| Conducting interrupted time-series analysis for single-and multiple-group comparisons A Linden The Stata Journal 15 (2), 480-500, 2015 | 1162 | 2015 |
| Measuring diagnostic and predictive accuracy in disease management: an introduction to receiver operating characteristic (ROC) analysis A Linden Journal of evaluation in clinical practice 12 (2), 132-139, 2006 | 442 | 2006 |
| Motivational interviewing‐based health coaching as a chronic care intervention A Linden, SW Butterworth, JO Prochaska Journal of evaluation in clinical practice 16 (1), 166-174, 2010 | 272 | 2010 |
| Applying a propensity score‐based weighting model to interrupted time series data: improving causal inference in programme evaluation A Linden, JL Adams Journal of evaluation in clinical practice 17 (6), 1231-1238, 2011 | 257 | 2011 |
| Effect of motivational interviewing-based health coaching on employees' physical and mental health status. S Butterworth, A Linden, W McClay, MC Leo Journal of occupational health psychology 11 (4), 358, 2006 | 216 | 2006 |
| Assessing regression to the mean effects in health care initiatives A Linden BMC medical research methodology 13, 1-7, 2013 | 193 | 2013 |
| Now trending: Coping with non-parallel trends in difference-in-differences analysis AM Ryan, E Kontopantelis, A Linden, JF Burgess Jr. Statistical Methods in Medical Research, 2018 | 178 | 2018 |
| Using balance statistics to determine the optimal number of controls in matching studies A Linden, SJ Samuels Journal of evaluation in clinical practice 19 (5), 968-975, 2013 | 166 | 2013 |
| Health coaching as an intervention in health management programs SW Butterworth, A Linden, W McClay Disease Management & Health Outcomes 15, 299-307, 2007 | 155 | 2007 |
| Estimating causal effects for multivalued treatments: a comparison of approaches A Linden, SD Uysal, A Ryan, JL Adams Statistics in Medicine 35 (4), 534-552, 2016 | 147 | 2016 |
| A comprehensive set of postestimation measures to enrich interrupted time-series analysis A Linden The Stata Journal 17 (1), 73–88, 2017 | 136 | 2017 |
| Conducting sensitivity analysis for unmeasured confounding in observational studies using E-values: the evalue package A Linden, MB Mathur, TJ VanderWeele The Stata Journal 20 (1), 162-175, 2020 | 116 | 2020 |
| Challenges to validity in single-group interrupted time series analysis A Linden Journal of Evaluation in Clinical Practice 23 (2), 413-418, 2017 | 96 | 2017 |
| A comprehensive hospital-based intervention to reduce readmissions for chronically ill patients: a randomized controlled trial A Linden, SW Butterworth Am J Manag Care 20 (10), 783-792, 2014 | 96 | 2014 |
| Combining machine learning and propensity score weighting to estimate causal effects in multivalued treatments A Linden, PR Yarnold Journal of Evaluation in Clinical Practice 22 (6), 875-885, 2016 | 94 | 2016 |
| Using data mining techniques to characterize participation in observational studies A Linden, PR Yarnold Journal of Evaluation in Clinical Practice 22 (6), 839-847, 2016 | 94 | 2016 |
| Modeling time‐to‐event (survival) data using classification tree analysis A Linden, PR Yarnold Journal of Evaluation in Clinical Practice 23, 1299-1308, 2017 | 86 | 2017 |
| Combining machine learning and matching techniques to improve causal inference in program evaluation A Linden, PR Yarnold Journal of Evaluation in Clinical Practice 22 (6), 868-874, 2016 | 84 | 2016 |
| Using machine learning to assess covariate balance in matching studies A Linden, PR Yarnold Journal of Evaluation in Clinical Practice 22 (6), 848-854, 2016 | 80 | 2016 |
| Combining propensity score‐based stratification and weighting to improve causal inference in the evaluation of health care interventions A Linden Journal of evaluation in clinical practice 20 (6), 1065-1071, 2014 | 80 | 2014 |
Paul R Yarnold, Ph.D.CEO, Optimal Data AnalysisVerified email at planetyarnold.com
