scholarly journals Randomization methods and cluster size in cluster randomized trials conducted in elementary and high schools

2021 ◽  
pp. 87-87
Author(s):  
Mirjana Pajcin ◽  
Zoran Bukumiric ◽  
Jelena Tomasevic ◽  
Aleksandra Ilic
Keyword(s):  
Cluster Size ◽  
Literature Search ◽  
Randomized Trials ◽  
Study Groups ◽  
Cluster Randomization ◽  
Entire Sample ◽  
Significant Difference ◽  
Cluster Randomized ◽  
Larger Sample ◽  
Randomization Method

Background: Randomization allows study groups to be formed so that they are similar in all characteristics except outcomes. Aim: The aim of this study is to examine the frequency of randomization methods and their effect on achieving baseline balance in cluster randomization studies conducted in schools. Methods: A literature search of MEDLINE bibliographic database shows that the total number of collected articles in full text was 343, out of which 81 were eligible for inclusion. Each publication was reviewed by two independent reviewers, and data was extracted and analyzed. Results: Stratification was the most commonly applied randomization method, reported in 28 trials (34.6 %). There was no statistically significant difference in the number of subjects and clusters, as well as in cluster size between trial?s groups in studies in which simple randomization was used. However, there was a statistically significant difference in number of subjects and clusters between groups in trials in which restricted randomization methods were used. Yet, there was no difference in the cluster size. Conclusion: Although there is no difference in the size of clusters between trial arms, either at the level of the entire sample or in relation to randomization methods applied, additional research should be conducted on larger sample in order to establish the effect of randomization methods on baseline balance, when the size of clusters is in question.

scholarly journals Comparative Study of Intralesional Dexamethasone Plus Hyaluronidase & Oral Colchicine in Patients with Oral Submucous Fibrosis

2018 ◽  
Vol 14 (2) ◽  
pp. 60-65
Author(s):  
Ganesh Prasad Neupane ◽  
Maya Rai ◽  
R. S. Rathore ◽  
V. K. Bhargava ◽  
A. K. Mahat ◽  
...  
Keyword(s):  
Treatment Outcomes ◽  
Oral Mucosa ◽  
Oral Submucous Fibrosis ◽  
Mouth Opening ◽  
Study Groups ◽  
T Test ◽  
Significant Difference ◽  
Intralesional Injections ◽  
Submucous Fibrosis ◽  
Larger Sample

Introduction: Oral Submucous Fibrosis (OSMF) is a precancerous condition of the oral mucosa. It is characterized by excessive production of collagen leading to inelasticity of the oral mucosa and atrophic changes of the epithelium.Aim and objective: To evaluate the efficacy of oral Colchicine in comparison to intralesional injections of Dexamethasone plus Hyaluronidase in the management of OSMF patients.Materials and Methods: Fourty patients with OSMF were randomly divided equally into two groups. 20 patients in Dexamethasone group received biweekly intralesional injections of Dexamethasone (4mg/ml) plus Hyaluronidase 1500 IU in buccal mucosa for a period of 12 weeks. Other 20 patients in Colchicine group received oral Colchicine 0.5 mg tablets twice daily for 12 weeks. Parameters taken in the study were burning sensation, and mouth opening. Descriptive statistics, paired t test and unpaired t test were used for statistical analysis.Results and Conclusions: The pre- and post-treatment differences were found to be statistically significant for both the groups (p<0.001) and for both the treatment outcomes. When the average difference of the treatment outcomes was compared between the two study groups, statistically highly significant difference was noted (p <0.001) only in mouth opening but not in burning sensation.These encouraging results should prompt further clinical trials with Colchicine on a larger sample size to broaden the therapeutic usefulness of the drug in the management of OSMF. JNGMC,  Vol. 14 No. 2 December 2016, Page: 60-65

Sample size estimation for modified Poisson analysis of cluster randomized trials with a binary outcome

2021 ◽  
pp. 096228022199041
Author(s):  
Fan Li ◽  
Guangyu Tong
Keyword(s):  
Relative Risk ◽  
Sample Size ◽  
Poisson Regression ◽  
Cluster Size ◽  
Randomized Trials ◽  
Cluster Randomized Trials ◽  
Binomial Regression ◽  
Size Variability ◽  
Working Correlation ◽  
Cluster Randomized

