Intra-cluster correlation coefficients of 20 infections calculated from the results of cluster-sample surveys

Background: Sample size calculations for longitudinal cluster randomised trials, such as crossover and stepped-wedge trials, require estimates of the assumed correlation structure. This includes both within-period intra-cluster correlations, which importantly differ from conventional intra-cluster correlations by their dependence on period, and also cluster autocorrelation coefficients to model correlation decay. There are limited resources to inform these estimates. In this article, we provide a repository of correlation estimates from a bank of real-world clustered datasets. These are provided under several assumed correlation structures, namely exchangeable, block-exchangeable and discrete-time decay correlation structures. Methods: Longitudinal studies with clustered outcomes were collected to form the CLustered OUtcome Dataset bank. Forty-four available continuous outcomes from 29 datasets were obtained and analysed using each correlation structure. Patterns of within-period intra-cluster correlation coefficient and cluster autocorrelation coefficients were explored by study characteristics. Results: The median within-period intra-cluster correlation coefficient for the discrete-time decay model was 0.05 (interquartile range: 0.02–0.09) with a median cluster autocorrelation of 0.73 (interquartile range: 0.19–0.91). The within-period intra-cluster correlation coefficients were similar for the exchangeable, block-exchangeable and discrete-time decay correlation structures. Within-period intra-cluster correlation coefficients and cluster autocorrelations were found to vary with the number of participants per cluster-period, the period-length, type of cluster (primary care, secondary care, community or school) and country income status (high-income country or low- and middle-income country). The within-period intra-cluster correlation coefficients tended to decrease with increasing period-length and slightly decrease with increasing cluster-period sizes, while the cluster autocorrelations tended to move closer to 1 with increasing cluster-period size. Using the CLustered OUtcome Dataset bank, an RShiny app has been developed for determining plausible values of correlation coefficients for use in sample size calculations. Discussion: This study provides a repository of intra-cluster correlations and cluster autocorrelations for longitudinal cluster trials. This can help inform sample size calculations for future longitudinal cluster randomised trials.

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A nomogram for single-stage cluster-sample surveys in a community for estimation of a prevalence rate

International Journal of Epidemiology ◽

10.1093/ije/31.2.463 ◽

2002 ◽

Vol 31 (2) ◽

pp. 463-467 ◽

Cited By ~ 3

Author(s):

R. Kumar

Keyword(s):

Prevalence Rate ◽

Single Stage ◽

Sample Surveys ◽

Cluster Sample

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Guideline for Selecting Types of Reliability and Suitable Intra-class Correlation Coefficients in Clinical Research

Journal of Biostatistics and Epidemiology ◽

10.18502/jbe.v7i3.7301 ◽

2021 ◽

Author(s):

Khalil Taherzadeh Chenani ◽

Farzan Madadizadeh

Keyword(s):

Clinical Research ◽

Correlation Coefficients ◽

Reliability Test ◽

Research Information ◽

Rater Reliability ◽

Intra Class Correlation ◽

Medical Researchers ◽

Different Types ◽

Test Retest Reliability ◽

Cluster Correlation

Introduction: Reliability is an integral part of measuring the reproducibility of research information. Intra-cluster correlation coefficient (ICC) is one of the necessary indicators for reliability reporting, which can be misleading in terms of its diversity. The main purpose of this study was to introduce the types of reliability and appropriate ICC indices. Methods: In this tutorial article, useful information about the types of reliability and indicators needed to report the results, as well as the types of ICC and its applications were explained for dummies. Results: Three general types of reliability include inter-rater reliability, test-retest reliability, and intra-rater reliability was presented. 10 different types of ICC were also introduced and explained. Conclusion: The research results may be misleading if any of the reliability types and calculation criteria types are chosen incorrectly. Therefore, to make the results of the study more accurate and valuable. Medical researchers must seek help from relevant guidelines such as this study before conducting reliability analysis.

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Intra-cluster correlation coefficients in primary care patients with type 2 diabetes and hypertension

Trials ◽

10.1186/s13063-020-04349-4 ◽

2020 ◽

Vol 21 (1) ◽

Cited By ~ 1

Author(s):

Yi Lin Lee ◽

Yvonne Mei Fong Lim ◽

Kian Boon Law ◽

Sheamini Sivasampu

Keyword(s):

Type 2 Diabetes ◽

Primary Care ◽

Clinical Outcomes ◽

Patient Experience ◽

Correlation Coefficients ◽

Middle Income ◽

Processes Of Care ◽

Middle Income Countries ◽

Cluster Correlation

Abstract Introduction There are few sources of published data on intra-cluster correlation coefficients (ICCs) amongst patients with type 2 diabetes (T2D) and/or hypertension in primary care, particularly in low- and middle-income countries. ICC values are necessary for determining the sample sizes of cluster randomized trials. Hence, we aim to report the ICC values for a range of measures from a cluster-based interventional study conducted in Malaysia. Method Baseline data from a large study entitled Evaluation of Enhanced Primary Health Care interventions in public health clinics (EnPHC-EVA: Facility) were used in this analysis. Data from 40 public primary care clinics were collected through retrospective chart reviews and a patient exit survey. We calculated the ICCs for processes of care, clinical outcomes and patient experiences in patients with T2D and/or hypertension using the analysis of variance approach. Results Patient experience had the highest ICC values compared to processes of care and clinical outcomes. The ICC values ranged from 0.01 to 0.48 for processes of care. Generally, the ICC values for processes of care for patients with hypertension only are higher than those for T2D patients, with or without hypertension. However, both groups of patients have similar ICCs for antihypertensive medications use. In addition, similar ICC values were observed for clinical outcomes, ranging from 0.01 to 0.09. For patient experience, the ICCs were between 0.03 (proportion of patients who are willing to recommend the clinic to their friends and family) and 0.25 (for Patient Assessment of Chronic Illness Care item 9, Given a copy of my treatment plan). Conclusion The reported ICCs and their respective 95% confidence intervals for T2D and hypertension will be useful for estimating sample sizes and improving efficiency of cluster trials conducted in the primary care setting, particularly for low- and middle-income countries.

