Abstract
BackgroundIn randomized clinical trials or observational studies, it is common to collect biomarker values longitudinally on a cohort of individuals. The investigators may be interested in grouping individuals that share similar changes of biomarker values and use these groups for diagnosis or therapeutic purposes. However, most classical model-based classification methods rely mainly on empirical models such as splines or polynomials and do not reflect the physiological processes.MethodsA model-based classification method was developed for longitudinal biomarker measurements through a pharmacokinetic model that describes biomarker changes over time. The method is illustrated using data on human Chorionic Gonadotrophic Hormone measurements after curettage of hydatidiform moles.ResultsThe resulting classification was linked to the evolution toward gestational trophoblastic neoplasia and may be used as a tool for early diagnosis. The diagnostic accuracy of the pharmacokinetic model was more reproducible than the one of a purely mathematical model that did not take into account the biological processes.ConclusionThe use of pharmacokinetic models in model-based classification approaches can lead to clinically useful classifications.Electronic supplementary materialThe online version of this article (doi:10.1186/s12874-015-0106-y) contains supplementary material, which is available to authorized users.
Highlights
In randomized clinical trials or observational studies, it is common to collect biomarker values longitudinally on a cohort of individuals
Models with constant residual variance provided better specificities than models with varying residual variance because they classified fewer women in the upper group of hCG level, which was considered predictive of gestational trophoblastic neoplasia (GTN)
In the present study, a model-based classification was performed with the Classification Expectation Maximization (CEM) algorithm to identify typical trajectories associated with groups of individuals and assign each individual to one of these groups
Summary
In randomized clinical trials or observational studies, it is common to collect biomarker values longitudinally on a cohort of individuals. An increasing number of studies in clinical research and epidemiology are collecting repeated biomarker measurements during follow-ups of subjects with various conditions or diseases. These longitudinal measurements, referred to as trajectories, are often used for patient monitoring. One example is the monitoring of Prostate Specific Antigen (PSA) to diagnose relapse of prostate cancer [1]. Another example is the monitoring of creatinine phosphokinase to check the condition of kidney-transplant patients [2].
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