scholarly journals Intracluster correlation coefficients in a large cluster randomized vaccine trial in schools: Transmission and impact of shared characteristics

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0254330
Author(s):  
Jane Whelan ◽  
Helen Marshall ◽  
Thomas R. Sullivan
Keyword(s):  
Sample Size ◽  
Rural Areas ◽  
Vaccine Trial ◽  
South Australia ◽  
Correlation Coefficients ◽  
Personal Characteristics ◽  
Future Research ◽  
Intracluster Correlation ◽  
Highly Correlated ◽  
Cluster Randomized

Cluster randomized trials (cRCT) to assess vaccine effectiveness incorporate indirect effects of vaccination, helping to inform vaccination policy. To calculate the sample size for a cRCT, an estimate of the intracluster correlation coefficient (ICC) is required. For infectious diseases, shared characteristics and social mixing behaviours may increase susceptibility and exposure, promote transmission and be a source of clustering. We present ICCs from a school-based cRCT assessing the effectiveness of a meningococcal B vaccine (Bexsero, GlaxoSmithKline) on reducing oropharyngeal carriage of Neisseria meningitidis (Nm) in 34,489 adolescents from 237 schools in South Australia in 2017/2018. We also explore the contribution of shared behaviours and characteristics to these ICCs. The ICC for carriage of disease-causing Nm genogroups (primary outcome) pre-vaccination was 0.004 (95% CI: 0.002, 0.007) and for all Nm was 0.007 (95%CI: 0.004, 0.011). Adjustment for social behaviours and personal characteristics reduced the ICC for carriage of disease-causing and all Nm genogroups by 25% (to 0.003) and 43% (to 0.004), respectively. ICCs are also reported for risk factors here, which may be outcomes in future research. Higher ICCs were observed for susceptibility and/or exposure variables related to Nm carriage (having a cold, spending ≥1 night out socializing or kissing ≥1 person in the previous week). In metropolitan areas, nights out socializing was a highly correlated behaviour. By contrast, smoking was a highly correlated behaviour in rural areas. A practical example to inform future cRCT sample size estimates is provided.

GROWTH AND DEVELOPMENT OF PERUVIAN CHILDREN

PEDIATRICS ◽  
1956 ◽  
Vol 17 (4) ◽  
pp. 510-523
Author(s):  
M. F. Trulson ◽  
C. Collazos ◽  
D. M. Hegsted
Keyword(s):  
Rural Areas ◽  
Growth And Development ◽  
Partial Correlation ◽  
Correlation Coefficients ◽  
Significant Degree ◽  
Skeletal Age ◽  
Developmental Patterns ◽  
Specific Effects ◽  
Developmental Age ◽  
Highly Correlated

One hundred nine school children from 2 rural areas in the coastal area of Peru were measured and weighed and roentgenograms of the hand and wrist were obtained. Three-fourths of the children were below Stuart's tenth percentile in height. Roughly, a third of the children were below the tenth percentile in weight. Fifteen per cent of the girls and 30 per cent of the boys were above the fiftieth percentile in weight. Forty to forty-five per cent of the children were in the stocky to obese channels of the Wetzel grid; 5 to 10 per cent would be classified as fair to poor, and roughly half would be considered average. Developmental age (Wetzel) was 7.5 ± 15.6 months less than chronological age for boys, 10.5 ± 11.3 months less for girls. A third of the boys and 15 per cent of the girls were advanced in Wetzel developmental age. It was apparent that the heavier children were generally advanced in Wetzel developmental age. Roentgenograms of the hand and wrist were assessed by comparing the films to the Greulich-Pyle Standards. Skeletal age was -11.3 ± 12.7 months for boys and -7.1 ± 9.8 for girls. Eighteen per cent of the population were advanced in skeletal age. Boys were more retarded than girls in skeletal age. The correlation and partial correlation coefficients for all combinations of the 4 measurements (retardation in weight, retardation in height, retardation in skeletal age and retardation in developmental age) were calculated. The various pairs were all rather highly correlated, this being particularly true of weight and Wetzel developmental age. The partial correlation coefficients show, however, that skeletal age was not closely correlated with any of the other 3 measurements. Height and developmental age were negatively correlated to a significant degree, and developmental age and weight were so closely related that they appear to be measures of the same characteristic in this population. Individual dietary histories are not available from these children, but it is known that the diets in the area are considerably below recommended levels in certain nutriients. Whether dietary deficiencies are factors in the apparently abnormal developmental patterns, or if the patterns are truly abnormal for the Peruvian child or indicate an adverse effect on health, remain to be shown. It is pointed out that there are probably advantages in studies upon growth and development in different areas of the world where a variety of dietary or environmental factors may have specific effects.

