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Research methods
Research
To condition or not condition? Analysing ‘change’ in longitudinal randomised controlled trials
  1. Cynthia J Coffman1,2,
  2. David Edelman1,3,
  3. Robert F Woolson1,2
  1. 1Health Services Research, Durham Veterans Affairs Medical Center, Durham, North Carolina, USA
  2. 2Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, North Carolina, USA
  3. 3Department of Medicine, Duke University Medical Center, Durham, North Carolina, USA
  1. Correspondence to Dr Cynthia J Coffman; Cynthia.Coffman{at}duke.edu

Abstract

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.

  • Constrained longitudinal data analysis
  • ANCOVA
  • Longitudinal RCT

This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/

https://doi.org/10.1136/bmjopen-2016-013096

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    Footnotes

    • Contributors The primary author is a senior statistician in the Biostatistics Unit of the Durham VA Health Services Research and Development group and an Associate Professor in the Department of Biostatistics and Bioinformatics at Duke University Medical Center. All listed authors have contributed to the design and preparation of the manuscript (CJC, DE and RFW). All data analyses were conducted by CJC.

    • Funding The study was funded by the US Department of Veterans Affairs Health Services Research and Development Service IIR 03-084.

    • Competing interests None declared.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Data sharing statement No additional data are available.