Ariel Linden, DrPH
Research Specialist
Medicine
School of Medicine

Dr. Linden is a health services researcher with expertise in evaluating the effectiveness of health care interventions and policy changes. As a methodologist, he specializes in developing and implementing techniques to maximize causal inference with observational data. His recent interests include the use of machine learning tools for studying causality and predictive modeling. Dr. Linden has published over 160 peer-reviewed papers and has written over 80 statistical software packages for Stata.

Education & Training

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• DrPH Health Services University of California 1997

Interests

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• statistics
• health services research
• causal inference
• risk prediction
• research methods
• machine learning

Websites

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• Linden Consulting Group, LLC (lindenconsulting.org)

Publications (137)

Top publication keywords:
Data Interpretation, StatisticalTobacco ProductsPropensity ScoreModels, StatisticalMachine LearningResearch DesignBiasDiscriminant AnalysisCausalityProgram EvaluationDisease ManagementObservational Studies as TopicCommerceInterrupted Time Series AnalysisDecision Trees

• Electronic connectivity between hospital pairs: impact on emergency department-related utilization.
  Journal of the American Medical Informatics Association : JAMIA 2023 Adler-Milstein J, Linden A, Hsia RY, Everson J
• Modelling of intensive care unit (ICU) length of stay as a quality measure: a problematic exercise.
  BMC medical research methodology 2023 Moran JL, Duke GJ, Santamaria JD, Linden A, Australian & New Zealand Intensive Care Society (ANZICS) Centre for Outcomes & Resource Evaluation (
• Erratum: A comprehensive set of post-estimation measures to enrich interrupted time series analysis
  Stata Journal 2022 Linden A
• Computing the fragility index for randomized trials and meta-analyses using Stata
  Stata Journal 2022 Linden A
• Association of Primary Care Engagement in Value-Based Reform Programs With Health Services Outcomes: Participation and Synergies.
  JAMA health forum 2022 Adler-Milstein J, Linden A, Hollingsworth JM, Ryan AM