scholarly journals Standardising the measurement of physical activity in people receiving haemodialysis: considerations for research and practice

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Hannah M. L. Young ◽  
Mark W. Orme ◽  
Yan Song ◽  
Maurice Dungey ◽  
James O. Burton ◽  
...  
Keyword(s):  
Physical Activity ◽  
Sample Size ◽  
Repeated Measures ◽  
A Priori ◽  
Intraclass Correlation ◽  
Correlation Coefficients ◽  
Future Research ◽  
Wear Time ◽  
Minimum Number ◽  
The Uk

Abstract Background Physical activity (PA) is exceptionally low amongst the haemodialysis (HD) population, and physical inactivity is a powerful predictor of mortality, making it a prime focus for intervention. Objective measurement of PA using accelerometers is increasing, but standard reporting guidelines essential to effectively evaluate, compare and synthesise the effects of PA interventions are lacking. This study aims to (i) determine the measurement and processing guidance required to ensure representative PA data amongst a diverse HD population, and; (ii) to assess adherence to PA monitor wear amongst HD patients. Methods Clinically stable HD patients from the UK and China wore a SenseWear Armband accelerometer for 7 days. Step count between days (HD, Weekday, Weekend) were compared using repeated measures ANCOVA. Intraclass correlation coefficients (ICCs) determined reliability (≥0.80 acceptable). Spearman-Brown prophecy formula, in conjunction with a priori ≥  80% sample size retention, identified the minimum number of days required for representative PA data. Results Seventy-seven patients (64% men, mean ± SD age 56 ± 14 years, median (interquartile range) time on HD 40 (19–72) months, 40% Chinese, 60% British) participated. Participants took fewer steps on HD days compared with non-HD weekdays and weekend days (3402 [95% CI 2665–4140], 4914 [95% CI 3940–5887], 4633 [95% CI 3558–5707] steps/day, respectively, p < 0.001). PA on HD days were less variable than non-HD days, (ICC 0.723–0.839 versus 0.559–0.611) with ≥ 1 HD day and ≥  3 non-HD days required to provide representative data. Using these criteria, the most stringent wear-time retaining ≥ 80% of the sample was ≥7 h. Conclusions At group level, a wear-time of ≥7 h on ≥1HD day and ≥ 3 non-HD days is required to provide reliable PA data whilst retaining an acceptable sample size. PA is low across both HD and non- HD days and future research should focus on interventions designed to increase physical activity in both the intra and interdialytic period.

scholarly journals A Review of Assumed and Reported Intracluster Correlations in Cluster Randomized Trials

2019 ◽  
Author(s):  
Xiaoran Han ◽  
Jiaye Lin ◽  
Jinjing Xu ◽  
Maggie Wang ◽  
Benny Zee ◽  
...  
Keyword(s):  
Sample Size ◽  
Randomized Trials ◽  
Intraclass Correlation ◽  
Cluster Effect ◽  
Size Estimation ◽  
Cluster Randomized Trials ◽  
Intracluster Correlation ◽  
Eligibility Criteria ◽  
Sample Size Planning ◽  
Cluster Randomized

