Graph cluster randomization

2013 ◽  
Author(s):  
Johan Ugander ◽  
Brian Karrer ◽  
Lars Backstrom ◽  
Jon Kleinberg
Keyword(s):  
Cluster Randomization ◽  
Graph Cluster

scholarly journals Design and Analysis of Experiments in Networks: Reducing Bias from Interference

2016 ◽  
Vol 5 (1) ◽  
Author(s):  
Dean Eckles ◽  
Brian Karrer ◽  
Johan Ugander
Keyword(s):  
Sufficient Conditions ◽  
Bias Reduction ◽  
Experimental Unit ◽  
Randomized Experiments ◽  
Cluster Randomization ◽  
Graph Cluster ◽  
Unbiased Estimates ◽  
Bias Variance ◽  
Substantial Bias ◽  
Biased Estimates

AbstractEstimating the effects of interventions in networks is complicated due to interference, such that the outcomes for one experimental unit may depend on the treatment assignments of other units. Familiar statistical formalism, experimental designs, and analysis methods assume the absence of this interference, and result in biased estimates of causal effects when it exists. While some assumptions can lead to unbiased estimates, these assumptions are generally unrealistic in the context of a network and often amount to assuming away the interference. In this work, we evaluate methods for designing and analyzing randomized experiments under minimal, realistic assumptions compatible with broad interference, where the aim is to reduce bias and possibly overall error in estimates of average effects of a global treatment. In design, we consider the ability to perform random assignment to treatments that is correlated in the network, such as through graph cluster randomization. In analysis, we consider incorporating information about the treatment assignment of network neighbors. We prove sufficient conditions for bias reduction through both design and analysis in the presence of potentially global interference; these conditions also give lower bounds on treatment effects. Through simulations of the entire process of experimentation in networks, we measure the performance of these methods under varied network structure and varied social behaviors, finding substantial bias reductions and, despite a bias–variance tradeoff, error reductions. These improvements are largest for networks with more clustering and data generating processes with both stronger direct effects of the treatment and stronger interactions between units.

scholarly journals Temporal compliance trends in a cluster randomization with crossover trial of out-of-hospital cardiac arrest

2012 ◽  
Vol 9 (3) ◽  
pp. 314-321
Author(s):  
Robert H Schmicker ◽  
Brian G Leroux ◽  
Gena K Sears ◽  
Ian Stiell ◽  
Laurie J Morrison ◽  
...  
Keyword(s):  
Cardiac Arrest ◽  
Crossover Trial ◽  
Cluster Randomization ◽  
Hospital Cardiac Arrest

Comparison of Plastic Single-use and Metal Reusable Laryngoscope Blades for Orotracheal Intubation during Rapid Sequence Induction of Anesthesia

2006 ◽  
Vol 104 (1) ◽  
pp. 60-64 ◽  
Author(s):  
Julien Amour ◽  
Frédéric Marmion ◽  
Aurélie Birenbaum ◽  
Armelle Nicolas-Robin ◽  
Pierre Coriat ◽  
...  
Keyword(s):  
Rapid Sequence Induction ◽  
Orotracheal Intubation ◽  
Failed Intubation ◽  
Cluster Randomization ◽  
Rapid Sequence ◽  
Lehane Grade ◽  
Sequence Induction ◽  
Single Use ◽  
S Period ◽  
Laryngoscope Blades

Background Plastic single-use laryngoscope blades are inexpensive and carry a lower risk of infection compared with metal reusable blades, but their efficiency during rapid sequence induction remains a matter of debate. The authors therefore compared plastic and metal blades during rapid sequence induction in a prospective randomized trial. Methods Two hundred eighty-four adult patients undergoing general anesthesia requiring rapid sequence induction were randomly assigned on a weekly basis to either plastic single-use or reusable metal blades (cluster randomization). After induction, a 60-s period was allowed to complete intubation. In the case of failed intubation, a second attempt was performed using metal blade. The primary endpoint of the study was the rate of failed intubations, and the secondary endpoint was the incidence of complications (oxygen desaturation, lung aspiration, and oropharynx trauma). Results Both groups were similar in their main characteristics, including risk factors for difficult intubation. On the first attempt, the rate of failed intubation was significantly increased in plastic blade group (17 vs. 3%; P < 0.01). In metal blade group, 50% of failed intubations were still difficult after the second attempt. In plastic blade group, all initial failed intubations were successfully intubated using metal blade, with an improvement in Cormack and Lehane grade. There was a significant increase in the complication rate in plastic group (15 vs. 6%; P < 0.05). Conclusions In rapid sequence induction of anesthesia, the plastic laryngoscope blade is less efficient than a metal blade and thus should not be recommended for use in this clinical setting.

Confidence intervals for a difference between lognormal means in cluster randomization trials

2014 ◽  
Vol 26 (2) ◽  
pp. 598-614 ◽  
Author(s):  
Julia Poirier ◽  
GY Zou ◽  
John Koval
Keyword(s):  
Confidence Intervals ◽  
Community Acquired Pneumonia ◽  
Small Sample ◽  
Cluster Randomization ◽  
Critical Pathway ◽  
Arithmetic Means ◽  
Multiple Parameters ◽  
The Difference ◽  
Using Data ◽  
Small Sample Sizes

Cluster randomization trials, in which intact social units are randomized to different interventions, have become popular in the last 25 years. Outcomes from these trials in many cases are positively skewed, following approximately lognormal distributions. When inference is focused on the difference between treatment arm arithmetic means, existent confidence interval procedures either make restricting assumptions or are complex to implement. We approach this problem by assuming log-transformed outcomes from each treatment arm follow a one-way random effects model. The treatment arm means are functions of multiple parameters for which separate confidence intervals are readily available, suggesting that the method of variance estimates recovery may be applied to obtain closed-form confidence intervals. A simulation study showed that this simple approach performs well in small sample sizes in terms of empirical coverage, relatively balanced tail errors, and interval widths as compared to existing methods. The methods are illustrated using data arising from a cluster randomization trial investigating a critical pathway for the treatment of community acquired pneumonia.

scholarly journals Analyzing randomized controlled interventions: Three notes for applied linguists

2015 ◽  
Vol 5 (1) ◽  
pp. 135-152 ◽  
Author(s):  
Jan Vanhove
Keyword(s):  
Repeated Measures ◽  
Intervention Studies ◽  
Mixed Effects ◽  
Analysis Of Covariance ◽  
Cluster Randomization ◽  
Repeated Measures Anova ◽  
Randomized Controlled ◽  
Mixed Effects Modeling ◽  
Statistical Formalism ◽  
Relevant Covariates

I discuss three common practices that obfuscate or invalidate the statistical analysis of randomized controlled interventions in applied linguistics. These are (a) checking whether randomization produced groups that are balanced on a number of possibly relevant covariates, (b) using repeated measures ANOVA to analyze pretest-posttest designs, and (c) using traditional significance tests to analyze interventions in which whole groups were assigned to the conditions (cluster randomization). The first practice is labeled superfluous, and taking full advantage of important covariates regardless of balance is recommended. The second is needlessly complicated, and analysis of covariance is recommended as a more powerful alternative. The third produces dramatic inferential errors, which are largely, though not entirely, avoided when mixed-effects modeling is used. This discussion is geared towards applied linguists who need to design, analyze, or assess intervention studies or other randomized controlled trials. Statistical formalism is kept to a minimum throughout.

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.

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