PubMed · 15005120
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Amit Kumar Ghosh. 2004. Explaining risk.. https://pubmed.ncbi.nlm.nih.gov/15005120/
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Adjustment for baseline variables in a randomized trial can increase power to detect a treatment effect. However, when baseline data are partly missing, analysis of complete cases is inefficient. We consider various possible improvements in the case of normally distributed baseline and outcome variables. Joint modelling of baseline and outcome is the most efficient method. Mean imputation is an excellent alternative, subject to three conditions. Firstly, if baseline and outcome are correlated more than about 0.6 then weighting should be used to allow for the greater information from complete cases. Secondly, imputation should be carried out in a deterministic way, using other baseline variables if possible, but not using randomized arm or outcome. Thirdly, if baselines are not missing completely at random, then a dummy variable for missingness should be included as a covariate (the missing indicator method). The methods are illustrated in a randomized trial in community psychiatry.
Traditional study designs, such as individual-level studies and ecological studies, are unable to simultaneously examine the effects of individual-level and group-level factors on risk of disease. Multilevel analysis overcomes this limitation by allowing the simultaneous investigation of factors defined at multiple levels. Areas in which multilevel modeling can be applied to sexually transmitted infection (STI) research include examining how both group-level and individual-level factors are related to individual-level STI outcomes, assessing interactions between individual-level and group-level constructs, and exploring how factors at multiple levels contribute to group-to-group differences in rates of disease. In this article, we review the fundamentals of multilevel modeling, the applications of multilevel models for the examination of STIs, and the key challenges associated with using multilevel modeling for infectious-disease research.
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