Abstract Background Cluster randomized trials (CRTs) are widely adopted in health and primary care research. However, the cluster effect needs to be taken into account appropriately in the design and analysis of CRTs. The objectives of this study were (i) to review the reporting of intracluster correlations in CRTs; and (ii) to evaluate whether the assumed intracluster correlation measures in sample size planning are consistent with those obtained in the analysis. Methods The Aggregate Analysis of ClinicalTrials.gov database was searched to identify CRTs registered between January 1, 2004 and March 27, 2016. The selected CRTs with accessible publications were screened according to eligibility criteria. Results Of the 281 CRTs identified, the percentage of studies accounting for cluster effect increased annually. A total of 183 studies accounted for clustering in sample size estimation, among them 43% of CRTs adopted the intraclass correlation coefficient (ICC) but the exact estimated value of ICC was provided in only 26% of the included studies. In different intervention types, there were no statistically significant differences between the assumed and reported values of ICC (all p-values >0.05). Conclusion Although the difference between the values of ICC assumed in sample size planning and that reported in the analysis was not statistically significant, deficiencies in CRTs are still common, such as low rates of considering cluster effect in sample size and reporting intracluster correlation estimates. We also suggest that researchers ought to be familiar with the properties of statistical approaches to improve the analysis of CRTs. Thus, more recommendations and guidelines such as the CONSORT statement for CRTs should be suggested to researchers.

scholarly journals Sample size calculation for stepped-wedge cluster-randomized trials with more than two levels of clustering

2019 ◽  
Vol 16 (3) ◽  
pp. 225-236 ◽  
Author(s):  
Steven Teerenstra ◽  
Monica Taljaard ◽  
Anja Haenen ◽  
Anita Huis ◽  
Femke Atsma ◽  
...  
Keyword(s):  
Hand Hygiene ◽  
Sample Size ◽  
Sample Size Calculation ◽  
Correlation Coefficients ◽  
Stepped Wedge ◽  
Change Trial ◽  
Stepped Wedge Trials ◽  
Hygiene Compliance ◽  
Standard Designs ◽  
Cluster Randomized

Background/Aims: Power and sample size calculation formulas for stepped-wedge trials with two levels (subjects within clusters) are available. However, stepped-wedge trials with more than two levels are possible. An example is the CHANGE trial which randomizes nursing homes (level 4) consisting of nursing home wards (level 3) in which nurses (level 2) are observed with respect to their hand hygiene compliance during hand hygiene opportunities (level 1) in the care of patients. We provide power and sample size methods for such trials and illustrate these in the setting of the CHANGE trial. Methods: We extend the original sample size methodology derived for stepped-wedge trials based on a random intercepts model, to accommodate more than two levels of clustering. We derive expressions that can be used to determine power and sample size for p levels of clustering in terms of the variances at each level or, alternatively, in terms of intracluster correlation coefficients. We consider different scenarios, depending on whether the same units in a particular level are repeatedly measured as a cohort sample or whether different units are measured cross-sectionally. Results: A simple variance inflation factor is obtained that can be used to calculate power and sample size for continuous and by approximation for binary and rate outcomes. It is the product of (1) variance inflation due to the multilevel structure and (2) variance inflation due to the stepped-wedge manner of assigning interventions over time. Standard and non-standard designs (i.e. so-called “hybrid designs” and designs with more, less, or no data collection when the clusters are all in the control or are all in the intervention condition) are covered. Conclusions: The formulas derived enable power and sample size calculations for multilevel stepped-wedge trials. For the two-, three-, and four-level case of the standard stepped wedge, we provide programs to facilitate these calculations.

scholarly journals Sample size for the estimation of Pearson’s linear correlation in crotalaria species

2019 ◽  
Vol 54 ◽  
Author(s):  
Marcos Toebe ◽  
Letícia Nunes Machado ◽  
Francieli de Lima Tartaglia ◽  
Juliana Oliveira de Carvalho ◽  
Cirineu Tolfo Bandeira ◽  
...  
Keyword(s):  
Confidence Interval ◽  
Sample Size ◽  
Correlation Coefficient ◽  
Linear Correlation ◽  
Correlation Coefficients ◽  
Crotalaria Juncea ◽  
Species Pairs ◽  
Precision Level ◽  
Highly Correlated ◽  
Crotalaria Spectabilis