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• A regression-with-residuals method for analyzing causal mediation: The rwrmed package
  Stata Journal 2021 Linden A, Huber C, Wodtke GT
• Longitudinal participation in delivery and payment reform programs among US Primary Care Organizations.
  Health services research 2021 Adler-Milstein J, Linden A, Bernstein S, Hollingsworth J, Ryan A
• Conducting sensitivity analysis for unmeasured confounding in observational studies using E-values: The evalue package
  Stata Journal 2020 Linden A, Mathur MB, VanderWeele TJ.
• Implementing ODA from Within Stata: Implementing CTA from Within Stata: Minimizing Imbalances on Patient Characteristics Between Treatment Groups in Randomized Trials (Invited)
  Optimal Data Analysis 2020 Linden A
• Implementing ODA from Within Stata: Identifying Causal Mechanisms in Interventions (Invited)
  Optimal Data Analysis 2020 Linden A
• Implementing ODA from Within Stata: Modeling Time‐to‐Event (Survival) Data (Invited)
  Optimal Data Analysis 2020 Linden A
• Implementing ODA from Within Stata: Finding the Optimal Cut-Point of a Diagnostic Test or Index (Invited)
  Optimal Data Analysis 2020 Linden A
• Implementing CTA from Within Stata: Using CTA to Generate Propensity Score Weights (Invited)
  Optimal Data Analysis 2020 Linden A
• Implementing CTA from Within Stata: Characterizing Participation in Observational Studies (Invited)
  Optimal Data Analysis 2020 Linden A
• Implementing CTA from Within Stata: Assessing the Quality of the Randomization Process in Randomized Controlled Trials (Invited)
  Optimal Data Analysis 2020 Linden A
• Implementing ODA from Within Stata: Identifying Structural Breaks in Single-Group Interrupted Time Series Designs (Invited)
  Optimal Data Analysis 2020 Linden A
• Implementing ODA from Within Stata: Evaluating Treatment Effects in Multiple‐Group Interrupted Time Series Analysis (Invited)
  Optimal Data Analysis 2020 Linden A
• Implementing ODA from Within Stata: Evaluating Treatment Effects for Survival (Time‐to‐Event) Outcomes (Invited)
  Optimal Data Analysis 2020 Linden A
• Implementing ODA from Within Stata: Assessing Covariate Balance in Observational Studies (Invited)
  Optimal Data Analysis 2020 Linden A
• Implementing ODA from Within Stata: An Application to Dose-Response Relationships (Invited)
  Optimal Data Analysis 2020 Linden A
• Implementing ODA from Within Stata: An Application to Estimating Treatment Effects using Observational Data (Invited). 2020;9:14-20
  Optimal Data Analysis 2020 Linden A
• Implementing ODA from Within Stata: An Application to Data From a Randomized Controlled Trial (Invited)
  Optimal Data Analysis 2020 Linden A
• Assessing medication adherence using Stata
  Stata Journal 2019 Linden A
• Assessment of Self-monitoring of Blood Glucose in Individuals With Type 2 Diabetes Not Using Insulin.
  JAMA internal medicine 2019 Platt KD, Thompson AN, Lin P, Basu T, Linden A, Fendrick AM
• Effect of Sample Size on Discovery of Relationships in Random Data by Classification Algorithms
  Optimal Data Analysis 2019 Linden A, Yarnold PR
• Logistic Discriminant Analysis and Structural Equation Modeling Both Identify Effects in Random Data
  Optimal Data Analysis 2019 Linden A, Bryant FB, Yarnold PR
• Multi-layer perceptron neural net model identifies effect in random data
  Optimal Data Analysis 2019 Linden A, Yarnold PR
• Novometric stepwise CTA analysis discriminating three class Categories using two ordered attributes
  Optimal Data Analysis 2019 Yarnold PR, Linden A
• Optimizing Suboptimal Classification Trees: Matlab® CART Model Predicting Probability of Lower Limb Prosthesis User’s Functional Potential
  Optimal Data Analysis 2019 Yarnold PR, Linden A
• Some machine learning algorithms find relationships between variables when none exist -- CTA doesn’t
  Optimal Data Analysis 2019 Linden A, Yarnold PR
• Now trending: Coping with non-parallel trends in difference-in-differences analysis.
  Statistical methods in medical research 2018 Ryan AM, Kontopantelis E, Linden A, Burgess JF
• Practice strategies to improve primary care for chronic disease patients under a pay-for-value program.
  Healthcare (Amsterdam, Netherlands) 2018 Cross DA, Nong P, Harris-Lemak C, Cohen GR, Linden A, Adler-Milstein J
• Using randomization tests to assess treatment effects in multiple-group interrupted time series analysis.
  Journal of evaluation in clinical practice 2018 Linden A
• Using machine learning to evaluate treatment effects in multiple-group interrupted time series analysis.
  Journal of evaluation in clinical practice 2018 Linden A, Yarnold PR
• Using forecast modelling to evaluate treatment effects in single-group interrupted time series analysis.
  Journal of evaluation in clinical practice 2018 Linden A
• Using group-based trajectory modelling to enhance causal inference in interrupted time series analysis.
  Journal of evaluation in clinical practice 2018 Linden A
• Using permutation tests to enhance causal inference in interrupted time series analysis.
  Journal of evaluation in clinical practice 2018 Linden A
• Combining synthetic controls and interrupted time series analysis to improve causal inference in program evaluation.
