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585 results on '"Keele, Luke"'

101. Instrumental variables: Don't throw the baby out with the bathwater

Author

Keele, Luke and Small, Dylan

Subjects
Instrumental variables -- Evaluation -- Quality management -- Design and construction, Health, Business, Health care industry
Abstract

Soumerai and Koppel (1) make the case against the use of instrumental variables (IVs) in outcomes research. While they do not explicitly call for a halt to the use of [...]

Published
2019
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102. Comparison of Simulated Outcomes of Colorectal Cancer Surgery at the Highest-Performing vs Chosen Local Hospitals

Author

Finn, Caitlin B., primary, Wirtalla, Chris, additional, Roberts, Sanford E., additional, Collier, Karole, additional, Mehta, Shivan J., additional, Guerra, Carmen E., additional, Airoldi, Edoardo, additional, Zhang, Xu, additional, Keele, Luke, additional, Aarons, Cary B., additional, Jensen, Shane T., additional, and Kelz, Rachel R., additional

Published
2023
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103. Clinical effectiveness and cost-effectiveness of emergency surgery for adult emergency hospital admissions with common acute gastrointestinal conditions: the ESORT study

Author

Grieve, Richard, primary, Hutchings, Andrew, additional, Moler Zapata, Silvia, additional, O’Neill, Stephen, additional, Lugo-Palacios, David G, additional, Silverwood, Richard, additional, Cromwell, David, additional, Kircheis, Tommaso, additional, Silver, Elizabeth, additional, Snowdon, Claire, additional, Charlton, Paul, additional, Bellingan, Geoff, additional, Moonesinghe, Ramani, additional, Keele, Luke, additional, Smart, Neil, additional, and Hinchliffe, Robert, additional

Published
2023
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107. Concluding Comments

Author

Keele, Luke, Linn, Suzanna, and Webb, Clayton McLaughlin

Published
2016

111. Generalized Structural Mean Models for Evaluating Depression as a Post-treatment Effect Modifier of a Jobs Training Intervention

Author

Stephens Alisa, Keele Luke, and Joffe Marshall

Subjects
post-treatment interaction, structural mean model, causal odds-ratio, Mathematics, QA1-939, Probabilities. Mathematical statistics, QA273-280
Abstract

In randomized controlled trials, the evaluation of an overall treatment effect is often followed by effect modification or subgroup analyses, where the possibility of a different magnitude or direction of effect for varying values of a covariate is explored. While studies of effect modification are typically restricted to pretreatment covariates, longitudinal experimental designs permit the examination of treatment effect modification by intermediate outcomes, where intermediates are measured after treatment but before the final outcome. We present a novel application of generalized structural mean models (GSMMs) for simultaneously assessing effect modification by post-treatment covariates and accounting for noncompliance to assigned treatment status. The proposed approach may also be used to identify post-treatment effect modifiers in the absence of noncompliance. The methods are evaluated using a simulation study that demonstrates that our approach retains consistent estimation of effect modification by intermediate variables that are affected by treatment and also predict outcomes. We illustrate the method using a randomized trial designed to promote re-employment through teaching skills to enhance self-esteem and inoculate job seekers against setbacks in the job search process. Our analysis provides some evidence that the intervention was much less successful among subjects that displayed higher levels of depression at intermediate post-treatment waves of the study. We also compare the assumptions of our approach and principal stratification as alternatives to account for differences in effects by intermediate variables.

Published
2016
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123. Operative and Nonoperative Outcomes of Emergency General Surgery Conditions

Author

Kaufman, Elinore J., primary, Keele, Luke J., additional, Wirtalla, Christopher J., additional, Rosen, Claire B., additional, Roberts, Sanford E., additional, Mavroudis, Catherine L., additional, Reilly, Patrick M., additional, Holena, Daniel N., additional, McHugh, Matthew D., additional, Small, Dylan, additional, and Kelz, Rachel R., additional

Published
2022
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127. Local Instrumental Variable Methods to Address Confounding and Heterogeneity when Using Electronic Health Records: An Application to Emergency Surgery

Author

Moler-Zapata, Silvia, primary, Grieve, Richard, additional, Lugo-Palacios, David, additional, Hutchings, A., additional, Silverwood, R., additional, Keele, Luke, additional, Kircheis, Tommaso, additional, Cromwell, David, additional, Smart, Neil, additional, Hinchliffe, Robert, additional, and O’Neill, Stephen, additional

Published
2022
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129. A General Approach to Causal Mediation Analysis

Author

Imai, Kosuke, Keele, Luke, and Tingley, Dustin

Abstract

Traditionally in the social sciences, causal mediation analysis has been formulated, understood, and implemented within the framework of linear structural equation models. We argue and demonstrate that this is problematic for 3 reasons: the lack of a general definition of causal mediation effects independent of a particular statistical model, the inability to specify the key identification assumption, and the difficulty of extending the framework to nonlinear models. In this article, we propose an alternative approach that overcomes these limitations. Our approach is general because it offers the definition, identification, estimation, and sensitivity analysis of causal mediation effects without reference to any specific statistical model. Further, our approach explicitly links these 4 elements closely together within a single framework. As a result, the proposed framework can accommodate linear and nonlinear relationships, parametric and nonparametric models, continuous and discrete mediators, and various types of outcome variables. The general definition and identification result also allow us to develop sensitivity analysis in the context of commonly used models, which enables applied researchers to formally assess the robustness of their empirical conclusions to violations of the key assumption. We illustrate our approach by applying it to the Job Search Intervention Study. We also offer easy-to-use software that implements all our proposed methods. (Contains 7 figures, 7 tables and 14 footnotes.)

Published
2010
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132. sj-docx-1-mdm-10.1177_0272989X221100799 – Supplemental material for Local Instrumental Variable Methods to Address Confounding and Heterogeneity when Using Electronic Health Records: An Application to Emergency Surgery

Author

Moler-Zapata, Silvia, Grieve, Richard, Lugo-Palacios, David, Hutchings, A., Silverwood, R., Keele, Luke, Kircheis, Tommaso, Cromwell, David, Smart, Neil, Hinchliffe, Robert, and O’Neill, Stephen

Subjects
111708 Health and Community Services, 111799 Public Health and Health Services not elsewhere classified, 160807 Sociological Methodology and Research Methods, FOS: Health sciences, FOS: Sociology
Abstract

Supplemental material, sj-docx-1-mdm-10.1177_0272989X221100799 for Local Instrumental Variable Methods to Address Confounding and Heterogeneity when Using Electronic Health Records: An Application to Emergency Surgery by Silvia Moler-Zapata, Richard Grieve, David Lugo-Palacios, A. Hutchings, R. Silverwood, Luke Keele, Tommaso Kircheis, David Cromwell, Neil Smart, Robert Hinchliffe and Stephen O’Neill in Medical Decision Making

Published
2022
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