Dr. Nelson O. Onyango has a PhD in Biostatistics from the Technical University of Munich, Germany, a Master of Science degree (Biostatistics) and Bachelor’s degree in Education from the University of Nairobi. He has over 14 years teaching experience in biostatistics at the University of Nairobi, Kenya. Besides, he conducts research and manages research grant projects.

His research interests are in Mathematical Epidemiology, where he has published most of his articles, and medical statistics (particularly survival data analysis). He has supervised close to 20 Masters students and currently supervising about 3 PhD students in Biostatistics. He is an external examiner for Biostatistics courses and Thesis at both the Jomo Kenyatta University of Agriculture and Technology – Nairobi, and the University of

Dar Es Salaam, Tanzania.
His research projects experience includes the following:
[1.] He is part of a Consortium of African Universities that is promoting training in Biostatistics through a Wellcome Trust Funded Project named DELTAS SSACAB. Besides, he is also part of the Belgium VLIR funded

grant project for promoting Biostatistics in Kenya.
[2.] He is a UNITED NATIONS consultant in a number of projects including the Global Strategy for Improving Agricultural and Rural Statistics, a project of the United Nations Economic Commission for Africa (UNECA). In this project, he participated in training country statisticians in three main areas of agricultural statistics including

Master Sampling Frames for Agricultural Research, Computer Assisted Personal Interviewing and Remote Sensing for Agricultural Data Collection and Estimation of Nomadic Livestock Population.
[3.] Dr. Onyango also participated as a statistician in a Pilot Project on the Use of Mobile Technologies for data collection in Kenya for the United Nations Economic Commission for Africa (UNECA) in partnership with the

Kenya National Bureau of Statistics and School of Mathematics (SOM), University of Nairobi. Besides, he also participated as the Statistician in the Pilot Project on Global Fuel Economy Initiative (GFEI) for the Energy Regulation Commission of Kenya (ERC) and United Nations Environmental Program (UNEP).

He is currently serving in the Technical Committee - Strategic Information, at the National AIDS Control Council as a Statistician and Evaluation expert on the UNAIDS Global AIDS Monitoring project. He has interests in other statistics application areas such as Monitoring and Evaluation, Mixed Methods research, Systematic Review and Meta-Analysis. He has been involved in trainings, Baseline/End-line surveys jointly with Prof. Hesborn Wao of the University of Florida - USA and other colleagues from Measure Evaluation and Africa Capacity Alliance.



Dr. Onyango, Nelson Owuor

Dr. Nelson O. Onyango has a PhD in Biostatistics from the Technical University of Munich, Germany, a Master of Science degree (Biostatistics) and Bachelor’s degree in Education from the University of Nairobi. He has over 14 years teaching experience in biostatistics at the University of Nairobi, Kenya. Besides, he conducts research and manages research grant projects.



S Gachau, E Njeru Njagi, N Owuor, P Mwaniki, M Quartagno, Sarguta R, English M, Ayieko P.  2021.  Handling missing data in a composite outcome with partially observed components: Simulation study based on clustered paediatric routine data. Journal of Applied Statistics. AbstractWebsite

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.

Thierno Souleymane Barry, Oscar Ngesa, Onyango NO, Mwambi H.  2021.  Bayesian Spatial Modeling of Anemia among children under 5 years in Guinea. AbstractWebsite

Bacground: Anemia is a major public health problem in Africa with an increasing number of children under 5
years getting infected. Guinea is one of the most affected countries. In 2018, the prevalence rate was 75% in
children under 5 years. This study sought to identify the factors associated with anemia and to map spatial
variation of anemia across the eight (8) regions in Guinea for children under 5 years, which can provide
guidance for control programs for the reduction of the disease.
Methods: Data from the Guinea Multiple Indicator Cluster Survey (MICS5) 2016 was used for this study. A
total of 2609 children under 5 years who had full covariate information were used in the analysis. Spatial
binomial logistic regression methodology was undertaken via Bayesian estimation based on Markov chain
Monte Carlo (McMC) using WinBUGS software version 1.4.
Results: Our findings revealed that 77% of children under 5 years in Guinea had anemia and the prevalence in
the regions ranged from 70.32% (Conakry) to 83.60% (N’Zerekore) across the country. After adjusting for non
spatial and spatial random effects in the model, older children (48–59 months) (OR: 0.47, CI [0.29 0.70]) were
less likely to be anemic compared to those who are younger (0-11 months). Children whose mothers have
completed secondary education or more had a reduced chance of anemia infection by 33% (OR: 0.67, CI [0.49
0.90]) and Children from household heads from Kissi ethnic group are less likely to have anemia than their
counterparts whose leader is from Soussou (OR: 0.48, CI [0.22 0.91]).
Conclusion: The spatial analysis allowed the identification of high-risk areas as well as the identification of
socio-economic and demographic factors associated with anemia among children under 5 years. Such an
analysis is important in helping policy makers and health practitioners in developing programs geared towards
control and management of anemia among children under 5 years in the country.


