Adjusted Risk Difference
Estimation: An Assessment of Convergence Problems with Application to Malaria
Efficacy Studies by Mukaka Mavuto*, White SA, Mwapasa V, Kalilani-Phiri L,
Terlouw DJ and Faragher Brian E in Open Access Biostatistics &Bioinformatics
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.
