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Open Access Biostatistics & Bioinformatics

Adjusted Risk Difference Estimation: An Assessment of Convergence Problems with Application to Malaria Efficacy Studies

  • Open or Close Mavuto Mukaka2,4,5*, Sarah AW3, Victor M2, Linda KP2, Dianne JT1,2,3 and Brian Faragher3

    1College of Medicine, University of Malawi, South Africa

    2Department of Public Health, University of Malawi, South Africa

    3Liverpool School of Tropical Medicine, UK

    4Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Thailand

    5Nuffield Department of Medicine Research Building, University of Oxford, UK

    *Corresponding author: Mavuto Mukaka, Head of Statistics, Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, 60th Anniversary Chalermprakiat Building, 3rd Floor, 420/6 Ratchawithi Rd, Ratchathewi District, Bangkok, 10400, Thailand

Submission: November 10, 2017; Published: December 22, 2017

DOI: 10.31031/OABB.2017.01.000502

ISSN: 2578-0247
Volume1 Issue1


A common measure of treatment effect in malaria efficacy studies is the risk difference, which can be estimated using binomial regression models. These models can fail to provide estimates, however, due to model failure or model convergence problems. Such failure most commonly occurs when the rate is close to 0% or 100% (a “boundary problem”) but can also occur occasionally even when the rate is not close to a boundary. This paper reports the findings from simulation studies performed to evaluate the factors that may contribute to model failure when using binomial regression to derive risk difference estimates.

Convergence rates were found to fall:

i) As one or both efficacy rates moved towards a boundary value, irrespective of the number of covariates included in the model;

ii) As the numbers of covariates in the model increased;

iii) As the levels of correlation between covariates the covariates increased. In all circumstances, convergence was poor when the efficacy rate in either group was 90% or more.

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