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About 10,000 papers for “Intraclass correlation”

Intraclass correlation

Liuqing Tian · OSF Preprints (OSF Preprints) · 2024

在【元自我】公众号上的推文《信度分析：组内相关系数(ICC)知多少》的补充材料

Cited by 0

Intraclass Correlation

Andy P. Field · Encyclopedia of Statistics in Behavioral Science · 2005

Abstract Intraclass correlations are used to assess the consistency between measures of the same class (i.e., measures of the same thing). Two common uses are in comparing paired data (such as twins) on the same measure and assessing the consistency between judges' ratings of a set of objects. There are different conceptualizations of the intraclass correlation and the variance components used to calculate them in these different models. There is an important distinction between the cases when the measures are a single score or an average of multiple scores, and when a measure of consistency or a measure of absolute agreement is required.

Cited by 35

Intraclass Correlation

Elizabeth DeLong · NIH Collaboratory eBooks · 2017

CHAPTER SECTIONS Contributors Patrick J. Heagerty, PhD Elizabeth R. DeLong, PhD For the NIH Pragmatic Trials Collaboratory Biostatistics and Study Design Core Contributing Editors Damon M. Seils, MA Jonathan McCall, […]

Cited by 1

Intraclass correlation coefficients.

Gwenyth O. Lee (5024411), Germán Comina (128280), Gustavo Hernandez-Cordova (8946692) et al. · Figshare · 2020

<p>Shown here are intraclass correlation coefficients for each cough feature, describing the proportion of variability in each feature explained by within-individual variability. For example, 29% of the variability in COUGH EPISODE FREQUENCY can be explained by within-individual variability.</p> <p>(DOCX)</p>

Cited by 0Open access

Intraclass correlation coefficient results.

Francesca Tomlinson (20490028), Ella O’Neill (20490031), Nai-Chieh Liu (755569) et al. · Figshare · 2024

<p><i>(</i>A) Intra-rater repeatability results for one rater across up to 19 subjects of multiple dog breeds in the photographic measurements. (B) Inter-rater repeatability results for one rater across up to 20 subjects of multiple dog breeds in the photographic measurements. Skull index [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0315411#pone.0315411.ref001" target="_blank">1</a>] displaying ICC result for analysis performed on multiple dog breeds, and skull index [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0315411#pone.0315411.ref002" target="_blank">2</a>] displaying ICC value for analysis performed on smooth-coated breeds (Boston Terrier, Boxer and Staffordshire Bull Terrier) only. (C) Inter-rater repeatability results for soft tape measurements conducted by two raters across 30 subjects of multiple dog breeds.</p> <p>(DOCX)</p>

Cited by 0Open access

Comparison of Intraclass Correlation.

Stephen A. Gallo (443006), Afton S. Carpenter (443007), Scott R. Glisson (443008) · Figshare · 2015

<p>Intraclass correlation for 2009, 2010 (face-to-face) and 2011, 2012 (teleconference) reviews (p<0.01 for all years).</p>

Cited by 0Open access

The intraclass correlation coefficient.

Kare Chawicha Debessa (20660605), Keneni Gutema Negeri (13977856), Mesay Hailu Dangisso (8563317) · Figshare · 2025

