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A semi-parametric mixed models for longitudinally measured fasting blood sugar level of adult diabetic patients

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dc.contributor.author Aniley, Tafere Tilahun
dc.contributor.author Yimer, Wondwosen Kassahun
dc.contributor.author Nigussie, Zelalem Mehari
dc.contributor.author Yimer, Wondwosen Kassahun
dc.contributor.author Yimer, Belay Birlie
dc.date.accessioned 2019-02-01T04:57:33Z
dc.date.available 2019-02-01T04:57:33Z
dc.date.issued 2019-01-10
dc.identifier.citation BMC Medical Research Methodology. 2019 Jan 10;19(1):13
dc.identifier.uri https://doi.org/10.1186/s12874-018-0648-x
dc.identifier.uri http://hdl.handle.net/10500/25233
dc.description.abstract Abstract Background At the diabetic clinic of Jimma University Specialized Hospital, health professionals provide regular follow-up to help people with diabetes live long and relatively healthy lives. Based on patient condition, they also provide interventions in the form of counselling to promote a healthy diet and physical activity and prescribing medicines. The main purpose of this study is to estimate the rate of change of fasting blood sugar (FBS) profile experienced by patients over time. The change may help to assess the effectiveness of interventions taken by the clinic to regulate FBS level, where rates of change close to zero over time may indicate the interventions are good regulating the level. Methods In the analysis of longitudinal data, the mean profile is often estimated by parametric linear mixed effects model. However, the individual and mean profile plots of FBS level for diabetic patients are nonlinear and imposing parametric models may be too restrictive and yield unsatisfactory results. We propose a semi-parametric mixed model, in particular using spline smoothing to efficiently analyze a longitudinal measured fasting blood sugar level of adult diabetic patients accounting for correlation between observations through random effects. Results The semi-parametric mixed models had better fit than the linear mixed models for various variance structures of subject-specific random effects. The study revealed that the rate of change in FBS level in diabetic patients, due to the clinic interventions, does not continue as a steady pace but changes with time and weight of patients. Conclusions The proposed method can help a physician in clinical monitoring of diabetic patients and to assess the effect of intervention packages, such as healthy diet, physical activity and prescribed medicines, because individualized curve may be obtained to follow patient-specific FBS level trends.
dc.title A semi-parametric mixed models for longitudinally measured fasting blood sugar level of adult diabetic patients
dc.type Journal Article
dc.date.updated 2019-02-01T04:57:33Z
dc.language.rfc3066 en
dc.rights.holder The Author(s)


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