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5. The Design of Clustered Observational Studies in Education

Author

Page, Lindsay C., Lenard, Matthew A., and Keele, Luke

Abstract

Clustered observational studies (COSs) are a critical analytic tool for educational effectiveness research. We present a design framework for the development and critique of COSs. The framework is built on the counterfactual model for causal inference and promotes the concept of designing COSs that emulate the targeted randomized trial that would have been conducted were it feasible. We emphasize the key role of understanding the assignment mechanism to study design. We review methods for statistical adjustment and highlight a recently developed form of matching designed specifically for COSs. We review how regression models can be profitably combined with matching and note best practices for estimates of statistical uncertainty. Finally, we review how sensitivity analyses can determine whether conclusions are sensitive to bias from potential unobserved confounders. We demonstrate concepts with an evaluation of a summer school reading intervention in a large U.S. school district.

Published
2020

13. An Internally Validated Prognostic Risk-Score Model for Disease-Specific Survival in Clinical Stage I and II Merkel Cell Carcinoma

Author

Shannon, Adrienne B., Straker, III, Richard J., Carr, Michael J., Sun, James, Landa, Karenia, Baecher, Kirsten, Lynch, Kevin, Bartels, Harrison G., Panchaud, Robyn, Keele, Luke J., Lowe, Michael C., Slingluff, Craig L., Jameson, Mark J., Tsai, Kenneth Y., Faries, Mark B., Beasley, Georgia M., Sondak, Vernon K., Karakousis, Giorgos C., Zager, Jonathan S., and Miura, John T.

Published
2022
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18. Matching Methods for Clustered Observational Studies in Education

Author

Keele, Luke, Lenard, Matthew, and Page, Lindsay

Abstract

Many interventions in education occur in settings where treatments are applied to groups. For example, a reading intervention may be implemented for all students in some schools and withheld from students in other schools. When such treatments are nonrandomly allocated, outcomes across the treated and control groups may differ due to the treatment or due to baseline differences between groups. When this is the case, researchers can use statistical adjustment to make treated and control groups similar in terms of observed characteristics. Recent work in statistics has developed matching methods designed for contexts where treatments are clustered. This form of matching, known as multilevel matching, may be well suited to many education applications where treatments are assigned to schools. In this article, we provide an extensive evaluation of multilevel matching and compare it to multilevel regression modeling. We evaluate multilevel matching methods in two ways. "First, we use these matching methods in a within-study comparison design and attempt to recover treatment effect estimates from three clustered randomized trials." Second, we conduct a simulation study. We find evidence that generally favors an analytic approach to statistical adjustment that combines multilevel matching with regression adjustment. We conclude with an empirical application.

Published
2021
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20. ASO Visual Abstract: An Internally Validated Prognostic Risk Score Model for Disease-Specific Survival in Clinical Stage I and II Merkel Cell Carcinoma

Author

Shannon, Adrienne B., Straker, III, Richard J., Carr, Michael J., Sun, James, Landa, Karenia, Baecher, Kirsten, Lynch, Kevin, Bartels, Harrison G., Panchaud, Robyn, Keele, Luke J., Lowe, Michael C., Slingluff, Jr, Craig L., Jameson, Mark J., Tsai, Kenneth Y., Faries, Mark B., Beasley, Georgia M., Sondak, Vernon K., Karakousis, Giorgos C., Zager, Jonathan S., and Miura, John T.

Published
2022
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23. Approximate balancing weights for clustered observational study designs.

Author

Ben‐Michael, Eli, Page, Lindsay, and Keele, Luke

Subjects
SCIENTIFIC observation, RANDOM effects model, INTRACLASS correlation, SIGNAL-to-noise ratio
Abstract

In a clustered observational study, a treatment is assigned to groups and all units within the group are exposed to the treatment. We develop a new method for statistical adjustment in clustered observational studies using approximate balancing weights, a generalization of inverse propensity score weights that solve a convex optimization problem to find a set of weights that directly minimize a measure of covariate imbalance, subject to an additional penalty on the variance of the weights. We tailor the approximate balancing weights optimization problem to the clustered observational study setting by deriving an upper bound on the mean square error and finding weights that minimize this upper bound, linking the level of covariate balance to a bound on the bias. We implement the procedure by specializing the bound to a random cluster‐level effects model, leading to a variance penalty that incorporates the signal‐to‐noise ratio and penalizes the weight on individuals and the total weight on groups differently according to the the intra‐class correlation. [ABSTRACT FROM AUTHOR]

Published
2024
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31. Overlap Violations in Clustered Observational Studies of Educational Interventions.

Author

Keele, Luke, Lenard, Matthew, and Page, Lindsay

Subjects
SCIENTIFIC observation, CATHOLIC schools, TEST methods, DIAGNOSIS methods, CAUSAL inference
Abstract

In education settings, treatments are often non-randomly assigned to clusters, such as schools or classrooms, while outcomes are measured for students. This research design is called the clustered observational study (COS). We examine the consequences of common support violations in the COS context. Common support violations occur when the covariate distributions of treated and control units have substantial areas of non-overlap. Such violations are likely to occur in a COS, especially with a small number of treated clusters. We provide a comprehensive review of methods for overlap violations in the context of COS designs. We provide an overview of diagnostic tests and trimming methods to ensure overlap holds for the distributions of treated and control covariates. We then outline how trimming changes the estimand and how profiling can be used to understand the causal quantity for which overlap holds. Finally, we demonstrate how steps to achieve adequate overlap can result in very narrowly defined causal effects that may have little policy relevance. We use data on Catholic schools to illustrate concepts throughout. [ABSTRACT FROM AUTHOR]

Published
2024
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49. A guide to regression discontinuity designs in medical applications.

Author

Cattaneo, Matias D., Keele, Luke, and Titiunik, Rocío

Subjects
*REGRESSION discontinuity design, *REGRESSION analysis, *FIX-point estimation, *ANTIRETROVIRAL agents, *MEDICAL research, *CANCER relapse
Abstract

We present a practical guide for the analysis of regression discontinuity (RD) designs in biomedical contexts. We begin by introducing key concepts, assumptions, and estimands within both the continuity‐based framework and the local randomization framework. We then discuss modern estimation and inference methods within both frameworks, including approaches for bandwidth or local neighborhood selection, optimal treatment effect point estimation, and robust bias‐corrected inference methods for uncertainty quantification. We also overview empirical falsification tests that can be used to support key assumptions. Our discussion focuses on two particular features that are relevant in biomedical research: (i) fuzzy RD designs, which often arise when therapeutic treatments are based on clinical guidelines, but patients with scores near the cutoff are treated contrary to the assignment rule; and (ii) RD designs with discrete scores, which are ubiquitous in biomedical applications. We illustrate our discussion with three empirical applications: the effect CD4 guidelines for anti‐retroviral therapy on retention of HIV patients in South Africa, the effect of genetic guidelines for chemotherapy on breast cancer recurrence in the United States, and the effects of age‐based patient cost‐sharing on healthcare utilization in Taiwan. Complete replication materials employing publicly available data and statistical software in Python, R and Stata are provided, offering researchers all necessary tools to conduct an RD analysis. [ABSTRACT FROM AUTHOR]

Published
2023
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