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  1. Design, Analysis, Power, and Sample Size Calculation for Three‐Phase Interrupted Time Series Analysis in Evaluation of Health Policy Interventions.Bo Zhang, Wei Liu, Stephenie C. Lemon, Bruce A. Barton, Melissa A. Fischer, Colleen Lawrence, Elizabeth J. Rahn, Maria I. Danila, Kenneth G. Saag, Paul A. Harris & Jeroan J. Allison - forthcoming - Journal of Evaluation in Clinical Practice.
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  • Using Group-Based Trajectory Modelling to Enhance Causal Inference in Interrupted Time Series Analysis.Ariel Linden - 2018 - Journal of Evaluation in Clinical Practice 24 (3):502-507.
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  • Combining Machine Learning and Matching Techniques to Improve Causal Inference in Program Evaluation.Ariel Linden & Paul R. Yarnold - 2016 - Journal of Evaluation in Clinical Practice 22 (6):868-874.
  • Using Permutation Tests to Enhance Causal Inference in Interrupted Time Series Analysis.Ariel Linden - 2018 - Journal of Evaluation in Clinical Practice 24 (3):496-501.
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  • Combining Synthetic Controls and Interrupted Time Series Analysis to Improve Causal Inference in Program Evaluation.Ariel Linden - 2018 - Journal of Evaluation in Clinical Practice 24 (2):447-453.
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  • A Matching Framework to Improve Causal Inference in Interrupted Time-Series Analysis.Ariel Linden - 2018 - Journal of Evaluation in Clinical Practice 24 (2):408-415.
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  • Persistent Threats to Validity in Single‐Group Interrupted Time Series Analysis with a Cross Over Design.Ariel Linden - 2017 - Journal of Evaluation in Clinical Practice 23 (2):419-425.
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  • Challenges to Validity in Single‐Group Interrupted Time Series Analysis.Ariel Linden - 2017 - Journal of Evaluation in Clinical Practice 23 (2):413-418.
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  • Using Forecast Modelling to Evaluate Treatment Effects in Single-Group Interrupted Time Series Analysis.Ariel Linden - 2018 - Journal of Evaluation in Clinical Practice 24 (4):695-700.
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  • Using Machine Learning to Identify Structural Breaks in Single-Group Interrupted Time Series Designs.Ariel Linden & Paul R. Yarnold - 2016 - Journal of Evaluation in Clinical Practice 22 (6):855-859.
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  • Combining Machine Learning and Propensity Score Weighting to Estimate Causal Effects in Multivalued Treatments.Ariel Linden & Paul R. Yarnold - 2016 - Journal of Evaluation in Clinical Practice 22 (6):875-885.
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  • Using Machine Learning to Assess Covariate Balance in Matching Studies.Ariel Linden & Paul R. Yarnold - 2016 - Journal of Evaluation in Clinical Practice 22 (6):848-854.
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  • Using Machine Learning to Model Dose-Response Relationships.Ariel Linden, Paul R. Yarnold & Brahmajee K. Nallamothu - 2016 - Journal of Evaluation in Clinical Practice 22 (6):860-867.
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  • Using Randomization Tests to Assess Treatment Effects in Multiple-Group Interrupted Time Series Analysis.Ariel Linden - 2019 - Journal of Evaluation in Clinical Practice 25 (1):5-10.
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  • Using Machine Learning to Evaluate Treatment Effects in Multiple-Group Interrupted Time Series Analysis.Ariel Linden & Paul R. Yarnold - 2018 - Journal of Evaluation in Clinical Practice 24 (4):740-744.
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  • Maximizing Predictive Accuracy By P. R. Yarnold R. C. Soltysik. ODA Books, Chicago, IL, 2016, $98.00, 396 Pp. ISBN 0 692 70092 7. [REVIEW]Ariel Linden - 2016 - Journal of Evaluation in Clinical Practice 22 (6):835-838.
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