A Utilitarian Perspective on Risk Quantification for Clinical Significance in Binary Outcomes.
Null hypothesis significance testing (NHST) in medical research is increasingly being supplemented by estimation statistics, focusing on effect sizes (ESs) and confidence intervals (CIs). This study evaluates the expression of ESs and CIs for binary outcomes. A utilitarian framework is proposed, emp...
| Publicado en: | Inquiry (00469580) pp. 1 - 13 |
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| Autor principal: | |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
4/24/2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=176845193&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 176845193 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00469580 INQ jtl: Inquiry (00469580) issn: 00469580 maglogo: Y pubinfo: dt: 4/24/2024 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 176845193 176845193 176845193 10.1177/00469580241248134 176845193 ppf: 1 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A Utilitarian Perspective on Risk Quantification for Clinical Significance in Binary Outcomes. aug: au: Park, Junhui affil: Pukyong National University, Busan, Korea sug: subj: Effect Size Confidence Intervals Risk Assessment Randomized Controlled Trials Significance Test Conceptual Framework Outcomes Research Human Meta Analysis Funding Source Clinical Effectiveness Data Analysis Software Sample Size Decision Making, Clinical Descriptive Statistics Odds Ratio Relative Risk ab: Null hypothesis significance testing (NHST) in medical research is increasingly being supplemented by estimation statistics, focusing on effect sizes (ESs) and confidence intervals (CIs). This study evaluates the expression of ESs and CIs for binary outcomes. A utilitarian framework is proposed, emphasizing the number of beneficiaries and the impact level. To evaluate clinical significance, minimal clinically important risk difference (MCIRD) is proposed based on event magnitude (EM). Within this framework, risk difference (RD) is introduced as the primary measure. To assess the performance of RD, we compared its statistical power against other measures (risk ratio, RR; odds ratio, OR; Cohen's h) in individual study scenarios, and visual information conveyance in meta-analysis scenarios. RDs maintain statistical power in comparison to other measures in individual studies. They provide clarity on the true impact of clinical interventions without compromising statistical integrity. Meta-analytic results indicate that using RDs directly enhances transparency, uncovers heterogeneity, and addresses misaligned assumptions. This approach, by quantifying clinical effectiveness under a utilitarian perspective, facilitates the applicability of research to patient care and encourages shared decision-making. The study advocates for reporting baseline risks (BRs) with RDs and recommends a standardized presentation of these statistics. In a utilitarian perspective, adopting RD as the preferred ES can foster a transparent, patient-focused research ethos. This aids in accurately presenting the magnitude and variability of treatment effects, offering a new direction in methodology. pubtype: Academic Journal doctype: equations & formulas meta analysis research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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