RMME/STAT Joint Colloquium

Machine Learning Prediction Modeling for Longitudinal Outcomes in Older Adults

Dr. Jaime Lynn Speiser
Wake Forest School of Medicine

Friday, December 10th, at 12:00PM ET

https://uconn-cmr.webex.com/uconn-cmr/j.php?MTID=macf5fd1f3af4a057a735eeefe6e40af0

Prediction models aim to help medical providers, individuals and caretakers make informed, data-driven decisions about risk of developing poor health outcomes, such as fall injury or mobility limitation in older adults. Most models for outcomes in older adults use cross-sectional data, although leveraging repeated measurements of predictors and outcomes over time may result in higher prediction accuracy. This seminar talk will focus on longitudinal risk prediction models for mobility limitation in older adults using the Health, Aging, and Body Composition dataset with a novel machine learning method called Binary Mixed Model (BiMM) forest. I will give an overview of two common machine learning methods, decision tree and random forest, before introducing the BiMM forest method. I will then apply the BiMM forest method for developing prediction models for mobility limitation in older adults.

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RMME Evaluation Colloquium

Behind the Evaluation: Holli Bayonas

Dr. Holli Bayonas
iEvaluate, LLC

Friday, November 19th, at 12:00PM ET

https://uconn-cmr.webex.com/uconn-cmr/j.php?MTID=m83cfb05ec06ab0e6aecf026ad3e414f6

This colloquium gives participants an inside look at one evaluator’s pathway to becoming an evaluation professional. Dr. Bayonas will describe her personal career trajectory, along with the day-to-day responsibilities associated with her current position at iEvaluate. She will compare and contrast her opportunities to work in industry versus working for herself as an independent evaluation consultant. In addition, Dr. Bayonas will discuss her approach to balancing career/professional goals and the demands of homelife, including how she and her partner navigated the prioritization and support of each other’s career aspirations. She will close this talk with career and personal advice for her younger self.

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RMME/STAT Joint Colloquium

How Auxiliary Information Can Help Your Missing Data Problem

Dr. Jerry Reiter
Duke University

Friday, November 5th, at 12:00PM ET

https://uconn-cmr.webex.com/uconn-cmr/j.php?MTID=m86ce051dbd968c3317ff09c343d31f40

Many surveys (and other types of databases) suffer from unit and item nonresponse. Typical practice accounts for unit nonresponse by inflating respondents’ survey weights, and accounts for item nonresponse using some form of imputation. Most methods implicitly treat both sources of nonresponse as missing at random. Sometimes, however, one knows information about the marginal distributions of some of the variables subject to missingness. In this talk, I discuss how such information can be leveraged to handle nonignorable missing data, including allowing different mechanisms for unit and item nonresponse (e.g., nonignorable unit nonresponse and ignorable item nonresponse). I illustrate the methods using data on voter turnout from the Current Population Survey.

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RMME/STAT Joint Colloquium

Overlap Weighting for Causal Inference

Dr. Fan Li
Duke University

Friday, October 1st, at 12:00PM ET

https://uconn-cmr.webex.com/uconn-cmr/j.php?MTID=ma4999c9bf3ac28d40a9686eec33d70ed

Covariate balance is crucial for causal comparisons. Weighting is a common strategy to balance covariates in observational studies. We propose a general class of weights—the balancing weights—that balance the weighted distributions of the covariates between treatment groups. These weights incorporate the propensity score to weight each group to an analyst-selected target population. This class unifies existing weighting methods, including commonly used weights such as inverse-probability weights as special cases. Within the class, we highlight the overlap weighting method, which has been widely adopted in applied research. The overlap weight of each unit is proportional to the probability of that unit being assigned to the opposite group. The overlap weights are bounded and minimize the asymptotic variance of the weighted average treatment effect among the class of balancing weights. The overlap weights also possess a desirable exact balance property. Extension of overlap weighting to multiple treatments, survival outcomes, and subgroup analysis will also be discussed.

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RMME/STAT Joint Colloquium

Assessing Personalization in Digital Health

Dr. Susan Murphy
Harvard University

Friday, September 10th, at 12:00PM ET

https://uconn-cmr.webex.com/uconn-cmr/j.php?MTID=m883b79a16b8b2c21038a80da6301cba3

Reinforcement Learning provides an attractive suite of online learning methods for personalizing interventions in Digital Health. However, after a reinforcement learning algorithm has been run in a clinical study, how do we assess whether personalization occurred? We might find users for whom it appears that the algorithm has indeed learned in which contexts the user is more responsive to a particular intervention. But could this have happened completely by chance? I discuss some first approaches to addressing these questions.

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