Ms Sarguta Rachel

Gachau, S; Njeru Njagi, E; Owuor, N; Mwaniki, P; Quartagno, M; Sarguta, R; English, M; Gachau, S; Njeru Njagi, E; Owuor, N; Mwaniki, P; Quartagno, M; Sarguta, R; English, M; Ayieko, P; - view fewer (2021) Handling missing data in a composite outcome with partially observed components: Simulation study based on clustered paediatric routine data. Journal of Applied Statistics (In press) … This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.

Gachau, S; Njeru Njagi, E; Owuor, N; Mwaniki, P; Quartagno, M; Sarguta, R; English, M; Gachau, S; Njeru Njagi, E; Owuor, N; Mwaniki, P; Quartagno, M; Sarguta, R; English, M; Ayieko, P; - view fewer (2021) Handling missing data in a composite outcome with partially observed components: Simulation study based on clustered paediatric routine data. Journal of Applied Statistics (In press) … This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.

Introduction: In low-and middle-income countries (LMICs) where healthcare resources are often limited, making decisions on appropriate treatment choices is critical in ensuring reduction of paediatric deaths as well as instilling proper utilisation of the already constrained healthcare resources. Well-developed and validated prognostic models can aid in early recognition of potential risks thus contributing to the reduction of mortality rates. The aim of the planned systematic review is to identify and appraise the methodological rigor of multivariable prognostic models predicting in-hospital paediatric mortality in LMIC in order to identify statistical and methodological shortcomings deserving special attention and to identify models for external validation.
Methods and analysis: This protocol has followed the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Protocols. A search of …

Objectives
To identify and appraise the methodological rigour of multivariable prognostic models predicting in-hospital paediatric mortality in low-income and middle-income countries (LMICs).
Design
Systematic review of peer-reviewed journals.
Data sources
MEDLINE, CINAHL, Google Scholar and Web of Science electronic databases since inception to August 2019.
Eligibility criteria
We included model development studies predicting in-hospital paediatric mortality in LMIC.
Data extraction and synthesis
This systematic review followed the Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies framework. The risk of bias assessment was conducted using Prediction model Risk of Bias Assessment Tool (PROBAST). No quantitative summary was conducted due to substantial heterogeneity that was observed after assessing the studies included.