Laura Forastiere works at the intersection between statistical methodology and applied global public health research. Her methodological research is focused on methods for assessing causal inference for evidence-based research, exploring the mechanisms underlying the effect of an intervention including causal pathways through intermediate variables or mechanisms of peer influence and spillover between connected units. Her research explores modeling, inferential, and other methodological issues that often arise in applied problems with complex clustered and network data, and standard statistical theory and methods are no longer adequate to support the goals of the analysis.Her work on this topic has provided statistical methods for investigating causal mechanisms and spillover effects in clustered encouragement designs and observational network data, statistical methods for assessing the effect of an intervention on changes in the network structure, as well as experimental designs for evaluating the effectiveness of targeting strategies on networks. Another major component of her research refers to analyzing experiments affected by post-treatment variables, such as non-compliance or truncation by death, or irregular designs where the treatment is assigned according to some cutoff rule.
Behavioral Sciences; Health Plan Implementation; HIV; Global Health; Causality; Clinical Trial; Biostatistics; Social Networking; Observational Study
Global Health; Infectious Diseases; Bayesian Statistics; Health Equity, Disparities, Social Determinants and Justice; Implementation Science