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Cluster-sample surveys and lot quality assurance sampling to evaluate yellow fever immunisation coverage following a national campaign, Bolivia, 2007

Tropical Medicine & International Health ◽

10.1111/j.1365-3156.2009.02231.x ◽

2009 ◽

Vol 14 (3) ◽

pp. 355-361 ◽

Cited By ~ 17

Author(s):

Lorenzo Pezzoli ◽

Silvia Pineda ◽

Percy Halkyer ◽

Gladys Crespo ◽

Nick Andrews ◽

...

Keyword(s):

Quality Assurance ◽

Yellow Fever ◽

Sample Surveys ◽

Immunisation Coverage ◽

Cluster Sample ◽

Lot Quality Assurance Sampling

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Association between Rift Valley fever virus seroprevalences in livestock and humans and their respective intra-cluster correlation coefficients, Tana River County, Kenya

Epidemiology and Infection ◽

10.1017/s0950268818003242 ◽

2018 ◽

Vol 147 ◽

Cited By ~ 4

Author(s):

B. Bett ◽

J. Lindahl ◽

R. Sang ◽

M. Wainaina ◽

S. Kairu-Wanyoike ◽

...

Keyword(s):

Rift Valley ◽

Rift Valley Fever ◽

Rift Valley Fever Virus ◽

Correlation Coefficients ◽

Enzyme Linked Immunosorbent Assay ◽

Cross Sectional ◽

Valley Fever ◽

Tana River ◽

Seropositive Animal ◽

Cluster Correlation

AbstractWe implemented a cross-sectional study in Tana River County, Kenya, a Rift Valley fever (RVF)-endemic area, to quantify the strength of association between RVF virus (RVFv) seroprevalences in livestock and humans, and their respective intra-cluster correlation coefficients (ICCs). The study involved 1932 livestock from 152 households and 552 humans from 170 households. Serum samples were collected and screened for anti-RVFv immunoglobulin G (IgG) antibodies using inhibition IgG enzyme-linked immunosorbent assay (ELISA). Data collected were analysed using generalised linear mixed effects models, with herd/household and village being fitted as random variables. The overall RVFv seroprevalences in livestock and humans were 25.41% (95% confidence interval (CI) 23.49–27.42%) and 21.20% (17.86–24.85%), respectively. The presence of at least one seropositive animal in a household was associated with an increased odds of exposure in people of 2.23 (95% CI 1.03–4.84). The ICCs associated with RVF virus seroprevalence in livestock were 0.30 (95% CI 0.19–0.44) and 0.22 (95% CI 0.12–0.38) within and between herds, respectively. These findings suggest that there is a greater variability of RVF virus exposure between than within herds. We discuss ways of using these ICC estimates in observational surveys for RVF in endemic areas and postulate that the design of the sentinel herd surveillance should consider patterns of RVF clustering to enhance its effectiveness as an early warning system for RVF epidemics.

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A Unique Model for ONSD Part II: Inter/Intra-operator Variability

Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques ◽

10.1017/s0317167100018448 ◽

2014 ◽

Vol 41 (4) ◽

pp. 430-435 ◽

Cited By ~ 11

Author(s):

F.A. Zeiler ◽

B. Unger ◽

Q. Zhu ◽

J. Xiao ◽

A.W. Kirkpatrick ◽

...

Keyword(s):

Potential Benefit ◽

Correlation Coefficients ◽

Optic Nerve Sheath Diameter ◽

Measurement Reliability ◽

Nerve Sheath ◽

Unique Model ◽

Experienced Operator ◽

Disc Size ◽

Novel Model ◽

Cluster Correlation

Objective:To evauluate our novel ultrasound model for measurement of optic nerve sheath diameter (ONSD) and determine the intra- and inter-operator variability associated with this technique.Methods:We conducted ten measurements of ONSD per model amongst eight different models with a single experienced operator to examine intra-operator variability. Similarly, we had seven different operators measure the OSND twice in eight different models, in order to determine inter-operator variability analyzed with a three level linear statistical model.Results:For intra-operator variability, the intra-cluster correlation coefficients for the experienced and novice operators were 0.643 and 0.453 respectively. This displayed improvement in intra-operator variability with experience. The inter-cluster correlation coefficient was 0 for the group of novice operators, indicating negligible difference amongst multiple operators in measuring any given model of ONSD. A strong, statistically significant, linear relationship between the actual model disc size and the ultrasound ONSD measures was identified, implying the reliability of the images produced by our novel model.Conclusions:Utilizing a novel model for ONSD ultrasonography, we have determined the intraoperator reliability of ONSD measurement to be moderate, with no appreciable difference amongst multiple operators. Improvement in measurement reliability has been demonstrated between expert and novice operators with our model, indicating the potential benefit of simulation platforms for teaching the technique of ONSD ultrasound.

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