Abstract: The objective of this work was to determine the necessary sample size to estimate Pearson’s linear correlation coefficients of four species of crotalaria at precision levels. The experiment was carried out with Crotalaria juncea, Crotalaria spectabilis, Crotalaria breviflora, and Crotalaria ochroleuca, during the 2014/2015 crop year. Eight crotalaria traits were evaluated in 1,000 randomly collected pods per species. For each species, the correlation coefficients were estimated for the 28 pairs of traits, and the sample size necessary to estimate the correlation coefficients was determined at four precision levels [0.10, 0.20, 0.30, and 0.40 amplitudes of the 95% (CI95%) confidence interval] by resampling with replacement. The sample size varies between crotalaria species and, especially, between pairs of traits, as a function of the magnitude of the correlation coefficient. At a certain precision level, the smallest sample size is required to estimate the correlation coefficients between highly correlated traits and vice-versa. To estimate the correlation coefficients with CI95% of 0.20, 10 to 440 pods are required, depending on the species, pairs of traits, and magnitude of the correlation coefficient.

scholarly journals Intracluster correlation coefficients in the Greater Mekong Subregion for sample size calculations of cluster randomized malaria trials

2019 ◽  
Vol 18 (1) ◽  
Author(s):  
Pimnara Peerawaranun ◽  
Jordi Landier ◽  
Francois H. Nosten ◽  
Thuy-Nhien Nguyen ◽  
Tran Tinh Hien ◽  
...  
Keyword(s):  
Randomized Trial ◽  
Sample Size ◽  
Randomized Trials ◽  
Cluster Randomized Trial ◽  
Cluster Randomized Trials ◽  
Intracluster Correlation ◽  
Variable Approach ◽  
Sample Size Calculations ◽  
Cluster Randomized ◽  
Greater Mekong Subregion

Abstract Background Sample size calculations for cluster randomized trials are a recognized methodological challenge for malaria research in pre-elimination settings. Positively correlated responses from the participants in the same cluster are a key feature in the estimated sample size required for a cluster randomized trial. The degree of correlation is measured by the intracluster correlation coefficient (ICC) where a higher coefficient suggests a closer correlation hence less heterogeneity within clusters but more heterogeneity between clusters. Methods Data on uPCR-detected Plasmodium falciparum and Plasmodium vivax infections from a recent cluster randomized trial which aimed at interrupting malaria transmission through mass drug administrations were used to calculate the ICCs for prevalence and incidence of Plasmodium infections. The trial was conducted in four countries in the Greater Mekong Subregion, Laos, Myanmar, Vietnam and Cambodia. Exact and simulation approaches were used to estimate ICC values for both the prevalence and the incidence of parasitaemia. In addition, the latent variable approach to estimate ICCs for the prevalence was utilized. Results The ICCs for prevalence ranged between 0.001 and 0.082 for all countries. The ICC from the combined 16 villages in the Greater Mekong Subregion were 0.26 and 0.21 for P. falciparum and P. vivax respectively. The ICCs for incidence of parasitaemia ranged between 0.002 and 0.075 for Myanmar, Cambodia and Vietnam. There were very high ICCs for incidence in the range of 0.701 to 0.806 in Laos during follow-up. Conclusion ICC estimates can help researchers when designing malaria cluster randomized trials. A high variability in ICCs and hence sample size requirements between study sites was observed. Realistic sample size estimates for cluster randomized malaria trials in the Greater Mekong Subregion have to assume high between cluster heterogeneity and ICCs. This work focused on uPCR-detected infections; there remains a need to develop more ICC references for trials designed around prevalence and incidence of clinical outcomes. Adequately powered trials are critical to estimate the benefit of interventions to malaria in a reliable and reproducible fashion. Trial registration: ClinicalTrials.govNCT01872702. Registered 7 June 2013. Retrospectively registered. https://clinicaltrials.gov/ct2/show/NCT01872702

scholarly journals Power and sample size calculations for cluster randomized trials with binary outcomes when intracluster correlation coefficients vary by treatment arm

