To illustrate the relevance of such non-random missingness in outcome data as well as the possibility to correct for such biases using Heckman-type selection model, we focus on birth weight (BW) as primary outcome variable in this paper. Even though missing values can in principle be imputed using multiple imputations, this approach can lead to biased estimates if unobservable or unmeasured factors – such as individual health knowledge or attitudes – affect both the outcome of interest and the likelihood of missing data. Missingness in the outcome variable is of particular importance in the context of clinical data in low-income settings, where accurate measures of clinical outcomes often is only available for a relatively small proportion of the population. Slightly less attention has been given to the often substantial degree of missingness in outcome variables. A substantial body of literature has highlighted the importance of potential confounding variables in observational studies. While this gap may to a certain extent reflect differences in programme implementation and differential adherence to treatment protocols in non-clinical settings, biases in observational studies seem also plausible. Heckman-selection models can correct for this selection bias and yield unbiased estimates, even when the proportion of missing data is substantial.Ī growing literature has highlighted the often substantial differences between evidence based on efficacy trials and empirically observed associations between intervention exposure and health outcomes.
Missingness in health outcome can lead to substantial bias. Using Heckman-type selection models to correct for missingness in our empirical application, we found supplementation effect sizes that were very close to those reported in the most recent systematic review of clinical AS trials. When missingness in the outcome data was related to unobserved determinants of the outcome, large and systematic biases were found for CCA and MICE, while Heckman-style selection models yielded unbiased estimates. ResultsĪll models performed well when data were missing at random. Last, we use a large population-representative data set on antenatal supplementation (AS) and birth outcomes from Côte d’Ivoire to illustrate the empirical relevance of this method. We introduce the basic Heckman model in a first stage, and then use simulation models to compare the performance of the model to alternative approaches used in the literature for missing outcome data, including complete case analysis (CCA), multiple imputations by chained equations (MICE) and pattern imputation with delta adjustment (PIDA). The aim of this study was to assess the extent to which Heckman-type selection models can create unbiased estimates in such settings. In low-income settings, key outcomes such as biomarkers or clinical assessments are often missing for a substantial proportion of the study population.
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