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Advantages and Disadvantages of Research Metrics used to Evaluate a Researcher’s Impact or Influence
By: Chad E Cook PT, PhD, FAPTA
Background:
Each year, in Duke University’s Division of Physical Therapy, I teach a class on research methodology. One of the topics we discuss in class involves ways to measure research impact among physical therapists’ (and other professions’) researchers. The discussion is complimentary to those that occur during the Appointment, Promotion and Tenure (AP&T) committee in the Department of Orthopaedics, of which I am a committee member. By definition, research impact metrics are quantitative tools used to assess the influence and productivity of researchers, to give some understanding who are leaders in their fields. Without fail, in the class (each year), there is some debate on the best methods. This blog will discuss four of the most common methods and will evaluate their advantages and disadvantages. The order presented does not imply superiority and these methods are not transferable with evaluating the impact of a single journal publication.
“It’s Not You, It’s Us…”: Heterogeneity of Treatment Effects as a Challenge to Effectiveness Trials.
By: Damian L Keter, PT, DPT, PhD
Background:
Comparative effectiveness studies are the cornerstone of medicine and health sciences research. They have a goal of finding ‘the best’ treatment for each associated condition. In comparative effectiveness studies, statistical models are able to provide ‘average’ treatment effects, which are often used to establish standardized mean difference between the interventions; however, it is clear across interventional studies that the ‘average’ effect is not to be consistently expected. Whereas interventional design focuses on central tendency (mean or median of the population), one may more importantly consider the dispersion of data around that point.
Heterogeneity of treatment effects (HTE) refers to the variation in how different individuals respond to the same treatment or intervention. HTE are often represented by standard deviations which are impacted by outliers, demonstrating individuals who respond ‘differently’ to the intervention than the ‘average’ results. There are a number of ways in which HTE can be analyzed or managed in secondary analyses including subgroup analysis of covariates and specific statistical methods to identify heterogeneity [1]. HTE is critically important in interpretation of results in interventional studies; however, they are often poorly reported [1]. Two factors must be considered when understanding HTE in interventional effectiveness trials: 1) what factors contribute to HTE, 2) the limitations and challenges in attempting to control HTE.
