Evaluation of a multivariate model predicting noncompliance with medication regimens among renal transplant patients

Stuart Greenstein, Bonita Siegal

Research output: Contribution to journalArticle

16 Scopus citations

Abstract

Background. Because noncompliance with medication regimens is a major cause of renal allograft failure, we evaluated the stability over time of two logistic regression models (sets of variables) that predict noncompliance with immunosuppressive regimens. Methods. Models were based on questionnaire data from 1402 patients (all over 18, receiving cyclosporine or a cyclosporine-like replacement drug, and with a functioning renal graft). The same questionnaire was completed by a subset of 548 (39.1%) patients approximately 18 months later. The goodness of fit of each model to the new data set was tested. Results. The noncompliance logistic regression model including patient beliefs as well as patient and transplant characteristics was an excellent fit to the second data set. A noncompliance model composed of only patient and transplant characteristics fit the new data set less well. Conclusions. Clinicians and educators need to take explicit account of renal transplant patients' attitudes when evaluating risks of noncompliance and when developing interventions and educational programs to minimize noncompliance.

Original languageEnglish (US)
Pages (from-to)2226-2228
Number of pages3
JournalTransplantation
Volume69
Issue number10
DOIs
StatePublished - May 27 2000

ASJC Scopus subject areas

  • Transplantation

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