Format

Send to

Choose Destination
Front Public Health. 2013 Aug 23;1:29. doi: 10.3389/fpubh.2013.00029. eCollection 2013.

Assessing the Relationship between the Baseline Value of a Continuous Variable and Subsequent Change Over Time.

Author information

1
University Hospital Center, Institute of Social and Preventive Medicine (IUMSP), University of Lausanne , Lausanne , Switzerland ; Department of Epidemiology, Biostatistics, and Occupational Health, McGill University , Montreal, QC , Canada.
2
Department of Epidemiology, Biostatistics, and Occupational Health, McGill University , Montreal, QC , Canada ; McGill University Health Center Research Institute , Montreal, QC , Canada ; Public Health Institute of Quebec , Montreal, QC , Canada.
3
Department of Epidemiology, Biostatistics, and Occupational Health, McGill University , Montreal, QC , Canada.

Abstract

Analyzing the relationship between the baseline value and subsequent change of a continuous variable is a frequent matter of inquiry in cohort studies. These analyses are surprisingly complex, particularly if only two waves of data are available. It is unclear for non-biostatisticians where the complexity of this analysis lies and which statistical method is adequate. With the help of simulated longitudinal data of body mass index in children, we review statistical methods for the analysis of the association between the baseline value and subsequent change, assuming linear growth with time. Key issues in such analyses are mathematical coupling, measurement error, variability of change between individuals, and regression to the mean. Ideally, it is better to rely on multiple repeated measurements at different times and a linear random effects model is a standard approach if more than two waves of data are available. If only two waves of data are available, our simulations show that Blomqvist's method - which consists in adjusting for measurement error variance the estimated regression coefficient of observed change on baseline value - provides accurate estimates. The adequacy of the methods to assess the relationship between the baseline value and subsequent change depends on the number of data waves, the availability of information on measurement error, and the variability of change between individuals.

KEYWORDS:

baseline value; change; mathematical coupling; measurement error; regression to the mean

Supplemental Content

Full text links

Icon for Frontiers Media SA Icon for PubMed Central
Loading ...
Support Center