Abstract
BackgroundThere has been some debate in the literature as to whether baseline values of a measurement of interest at treatment initiation should be treated as an outcome variable as part of a model for longitudinal change or instead used as a predictive variable with respect to the response to treatment. We develop a new approach that involves a combined statistical model for all pre- and post-treatment observations of the biomarker of interest, in which the characteristics of response to treatment are treated as a function of the ‘true’ value of the biomarker at treatment initiation.MethodsThe modelling strategy developed is applied to a dataset of CD4 counts from patients in the UK Register of HIV Seroconverters (UKR) cohort who initiated highly active antiretroviral therapy (HAART). The post-HAART recovery in CD4 counts for each individual is modelled as following an asymptotic curve in which the speed of response to treatment and long-term maximum are functions of the ‘true’ underlying CD4 count at initiation of HAART and the time elapsed since seroconversion. Following previous research in this field, the models developed incorporate non-stationary stochastic process components, and the possibility of between-patient differences in variability over time was also considered.ResultsA variety of novel models were successfully fitted to the UKR dataset. These provide reinforcing evidence for findings that have previously been reported in the literature, in particular that there is a strong positive relationship between CD4 count at initiation of HAART and the long-term maximum in each patient, but also reveal potentially important features of the data that would not have been easily identified by other methods of analysis.ConclusionOur proposed methodology provides a unified framework for the analysis of pre- and post-treatment longitudinal biomarker data that will be useful for epidemiological investigations and simulations in this context. The approach developed allows use of all relevant data from observational cohorts in which many patients are missing pre-treatment measurements and in which the timing and number of observations vary widely between patients.Electronic supplementary materialThe online version of this article (doi:10.1186/s12874-016-0187-2) contains supplementary material, which is available to authorized users.
Highlights
There has been some debate in the literature as to whether baseline values of a measurement of interest at treatment initiation should be treated as an outcome variable as part of a model for longitudinal change or instead used as a predictive variable with respect to the response to treatment
Recruitment to the cohort began in 1994, but, as we are interested in modelling the response to modern highly active antiretroviral therapy (HAART) regimens, we restrict our analysis to patients with an estimated date of human immunodeficiency virus (HIV)-1 seroconversion during or after 2003
Model fitting Summaries of the set of models fitted to the United Kingdom (UK) Register of HIV Seroconverters dataset are presented in Table 1, and to facilitate their interpretation Table 2 provides a description of each model parameter
Summary
There has been some debate in the literature as to whether baseline values of a measurement of interest at treatment initiation should be treated as an outcome variable as part of a model for longitudinal change or instead used as a predictive variable with respect to the response to treatment. In the setting of randomised controlled trials (RCTs), designed primarily to assess the difference between treatment conditions, some authors have argued that optimal efficiency is gained by treating the baseline measurement as an outcome variable within a parametric model [1, 2], whilst Senn has argued that conditioning estimation of treatment effect on the baseline observation through the use of ANCOVA is preferable in most trial situations [3] and Kenward et al demonstrated that with correct adjustments for sample size the two approaches have nearly identical properties [4] Both of these approaches can be problematic when applied to the estimation of response to treatment using longitudinal observational datasets, in which the timing and choice of Stirrup et al BMC Medical Research Methodology (2016) 16:121 treatment have not been randomised and in which baseline observations immediately prior to treatment may not be available for all patients. The models developed are applied to CD4 cell counts in human immunodeficiency virus (HIV)positive patients who initiate highly active antiretroviral therapy (HAART)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
