Limitations of Applying Summary Results of Clinical Trials to Individual Patients: The Need for Risk Stratification Editorial Summary One of the limiting factors in contemporary wound care research is the elidation and ignorance of the pathologies with wound care data. This leads to the use of statistical tools that cannot deal with the nature of the data. This article explores some of the problems and solutions with the application of wound care data, and outlines the need for risk stratification.
Introduction
W
hen I began writing this article I originally titled it ‘The Pathology of Wound Care Data’, but the title did not seem quite right. Pathology refers to the ‘study of causes and effects of the disease or injury’; this was not quite what I wanted to write about regarding wound care data. The more I thought about it, pathophysiology seemed to fit better. Pathophysiology is the study of a ‘disordered physiological processes that cause, result from, or otherwise are otherwise associated with a disease or injury’. Wound care data in particular, but data in general, can be thought of as the result of a data generating process in the real world. In a perfect world, researchers design experiments where the data generating process is not one of these ‘disordered physiological processes’; however, in the real world, wound care data is often ‘diseased or injured’. Wound care data possesses many statistical pathologies that make analysis and inference difficult, and if not identified and treated, these pathologies can cause analysts and researchers to arrive at conclusions that range from completely wrong, to less correct than they could be. I have often thought of creating something like a DSM-4 (The Diagnostic and Statistical Manual of Mental Disorders, fourth edition, text revision (DSM-IV-TR) (American Psychiatric Association [APA], 2000) is a compendium of mental disorders, a listing of the criteria used to diagnose them, and a detailed system for their definition, organization, and classification) of wound care data; a guide on how to identify
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Wound Masterclass - Vol 2 - March 2023
these statistical pathologies, and how to attend to them in a principled and ethical manner. This article is an abridged version of such a guide, where we focus mostly on the diagnosis and description of the pathologies that I come across most often in my work: censoring and truncation, measurement error, fat tails, and zero-inflation.
Censoring and Truncation Censoring occurs when the value of an observation is only partly known. An example of censoring, when the number of observations is equal to one, would be an unstageable pressure ulcer. As there is an obstruction to viewing the underlying tissue, the clinician cannot determine whether the wound is actually a stage 3 or 4 pressure ulcer, and thus the ‘unstageable moniker’ is used, which explicitly indicates that censoring has occurred. Carrying this idea forward, one could think of a deep tissue injury as a censored stage 1, 2, 3, or 4 pressure ulcer. Censoring most often occurs in the longitudinal analysis of wound care data; longitudinal wound care data comprises repeated measurements of the same wounds over time. The most often used longitudinal design is a pre-post test (one repeat measurement), where there is one initial baseline measurement, and then one single follow-up measurement. When there is more than one repeat measurement, we call this a longitudinal cohort study. In the context of longitudinal data, there are 3 types of censoring: left-censoring, right-censoring, and interval-censoring. Interval-censoring can combine with left or right censoring, and a wound can have multiple occurrences of
Mr Zwelithini Tunyiswa Open Wound Research, University of Pennsylvania Concord NC, United States
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