  Journal of evaluation in clinical practice 2018 Linden A
• Comparative accuracy of a diagnostic index modeled using (optimized) regression vs. novometrics
  Optimal Data Analysis 2018 Linden A, Yarnold PR.
• Identifying maximum-accuracy cut-points for diagnostic indexes via ODA
  Optimal Data Analysis 2018 Linden A, Yarnold PR.
• Reanalysis of the National Supported Work Experiment Using ODA
  Optimal Data Analysis 2018 Linden A, Yarnold PR.
• Review of “A Course in Item Response Theory and Modeling with Stata” by Raykov and Marcoulides
  Stata Journal 2018 Linden A.
• The Australian gun buy-back program and the rate of suicide by firearm
  Optimal Data Analysis 2018 Linden A, Yarnold PR.
• Using ODA in the evaluation of randomized controlled trials
  Optimal Data Analysis 2018 Linden A, Yarnold PR.
• Using ODA in the evaluation of randomized controlled trials: application to survival outcomes
  Optimal Data Analysis 2018 Linden A, Yarnold PR.
• A matching framework to improve causal inference in interrupted time-series analysis.
  Journal of evaluation in clinical practice 2017 Linden A
• Estimating causal effects for survival (time-to-event) outcomes by combining classification tree analysis and propensity score weighting.
  Journal of evaluation in clinical practice 2017 Linden A, Yarnold PR
• Identifying causal mechanisms in health care interventions using classification tree analysis.
  Journal of evaluation in clinical practice 2017 Linden A, Yarnold PR
• Precommitting to choose wisely about low-value services: a stepped wedge cluster randomised trial.
  BMJ quality & safety 2017 Kullgren JT, Krupka E, Schachter A, Linden A, Miller J, Acharya Y, Alford J, Duffy R, Adler-Milstein J
• Minimizing imbalances on patient characteristics between treatment groups in randomized trials using classification tree analysis.
  Journal of evaluation in clinical practice 2017 Linden A, Yarnold PR
• Modeling time-to-event (survival) data using classification tree analysis.
  Journal of evaluation in clinical practice 2017 Linden A, Yarnold PR
• Using classification tree analysis to generate propensity score weights.
  Journal of evaluation in clinical practice 2017 Linden A, Yarnold PR
• Improving causal inference with a doubly robust estimator that combines propensity score stratification and weighting.
  Journal of evaluation in clinical practice 2017 Linden A
• A comparison of approaches for stratifying on the propensity score to reduce bias.
  Journal of evaluation in clinical practice 2017 Linden A
• A comprehensive set of post-estimation measures to enrich interrupted time series analysis
  Stata Journal 2017 Linden A.
• Computing propensity score weights for CTA models involving perfectly predicted endpoints
  Optimal Data Analysis 2017 Yarnold PR, Linden A
• Persistent threats to validity in single-group interrupted time series analysis with a cross over design.
  Journal of evaluation in clinical practice 2016 Linden A
• Challenges to validity in single-group interrupted time series analysis.
  Journal of evaluation in clinical practice 2016 Linden A
• Combining machine learning and propensity score weighting to estimate causal effects in multivalued treatments.
  Journal of evaluation in clinical practice 2016 Linden A, Yarnold PR
• Combining machine learning and matching techniques to improve causal inference in program evaluation.
  Journal of evaluation in clinical practice 2016 Linden A, Yarnold PR
• Using machine learning to model dose-response relationships.
  Journal of evaluation in clinical practice 2016 Linden A, Yarnold PR, Nallamothu BK
• Using machine learning to identify structural breaks in single-group interrupted time series designs.
  Journal of evaluation in clinical practice 2016 Linden A, Yarnold PR
• Using machine learning to assess covariate balance in matching studies.
  Journal of evaluation in clinical practice 2016 Linden A, Yarnold PR
• Using data mining techniques to characterize participation in observational studies.
  Journal of evaluation in clinical practice 2016 Linden A, Yarnold PR
• Novometric analysis with ordered class variables: The optimal alternative to linear regression analysis
  Optimal Data Analysis 2016 Yarnold PR, Linden A.
• Review of “Maximizing Predictive Accuracy By P. R. Yarnold R. C. Soltysik. ODA Books, Chicago, IL, 2016, $98.00, 396 pp. ISBN 0 692 70092 7”
  J Eval Clin Pract 2016 Linden A.
• Theoretical aspects of the D statistic
  Optimal Data Analysis 2016 Yarnold PR, Linden A.
• Using machine learning to model dose-response relationships via ODA: eliminating response variable baseline variation by ipsative standardization
  Optimal Data Analysis 2016 Yarnold PR, Linden A.
• Estimating Measurement Error of the Patient Activation Measure for Respondents with Partially Missing Data.
  BioMed research international 2015 Linden A
• Estimating causal effects for multivalued treatments: a comparison of approaches.
  Statistics in medicine 2015 Linden A, Uysal SD, Ryan A, Adams JL
• Conducting interrupted time-series analysis for single- and multiple-group comparisons
  Stata Journal 2015 Linden A.
• Graphical displays for assessing covariate balance in matching studies.
