scholarly journals Combining propensity score-based stratification and weighting to improve causal inference in the evaluation of health care interventions

2014 ◽  
Vol 20 (6) ◽  
pp. 1065-1071 ◽  
Author(s):  
Ariel Linden
Keyword(s):  
Health Care ◽  
Propensity Score ◽  
Causal Inference ◽  
Health Care Interventions

scholarly journals Reflective Choice in Health Care: Using Information Technology to Present Allocation Options

1999 ◽  
Vol 25 (2-3) ◽  
pp. 387-402
Author(s):  
Arti K. Rai
Keyword(s):  
Health Care ◽  
Information Technology ◽  
Health Care System ◽  
Medical Technology ◽  
Health Benefit ◽  
Cost Benefit ◽  
Alternative Funding ◽  
Health Care Interventions ◽  
The U.S ◽  
Care System

Over the last few decades, the U.S. health care system has been the beneficiary of tremendous growth in the power and sheer quantity of useful medical technology. As a consequence, our society has, for some time, had to make cost-benefit tradeoffs in health care. The alternative—funding all health care interventions that would produce some health benefit for some patient—is not feasible, because it would effectively consume all of our resources.

scholarly journals The PRISMA Statement for Reporting Systematic Reviews and Meta-Analyses of Studies That Evaluate Health Care Interventions: Explanation and Elaboration

PLoS Medicine ◽  
2009 ◽  
Vol 6 (7) ◽  
pp. e1000100 ◽  
Author(s):  
Alessandro Liberati ◽  
Douglas G. Altman ◽  
Jennifer Tetzlaff ◽  
Cynthia Mulrow ◽  
Peter C. Gøtzsche ◽  
...  
Keyword(s):  
Health Care ◽  
Systematic Reviews ◽  
Health Care Interventions ◽  
Prisma Statement ◽  
Meta Analyses

scholarly journals PRM11 Learning Effect in Economic Evaluations of Health Care Interventions

2011 ◽  
Vol 14 (7) ◽  
pp. A422-A423
Author(s):  
S. Chang ◽  
D. Sungher ◽  
A. Diamantopoulos
Keyword(s):  
Health Care ◽  
Learning Effect ◽  
Economic Evaluations ◽  
Health Care Interventions

scholarly journals Using logic models to enhance the methodological quality of primary health-care interventions: guidance from an intervention to promote nutrition care by general practitioners and practice nurses

2017 ◽  
Vol 23 (1) ◽  
pp. 53 ◽  
Author(s):  
Lauren Ball ◽  
Dianne Ball ◽  
Michael Leveritt ◽  
Sumantra Ray ◽  
Clare Collins ◽  
...  
Keyword(s):  
Health Care ◽  
Primary Health Care ◽  
Care Setting ◽  
Logic Model ◽  
Practice Nurses ◽  
Nutrition Care ◽  
Health Care Interventions ◽  
Primary Health ◽  
Health Care Practitioners ◽  
Logic Models

The methodological designs underpinning many primary health-care interventions are not rigorous. Logic models can be used to support intervention planning, implementation and evaluation in the primary health-care setting. Logic models provide a systematic and visual way of facilitating shared understanding of the rationale for the intervention, the planned activities, expected outcomes, evaluation strategy and required resources. This article provides guidance for primary health-care practitioners and researchers on the use of logic models for enhancing methodological rigour of interventions. The article outlines the recommended steps in developing a logic model using the ‘NutriCare’ intervention as an example. The ‘NutriCare’ intervention is based in the Australian primary health-care setting and promotes nutrition care by general practitioners and practice nurses. The recommended approach involves canvassing the views of all stakeholders who have valuable and informed opinions about the planned project. The following four targeted, iterative steps are recommended: (1) confirm situation, intervention aim and target population; (2) document expected outcomes and outputs of the intervention; (3) identify and describe assumptions, external factors and inputs; and (4) confirm intervention components. Over a period of 2 months, three primary health-care researchers and one health-services consultant led the collaborative development of the ‘NutriCare’ logic model. Primary health-care practitioners and researchers are encouraged to develop a logic model when planning interventions to maximise the methodological rigour of studies, confirm that data required to answer the question are captured and ensure that the intervention meets the project goals.

scholarly journals Using a monotone single-index model to stabilize the propensity score in missing data problems and causal inference

2018 ◽  
Vol 38 (8) ◽  
pp. 1442-1458 ◽  
Author(s):  
Jing Qin ◽  
Tao Yu ◽  
Pengfei Li ◽  
Hao Liu ◽  
Baojiang Chen
Keyword(s):  
Missing Data ◽  
Propensity Score ◽  
Causal Inference ◽  
Single Index ◽  
Index Model ◽  
Single Index Model
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