Latent class piecewise linear trajectory modelling for short-term cognition responses after chemotherapy for breast cancer patients

M. I. Rolfe, K. Mengersen, G. Beadle, K. Vearncombe, B. Andrew, H. L. Johnson and C. Walsh

Journal of Applied Statistics, 2010, vol. 37, issue 5, 725-738

Abstract: This paper investigates the impact of chemotherapy on cognitive function of breast cancer patients and whether this response is homogeneous for all patients. Latent class piecewise linear trajectory (growth) models were employed to describe changes and identify subgroups in three Auditory Verbal Learning Test measures (learning, immediate retention and delayed recall) in 130 breast cancer patients taken at three time periods: before chemotherapy and 1 and 6 months post-chemotherapy. Two distinct subgroups of women exhibiting different patterns of response were identified for learning and delayed recall and three for immediate retention. The groups differed in level (intercept) at 1 month post-chemotherapy and patterns of decline and recovery. Binomial and multinomial logistic regressions on the latent classes found that age, initial National Adult Reading Test (NART)-predicted IQ, stage of cancer and the initial Functional Assessment of Cancer Therapy-Breast subscale (or subsets thereof) to be significant predictors of classes.

Keywords: latent class; piecewise linear; trajectory; cognition; breast cancer; chemotherapy; growth models; mixtures (search for similar items in EconPapers)
Date: 2010
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.tandfonline.com/doi/abs/10.1080/02664760902729641 (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:japsta:v:37:y:2010:i:5:p:725-738

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/CJAS20

DOI: 10.1080/02664760902729641

Access Statistics for this article

Journal of Applied Statistics is currently edited by Robert Aykroyd

More articles in Journal of Applied Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().