scholarly journals Patient Partner Perspectives Regarding Ethically and Clinically Important Aspects of Trial Design in Pragmatic Cluster Randomized Trials for Hemodialysis

2021 ◽  
Vol 8 ◽  
pp. 205435812110328
Author(s):  
Stuart G. Nicholls ◽  
Kelly Carroll ◽  
Cory E. Goldstein ◽  
Jamie C. Brehaut ◽  
Charles Weijer ◽  
...  

Background: Cluster randomized trials (CRTs) are trials in which intact groups such as hemodialysis centers or shifts are randomized to treatment or control arms. Pragmatic CRTs have been promoted as a promising trial design for nephrology research yet may also pose ethical challenges. While randomization occurs at the cluster level, the intervention and data collection may vary in a CRT, challenging the identification of research participants. Moreover, when a waiver of patient consent is granted by a research ethics committee, there is an open question as to whether and to what degree patients should be notified about ongoing research or be provided with a debrief regarding the nature and results of the trial upon completion. While empirical and conceptual research exploring ethical issues in pragmatic CRTs has begun to emerge, there has been limited discussion with patients, families, or caregivers of patients undergoing hemodialysis. Objective: To explore with patients and families with experience of hemodialysis research the challenges raised by different approaches to designing pragmatic CRTs in hemodialysis. Specifically, their perceptions of (1) the use of a waiver of consent, (2) notification processes and information provided to participants, and (3) any other concerns about cluster randomized designs in hemodialysis. Design: Focus group and interview discussions of hypothetical clinical trial designs. Setting: Focus groups and interviews were conducted in-person or via videoconference or telephone. Participants: Patient partners in hemodialysis research, defined as patients with personal experience of dialysis or a family member who had experience supporting a patient receiving hemodialysis, who have been actively involved in discussions to advise a research team on the design, conduct, or implementation of a hemodialysis trial. Methods: Participants were invited to participate in focus groups or individual discussions that were audio recorded with consent. Recorded interviews were transcribed verbatim prior to analysis. Transcripts were analyzed using a thematic analysis approach. Results: Two focus groups, three individual interviews, and one interview involving a patient and family member were conducted with 17 individuals between February 2019 and May 2020. Participants expressed support for approaches that emphasized patient choice. Disclosure of patient-relevant risks and information were key themes. Both consent and notification processes served to generate trust, but bypassing patient choice was perceived as undermining this trust. Participants did not dismiss the option of a waiver of consent. They were, however, more restrictive in their views about when a waiver of consent may be acceptable. Patient partners were skeptical of claims to impracticability based on costs or the time commitments for staff. Limitations: All participants were from Canada and had been involved in the design or conduct of a trial, limiting the degree to which results may be extrapolated. Conclusions: Given the preferences of participants to be afforded the opportunity to decide about trial participation, we argue that investigators should thoroughly investigate approaches that allow participants to make an informed choice regarding trial participation. In keeping with the preference for autonomous choice, there remains a need to further explore how consent approaches can be designed to facilitate clinical trial conduct while meeting their ethical requirements. Finally, further work is needed to define the limited circumstances in which waivers of consent are appropriate.

2011 ◽  
Vol 59 (12) ◽  
pp. 2332-2336 ◽  
Author(s):  
Jennifer Tjia ◽  
Kathleen M. Mazor ◽  
Terry Field ◽  
Peter Doherty ◽  
Ann Spenard ◽  
...  

Author(s):  
Eva Lorenz ◽  
Sabine Gabrysch

In cluster-randomized trials, groups or clusters of individuals, rather than individuals themselves, are randomly allocated to intervention or control. In this article, we describe a new command, ccrand, that implements a covariate-constrained randomization procedure for cluster-randomized trials. It can ensure balance of one or more baseline covariates between trial arms by restriction to allocations that meet specified balance criteria. We provide a brief overview of the theoretical background, describe ccrand and its options, and illustrate it using an example.


2010 ◽  
Vol 8 (1) ◽  
pp. 27-36 ◽  
Author(s):  
Zhiying You ◽  
O Dale Williams ◽  
Inmaculada Aban ◽  
Edmond Kato Kabagambe ◽  
Hemant K Tiwari ◽  
...  

2021 ◽  
Author(s):  
L Miriam Dickinson ◽  
Patrick Hosokawa ◽  
Jeanette A Waxmonsky ◽  
Bethany M Kwan

Author(s):  
John A. Gallis ◽  
Fan Li ◽  
Elizabeth L. Turner

Cluster randomized trials, where clusters (for example, schools or clinics) are randomized to comparison arms but measurements are taken on individuals, are commonly used to evaluate interventions in public health, education, and the social sciences. Analysis is often conducted on individual-level outcomes, and such analysis methods must consider that outcomes for members of the same cluster tend to be more similar than outcomes for members of other clusters. A popular individual-level analysis technique is generalized estimating equations (GEE). However, it is common to randomize a small number of clusters (for example, 30 or fewer), and in this case, the GEE standard errors obtained from the sandwich variance estimator will be biased, leading to inflated type I errors. Some bias-corrected standard errors have been proposed and studied to account for this finite-sample bias, but none has yet been implemented in Stata. In this article, we describe several popular bias corrections to the robust sandwich variance. We then introduce our newly created command, xtgeebcv, which will allow Stata users to easily apply finite-sample corrections to standard errors obtained from GEE models. We then provide examples to demonstrate the use of xtgeebcv. Finally, we discuss suggestions about which finite-sample corrections to use in which situations and consider areas of future research that may improve xtgeebcv.


2021 ◽  
pp. 096228022199041
Author(s):  
Fan Li ◽  
Guangyu Tong

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.


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