A mixture of mixed regressions model for longitudinal data, with application to clinical laboratory measurements

Jon Helgeland and Petter Laake

Communications in Statistics - Theory and Methods, 2025, vol. 54, issue 22, 7158-7174

Abstract: We study a statistical model for longitudinal data from a mixture of populations with different mean structures, with a view to analyzing clinical laboratory data. Different configurations of learning data are discussed, including the case where external summary statistics are known for one population. An EM algorithm is used for fitting the model. We discuss the asymptotic properties of the model, and show that there exists a consistent root of the likelihood equations with standard properties. A simulation study with linear and fractional polynomial models to study finite sample bias, variance, as well as parameter standard deviation estimators based on nonparametric bootstrapping and asymptotic distributions, is included. The separation between populations was the most important factor for estimator performance. Nonparametric bootstrapping performed reasonably well in general when separation was large, but was unsatisfactory in some cases with small separation. The asymptotic distributions were in general reasonable for large separation, but very inaccurate in other cases, and seem to be of limited practical value. We also present an example on measurements of cardiac enzymes for data sets comprising both healthy patients and patients with acute myocardial infarction.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2025.2467202 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:lstaxx:v:54:y:2025:i:22:p:7158-7174

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/lsta20

DOI: 10.1080/03610926.2025.2467202

Access Statistics for this article

Communications in Statistics - Theory and Methods is currently edited by Debbie Iscoe

More articles in Communications in Statistics - Theory and Methods from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().