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

Amaral, Emilyane de Oliveira Santana; Line, Sérgio Roberto Peres

doi:10.1007/s11192-021-04150-3

Cited by 8 publications

(5 citation statements)

References 63 publications

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“…6 The isolated use of p-value is a matter of debate and raised issues like replicability, veracity, and reliability of the generated conclusions. 7 Therefore, there are some alternatives/complementary to the use of p-value. Complementing p-value with other statistics such as confidence interval, effect sizes, and Bayes factors is recommended.…”

Section: Highly Significant When the P-value Is <0001mentioning

confidence: 99%

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Synchronous use of two markers of research: p-value and confidence interval

Subedi¹,

Subedi²

2022

JGMC Nepal

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Section: Highly Significant When the P-value Is <0001mentioning

confidence: 99%

“…The relative use of CI is low in clinical and biomedical research. 7 A CI is a degree of uncertainty around the effect estimate. It consists of an upper and lower limit, which shows that the true (unknown) effect may fall within this interval.…”

Section: Confidence Interval (Ci)mentioning

confidence: 99%

Synchronous use of two markers of research: p-value and confidence interval

Subedi¹,

Subedi²

2022

JGMC Nepal

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“…Effect size quantifies the magnitude of the difference or the strength of the association between variables. There are several reasons why calculating and reporting the effect size and the CIs around the effect size is important in clinical research . First, effect size is needed during study planning to calculate the sample size.…”

Section: Introductionmentioning

confidence: 99%

“…There are several reasons why calculating and reporting the effect size and the CIs around the effect size is important in clinical research. [4][5][6] First, effect size is needed during study planning to calculate the sample size. Second, once the study is completed, investigators estimate the size of the effect, the precision of the estimate, and whether the results are compatible with clinically meaningful effects.…”

mentioning

confidence: 99%

A Simple Guide to Effect Size Measures

Kallogjeri

Piccirillo

2023

JAMA Otolaryngol Head Neck Surg

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ImportanceEffect size quantifies the magnitude of the difference or the strength of the association between variables. In clinical research it is important to calculate and report the effect size and the confidence interval (CI) because it is needed for sample size calculation, meaningful interpretation of results, and meta-analyses.ObservationsThere are many different effect size measures that can be organized into 2 families or groups—d family and r family. The d family includes measures that quantify the differences between groups. The r family includes measures that quantify the strength of the association. Effect sizes that are presented in the same units as the characteristic being measured and compared are known as nonstandardized or simple effect sizes. The nonstandardized effect sizes have the advantage of being more informative, easier to interpret, and easier to evaluate in the light of clinical significance or practical relevance. Standardized effect sizes are unit-less and are helpful for combining and comparing effects of different outcome measures or across different studies (ie, meta-analysis).Conclusions and RelevanceThe choice of the correct effect size measure depends on the research question, study design, targeted audience, and the statistical assumptions being made. For a complete and meaningful interpretation of results from a clinical research study, the investigator should make clear the type of effect size being reported, its magnitude and direction, degree of uncertainty of the effect size estimate as presented by the CIs, and whether the results are compatible with a clinically meaningful effect.

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“…Fritz et al ( 2012 ) reviewed articles published in 2009 and 2010 in the Journal of Experimental Psychology: General , and noted that less than half of the articles they reviewed reported effect sizes and no article reported a confidence interval for an effect size. More recently, Amaral & Line ( 2021 ) found low reporting rate of effect sizes in 119,558 clinical or biomedical studies published between 2019 and 2020 and advocated greater emphasis on reporting them. The barriers stopping researchers from easily reporting effect sizes along with their CIs not only lie in their unfamiliarity with different effect size indices but also in the lack of guidance of how to correctly estimate the CI for a specific effect size index.…”

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confidence: 99%

Accurate Confidence and Bayesian Interval Estimation for Non-centrality Parameters and Effect Size Indices

et al. 2023

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Reporting effect size index estimates with their confidence intervals (CIs) can be an excellent way to simultaneously communicate the strength and precision of the observed evidence. We recently proposed a robust effect size index (RESI) that is advantageous over common indices because it’s widely applicable to different types of data. Here, we use statistical theory and simulations to develop and evaluate RESI estimators and confidence/credible intervals that rely on different covariance estimators. Our results show (1) counter to intuition, the randomness of covariates reduces coverage for Chi-squared and F CIs; (2) when the variance of the estimators is estimated, the non-central Chi-squared and F CIs using the parametric and robust RESI estimators fail to cover the true effect size at the nominal level. Using the robust estimator along with the proposed nonparametric bootstrap or Bayesian (credible) intervals provides valid inference for the RESI, even when model assumptions may be violated. This work forms a unified effect size reporting procedure, such that effect sizes with confidence/credible intervals can be easily reported in an analysis of variance (ANOVA) table format.

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

Cited by 8 publications

References 63 publications

Synchronous use of two markers of research: p-value and confidence interval

Synchronous use of two markers of research: p-value and confidence interval

A Simple Guide to Effect Size Measures

Accurate Confidence and Bayesian Interval Estimation for Non-centrality Parameters and Effect Size Indices

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