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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\u2010effects 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\u2010dependent covariates can be included. Additionally, we extend the method to adjust for baseline measurement error of other time\u2010varying covariates. We apply the methodology to data from the Swiss HIV Cohort Study to address the question if a joint infection with HIV\u20101 and hepatitis C virus leads to a slower increase of CD4 lymphocyte counts over time after the start of antiretroviral therapy. 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