2021 ◽  
pp. 174077452110598
Author(s):  
Lee Kennedy-Shaffer ◽  
Michael D Hughes
Keyword(s):  
Sample Size ◽  
Correlation Coefficient ◽  
Generalized Estimating Equations ◽  
Randomized Trials ◽  
Asymptotic Variance ◽  
Correlation Structure ◽  
Intracluster Correlation ◽  
Intracluster Correlation Coefficient ◽  
Working Correlation ◽  
Cluster Randomized

Background/Aims Generalized estimating equations are commonly used to fit logistic regression models to clustered binary data from cluster randomized trials. A commonly used correlation structure assumes that the intracluster correlation coefficient does not vary by treatment arm or other covariates, but the consequences of this assumption are understudied. We aim to evaluate the effect of allowing variation of the intracluster correlation coefficient by treatment or other covariates on the efficiency of analysis and show how to account for such variation in sample size calculations. Methods We develop formulae for the asymptotic variance of the estimated difference in outcome between treatment arms obtained when the true exchangeable correlation structure depends on the treatment arm and the working correlation structure used in the generalized estimating equations analysis is: (i) correctly specified, (ii) independent, or (iii) exchangeable with no dependence on treatment arm. These formulae require a known distribution of cluster sizes; we also develop simplifications for the case when cluster sizes do not vary and approximations that can be used when the first two moments of the cluster size distribution are known. We then extend the results to settings with adjustment for a second binary cluster-level covariate. We provide formulae to calculate the required sample size for cluster randomized trials using these variances. Results We show that the asymptotic variance of the estimated difference in outcome between treatment arms using these three working correlation structures is the same if all clusters have the same size, and this asymptotic variance is approximately the same when intracluster correlation coefficient values are small. We illustrate these results using data from a recent cluster randomized trial for infectious disease prevention in which the clusters are groups of households and modest in size (mean 9.6 individuals), with intracluster correlation coefficient values of 0.078 in the control arm and 0.057 in an intervention arm. In this application, we found a negligible difference between the variances calculated using structures (i) and (iii) and only a small increase (typically [Formula: see text]) for the independent correlation structure (ii), and hence minimal effect on power or sample size requirements. The impact may be larger in other applications if there is greater variation in the ICC between treatment arms or with an additional covariate. Conclusion The common approach of fitting generalized estimating equations with an exchangeable working correlation structure with a common intracluster correlation coefficient across arms likely does not substantially reduce the power or efficiency of the analysis in the setting of a large number of small or modest-sized clusters, even if the intracluster correlation coefficient varies by treatment arm. Our formulae, however, allow formal evaluation of this and may identify situations in which variation in intracluster correlation coefficient by treatment arm or another binary covariate may have a more substantial impact on power and hence sample size requirements.

scholarly journals Methodological challenges in pragmatic trials in Alzheimer’s disease and related dementias: Opportunities for improvement

2021 ◽  
pp. 174077452110466
Author(s):  
Monica Taljaard ◽  
Fan Li ◽  
Bo Qin ◽  
Caroline Cui ◽  
Leyi Zhang ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Sample Size ◽  
Group Treatment ◽  
Repeated Measures ◽  
Repeated Measurements ◽  
Intracluster Correlation ◽  
Pragmatic Trials ◽  
Sample Size Calculations ◽  
Cluster Randomized

