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Dr Edmund Njeru Njagi

Associate Professor

United Kingdom

My research work spans the cancer care pathway: routes to diagnosis, access to diagnostic investigations, staging, treatment and related/subsequent outcomes. Disentangling drivers of variation in cancer care and cancer outcomes through robust statistical and epidemiological approaches on large linked national administrative and clinical datasets encapsulates my day-to-day activities.

I am part of the National Cancer Audit Collaborating Centre within the Clinical Effectiveness Unit, long-standing academic collaboration between the London School of Hygiene & Tropical Medicine (LSHTM) and the Royal College of Surgeons of England.

Broadly, I study the quality of, and variation in, cancer care and cancer outcomes in England and Wales, in the context of cancer services research commissioned by the Healthcare Quality Improvement Partnership on behalf of the National Health Service England and the Welsh Government. I previously worked with the Inequalities in Cancer Outcomes Network at LSHTM on Cancer Research UK-funded studies on variation in cancer care and cancer outcomes in England.

I trained in Biostatistics at Universiteit Hasselt in Belgium within the Interuniversity Institute for Biostatistics and statistical Bioinformatics, where I obtained my PhD in Biostatistics focusing on statistical methodology for longitudinal and missing data analysis, and where I subsequently consulted for the European Food Safety Authority.

Internationally, I regularly speak on the topic of missing data and multiple imputation in cancer epidemiology for the biennial Corsican Summer School on Modern Methods in Biostatistics and Epidemiology in France. I have also had invited engagements at Stellenbosch University in Cape Town and the University of Nairobi, ranging from invited lectures to research supervision.

Affiliations

Department of Health Services Research and Policy
Faculty of Public Health and Policy

Teaching

- Statistics

- Epidemiology

Research

Variation/inequalities in cancer care and cancer outcomes, focusing on breast cancer (primary and metastatic), ovarian, pancreatic, non-Hodgkin lymphoma and kidney cancer.

Research Area
Quality of care
Health care policy
Health inequalities
Health outcomes
Health policy
Health services
Health services research
Public health
Applied statistics (medical)
Epidemiology
Disease and Health Conditions
Cancer
Country
United Kingdom

Selected Publications

Pairwise joint modeling of clustered and high-dimensional outcomes with covariate missingness in pediatric pneumonia care.
Gachau, S; NJAGI, EN; Molenberghs, G; Owuor, N; Sarguta, R; English, M; AYIEKO, P;
2022
PHARMACEUTICAL STATISTICS
Excess Mortality by Multimorbidity, Socioeconomic, and Healthcare Factors, amongst Patients Diagnosed with Diffuse Large B-Cell or Follicular Lymphoma in England.
SMITH, MJ; BELOT, A; Quartagno, M; LUQUE FERNANDEZ, MA; Bonaventure, A; Gachau, S; Benitez Majano, S; RACHET, B; NJAGI, EN;
2021
Cancers
Dealing with missing information on covariates for excess mortality hazard regression models - Making the imputation model compatible with the substantive model.
Antunes, L; Mendonça, D; Bento, MJ; NJAGI, EN; BELOT, A; RACHET, B;
2021
Statistical methods in medical research
Investigating the inequalities in route to diagnosis amongst patients with diffuse large B-cell or follicular lymphoma in England.
SMITH, MJ; FERNANDEZ, MA L; BELOT, A; Quartagno, M; Bonaventure, A; Majano, SB; RACHET, B; NJAGI, EN;
2021
British journal of cancer
Improved longitudinal data analysis for cross-over design settings, with a piecewise linear mixed-effects model
Mwangi, M; Verbeke, G; NJAGI, EN; Mwalili, S; Ivanova, A; Bukania, ZN; Molenberghs, G;
2021
Communications in Statistics: Case Studies, Data Analysis and Applications
Handling missing data in a composite outcome with partially observed components: simulation study based on clustered paediatric routine data.
Gachau, S; NJAGI, EN; Owuor, N; Mwaniki, P; Quartagno, M; Sarguta, R; English, M; AYIEKO, P;
2021
Journal of applied statistics
Handling missing data in modelling quality of clinician-prescribed routine care: Sensitivity analysis of departure from missing at random assumption.
Gachau, S; Quartagno, M; NJAGI, EN; Owuor, N; English, M; AYIEKO, P;
2020
Statistical methods in medical research
O17 Modelling longitudinal patient-reported outcome measures in JDM
Baptiste, PJ; Wedderburn, LR; Deakin, CT; Stavola, BL D; NJAGI, EN;
2020
Rheumatology
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