Morris Ogero, Rachel Jelagat Sarguta, Malla L, Aluvaala J, Agweyu A, English M, Onyango NO, Akech S.  2020.  Prognostic models for predicting in-hospital paediatric mortality in resource-limited countries: a systematic review. BMJ open. 10(10):035045. AbstractWebsite

To identify and appraise the methodological rigour of multivariable prognostic models predicting in-hospital paediatric mortality in low-income and middle-income countries (LMICs).
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.

Susan Gachau, Matteo Quartagno, NJAGI EDMUNDNJERU, Nelson Owuor, English M, Ayieko P.  2020.  Handling missing data in modelling quality of clinician-prescribed routine care: sensitivity analysis of departure from missing at random assumption. Statistical methods in medical research. 29(10):3076-3092. AbstractWebsite

Missing information is a major drawback in analyzing data collected in many routine health care settings. Multiple imputation assuming a missing at random mechanism is a popular method to handle missing data. The missing at random assumption cannot be confirmed from the observed data alone, hence the need for sensitivity analysis to assess robustness of inference. However, sensitivity analysis is rarely conducted and reported in practice. We analyzed routine paediatric data collected during a cluster randomized trial conducted in Kenyan hospitals. We imputed missing patient and clinician-level variables assuming the missing at random mechanism. We also imputed missing clinician-level variables assuming a missing not at random mechanism. We incorporated opinions from 15 clinical experts in the form of prior distributions and shift parameters in the delta adjustment method. An interaction between trial intervention arm and follow-up time, hospital, clinician and patient-level factors were included in a proportional odds random-effects analysis model. We performed these analyses using R functions derived from the jomo package. Parameter estimates from multiple imputation under the missing at random mechanism were similar to multiple imputation estimates assuming the missing not at random mechanism. Our inferences were insensitive to departures from the missing at random assumption using either the prior distributions or shift parameters sensitivity analysis approach

Chirwa, TF, Zingoni ZM, Munyewende P, Manda SO, Mwambi H, Kandala N-B, Kinyanjui S, Young T, Musenge E, Simbeye J, Musonda P, Mahande MJ, Weke P, Onyango NO, Kazembe L.  2020.  Developing excellence in biostatistics leadership, training and science in Africa: How the Sub-Saharan Africa Consortium for Advanced Biostatistics (SSACAB) training unites …. AAS Open Research. 3(51) AbstractWebsite

The increase in health research in sub-Saharan Africa (SSA) has led to a high demand for biostatisticians to develop study designs, contribute and apply statistical methods in data analyses. Initiatives exist to address the dearth in statistical capacity and lack of local biostatisticians in SSA health projects. The Sub-Saharan African Consortium for Advanced Biostatistics (SSACAB) led by African institutions was initiated to improve biostatistical capacity according to the needs identified by African institutions, through collaborative masters and doctoral training in biostatistics. SACCAB has created a critical mass of biostatisticians and a network of institutions over the last five years and has strengthened biostatistics resources and capacity for health research studies in SSA. SSACAB comprises 11 universities and four research institutions which are supported by four European universities. In 2015, only four universities …


Susan Gachau, Nelson Owuor, NJAGI EDMUNDNJERU, Ayieko P, English M.  2019.  Analysis of Hierarchical Routine Data With Covariate Missingness: Effects of Audit & Feedback on Clinicians' Prescribed Pediatric Pneumonia Care in Kenyan Hospitals. Frontiers in public health. 7( ):198. AbstractWebsite