Background and Aims We need more pragmatic trials of interventions to improve care and outcomes for people living with Alzheimer’s disease and related dementias. However, these trials present unique methodological challenges in their design, analysis, and reporting—often, due to the presence of one or more sources of clustering. Failure to account for clustering in the design and analysis can lead to increased risks of Type I and Type II errors. We conducted a review to describe key methodological characteristics and obtain a “baseline assessment” of methodological quality of pragmatic trials in dementia research, with a view to developing new methods and practical guidance to support investigators and methodologists conducting pragmatic trials in this field. Methods We used a published search filter in MEDLINE to identify trials more likely to be pragmatic and identified a subset that focused on people living with Alzheimer’s disease or other dementias or included them as a defined subgroup. Pairs of reviewers extracted descriptive information and key methodological quality indicators from each trial. Results We identified N = 62 eligible primary trial reports published across 36 different journals. There were 15 (24%) individually randomized, 38 (61%) cluster randomized, and 9 (15%) individually randomized group treatment designs; 54 (87%) trials used repeated measures on the same individual and/or cluster over time and 17 (27%) had a multivariate primary outcome (e.g. due to measuring an outcome on both the patient and their caregiver). Of the 38 cluster randomized trials, 16 (42%) did not report sample size calculations accounting for the intracluster correlation and 13 (34%) did not account for intracluster correlation in the analysis. Of the 9 individually randomized group treatment trials, 6 (67%) did not report sample size calculations accounting for intracluster correlation and 8 (89%) did not account for it in the analysis. Of the 54 trials with repeated measurements, 45 (83%) did not report sample size calculations accounting for repeated measurements and 19 (35%) did not utilize at least some of the repeated measures in the analysis. No trials accounted for the multivariate nature of their primary outcomes in sample size calculation; only one did so in the analysis. Conclusion There is a need and opportunity to improve the design, analysis, and reporting of pragmatic trials in dementia research. Investigators should pay attention to the potential presence of one or more sources of clustering. While methods for longitudinal and cluster randomized trials are well developed, accessible resources and new methods for dealing with multiple sources of clustering are required. Involvement of a statistician with expertise in longitudinal and clustered designs is recommended.

Implementation Research on Shared Decision Making in Primary Care: Inventory of Intracluster Correlation Coefficients

2019 ◽  
Vol 39 (6) ◽  
pp. 661-672
Author(s):  
Ali Ben Charif ◽  
Jordie Croteau ◽  
Rhéda Adekpedjou ◽  
Hervé Tchala Vignon Zomahoun ◽  
Evehouenou Lionel Adisso ◽  
...  
Keyword(s):  
Primary Care ◽  
Decision Making ◽  
Shared Decision Making ◽  
Randomized Trials ◽  
Correlation Coefficients ◽  
Shared Decision ◽  
Cluster Randomized Trials ◽  
Intracluster Correlation ◽  
Convenience Sample ◽  
Cluster Randomized

Background. Cluster randomized trials are important sources of information on evidence-based practices in primary care. However, there are few sources of intracluster correlation coefficients (ICCs) for designing such trials. We inventoried ICC estimates for shared decision-making (SDM) measures in primary care. Methods. Data sources were studies led by the Canada Research Chair in Shared Decision Making and Knowledge Transition. Eligible studies were conducted in primary care, included at least 2 hierarchical levels, included SDM measures for individual units nested under any type of cluster (area, clinic, or provider), and were approved by an ethics committee. We classified measures into decision antecedents, decision processes, and decision outcomes. We used Bayesian random-effect models to estimate mode ICCs and the 95% highest probability density interval (HPDI). We summarized estimates by calculating median and interquartile range (IQR). Results. Six of 14 studies were included. There were 97 ICC estimates for 17 measures. ICC estimates ranged from 0 to 0.5 (median, 0.03; IRQ, 0–0.07). They were higher for process measures (median, 0.03; IQR, 0–0.07) than for antecedent measures (0.02; 0–0.07) or outcome measures (0.02; 0–0.06), for which, respectively, “decisional conflict” (mode, 0.48; 95% HPDI, 0.39–0.57), “reluctance to disclose uncertainty to patients” (0.5; 0.11–0.89), and “quality of the decision” (0.45; 0.14–0.84) had the highest ICCs. ICCs for provider-level clustering (median, 0.06; IQR, 0–0.13) were higher than for other levels. Limitations. This convenience sample of studies may not reflect all potential ICC ranges for primary care SDM measures. Conclusions. Our inventory of ICC estimates for SDM measures in primary care will improve the ease and accuracy of power calculations in cluster randomized trials and inspire its further expansion in SDM contexts.

Sign in / Sign up

Export Citation Format

Share Document