Many studies in public health, including comparative effectiveness research, aim to answer questions such as """"""""what works for whom?"""""""" or """"""""under what conditions does it work?"""""""" Such questions can often only be answered in the context of a non-experimental study. Propensity scores are a key statistical tool for non-experimental studies because they facilitate the comparison of """"""""apples with apples."""""""" However, many non-experimental studies require combining information from multiple data sets, which may have slightly different measures available. Examples include depression measured using two different scales, or direct- versus parent-reported measures of behavior among children with autism. Existing propensity score methods cannot handle situations where the covariates are measured with error or are measured differently across treatment and comparison groups. This is a particular challenge in mental health research, where many of the disorders and factors under study are not directly observable and are instead modeled as latent constructs. This project will develop and assess new statistical methods for estimating treatment effects in non-experimental studies when the covariates are measured with error or are measured in different ways across the treatment and comparison groups. The work will tie together propensity score methods for estimating treatment effects, latent variable methods, and multiple imputation methods for handling missing data.
The aims are: 1) Investigate the implications of measurement error in the covariates when using propensity score methods to estimate treatment effects, 2) Develop and assess propensity score methods for settings where some of the covariates are measured with error or are measured differently in the treatment and comparison groups, and 3) Use the results and methods from Aims 1 and 2 to estimate treatment effects in three studies, each comparing a group receiving the intervention to an external comparison group, and then test the methods by comparing those estimates to the reported treatment effects from randomized trials of the same interventions. The methods will be examined in the context of three studies in mental health evaluating the effectiveness of (1) early intervention for children with autism, (2) perinatal depression prevention, and (3) the use of ginkgo biloba to prevent dementia and Alzheimer's disease.
Many important questions regarding the effects of policies, treatments, or interventions in public health can only be answered using non-experimental methods due to ethical or feasibility concerns with randomized controlled trials. New statistical methods are needed to allow researchers to take full advantage of existing data to estimate treatment effects in non- experimental studies.
|Austin, Peter C; Stuart, Elizabeth A (2017) Estimating the effect of treatment on binary outcomes using full matching on the propensity score. Stat Methods Med Res 26:2505-2525|
|Rudolph, Kara E; Stuart, Elizabeth A (2017) Using sensitivity analyses for unobserved confounding to address covariate measurement error in propensity score methods. Am J Epidemiol :|
|George, Brandon J; Beasley, T Mark; Brown, Andrew W et al. (2016) Common scientific and statistical errors in obesity research. Obesity (Silver Spring) 24:781-90|
|Rudolph, Kara E; Colson, K Ellicott; Stuart, Elizabeth A et al. (2016) Optimally combining propensity score subclasses. Stat Med 35:4937-4947|
|Nguyen, Trang Quynh; Webb-Vargas, Yenny; Koning, Ina M et al. (2016) Causal mediation analysis with a binary outcome and multiple continuous or ordinal mediators: Simulations and application to an alcohol intervention. Struct Equ Modeling 23:368-383|
|Colson, K Ellicott; Rudolph, Kara E; Zimmerman, Scott C et al. (2016) Optimizing matching and analysis combinations for estimating causal effects. Sci Rep 6:23222|
|Austin, Peter C; Stuart, Elizabeth A (2015) Optimal full matching for survival outcomes: a method that merits more widespread use. Stat Med 34:3949-67|
|Austin, Peter C; Stuart, Elizabeth A (2015) Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies. Stat Med 34:3661-79|
|Li, Peng; Stuart, Elizabeth A; Allison, David B (2015) Multiple Imputation: A Flexible Tool for Handling Missing Data. JAMA 314:1966-7|
|Schuler, Megan S; Leoutsakos, Jeannie-Marie S; Stuart, Elizabeth A (2014) ADDRESSING CONFOUNDING WHEN ESTIMATING THE EFFECTS OF LATENT CLASSES ON A DISTAL OUTCOME. Health Serv Outcomes Res Methodol 14:232-254|
Showing the most recent 10 out of 11 publications