Routine clinical data are widely used in many countries to monitor quality of care. A limitation of routine data is missing information which occur due to lack of documentation of care processes by health care providers, poor record keeping or limited health care technology at facility level. Our objective was to address missing covariates while properly accounting for hierarchical structure in routine paediatric pneumonia care.
We analysed routine data collected during a cluster randomized trial to investigating the effect of audit and feedback (A&F) over time on inpatient pneumonia care among children admitted in 12 Kenyan hospitals between March and November 2016. Six hospitals in the intervention arm received enhance A&F on classification and treatment of pneumonia cases in addition to a standard A&F report on general inpatient paediatric care. The remaining six in control arm received standard A&F alone. We derived and analysed a composite outcome known as Paediatric Admission Quality of Care (PAQC) score. In our analysis, we adjusted for patients, clinician and hospital level factors. Missing data occurred in patient and clinician level variables. We did multiple imputation of missing covariates within the joint model imputation framework. We fitted proportion odds random effects model and generalized estimating equation (GEE) models to the data before and after multilevel multiple imputation.

DN Kareithi, Salifu D, N Owuor, Subramanian S, Tonnang EZH.  2019.  An algorithm for data reconstruction from published articles–Application on insect life tables. Cogent Mathematics & Statistics. 6(1):1701377. AbstractWebsite

Data collection in life table experiments is generally time-consuming and costly such that data reconstruction of published information provides an avenue to access the original data for purposes of further investigation. In this paper, we present an algorithm that reconstructs life table raw data using a summary of results from published articles. We present the steps of the development and implementation (in the R computer language) of the algorithm, its scope of application, assumptions, and limitations. Statistical background of the algorithm is also presented. The developed algorithm was then applied to reconstruction of life table data of two insect species, Chilo partellus and Busseola fusca, from published information. Welch’s two-sample t-test was applied to test the difference between the original and reconstructed data of the insect life stages. C. Partellus results were not significantly different, but, for B. fusca, pupa development time, and larva and pupa development rate were significantly different at the 95% confidence level. It is concluded that the algorithm could be used to reconstruct original data sets from cohort life table data sets of insects, given published information and sample sizes.


Nuwasiima, A, et al.  2017.  Predictors of HIV prevention knowledge and sexual behaviors among students at Makerere University Kampala, Uganda. al Epidemiology, Biostatistics and Public Health. 14(4)


Otieno, S, et al.  2015.  The role of agri-business incentive on under-five child immunization in Trans-Nzoia County. East African Journal of Public Health. 12(12):1054-1059.
Orowe, I, Weke P.  2015.  Multistate Modelling Vertical Transmission and Determination of R0 Using Transition Intensities.. HIKARI Applied Mathematical Sciences. 9(79):3941–3956.


Onyango Nelson O., MJ.  2014.  Determination of optimal vaccination strategies using an orbital stability threshold from periodically driven systems. Journal of Mathematical Biology. 68(3):763--784.
Onyango N.O., Mueller J., MSK.  2014.  Optimal Vaccination Strategies in an SIR epidemic model with time scales. , Rennes, France


N.O., O.  2010.  Theory and Practice of Mixed Modeling. , Saarbrucken: VDM-Verlag Dr.Mueller
NO, O.  2010.  Optimal Vaccination Strategies in periodic settings and Threshold conditions: A Survey.. . International Journal of Biomathematics and Biostatistics . 1(2):193-201.


Onyango, NO.  2009.  On the Linear Mixed Effects Regression (lmer) R Function for Nested Animal Breeding Data. AbstractOn the Linear Mixed Effects Regression (lmer) R Function for Nested Animal Breeding Data

This work highlights aspects of the R lmer function for a case where the dataset is nested, highly unbalanced, involves mixed effects and repeated measurements. The lmer function is part of the lme4 package of the statistical software R. The dataset used in the study is simulated from a survey of cow milk off takes from a group of Herds in Uganda, Africa. The purpose of the survey was to identify quality breeds of African Indigenous cattle for purposes of genetic breeding following the difficulties involved in implantation of foreign breeds of cattle in Africa. The work highlights the use of mixed model analysis in the context of animal breed selection. The exposition is accessible to readers with an intermediate background in statistics. Some previous exposure to R is helpful as well as some familiarity with mixed models.

NO, O.  2009.  On the lmer function for nested animal breeding data. CSBIGS. 4(1):44-58.


Onyango N. O., Achia T., RJ.  2007.  Case Study 2: Identification of Elite Ankole Cattle in a Herd Monitoring Study in Uganda.. Biometrics and Research Methods Teaching Resource Version 1. , Nairobi: ILRI

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