records attributed to G L Gadbury.

3 recordsLinked to original sources

PubMed2003

Modern statistical methods for handling missing repeated measurements in obesity trial data: beyond LOCF.

This paper brings together some modern statistical methods to address the problem of missing data in obesity trials with repeated measurements. Such missing data occur when subjects miss one or more follow-up visits, or drop out early from an obesity trial. A common approach to dealing with missing data because of dropout is 'last observation carried forward' (LOCF). This method, although intuitively appealing, requires restrictive assumptions to produce valid statistical conclusions. We review the need for obesity trials, the assumptions that must be made regarding missing data in such trials, and some modern statistical methods for analysing data containing missing repeated measurements. These modern methods have fewer limitations and less restrictive assumptions than required for LOCF. Moreover, their recent introduction into current releases of statistical software and textbooks makes them more readily available to the applied data analyses.

Clinical Trials as Topic↗

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↗