Welcome to the Linden Consulting Group

Linden Consulting Group

Statistical Software

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With over 25 years of experience in medical - healthcare research and management, we can help you design, implement, and evaluate

Medical management, disease management and health promotion programs
Medical home model, accountable care organizations
Clinical quality improvement initiatives, operational processes
Government, accreditation, and contractual requirements
Financial planning and measurement
Most any other needs of today’s healthcare market

Awards and Recognition

"Leader in Disease Management" by Managed Healthcare Executive
"Outstanding Journal Article" Award by Disease Management Association of America
"Best of Business" by the Small Business Commerce Association

	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
	Linden A, Yarnold PR. Using machine learning to evaluate treatment effects in multiple-group interrupted time series analysis. Journal of Evaluation in Clinical Practice DOI: 10.1111/jep.12966
	Linden A. Using forecast modelling to evaluate treatment effects in single-group interrupted time series analysis. Journal of Evaluation in Clinical Practice DOI: 10.1111/jep.12946
	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
	Linden A. Review of “A Course in Item Response Theory and Modeling with Stata” by Raykov and Marcoulides. Stata Journal 2018;18:485-488.
	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.
	Linden A. Using permutation tests to enhance causal inference in interrupted time series analysis. Journal of Evaluation in Clinical Practice 2018;24:496-501.
	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.
	Linden A. A matching framework to improve causal inference in interrupted time series analysis. Journal of Evaluation in Clinical Practice 2018;24:408-415.
	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.
	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.

designed by: Homer Gaines

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