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dc.contributor.authorMaronga, Christopher Sianyo
dc.date.accessioned2022-06-13T07:16:34Z
dc.date.available2022-06-13T07:16:34Z
dc.date.issued2021
dc.identifier.urihttp://hdl.handle.net/11071/12817
dc.descriptionA Research Thesis Submitted to the Graduate School in partial fulfillment of the requirements for the Award of Master of Science Degree in Statistical Sciences at Strathmore Universityen_US
dc.description.abstractBackground: In follow up studies, interest often lies in understanding the association between biomarkers measured over time and a time-to-event outcome. For this, a two-stage separate analysis or the use of time-dependent Cox models are often used. The former approach does not account for shared features between the two processes while the latter ignores the indigeneity in the biomarker, resulting in inefficient and biased estimates. The objective of this project was to _x joint models on longitudinal anthropometry and time to death among children hospitalized with complicated SAM in four hospitals in Kenya. Methods: Data from a randomised placebo-controlled trial for 1,778 children aged 2 to 59 months admitted to hospital with complicated Severe Acute Malnutrition (SAM) but without HIV was analysed. We used Linear mixed effects models to model longitudinal anthropometry and Cox proportional hazards model to assess the effect of a priori selected baseline covariates on mortality. The two models were linked through current value and slope association to create a joint model used to study the effect of longitudinal anthropometry on risk of death. Results: The joint model results showed that a unit centimetre gain in monthly midupper arm circumference (MUAC) was associated with 46.8% reduction in hazard of death, 0.532(95% CI: 0.476-0.596), while a unit gain in standard deviation (SD) for weight-forheight WHZ) was associated with 37.1% reduction in the risk of death, 0.629(95% CI:0.579- 0.683). A unit gain in SD for monthly weight-for-age (WAZ) and height-for-age (HAZ) was associated with 21.2%, 0.788(95% CI: 0.742-0.837) and 2.5%, 0.227(95% C.I: 0.008 - 6.556) reduction in risk of mortality respectively. Conclusion: In studying the relationship between survival outcome and covariates, researchers often use baseline values of the covariates which fails to account for the interdependencies. Using joint modelling framework, we quantified the association between four longitudinal anthropometry and risk of death. Through current value and slope association, MUAC and WHZ have the strongest association with risk of death respectively hence are better metrics and can be used to screen and identify high-risk children.en_US
dc.language.isoenen_US
dc.publisherStrathmore Universityen_US
dc.subjectModelling approachen_US
dc.subjectAnthropometryen_US
dc.subjectMalnourished children_Kenyaen_US
dc.titleA Joint modelling approach of monthly anthropometry and time to death among hospitalized severe malnourished children in Kenyaen_US
dc.typeThesisen_US


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