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Linden A. Using Randomization tests to assess treatment effects in multiple-group interrupted time series analysis. Journal of Evaluation in Clinical Practice DOI: 10.1111/jep.12995 |
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Cross DA, Nong P, Lemak CH, Cohen GR, Linden A, Adler-Milstein J. Practice strategies to improve primary care for high-needs patients under a pay-for-value program. Healthcare DOI: 10.1016/j.hjdsi.2018.08.004 |
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Linden A, Yarnold PR. Using machine learning to evaluate treatment effects in multiple-group interrupted time series analysis. Journal of Evaluation in Clinical Practice 2018;24:740-744. |
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Linden A. Using forecast modelling to evaluate treatment effects in single-group interrupted time series analysis. Journal of Evaluation in Clinical Practice 2018;24:695-700. |
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Kullgren JT, Krupka E, Schachter A, Linden A, Miller J, Acharya Y, Alford J, Duffy R, Adler-Milstein J. Precommitting to choose wisely about low-value services: A stepped wedge cluster randomized trial. BMJ – Quality & Safety DOI: 10.1136/bmjqs-2017-006699 |
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Linden A. Review of “A Course in Item Response Theory and Modeling with Stata” by Raykov and Marcoulides. Stata Journal 2018;18:485-488. |
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Linden A, Yarnold PR. Comparative accuracy of a diagnostic index modeled using (optimized) regression vs. novometrics. Optimal Data Analysis 2018;7:66-71. |
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Linden A, Yarnold PR. Identifying maximum-accuracy cut-points for diagnostic indexes via ODA. Optimal Data Analysis 2018;7:59-65. |
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Linden A, Yarnold PR. Reanalysis of the National Supported Work experiment using ODA. Optimal Data Analysis 2018;7:54-58. |
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Linden A, Yarnold PR. Using ODA in the evaluation of randomized controlled trials: application to survival outcome. Optimal Data Analysis 2018;7:50-53. |
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Linden A, Yarnold PR. Using ODA in the evaluation of randomized controlled trials. Optimal Data Analysis 2018;7:46-49. |
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Linden A. Using group-based trajectory modelling to enhance causal inference in interrupted time series analysis. Journal of Evaluation in Clinical Practice 2018;24:502-507. |
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Linden A. Using permutation tests to enhance causal inference in interrupted time series analysis. Journal of Evaluation in Clinical Practice 2018;24:496-501. |
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Linden A. Combining synthetic controls and interrupted time series analysis to improve causal inference in program evaluation. Journal of Evaluation in Clinical Practice 2018;24:447-453. |
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Linden A. A matching framework to improve causal inference in interrupted time series analysis. Journal of Evaluation in Clinical Practice 2018;24:408-415. |
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Linden A, Yarnold PR. Estimating causal effects for survival (time-to-event) outcomes by combining classification tree analysis and propensity score weighting. Journal of Evaluation in Clinical Practice 2018;24:380-387. |
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Linden A, Yarnold PR. Identifying causal mechanisms in health care interventions using classification tree analysis. Journal of Evaluation in Clinical Practice 2018;24:353-361. |