A Simple Approach for Sample Size Calculation for Comparing Two Concordance Correlation Coefficients Estimated on the Same Subjects

Lin, Hung‐Mo; Williamson, John

doi:10.1080/10543406.2014.971163

Cited by 5 publications

(12 citation statements)

References 21 publications

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“…In order to compare the sensitivity and specificity of AGILE vs. IFi in predicting the outcomes, Receiver Operating Characteristics (ROC) curves were analyzed by logistic regression analysis; Area Under Curve (AUC) between 0.7 and 0.9 was considered significant. Finally, in this study we use Lin's Concordance Correlation Coefficient, a method currently used to compare a new measurement method (AGILE) with the standard method (IFi) [20]. The sample size was 350 subjects with a study power of 0.90 (one-sided z test with α = 0.05) with a CCC value between 0.80-0.75.…”

Section: Discussionmentioning

confidence: 99%

Validation of “(fr)AGILE”: a quick tool to identify multidimensional frailty in the elderly

et al. 2020

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Background Several tools have been proposed and validated to operationally define frailty. Recently, the Italian Frailty index (IFi), an Italian modified version of Frailty index, has been validated but its use in clinical practice is limited by long time of administration. Therefore, the aim of this study was to create and validate a quick version of the IFi (AGILE). Methods Validation study was performed by administering IFi and AGILE, after a Comprehensive Geriatric Assessment (CGA) in 401 subjects aged 65 or over (77 ± 7 years). AGILE was a 10-items tool created starting from the more predictive items of the four domains of frailty investigated by IFi (mental, physical, socioeconomic and nutritional). AGILE scores were stratified in light, moderate and severe frailty. At 24 months of follow-up, death, disability (taking into account an increase in ADL lost ≥1 from the baseline) and hospitalization were considered. Area under curve (AUC) was evaluated for both IFi and AGILE. Results Administration time was 9.5 ± 3.8 min for IFi administered after a CGA, and 2.4 ± 1.2 min for AGILE, regardless of CGA (p < 0.001). With increasing degree of frailty, prevalence of mortality increased progressively from 6.5 to 41.8% and from 9.0 to 33.3%, disability from 16.1 to 64.2% and from 22.1 to 59.8% and hospitalization from 17.2 to 58.7% and from 27.0 to 52.2% with AGILE and IFi, respectively (p = NS). Relative Risk for each unit of increase in AGILE was 56, 44 and 24% for mortality, disability and hospitalization, respectively and was lower for IFi (8, 7 and 4% for mortality, disability and hospitalization, respectively). The AUC was higher in AGILE vs. IFi for mortality (0.729 vs. 0.698), disability (0.715 vs. 0.682) and hospitalization (0.645 vs. 0.630). Conclusions Our study shows that AGILE is a rapid and effective tool for screening multidimensional frailty, able to predict mortality, disability and hospitalization, especially useful in care settings that require reliable assessment instruments with short administration time.

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Section: Discussionmentioning

confidence: 99%

Validation of “(fr)AGILE”: a quick tool to identify multidimensional frailty in the elderly

et al. 2020

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“…These data are then transferred into a matrix for analysis (see Figure 2.). 39 For SMMs, the pairwise rating method is based on relationships and organization of words held in the memory, 39 connected through a hierarchical and stereotypical relationship. 39 Pairwise comparison has been used to measure patients, healthcare teams, healthcare education/training, and medical sciences and pharmaceutical research.…”

Section: Pairwise Ratingsmentioning

confidence: 99%

“…39 For SMMs, the pairwise rating method is based on relationships and organization of words held in the memory, 39 connected through a hierarchical and stereotypical relationship. 39 Pairwise comparison has been used to measure patients, healthcare teams, healthcare education/training, and medical sciences and pharmaceutical research. [40][41][42][43] Pairwise ratings have four advantages: time efficiency, ease of use, the ability to provide a more robust data set, and the ability to create a statistically rendered mental model.…”

Section: Pairwise Ratingsmentioning

confidence: 99%

“…47 This technique allows for the comparison of complex networks between individuals or within a group. 39,45 The data set that is rendered using this technique is robust and rich, providing not just the relationship between concepts but the direction of that relationship. A broad sample is required to achieve a rich data, which becomes increasingly difficult to analyze as the matrices expand.…”

