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Minimal Clinically Important Difference (MCID) in Patient-Reported Outcome Measures for Neurological Conditions: Review of Concept and Methods.
Mishra, Biswamohan; Sudheer, Pachipala; Agarwal, Ayush; Srivastava, M Vasantha Padma; Vishnu, Venugopalan Y.
Afiliação
  • Mishra B; Department of Neurology, All India Institute of Medical Sciences, New Delhi, India.
  • Sudheer P; Department of Neurology, All India Institute of Medical Sciences, New Delhi, India.
  • Agarwal A; Department of Neurology, All India Institute of Medical Sciences, New Delhi, India.
  • Srivastava MVP; Department of Neurology, All India Institute of Medical Sciences, New Delhi, India.
  • Nilima; Department of Biostatistics, All India Institute of Medical Sciences, New Delhi, India.
  • Vishnu VY; Department of Neurology, All India Institute of Medical Sciences, New Delhi, India.
Ann Indian Acad Neurol ; 26(4): 334-343, 2023.
Article em En | MEDLINE | ID: mdl-37970301
ABSTRACT
The concept of the minimal clinically important difference (MCID) emerged from the recognition that statistical significance alone is not enough to determine the clinical relevance of treatment effects in clinical research. In many cases, statistically significant changes in outcomes may not be meaningful to patients or may not result in any tangible improvements in their health. This has led to a growing emphasis on the importance of measuring patient-reported outcome measures (PROMs) in clinical trials and other research studies, in order to capture the patient perspective on treatment effectiveness. MCID is defined as the smallest change in scores that is considered meaningful or important to patients. MCID is particularly important in fields such as neurology, where many of the outcomes of interest are subjective or based on patient-reported symptoms. This review discusses the challenges associated with interpreting outcomes of clinical trials based solely on statistical significance, highlighting the importance of considering clinical relevance and patient perception of change. There are two main approaches to estimating MCID anchor-based and distribution-based. Anchor-based approaches compare change scores using an external anchor, while distribution-based approaches estimate MCID values based on statistical characteristics of scores within a sample. MCID is dynamic and context-specific, and there is no single 'gold standard' method for estimating it. A range of MCID thresholds should be defined using multiple methods for a disease under targeted intervention, rather than relying on a single absolute value. The use of MCID thresholds can be an important tool for researchers, neurophysicians and patients in evaluating the effectiveness of treatments and interventions, and in making informed decisions about care.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article