Use of 95% confidence intervals in the reporting of between-group differences in randomized controlled trials: analysis of a representative sample of 200 physical therapy trials

Freire, Ana Paula Coelho Figueira; Elkins, Mark R.; Ramos, Ercy Mara Cípulo; Moseley, Anne M.

doi:10.1016/j.bjpt.2018.10.004

Cited by 23 publications

(22 citation statements)

References 25 publications

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“…A paper published within this issue of the Brazilian Journal of Physical Therapy (BJPT) raised a very interesting, important and relevant matter for evidence-based practice: the use of the 95% confidence interval (CI) for reporting the uncertainty around between-group comparisons in randomized controlled trials investigating the effects of physical therapy interventions. 1 Briefly, the study found that: (1) only less than one-third of physical therapy trials report CIs; (2) trials with lower risk of bias (i.e., higher quality) are more likely to report CIs; and (3) there has been a consistent increase in reporting CIs over time. 1 The increasing trend on reporting CIs is good news for physical therapy evidencebased practice.…”

Section: Introductionmentioning

confidence: 99%

“…1 Briefly, the study found that: (1) only less than one-third of physical therapy trials report CIs; (2) trials with lower risk of bias (i.e., higher quality) are more likely to report CIs; and (3) there has been a consistent increase in reporting CIs over time. 1 The increasing trend on reporting CIs is good news for physical therapy evidencebased practice. Nevertheless, clinicians should understand CIs so they can appropriately interpret results of trials in order to better implement such evidence in practice.…”

Section: Introductionmentioning

confidence: 99%

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Understanding and interpreting confidence and credible intervals around effect estimates

Hespanhol

Vallio

Costa

et al. 2019

Brazilian Journal of Physical Therapy

237

150

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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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Understanding and interpreting confidence and credible intervals around effect estimates

Hespanhol

Vallio

Costa

et al. 2019

Brazilian Journal of Physical Therapy

237

150

View full text Add to dashboard Cite

show abstract

“…Therefore, the confidence interval of the average value is − / , + / , where = / is the corresponding standard score. In general, the confidence probability in the literature [42,45,46] is 90% or 95%, and / is 1.645 or 1.96, respectively.…”

Section: Confidence Intervalmentioning

confidence: 99%

“…Nearly 100 factors were summarized to reveal the commonly used factors. In accordance with the relevant studies [29,41,42] and the study area, we select eight influencing factors: elevation, topography, slope, lithology, distance to a structure line, distance to a river, average annual rainfall, and distances to roads. Table 1 lists all the DEM data for the study area and their sources.…”

Section: Influencing Factorsmentioning

confidence: 99%

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Exploring the Impact of Multitemporal DEM Data on the Susceptibility Mapping of Landslides

Wang

Han

et al. 2020

Applied Sciences

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Digital elevation models (DEMs) are fundamental data models used for susceptibility assessment of landslides. Due to landscape change and reshaping processes, a DEM can show obvious temporal variation and has a significant influence on assessment results. To explore the impact of DEM temporal variation on hazard susceptibility, the southern area of Sichuan province in China is selected as a study area. Multitemporal DEM data spanning over 17 years are collected and the topographic variation of the landscape in this area is investigated. Multitemporal susceptibility maps of landslides are subsequently generated using the widely accepted logistic regression model (LRM). A positive correlation between the topographic variation and landslide susceptibility that was supported by previous studies is quantitatively verified. The ratio of the number of landslides to the susceptibility level areas (RNA) in which the hazards occur is introduced. The RNA demonstrates a general decrease in the susceptibility level from 2000 to 2009, while the ratio of the decreased level is more than fifteen times greater than that of the ratio of the increased level. The impact of the multitemporal DEM on susceptibility mapping is demonstrated to be significant. As such, susceptibility assessments should use DEM data at the time of study.

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Current use of effect size or confidence interval analyses in clinical and biomedical research

Amaral

Line

2021

Scientometrics

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The isolated use of the statistical hypothesis testing for two group comparison has limitations, and its combination with effect size or confidence interval analysis as complementary statistical tests is recommended. In the present work, we estimate the use of these complementary statistical tests (i.e. effect size or confidence interval) in recently published in research articles in clinical and biomedical areas. Methods : The ProQuest database was used to search published studies in academic journals between 2019 and 2020. The analysis was carried out using terms that represent five areas of clinical and biomedical research: “brain”, “liver”, “heart”, “dental”, and “covid-19”. A total of 119,558 published articles were retrieved. Results : The relative use of complementary statistical tests in clinical and biomedical publications was low. The highest frequency usage of complementary statistical tests was among articles that also used statistical hypothesis testing for two-sample comparison. Publications with the term “covid-19” showed the lowest usage rate of complementary statistical tests when all article were analyzed but presented the highest rate among articles that used hypothesis testing. Conclusion : The low use of effect size or confidence interval in two-sample comparison suggests that coordinate measures should be taken in order to increase the use of this analysis in clinical and biomedical research. Their use should be emphasized in statistical disciplines for college and graduate students, become a routine procedure in research laboratories, and recommended by reviewers and editors of scientific journals. Supplementary Information The online version contains supplementary material available at 10.1007/s11192-021-04150-3.

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Use of 95% confidence intervals in the reporting of between-group differences in randomized controlled trials: analysis of a representative sample of 200 physical therapy trials

Abstract: Objectives: To assess the prevalence of the use of 95% confidence intervals in the reporting of between-group differences in randomized controlled trials of physical therapy interventions and to determine if the prevalence is changing over time.

Cited by 23 publications

References 25 publications

Understanding and interpreting confidence and credible intervals around effect estimates

Understanding and interpreting confidence and credible intervals around effect estimates

Exploring the Impact of Multitemporal DEM Data on the Susceptibility Mapping of Landslides

Current use of effect size or confidence interval analyses in clinical and biomedical research

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