  Journal of evaluation in clinical practice 2014 Linden A
• A comprehensive hospital-based intervention to reduce readmissions for chronically ill patients: a randomized controlled trial.
  The American journal of managed care 2014 Linden A, Butterworth S
• Combining propensity score-based stratification and weighting to improve causal inference in the evaluation of health care interventions.
  Journal of evaluation in clinical practice 2014 Linden A
• Review of “An Introduction to Stata for Health Researchers, Fourth Edition, by Juul and Frydenberg.”
  Stata Journal 2014 Linden A.
• Assessing regression to the mean effects in health care initiatives.
  BMC medical research methodology 2013 Linden A
• Using balance statistics to determine the optimal number of controls in matching studies.
  Journal of evaluation in clinical practice 2013 Linden A, Samuels SJ
• Advancing current approaches to disease management evaluation: capitalizing on heterogeneity to understand what works and for whom.
  BMC medical research methodology 2013 Elissen AM, Adams JL, Spreeuwenberg M, Duimel-Peeters IG, Spreeuwenberg C, Linden A, Vrijhoef HJ
• Using mediation analysis to identify causal mechanisms in disease management interventions
  Health Services and Outcomes Research Methodology 2013 Linden A, Karlson KB.
• Estimating measurement error when annualizing health care costs.
  Journal of evaluation in clinical practice 2012 Linden A, Samuels SJ
• Combining the regression discontinuity design and propensity score-based weighting to improve causal inference in program evaluation.
  Journal of evaluation in clinical practice 2012 Linden A, Adams JL
• Identifying spin in health management evaluations.
  Journal of evaluation in clinical practice 2011 Linden A
• A successful hospital-based disease management program to reduce admissions among patients with multiple chronic illnesses
  International Journal of Person Centered Medicine 2011 Linden A, Bonollo M, Fiddes K.
• Use of an algorithm applied to urine drug screening to assess adherence to a hydrocodone regimen
  Journal of Clinical Pharmacology & Therapeutics 2011 Couto J, Webster L, Romney M, Leider H, Linden A.
• Applying a propensity score-based weighting model to interrupted time series data: improving causal inference in programme evaluation.
  Journal of evaluation in clinical practice 2010 Linden A, Adams JL
• Designing a prospective study when randomization is not feasible.
  Evaluation & the health professions 2010 Linden A
• Help patients with chronic kidney disease stave off dialysis.
  The Journal of family practice 2010 Linden A, Biuso TJ, Butterworth SW
• Evaluating health management programmes over time: application of propensity score-based weighting to longitudinal data.
  Journal of evaluation in clinical practice 2010 Linden A, Adams JL
• Motivational interviewing-based health coaching as a chronic care intervention.
  Journal of evaluation in clinical practice 2010 Linden A, Butterworth SW, Prochaska JO
• Using propensity score-based weighting in the evaluation of health management programme effectiveness.
  Journal of evaluation in clinical practice 2010 Linden A, Adams JL
• Use of an algorithm applied to urine drug screening to assess adherence to an oxycontin regimen.
  Journal of opioid management 2009 Couto JE, Webster L, Romney MC, Leider HL, Linden A
• Improving participant selection in disease management programmes: insights gained from propensity score stratification.
  Journal of evaluation in clinical practice 2008 Linden A, Adams JL
• Disease management: truth in advertising.
  Health affairs (Project Hope) 2008 Linden A
• Narrow model.
  Health affairs (Project Hope) 2008 Linden A
• Sample size in disease management program evaluation: the challenge of demonstrating a statistically significant reduction in admissions.
  Disease management : DM 2008 Linden A
• Disease management in chronic kidney disease.
  Advances in chronic kidney disease 2008 Rastogi A, Linden A, Nissenson AR
• Medicare disease management in policy context.
  Health care financing review 2008 Linden A, Adler-Milstein J
• The case-mix of chronic illness hospitalization rates in a managed care population: implications for health management programmes.
  Journal of evaluation in clinical practice 2007 Linden A, Goldberg S
• Evaluation of a medicaid asthma disease management program.
  Disease management : DM 2007 Linden A, Berg GD, Wadhwa S
• Determining if disease management saves money: an introduction to meta-analysis.
  Journal of evaluation in clinical practice 2007 Linden A, Adams JL
• A conceptual framework for targeting prediabetes with lifestyle, clinical, and behavioral management interventions.
  Disease management : DM 2007 Biuso TJ, Butterworth S, Linden A
• Consensus development and application of ICD-9 codes for defining chronic illnesses and their complications
  Dis Manage Health Outcomes 2007 Linden A, Biuso TJ, Gopal A, Barker AF, Cigarroa J, Haranath SP, Rinkevich D, Stajduhar K.
• Estimating the effect of regression to the mean in health management programs
  Dis Manage Health Outcomes 2007 Linden A.
• Health coaching as an intervention in health management programs
