TY - JOUR
T1 - Understanding and interpreting confidence and credible intervals around effect estimates
AU - Junior, Luiz Carlos Hespanhol
AU - Vallio, Caio Sain
AU - Saragiotto, Bruno Tirotti
AU - Costa, Lucíola Menezes
N1 - Funding Information:
Luiz Hespanhol was granted with a Young Investigator Grant from the São Paulo Research Foundation (FAPESP), grant 2016/09220-1 . Caio Sain Vallio was granted with a PhD scholarship from FAPESP, process number 2017/11665-4. Bruno T Saragiotto was granted with a Young Investigator Grant from FAPESP, grant 2016/24217-7.
Publisher Copyright:
© 2019 Associação Brasileira de Pesquisa e Pós-Graduação em Fisioterapia
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2019/7/1
Y1 - 2019/7/1
N2 - Introduction: Reporting confidence intervals in scientific articles is important and relevant for evidence-based practice. Clinicians should understand confidence intervals in order to determine if they can realistically expect results similar to those presented in research studies when they implement the scientific evidence in clinical practice. The aims of this masterclass are: (1) to discuss confidence intervals around effect estimates; (2) to understand confidence intervals estimation (frequentist and Bayesian approaches); and (3) to interpret such uncertainty measures. Content: Confidence intervals are measures of uncertainty around effect estimates. Interpretation of the frequentist 95% confidence interval: we can be 95% confident that the true (unknown) estimate would lie within the lower and upper limits of the interval, based on hypothesized repeats of the experiment. Many researchers and health professionals oversimplify the interpretation of the frequentist 95% confidence interval by dichotomizing it in statistically significant or non-statistically significant, hampering a proper discussion on the values, the width (precision) and the practical implications of such interval. Interpretation of the Bayesian 95% confidence interval (which is known as credible interval): there is a 95% probability that the true (unknown) estimate would lie within the interval, given the evidence provided by the observed data. Conclusions: The use and reporting of confidence intervals should be encouraged in all scientific articles. Clinicians should consider using the interpretation, relevance and applicability of confidence intervals in real-world decision-making. Training and education may enhance knowledge and skills related to estimating, understanding and interpreting uncertainty measures, reducing the barriers for their use under either frequentist or Bayesian approaches.
AB - Introduction: Reporting confidence intervals in scientific articles is important and relevant for evidence-based practice. Clinicians should understand confidence intervals in order to determine if they can realistically expect results similar to those presented in research studies when they implement the scientific evidence in clinical practice. The aims of this masterclass are: (1) to discuss confidence intervals around effect estimates; (2) to understand confidence intervals estimation (frequentist and Bayesian approaches); and (3) to interpret such uncertainty measures. Content: Confidence intervals are measures of uncertainty around effect estimates. Interpretation of the frequentist 95% confidence interval: we can be 95% confident that the true (unknown) estimate would lie within the lower and upper limits of the interval, based on hypothesized repeats of the experiment. Many researchers and health professionals oversimplify the interpretation of the frequentist 95% confidence interval by dichotomizing it in statistically significant or non-statistically significant, hampering a proper discussion on the values, the width (precision) and the practical implications of such interval. Interpretation of the Bayesian 95% confidence interval (which is known as credible interval): there is a 95% probability that the true (unknown) estimate would lie within the interval, given the evidence provided by the observed data. Conclusions: The use and reporting of confidence intervals should be encouraged in all scientific articles. Clinicians should consider using the interpretation, relevance and applicability of confidence intervals in real-world decision-making. Training and education may enhance knowledge and skills related to estimating, understanding and interpreting uncertainty measures, reducing the barriers for their use under either frequentist or Bayesian approaches.
KW - Biostatistics
KW - Confidence intervals
KW - Evidence-based practice
KW - Physical therapy specialty
KW - Statistical data analysis
UR - http://www.scopus.com/inward/record.url?scp=85059752239&partnerID=8YFLogxK
U2 - 10.1016/j.bjpt.2018.12.006
DO - 10.1016/j.bjpt.2018.12.006
M3 - Review article
C2 - 30638956
VL - 23
SP - 290
EP - 301
JO - Brazilian Journal of Physical Therapy
JF - Brazilian Journal of Physical Therapy
SN - 1809-9246
IS - 4
ER -