The modified Poisson regression coupled with a robust sandwich variance has become a viable alternative to log-binomial regression for estimating the marginal relative risk in cluster randomized trials. However, a corresponding sample size formula for relative risk regression via the modified Poisson model is currently not available for cluster randomized trials. Through analytical derivations, we show that there is no loss of asymptotic efficiency for estimating the marginal relative risk via the modified Poisson regression relative to the log-binomial regression. This finding holds both under the independence working correlation and under the exchangeable working correlation provided a simple modification is used to obtain the consistent intraclass correlation coefficient estimate. Therefore, the sample size formulas developed for log-binomial regression naturally apply to the modified Poisson regression in cluster randomized trials. We further extend the sample size formulas to accommodate variable cluster sizes. An extensive Monte Carlo simulation study is carried out to validate the proposed formulas. We find that the proposed formulas have satisfactory performance across a range of cluster size variability, as long as suitable finite-sample corrections are applied to the sandwich variance estimator and the number of clusters is at least 10. Our findings also suggest that the sample size estimate under the exchangeable working correlation is more robust to cluster size variability, and recommend the use of an exchangeable working correlation over an independence working correlation for both design and analysis. The proposed sample size formulas are illustrated using the Stop Colorectal Cancer (STOP CRC) trial.

scholarly journals Cluster over individual randomization: are study design choices appropriately justified? Review of a random sample of trials

2020 ◽  
Vol 17 (3) ◽  
pp. 253-263 ◽  
Author(s):  
Monica Taljaard ◽  
Cory E Goldstein ◽  
Bruno Giraudeau ◽  
Stuart G Nicholls ◽  
Kelly Carroll ◽  
...  
Keyword(s):  
Health Care ◽  
Informed Consent ◽  
Random Sample ◽  
Randomized Trials ◽  
Cluster Randomized Trials ◽  
Cluster Randomization ◽  
Pragmatic Trials ◽  
Individual Level ◽  
Health Care Interventions ◽  
Cluster Randomized

Background: Novel rationales for randomizing clusters rather than individuals appear to be emerging from the push for more pragmatic trials, for example, to facilitate trial recruitment, reduce the costs of research, and improve external validity. Such rationales may be driven by a mistaken perception that choosing cluster randomization lessens the need for informed consent. We reviewed a random sample of published cluster randomized trials involving only individual-level health care interventions to determine (a) the prevalence of reporting a rationale for the choice of cluster randomization; (b) the types of explicit, or if absent, apparent rationales for the use of cluster randomization; (c) the prevalence of reporting patient informed consent for study interventions; and (d) the types of justifications provided for waivers of consent. We considered cluster randomized trials for evaluating exclusively the individual-level health care interventions to focus on clinical trials where individual randomization is only theoretically possible and where there is a general expectation of informed consent. Methods: A random sample of 40 cluster randomized trials were identified by implementing a validated electronic search filter in two electronic databases (Ovid MEDLINE and Embase), with two reviewers independently extracting information from each trial. Inclusion criteria were the following: primary report of a cluster randomized trial, evaluating exclusively an individual-level health care intervention, published between 2007 and 2016, and conducted in Canada, the United States, European Union, Australia, or low- and middle-income country settings. Results: Twenty-five trials (62.5%, 95% confidence interval = 47.5%–77.5%) reported an explicit rationale for the use of cluster randomization. The most commonly reported rationales were those with logistical or administrative convenience (15 trials, 60%) and those that need to avoid contamination (13 trials, 52%); five trials (20%) were cited rationales related to the push for more pragmatic trials. Twenty-one trials (52.5%, 95% confidence interval = 37%–68%) reported written informed consent for the intervention, two (5%) reported verbal consent, and eight (20%) reported waivers of consent, while in nine trials (22.5%) consent was unclear or not mentioned. Reported justifications for waivers of consent included that study interventions were already used in clinical practice, patients were not randomized individually, and the need to facilitate the pragmatic nature of the trial. Only one trial reported an explicit and appropriate justification for waiver of consent based on minimum criteria in international research ethics guidelines, namely, infeasibility and minimal risk. Conclusion: Rationales for adopting cluster over individual randomization and for adopting consent waivers are emerging, related to the need to facilitate pragmatic trials. Greater attention to clear reporting of study design rationales, informed consent procedures, as well as justification for waivers is needed to ensure that such trials meet appropriate ethical standards.

scholarly journals The Effect of Cluster Size Variability on Statistical Power in Cluster-Randomized Trials

PLoS ONE ◽  
2015 ◽  
Vol 10 (4) ◽  
pp. e0119074 ◽  
Author(s):  
Stephen A. Lauer ◽  
Ken P. Kleinman ◽  
Nicholas G. Reich
Keyword(s):  
Cluster Size ◽  
Statistical Power ◽  
Randomized Trials ◽  
Cluster Randomized Trials ◽  
Size Variability ◽  
Cluster Randomized

scholarly journals Vitamin K prophylaxis in newborns

2021 ◽  
Vol 21 (S1) ◽  
Author(s):  
Sophie Jullien
Keyword(s):  
Vitamin K ◽  
Literature Search ◽  
Randomized Trials ◽  
Supporting Evidence ◽  
Vitamin K Prophylaxis ◽  
Manual Search ◽  
Oral Regimen ◽  
Significant Difference ◽  
Haemorrhagic Disease ◽  
Key Terms

