dc.contributor.advisor | Haneuse, Sebastien | en_US |
dc.contributor.author | Smoot, Elizabeth | en_US |
dc.date.accessioned | 2015-07-17T16:29:08Z | |
dc.date.created | 2015-05 | en_US |
dc.date.issued | 2015-04-07 | en_US |
dc.date.submitted | 2015 | en_US |
dc.identifier.citation | Smoot, Elizabeth. 2015. Methods for Effectively Combining Group- and Individual-Level Data. Doctoral dissertation, Harvard University, Graduate School of Arts & Sciences. | en_US |
dc.identifier.uri | http://nrs.harvard.edu/urn-3:HUL.InstRepos:17463969 | |
dc.description.abstract | In observational studies researchers often have access to multiple sources of information but ultimately choose to apply well-established statistical methods that do not take advantage of the full range of information available. In this dissertation I discuss three methods that are able to incorporate this additional data and show how using each improves the quality of the analysis.
First, in Chapters 1 and 2, I focus on methods for improving estimator efficiency in studies in which both population (group) and individual-level data is available. In such settings, the hybrid design for ecological inference efficiently combines the two sources of information; however, in practice, maximizing the likelihood is often computationally intractable. I propose and develop an alternative, computationally efficient representation of the hybrid likelihood. I then demonstrate that this approximation incurs no penalty in terms of increased bias or reduced efficiency.
Second, in Chapters 3 and 4, I highlight the problem of applying standard analyses to outcome-dependent sampling schemes in settings in which study units are cluster-correlated. I demonstrate that incorporating known outcome totals into the likelihood via inverse probability weights results in valid estimation and inference. I further discuss the applicability of outcome-dependent sampling schemes in resource-limited settings, specifically to the analysis of national ART programs in sub-Saharan Africa. I propose the cluster-stratified case-control study as a valid and logistically reasonable study design in such resource-poor settings, discuss balanced versus unbalanced sampling techniques, and address the practical trade-off between logistic considerations and statistical efficiency of cluster-stratified case-control versus case-control studies.
Finally, in Chapter 5, I demonstrate the benefit of incorporating the full-range of possible outcomes into an observational data analysis, as opposed to running the analysis on a pre-selected set of outcomes. Testing all possible outcomes for associations with the exposure inherently incorporates negative controls into the analysis and further validates a study's statistically significant results. I apply this technique to an investigation of the relationship between particulate air pollution and hospital admission causes. | en_US |
dc.description.sponsorship | Biostatistics | en_US |
dc.format.mimetype | application/pdf | en_US |
dc.language.iso | en | en_US |
dash.license | LAA | en_US |
dc.subject | Statistics | en_US |
dc.title | Methods for Effectively Combining Group- and Individual-Level Data | en_US |
dc.type | Thesis or Dissertation | en_US |
dash.depositing.author | Smoot, Elizabeth | en_US |
dc.date.available | 2015-07-17T16:29:08Z | |
thesis.degree.date | 2015 | en_US |
thesis.degree.grantor | Graduate School of Arts & Sciences | en_US |
thesis.degree.level | Doctoral | en_US |
thesis.degree.name | Doctor of Philosophy | en_US |
dc.contributor.committeeMember | Dominici, Francesca | en_US |
dc.contributor.committeeMember | Coull, Brent | en_US |
dc.type.material | text | en_US |
thesis.degree.department | Biostatistics | en_US |
dash.identifier.vireo | http://etds.lib.harvard.edu/gsas/admin/view/201 | en_US |
dc.description.keywords | observational studies; hybrid design; ecological data; case-control; | en_US |
dash.author.email | beth.smoot@gmail.com | en_US |
dash.identifier.drs | urn-3:HUL.DRS.OBJECT:25163869 | en_US |
dash.contributor.affiliated | Smoot, Elizabeth | |