Section: Distance Ratio Formulamentioning

confidence: 99%

“…45,47 Additionally, if a link is absent between two concepts, the formula treats this absence as if the concept itself were missing. 39 Distance ratio formula has been applied across multiple fields of study including social relations, power grids, genes, and text analysis. [48][49][50][51][52] Specific to healthcare, this method can be applied to measure providers' (individual or team) SMMs throughout a variety of tasks, both qualitatively and quantitatively.…”

Section: Distance Ratio Formulamentioning

confidence: 99%

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Measuring shared mental models in healthcare

Gisick

Webster

Keebler

et al. 2018

Journal of Patient Safety and Risk Management

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Objective: To review common qualitative and quantitative methods of measuring shared mental models appropriate for use in the healthcare setting. Background: Shared mental models are the overlap of individuals' set of knowledge and/or assumptions that act as the basis for understanding and decision making between individuals. Within healthcare, shared mental models facilitate effective teamwork and theorized to influence clinical decision making and performance. With the current rapid growth and expansion of healthcare teams, it is critical that we understand and correctly use shared mental model measurement methods assess optimal team performance. Unfortunately, agreement on the proper measurement of shared mental models within healthcare remains diffuse. Method: This paper presents methods appropriate to measure shared mental models within healthcare. Results: Multiple shared mental model measurement methods are discussed with regard to their utility within this setting, ease of use, and difficulties in deploying within the healthcare operational environment. For rigorous analysis of shared mental models, it is recommended that a combination of qualitative and quantitative analyses be employed. Conclusion: There are multitude of shared mental model measurement methods that can be used in the healthcare domain; although there is no perfect solution for every situation. Researchers can utilize this article to determine the best approach for their needs.

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Computer assisted interpretation of Tc-99m mercaptoacetyltriglycine diuretic scintigraphy enhances resident performance

Taylor

Rahman

Folks

et al. 2023

Nuclear Medicine Communications

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Objective iRENEX is a software module that incorporates scintigraphic and clinical data to interpret 99m Tc- mercaptoacetyltriglycine (MAG3) diuretic studies and provide reasons for their conclusions. Our objectives were to compare iRENEX interpretations with those of expert physicians, use iRENEX to evaluate resident performance and determine if iRENEX could improve the diagnostic accuracy of experienced residents. Methods Baseline and furosemide 99m Tc-MAG3 acquisitions of 50 patients with suspected obstruction (mean age ± SD, 58.7 ± 15.8 years, 60% female) were randomly selected from an archived database and independently interpreted by iRENEX, three expert readers and four nuclear medicine residents with one full year of residency. All raters had access to scintigraphic data and a text file containing clinical information and scored each kidney on a scale from +1.0 to −1.0. Scores ≥0.20 represented obstruction with higher scores indicating greater confidence. Scores +0.19 to −0.19 were indeterminate; scores ≤−0.20 indicated no obstruction. Several months later, residents reinterpreted the studies with access to iRENEX. Receiver operating characteristic (ROC) analysis and concordance correlation coefficient (CCC) quantified agreement. Results The CCC among experts was higher than that among residents, 0.84, versus 0.39, respectively, P < 0.001. When residents reinterpreted the studies with iRENEX, their CCC improved from 0.39 to 0.73, P < 0.001. ROC analysis showed significant improvement in the ability of residents to distinguish between obstructed and non-obstructed kidneys using iRENEX ( P = 0.036). Conclusion iRENEX interpretations were comparable to those of experts. iRENEX reduced interobserver variability among experienced residents and led to better agreement between resident and expert interpretations.

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A Simple Approach for Sample Size Calculation for Comparing Two Concordance Correlation Coefficients Estimated on the Same Subjects

Cited by 5 publications

References 21 publications

Validation of “(fr)AGILE”: a quick tool to identify multidimensional frailty in the elderly

Validation of “(fr)AGILE”: a quick tool to identify multidimensional frailty in the elderly

Measuring shared mental models in healthcare

Computer assisted interpretation of Tc-99m mercaptoacetyltriglycine diuretic scintigraphy enhances resident performance

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