To condition or not condition? Analysing 'change' in longitudinal randomised controlled trials.

Published online

Journal Article

OBJECTIVE: The statistical analysis for a 2-arm randomised controlled trial (RCT) with a baseline outcome followed by a few assessments at fixed follow-up times typically invokes traditional analytic methods (eg, analysis of covariance (ANCOVA), longitudinal data analysis (LDA)). 'Constrained' longitudinal data analysis (cLDA) is a well-established unconditional technique that constrains means of baseline to be equal between arms. We use an analysis of fasting lipid profiles from the Group Medical Clinics (GMC) longitudinal RCT on patients with diabetes to illustrate applications of ANCOVA, LDA and cLDA to demonstrate theoretical concepts of these methods including the impact of missing data. METHODS: For the analysis of the illustrated example, all models were fit using linear mixed models to participants with only complete data and to participants using all available data. RESULTS: With complete data (n=195), 95% CI coverage are equivalent for ANCOVA and cLDA with an estimated 11.2 mg/dL (95% CI -19.2 to -3.3; p=0.006) lower mean low-density lipoprotein (LDL) cholesterol in GMC compared with usual care. With all available data (n=233), applying the cLDA model yielded an LDL improvement of 8.9 mg/dL (95% CI -16.7 to -1.0; p=0.03) for GMC compared with usual care. The less efficient, LDA analysis yielded an LDL improvement of 7.2 mg/dL (95% CI -17.2 to 2.8; p=0.15) for GMC compared with usual care. CONCLUSIONS: Under reasonable missing data assumptions, cLDA will yield efficient treatment effect estimates and robust inferential statistics. It may be regarded as the method of choice over ANCOVA and LDA.

Full Text

Duke Authors

Cited Authors

  • Coffman, CJ; Edelman, D; Woolson, RF

Published Date

  • December 30, 2016

Published In

Volume / Issue

  • 6 / 12

Start / End Page

  • e013096 -

PubMed ID

  • 28039292

Pubmed Central ID

  • 28039292

Electronic International Standard Serial Number (EISSN)

  • 2044-6055

Digital Object Identifier (DOI)

  • 10.1136/bmjopen-2016-013096

Language

  • eng

Conference Location

  • England