APPLYING MULTIPLE IMPUTATION FOR EXTERNAL CALIBRATION TO PROPENSTY SCORE ANALYSIS

Although covariate measurement error is likely the norm rather than the exception, methods for handling covariate measurement error in propensity score methods have not been widely investigated. We consider a multiple imputation-based approach that uses an external calibration sample with information on the true and mismeasured covariates, Multiple Imputation for External Calibration (MI-EC), to correct for the measurement error, and investigate its performance using simulation studies. As expected, using the covariate measured with error leads to bias in the treatment effect estimate. In contrast, the MI-EC method can eliminate almost all the bias. We confirm that the outcome must be used in the imputation process to obtain good results, a finding related to the idea of congenial imputation and analysis in the broader multiple imputation literature. We illustrate the MI-EC approach using a motivating example estimating the effects of living in a disadvantaged neighborhood on mental health and substance use outcomes among adolescents. These results show that estimating the propensity score using covariates measured with error leads to biased estimates of treatment effects, but when a calibration data set is available, MI-EC can be used to help correct for such bias

An Influenza A/H1N1/2009 Hemagglutinin Vaccine Produced in Escherichia coli

The A/H1N1/2009 influenza pandemic made evident the need for faster and higher-yield methods for the production of influenza vaccines. Platforms based on virus culture in mammalian or insect cells are currently under investigation. Alternatively, expression of fragments of the hemagglutinin (HA) protein in prokaryotic systems can potentially be the most efficacious strategy for the manufacture of large quantities of influenza vaccine in a short period of time. Despite experimental evidence on the immunogenic potential of HA protein constructs expressed in bacteria, it is still generally accepted that glycosylation should be a requirement for vaccine efficacy.We expressed the globular HA receptor binding domain, referred to here as HA(63-286)-RBD, of the influenza A/H1N1/2009 virus in Escherichia coli using a simple, robust and scalable process. The recombinant protein was refolded and purified from the insoluble fraction of the cellular lysate as a single species. Recombinant HA(63-286)-RBD appears to be properly folded, as shown by analytical ultracentrifugation and bio-recognition assays. It binds specifically to serum antibodies from influenza A/H1N1/2009 patients and was found to be immunogenic, to be capable of triggering the production of neutralizing antibodies, and to have protective activity in the ferret model.Projections based on our production/purification data indicate that this strategy could yield up to half a billion doses of vaccine per month in a medium-scale pharmaceutical production facility equipped for bacterial culture. Also, our findings demonstrate that glycosylation is not a mandatory requirement for influenza vaccine efficacy

Causal Inference Methods for Measurement Error and Mediation

Causal inference provides a principled way to investigate causal effects in public health, neuroscience and other areas. This thesis addresses two topics in causal inference: (i) the estimation of causal effects using covariates measured with error, and (ii) the investigation of mechanisms underlying causal effects. Although covariate measurement error is often present, methods for handling covariate measurement error in propensity score methods have not been widely investigated. We develop an imputation-based solution to using mismeasured covariates in propensity score methods that provide an estimate of a causal treatment effect, and use it to estimate the effects of living in a disadvantaged neighborhood on adolescent mental health and substance use. Furthermore, we can use mediation analysis to study how the causal effect that a treatment X has on an outcome variable Y is influenced by some intermediate variable M. Standard approaches toward assessing mediation require that each of the variables X, M, and Y take scalar values. However, in many situations this may not be reasonable or practical. We extend the standard and causal mediation framework, allowing one or more of the variables to be considered continuous functions of time. But making causal statements about mediation always poses a problem because, even if we can randomize the intervention, it is often difficult - or impossible - to randomize the assignment of the mediator. Within-subject designs, common in neuroscience experiments using functional Magnetic Resonance Imaging, open new possibilities for identification of the mediation counterfactuals. We establish a new set of identifiability conditions for estimating causal mediation effects and develop an estimation procedure that is robust to baseline confounding of the mediator-outcome relation. This thesis advances the causal inference literature in innovative ways, enriching the principled thinking about effects and mediation with contributions from the measurement error and functional data analysis literature

Effects of different language and tDCS interventions in PPA and their neural correlates

Language intervention has been shown to critically affect the course of post-stroke language rehabilitation. Interventions at early or chronic stages (either by reperfusion, speech-language therapy or tDCS) have dramatically improved outcomes in language post-stroke rehabilitation. Little is known, however, about interventions in neurodegerative diseases and the changes in neural substrates they may induce. It has been shown that language therapy is indeed beneficial and improves language outcomes, both with behavioral interventions and more recently with tDCS for longer lasting and more generalizable effects even in PPA(Cotelli et al., 2014; Tsapkini, Frangakis, Gomez, Davis, & Hillis, 2014). However, it has not been addressed how this is instantiated in the brain. In this paper we evaluate the effects of tDCS in 18 participants with PPA who received both sham and tDCS coupled with language therapy. Our primary aim was to evaluate the effects of intervention and determine the neural correlates of tDCS vs. sham interventions that led to improved language outcomes using resting-state fMRI (rs-fMRI). Method: Eighteen patients diagnosed with PPA underwent written or oral language production intervention with and without tDCS (sham) in a within-subjects cross-over design. Participants received treatment for 2 weeks, 10 sessions for each condition. Each condition was separated by 3 months. Resting-state fMRI data were obtained on the first treatment condition from 13 participants who underwent written language intervention at 3 time-points: before, after and 2-months post-intervention. Participants were pseudo-randomly assigned in either tDCS or sham condition first in a between-subjects design. Results: First, we replicated our previous results obtained with fewer participants: all improved in both tDCS and sham conditions on trained items. Generalization of treatment on untrained items was significant only in tDCS condition. Therapy gains lasted longer in tDCS condition as well. Second, preliminary analyses of rs-fMRI show changes of functional connectivity between written language areas in the tDCS and sham conditions. Conclusions: tDCS represents an increasingly valuable treatment option in language rehabilitation even in neurodegeneration. Late intervention is as beneficial as early intervention but improvement seems more dramatic in early cases. Different possibilities are discussed: tDCS may indeed change the course of the disease, i.e., it may slow down the rate of decline or, language improvement due to tDCS (or delay in language deterioration due to the course of the disease) may hold the spread of decline in other cognitive functions, thus, early interventions appear more beneficial. The correlation between functional connectivity and language production outcomes is expected to shed light on how tDCS works in the brains of people with a neurodegenerative disease. Implications of functional connectivity changes between language areas involved in the targeted language function will inform further interventions