Eloise Kaizar
· Professor of Statistics, Department ChairOhio State University · Statistics
Active 2006–2026
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About
Eloise Kaizar is a Professor of Statistics and serves as the Department Chair in the Department of Statistics at The Ohio State University. She joined the faculty in 2006 and has a PhD from Carnegie Mellon University obtained in 2006. Her primary research interests include developing and evaluating methods to assess the effectiveness and safety of health care interventions, with a focus on research synthesis or meta-analysis. Her work aims to reduce the cost of important discoveries, answer system-wide questions, and empirically evaluate interventions for diverse and fragile populations. She also works on developing methods to evaluate study generalizability, synthesize data collected with randomized and observational designs, and assess treatment effects on rare event outcomes. Her research incorporates ideas from survey sampling, missing data, and causal analysis. In addition to her research, she works to advance the profession and improve the quality of statistical applications in medicine, including chairing the Gertrude M. Cox scholarship committee. Her research has been funded by the NIH, NSF, other federal agencies, and corporate collaborations.
Research topics
- Psychology
- Computer Science
- Artificial Intelligence
- Political Science
- Sociology
- Public relations
- Demography
- Statistics
- Psychiatry
- Economics
Selected publications
Statistics in Medicine · 2017-07-10 · 32 citations
articleSenior authorCorrespondingMultiple imputation is a popular method for addressing missing data, but its implementation is difficult when data have a multilevel structure and one or more variables are systematically missing. This systematic missing data pattern may commonly occur in meta-analysis of individual participant data, where some variables are never observed in some studies, but are present in other hierarchical data settings. In these cases, valid imputation must account for both relationships between variables a…
Journal of the International Neuropsychological Society · 2019-08-13 · 27 citations
articleOBJECTIVES: We conducted joint analyses from five randomized clinical trials (RCTs) of online family problem-solving therapy (OFPST) for children with traumatic brain injury (TBI) to identify child and parent outcomes most sensitive to OFPST and trajectories of recovery over time. METHODS: We examined data from 359 children with complicated mild to severe TBI, aged 5-18, randomized to OFPST or a control condition. Using profile analyses, we examined group differences on parent-reported child (in…
Exploring the direct and indirect effects of elite influence on public opinion
PLoS ONE · 2021 · 17 citations
Political elites both respond to public opinion and influence it. Elite policy messages can shape individual policy attitudes, but the extent to which they do is difficult to measure in a dynamic information environment. Furthermore, policy messages are not absorbed in isolation, but spread through the social networks in which individuals are embedded, and their effects must be evaluated in light of how they spread across social environments. Using a sample of 358 participants across thirty stud…
Comparing multiple imputation methods for systematically missing subject‐level data
Research Synthesis Methods · 2015-12-17 · 17 citations
articleSenior authorWhen conducting research synthesis, the collection of studies that will be combined often do not measure the same set of variables, which creates missing data. When the studies to combine are longitudinal, missing data can occur on the observation-level (time-varying) or the subject-level (non-time-varying). Traditionally, the focus of missing data methods for longitudinal data has been on missing observation-level variables. In this paper, we focus on missing subject-level variables and compare…
Journal of Neurotrauma · 2018-10-17 · 16 citations
reviewA series of five randomized controlled clinical trials (RCTs) conducted between 2002 and 2015 support the potential efficacy of online family problem-solving treatment (OFPST) in improving both child and parent/family outcomes after pediatric traumatic brain injury (TBI). However, small sample sizes and heterogeneity across individual studies have precluded examination of potentially important moderators. We jointly analyzed individual participant data (IPD) from these five RCTs, involving 359 c…
Recent grants
Statistical methods to support a model pediatric traumatic brain injury data bank
NIH · $147k · 2014–2018
Frequent coauthors
- 30 shared
Keith Owen Yeates
University of Calgary
- 27 shared
H. Gerry Taylor
Nationwide Children's Hospital
- 16 shared
Ann Dietrich
- 16 shared
Kathryn E. Nuss
- 16 shared
Martha Wright
The Ohio State University
- 16 shared
Barbara Bangert
Case Western Reserve University
- 16 shared
Jerome Rusin
Nationwide Children's Hospital
- 12 shared
Shari L. Wade
University of Cincinnati
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