AbstractWe looked at existing recommendations and supporting evidence on the effectiveness of vitamin K given after birth in preventing the haemorrhagic disease of the newborn (HDN).We conducted a literature search up to the 10th of December 2019 by using key terms and manual search in selected sources. We summarized the recommendations and the strength of the recommendation when and as reported by the authors. We summarized the main findings of systematic reviews with the certainty of the evidence as reported.All newborns should receive vitamin K prophylaxis, as it has been proven that oral and intramuscular prophylactic vitamin K given after birth are effective for preventing classical HDN. There are no randomized trials looking at the efficacy of vitamin K supplement on late HDN. There are no randomized trials comparing the oral and intramuscular route of administration of prophylactic vitamin K in newborns. From older trials and surveillance data, it seems that there is no significant difference between the intramuscular and the oral regimens for preventing classical and late HDN, provided that the oral regimen is duly completed. Evidence assessing vitamin K prophylaxis in preterm infants is scarce.

Sample Size Issues for Cluster Randomized Trials With Discrete-Time Survival Endpoints

Methodology ◽  
2012 ◽  
Vol 8 (4) ◽  
pp. 146-158 ◽  
Author(s):  
Mirjam Moerbeek
Keyword(s):  
Discrete Time ◽  
Cluster Size ◽  
Randomized Trials ◽  
Cluster Randomized Trial ◽  
Smoking Initiation ◽  
Fixed Number ◽  
Cluster Randomized Trials ◽  
Number Of Clusters ◽  
Sufficient Power ◽  
Cluster Randomized

With cluster randomized trials complete groups of subjects are randomized to treatment conditions. An important question might be whether and when the subjects experience a particular event, such as smoking initiation or recovery from disease. In the social sciences the timing of such events is often measured in discrete time by using time intervals. At the planning phase of a cluster randomized trial one should decide on the number of clusters and cluster size such that parameters are estimated accurately and sufficient power on the test on treatment effect is achieved. On basis of a simulation study it is concluded that regression coefficients are estimated more accurately than the variance of the random cluster effect. In addition, it is shown that power increases with cluster size and number of clusters, and that a sufficient power cannot always be achieved by using larger cluster sizes at a fixed number of clusters.

scholarly journals Statistical approaches to computing sample size in cluster randomized trials: a simulation study

2019 ◽  
Author(s):  
Ashutosh Ranjan ◽  
Guangzi Song ◽  
Christopher S Coffey ◽  
Leslie A McClure
Keyword(s):  
Sample Size ◽  
Simulation Study ◽  
Cluster Size ◽  
Randomized Trials ◽  
Sample Size Calculation ◽  
Minimum Variance ◽  
Type I ◽  
Cluster Randomized Trials ◽  
Weighted Analysis ◽  
Cluster Randomized

Abstract Background: Cluster randomized trials, which randomize groups of individuals to an intervention, are common in health services research when one wants to evaluate improvement in a subject's outcome by intervening at an organizational level. For many such trials sample size calculation is performed under the assumption of equal cluster size. Many trials that set out to recruit equal clusters end up with unequal clusters for various reasons. This leads to a misalignment between the method used for sample size calculation and the data analysis, which may affect trial power. Various weighted analysis methods for analyzing cluster means have been suggested to overcome the problem introduced by unbalanced clusters; however, the performance of such methods has not been evaluated extensively.Methods: We examine the use of the general linear model for analysis of clustered randomized trials assuming equal cluster sizes during the planning stage but ending up with unequal clusters. We demonstrate the performance of three approaches using different weights for analyzing the cluster means: (1) the standard analysis of cluster means, (2) weighting by cluster size, and (3) minimum variance weights. Several distributions are used to generate cluster sizes to cover a wide range of patterns of imbalance. The variability in cluster size is measured by the coefficient of variation (CV). By means of a simulation study, we assess the impact of using each of the three analysis methods with respect to type I error and power of the study and how it is affected by the variability in cluster size. Results: Analyses that assumes equal clusters provide a reasonable approximation when cluster sizes vary minimally (CV < 0.30). In an analysis weighted by cluster size type I errors were inflated, and that worsened as the variation in cluster size increases. However, a minimum variance weighted analysis best maintains target power and level of significance under all degrees of imbalance considered. Conclusion: The unweighted analysis works well as an approximate method when the variation in cluster size is minimal. However, using minimum variance weights performs much better across the full range of variation in cluster size and is recommended.

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