<div> Introduction Accessing affordable and high-quality healthcare remains a persistent challenge in low- and middle-income countries like Ethiopia. Community-based health insurance (CBHI) programs offer a promising solution to expand healthcare coverage and provide financial protection, particularly for vulnerable populations such as women. This study aimed to investigate the factors that influence CBHI enrollment among women in Sidama National Regional State, Ethiopia, using a multilevel analysis. Methods A community-based cross-sectional study was conducted using a multistage sampling technique from December 15<sup>th</sup> to January 12<sup>th</sup>, 2024, in the central Sidama zone, Sidama National Regional State. The study included 835 women aged 18 years and older, residing both in rural and urban areas. Face-to-face interviews were conducted using a pre-tested questionnaire to collect comprehensive data on socio-demographic, economic, and scheme-related factors. Data collection utilized the Open Data Kit mobile application, and data analysis was performed using Stata version 16, employing multilevel modified Poisson modeling to identify determinants of CBHI enrollment. Results Among 845 samples, 835 women were interviewed, resulting in a response rate of 98.8%. Individual-level factors such as older age (APR = 1.02, 95%CI: 1.01–1.03, p<0.001), larger family size (APR = 1.09, 95%CI: 1.03–1.16, p = 0.001), and moderate wealth index (APR = 2.72, 95%CI: 1.28–5.79, p = 0.009) showed positive associations with CBHI enrollment, depicted a higher likelihood of individuals joining the insurance scheme. In addition, at the community level, higher rates of women’s literacy (APR = 1.73, 1.18–2.55, p = 0.005), and women’s autonomy (APR = 2.64, 95%CI: 1.50–4.65, p = 0.001) were positively correlated with CBHI enrollment. Conclusions The study revealed that the enrollment rate among women in the CBHI scheme was 35%, indicating a need for improved outreach efforts. Significant factors that affected enrollment included older age, larger family size, and moderate wealth. Additionally, positive community-level influences such as higher literacy rates and increased autonomy for women contributed to higher enrollment. To improve CBHI enrollment, the target should focus on younger women and those from smaller families. Financial support, such as subsidies for low-income women, can also encourage participation. Investing in women’s literacy and empowerment programs will enable them to make informed health choices, thereby increasing enrollment. Finally, ongoing research is necessary to track enrollment trends and identify barriers. Utilizing qualitative methods will yield insights into women’s perceptions of CBHI, facilitating more effective strategies. Implementing these recommendations can enhance women’s access to healthcare through CBHI. </div>

Cited by 0Open access

Intraclass correlation coefficient estimation.

Camila Bonfim (20371251), Flávia Alves (20371254), Érika Fialho (20371257) et al. · Figshare · 2024

&lt;div&gt;&lt;p&gt;Background&lt;/p&gt;&lt;p&gt;Psychiatric patients experience lower life expectancy compared to the general population. Conditional cash transfer programmes (CCTPs) have shown promise in reducing mortality rates, but their impact on psychiatric patients has been unclear. This study tests the association between being a Brazilian Bolsa Família Programme (BFP) recipient and the risk of mortality among people previously hospitalised with any psychiatric disorders.&lt;/p&gt;&lt;p&gt;Methods and findings&lt;/p&gt;&lt;p&gt;This cohort study utilised Brazilian administrative datasets, linking social and health system data from the 100 Million Brazilian Cohort, a population-representative study. We followed individuals who applied for BFP following a single hospitalisation with a psychiatric disorder between 2008 and 2015. The outcome was mortality and specific causes, defined according to International Classification of Diseases 10th Revision (ICD-10). Cox proportional hazards models estimated the hazard ratio (HR) for overall mortality and competing risks models estimated the HR for specific causes of death, both associated with being a BFP recipient, adjusted for confounders, and weighted with a propensity score. We included 69,901 psychiatric patients aged between 10 and 120, with the majority being male (60.5%), and 26,556 (37.99%) received BFP following hospitalisation. BFP was associated with reduced overall mortality (HR 0.93, 95% CI 0.87,0.98, &lt;i&gt;p&lt;/i&gt; 0.018) and mortality due to natural causes (HR 0.89, 95% CI 0.83, 0.96, &lt;i&gt;p&lt;/i&gt; &lt; 0.001). Reduction in suicide (HR 0.90, 95% CI 0.68, 1.21, &lt;i&gt;p&lt;/i&gt; = 0.514) was observed, although it was not statistically significant. The BFP’s effects on overall mortality were more pronounced in females and younger individuals. In addition, 4% of deaths could have been prevented if BFP had been present (population attributable risk (PAF) = 4%, 95% CI 0.06, 7.10).&lt;/p&gt;&lt;p&gt;Conclusions&lt;/p&gt;&lt;p&gt;BFP appears to reduce mortality rates among psychiatric patients. While not designed to address elevated mortality risk in this population, this study highlights the potential for poverty alleviation programmes to mitigate mortality rates in one of the highest-risk population subgroups.&lt;/p&gt;&lt;/div&gt;

Cited by 0Open access