Accounting for baseline differences and measurement error in the analysis of change over time

Stat Med. 2014 Jan 15;33(1):2-16. doi: 10.1002/sim.5910. Epub 2013 Jul 30.


If change over time is compared in several groups, it is important to take into account baseline values so that the comparison is carried out under the same preconditions. As the observed baseline measurements are distorted by measurement error, it may not be sufficient to include them as covariate. By fitting a longitudinal mixed-effects model to all data including the baseline observations and subsequently calculating the expected change conditional on the underlying baseline value, a solution to this problem has been provided recently so that groups with the same baseline characteristics can be compared. In this article, we present an extended approach where a broader set of models can be used. Specifically, it is possible to include any desired set of interactions between the time variable and the other covariates, and also, time-dependent covariates can be included. Additionally, we extend the method to adjust for baseline measurement error of other time-varying covariates. We apply the methodology to data from the Swiss HIV Cohort Study to address the question if a joint infection with HIV-1 and hepatitis C virus leads to a slower increase of CD4 lymphocyte counts over time after the start of antiretroviral therapy.

Keywords: BIC; longitudinal mixed-effects models; measurement error; underlying baseline measurement.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Antiretroviral Therapy, Highly Active / standards
  • CD4 Lymphocyte Count
  • Cohort Studies*
  • Data Interpretation, Statistical*
  • HIV Infections / complications
  • HIV Infections / drug therapy
  • HIV Infections / virology
  • HIV-1 / growth & development
  • Hepacivirus / growth & development
  • Hepatitis C, Chronic / complications
  • Hepatitis C, Chronic / virology
  • Humans
  • Longitudinal Studies*
  • Models, Statistical*
  • Switzerland
  • Time Factors