PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 8672710

Analyzing bivariate repeated measures for discrete and continuous outcome variables.

J Rochon
View original source ↗Export metadata ↓

Abstract

A considerable body of literature has arisen over the past 15 years for analyzing univariate repeated measures data. However, it is rare in applied biomedical research for interest to be restricted to a single outcome measure. In this paper, we consider the case of bivariate repeated measures. We apply a generalized estimating equations (GEE) approach to relate each set of repeated measures to important explanatory variables. We then invoke the seemingly unrelated regression paradigm to combine these GEE models into an overall analysis framework. This approach provides a great deal of flexibility in modeling the relationships to fixed and time-dependent covariates for each set of outcome variables. Estimation and hypothesis testing issues are described and the methodology is illustrated with an example.

Explore related subjects

Analysis of VarianceAnti-Inflammatory Agents, Non-SteroidalBiometryCrohn DiseaseData Interpretation, StatisticalHumansLinear ModelsMethotrexateModels, StatisticalMulticenter Studies as TopicRandomized Controlled Trials as TopicRegression AnalysisJournal Article

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

J Rochon. 1996. Analyzing bivariate repeated measures for discrete and continuous outcome variables.. https://pubmed.ncbi.nlm.nih.gov/8672710/

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2014

Molecular heterochrony and the evolution of sociality in bumblebees (Bombus terrestris).

Sibling care is a hallmark of social insects, but its evolution remains challenging to explain at the molecular level. The hypothesis that sibling care evolved from ancestral maternal care in primitively eusocial insects has been elaborated to involve heterochronic changes in gene expression. This elaboration leads to the prediction that workers in these species will show patterns of gene expression more similar to foundress queens, who express maternal care behaviour, than to established queens engaged solely in reproductive behaviour. We tested this idea in bumblebees (Bombus terrestris) using a microarray platform with approximately 4500 genes. Unlike the wasp Polistes metricus, in which support for the above prediction has been obtained, we found that patterns of brain gene expression in foundress and queen bumblebees were more similar to each other than to workers. Comparisons of differentially expressed genes derived from this study and gene lists from microarray studies in Polistes and the honeybee Apis mellifera yielded a shared set of genes involved in the regulation of related social behaviours across independent eusocial lineages. Together, these results suggest that multiple independent evolutions of eusociality in the insects might have involved different evolutionary routes, but nevertheless involved some similarities at the molecular level.

Analysis of Variance↗
PubMed2007

Confidence intervals for the standardized effect arising in the comparison of two normal populations.

Confidence intervals for a standardized effect are derived after stabilizing the variance of the Welch t-statistic. Simulation studies demonstrate the viability of the resulting intervals for a wide range of parameter values and sample sizes as small as five. The methodology is extended to the combination of results from several studies, so as to obtain a confidence interval for a representative standardized effect for all the studies. The methods are illustrated on a recent meta-analytic study of systolic blood pressure reduction during a weight reducing regime, as well as the classical Mumford data on psychological intervention and hospital length of stay.

Analysis of Variance↗