  Dis Manage Health Outcomes 2007 Butterworth S, Linden A, McClay W.
• Use of the total population approach to measure U.S. disease management industry's cost savings: issues and implications
  Dis Manage Health Outcomes 2007 Linden A.
• Evaluating program effectiveness using the regression point displacement design.
  Evaluation & the health professions 2006 Linden A, Trochim WM, Adams JL
• Effect of motivational interviewing-based health coaching on employees' physical and mental health status.
  Journal of occupational health psychology 2006 Butterworth S, Linden A, McClay W, Leo MC
• Is Israel ready for disease management?
  The Israel Medical Association journal : IMAJ 2006 Linden A
• Disease management interventions II: What else is in the black box?
  Disease management : DM 2006 Linden A, Butterworth SW, Roberts N
• Evaluating disease management programme effectiveness: an introduction to instrumental variables.
  Journal of evaluation in clinical practice 2006 Linden A, Adams JL
• Evaluating disease management programme effectiveness: an introduction to the regression discontinuity design.
  Journal of evaluation in clinical practice 2006 Linden A, Adams JL, Roberts N
• Measuring diagnostic and predictive accuracy in disease management: an introduction to receiver operating characteristic (ROC) analysis.
  Journal of evaluation in clinical practice 2006 Linden A
• Strengthening the case for disease management effectiveness: un-hiding the hidden bias.
  Journal of evaluation in clinical practice 2006 Linden A, Adams JL, Roberts N
• What will it take for disease management to demonstrate a return on investment? New perspectives on an old theme.
  The American journal of managed care 2006 Linden AL
• Evaluating the effectiveness of home health as a disease management strategy
  Home Health Care Management & Practice 2006 Linden A.
• In search of financial savings from disease management: Applying the number needed to decrease (NND) analysis to a diabetic population
  Dis Manage Health Outcomes 2006 Linden A, Biuso TJ.
• Using visual displays as a tool to demonstrate disease management program effectiveness.
  Disease management : DM 2005 Linden A, Roberts N
• Letter regarding article by Galbreath et al, "Long-term healthcare and cost outcomes of disease management in a large, randomized, community-based population with heart failure".
  Circulation 2005 Linden A, Wilson T
• Potential bias in "controls" used in a heart failure disease-management program.
  Journal of the American Geriatrics Society 2005 Wilson T, Linden A
• Financial interests, detailed description of methods, and use of randomized design in a real-world setting.
  The American journal of managed care 2005 Wilson T, Linden A
• Asthma disease management: regression to the mean or better?
  The American journal of managed care 2005 Wilson T, Linden A
• Care management for heart failure.
  Annals of internal medicine 2005 Linden A, Wilson T
• Disease management's economic impact: unproven?
  Health affairs (Project Hope) 2005 Wilson T, Linden A
• A user's guide to the disease management literature: recommendations for reporting and assessing program outcomes.
  The American journal of managed care 2005 Linden A, Roberts N
• Evaluating disease management program effectiveness: An introduction to the bootstrap technique
  Dis Manage Health Outcomes 2005 Linden A, Adams J, Roberts N.
• Using propensity scores to construct comparable control groups for disease management program evaluation
  Dis Manage Health Outcomes 2005 Linden A, Adams J, Roberts N.
• Measuring diabetes management.
  Health affairs (Project Hope) 2004 Wilson T, Linden A
• Generalizing disease management program results: how to get from here to there.
  Managed care interface 2004 Linden A, Adams JL, Roberts N
• Disease management interventions: what's in the black box?
  Disease management : DM 2004 Linden A, Roberts N
• Evaluating disease management program effectiveness adjusting for enrollment (tenure) and seasonality
  Research in Health Financial Management 2004 Linden A, Adams J, Roberts N.
• Evaluating disease management program effectiveness: an introduction to survival analysis.
  Disease management : DM 2004 Linden A, Adams JL, Roberts N
• Using an empirical method for establishing clinical outcome targets in disease management programs.
  Disease management : DM 2004 Linden A, Adams JL, Roberts N
• An assessment of the total population approach for evaluating disease management program effectiveness.
  Disease management : DM 2003 Linden A, Adams JL, Roberts N
• Evaluating disease management program effectiveness: an introduction to time-series analysis.
  Disease management : DM 2003 Linden A, Adams JL, Roberts N
• The complete "how to" guide for selecting a disease management vendor.
  Disease management : DM 2003 Linden A, Roberts N, Keck K
• A Risk Adjusted Method of Analysis for Bed-day Reporting at CareAmerica Health Plans
  A Risk Adjusted Method of Analysis for Bed-day Reporting at CareAmerica Health Plans. 1997 Linden A.
• A physiological comparison of forward vs reverse wheelchair ergometry
  Medicine & Science in Sports & Exercise 1993 Linden A, Holland GJ, Loy SF, Vincent WJ.