Modelling blood pressure as a continuous outcome variable in a co-twin control study
- J T Quirka,
- S Bergb,
- V M Chinchillic,
- B Johanssonb,
- G E McClearnd,
- G P Voglerd
- aDepartment of Cancer Prevention, Epidemiology, and Biostatistics, Roswell Park Cancer Institute, A-334 Carlton House, Elm and Carlton Streets, Buffalo, NY, USA 14263, USA, bUniversity College of Health Sciences, Institute of Gerontology, Jönköping, Sweden, cDepartment of Health Evaluation Sciences, College of Medicine, The Pennsylvania State University, USA, dCenter for Developmental and Health Genetics, College of Health and Human Development, The Pennsylvania State University
- Dr Quirk (jeff.quirk{at}roswellpark.org)
- Accepted 24 April 2001
The co-twin control study is a popular research design that is used in many research disciplines, including epidemiology, medicine, and the behavioural sciences. In co-twin control studies of monozygotic (MZ) exposure discordant twin pairs, the appropriate statistical tests used to analyse data from continuous outcome variables are the parametric paired sample t test or the non-parametric Wilcoxon signed rank test. Results from these tests enable researchers to discover if an independent variable (the exposure) is significantly associated with the dependent variable (continuous outcome measure) while controlling for genotype, age, and sex. To date, little work has been done on modelling continuous outcome variables in co-twin control studies.
The general linear mixed model (GLMM) can be used to analyse several types of data, including repeated measurements, longitudinal, spatial, multivariate, and clustered data. As co-twin control studies yield clustered data (that is, each twin pair comprises a cluster and the members of the cluster are correlated), the GLMM provides a useful statistical approach for the regression analysis of data from co-twin control studies with continuous outcome measures. The GLMM incorporates fixed and/or random effects parameters, and assumes that a continuous outcome variable is linearly related to a set of predictor variables while allowing for possible correlation between observations (that …
