The “best balance” allocation led to optimal balance in cluster-controlled trials

Hoop, Esther de; Teerenstra, Steven; Gaal, Betsie van; Moerbeek, Mirjam; Borm, George F.

doi:10.1016/j.jclinepi.2011.05.006

Cited by 35 publications

(36 citation statements)

References 13 publications

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“…Especially when the number of clusters is small – as it is the case in pilot trials – an unequal distribution of clusters can occur [23]. Randomization at the patient level might have prevented unequal distribution, but this is not practical for a training program as an intervention, because knowledge exchange among the physicians in one hospital is highly probable, and might influence the results.…”

Section: Discussionmentioning

confidence: 99%

A consultation training program for physicians for communication about complementary medicine with breast cancer patients: a prospective, multi-center, cluster-randomized, mixed-method pilot study

et al. 2016

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BackgroundThe aim was to develop and evaluate a training program for physicians for communicating with breast cancer patients about complementary medicine (CM).MethodsIn a cluster-randomized pilot trial eight breast cancer centers (two physicians per center) were randomized to either a complementary communication training program (9 h e-learning + 20 h on-site skills training) or to a control group without training. Each physician was asked to consult ten patients for whom he or she is not the physician in charge. We used mixed methods: Quantitative outcomes included physicians’ assessments (empathy, complexity of consultation, knowledge transfer) and patients’ assessments (satisfaction, empathy, knowledge transfer). For qualitative analyses, 15 (eight in the training and seven in the control group) videotaped consultations were analyzed based on grounded theory, and separate focus groups with the physicians of both groups were conducted.ResultsA total of 137 patients were included. Although cluster-randomized, physicians in the two groups differed. Those in the training group were younger (33.4 ± 8.9 vs. 40.0 ± 8.5 years) and had less work experience (5.4 ± 8.9 vs. 11.1 ± 7.4 years). Patient satisfaction with the CM consultation was relatively high on a scale from 0 to 24 and was comparable in the two groups (training group: 19.4 ± 4.6; control group 20.5 ± 4.1). The qualitative findings showed that physicians structured majority of consultations as taught during the training. Comparing only the younger and less CM experienced physicians, those trained in CM communication felt more confident discussing CM-related topics than those without training.ConclusionA CM communication-training program might be especially beneficial for physicians with less consulting experience when communicating about CM-related issues. A larger trial using more suitable quantitative outcomes needs to confirm this.Trial registrationClinicalTrials.gov: NCT02223091, date of registration: 7 February 2014.

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Section: Discussionmentioning

confidence: 99%

A consultation training program for physicians for communication about complementary medicine with breast cancer patients: a prospective, multi-center, cluster-randomized, mixed-method pilot study

et al. 2016

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“…De Hoop et al , proposed a BB score, which is expressed as a double sum of squared differences across group‐level variables and across all levels for each variable:

BB = \sum_{l = 1}^{S} \sum_{τ} {(n_{0 lτ} - n_{1 lτ})}^{2},

where n 0 l τ , n 1 l τ are the number of groups assigned to the two different treatment arms that have the τ th level of the l th variable. This metric is similar to the imbalance score, but it only accommodates categorical group‐level variables.…”

Section: Methods For the Simulation Studiesmentioning

confidence: 99%

An evaluation of constrained randomization for the design and analysis of group‐randomized trials

Lokhnygina

Murray

et al. 2015

Statistics in Medicine

154

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In group-randomized trials, a frequent practical limitation to adopting rigorous research designs is that only a small number of groups may be available, and therefore simple randomization cannot be relied upon to balance key group-level prognostic factors across the comparison arms. Constrained randomization is an allocation technique proposed for ensuring balance, and can be used together with a permutation test for randomization-based inference. However, several statistical issues have not been thoroughly studied when constrained randomization is considered. Therefore, we used simulations to evaluate key issues including: the impact of the choice of the candidate set size and the balance metric used to guide randomization; the choice of adjusted versus unadjusted analysis; and the use of model-based versus randomization-based tests. We conducted a simulation study to compare the type I error and power of the F-test and the permutation test in the presence of group-level potential confounders. Our results indicate that the adjusted F-test and the permutation test perform similarly and slightly better for constrained randomization relative to simple randomization in terms of power, and the candidate set size does not substantially affect their power. Under constrained randomization, however, the unadjusted F-test is conservative while the unadjusted permutation test carries the desired type I error rate as long as the candidate set size is not too small; the unadjusted permutation test is consistently more powerful than the unadjusted F-test, and gains power as candidate set size changes. Finally, we caution against the inappropriate specification of permutation distribution under constrained randomization. An ongoing group-randomized trial is used as an illustrative example for the constrained randomization design.

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“…This l 2 metric accommodates both continuous and categorical variables (dummy variables are used to represent a multicategory factor) and is invariant to linear transformations of covariates with the default choice of ω k . Two alternative balance metrics were developed with constrained randomization, but they were specifically designed to balance group‐level factors and may not accommodate continuous variables. To compare with the l 2 metric, we propose an l 1 analog

B_{false(l 1 false)} = \sum_{k} {\overset{ω}{true}}_{k} false| {\overset{x}{true}}_{T k} - {\overset{x}{true}}_{C k} false|,

where

{\overset{x}{true}}_{T k}, {\overset{x}{true}}_{C k}

and

{\overset{ω}{true}}_{k}

are defined similarly, and by default,

{\overset{ω}{true}}_{k}

is taken to be the inverse standard deviation of the group means.…”

Section: Motivating Examplesmentioning

confidence: 99%

An evaluation of constrained randomization for the design and analysis of group‐randomized trials with binary outcomes

Turner

Heagerty

et al. 2017

Statistics in Medicine

141

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Group-randomized trials (GRTs) are randomized studies that allocate intact groups of individuals to different comparison arms. A frequent practical limitation to adopting such research designs is that only a limited number of groups may be available, and therefore, simple randomization is unable to adequately balance multiple group-level covariates between arms. Therefore, covariate-based constrained randomization was proposed as an allocation technique to achieve balance. Constrained randomization involves generating a large number of possible allocation schemes, calculating a balance score that assesses covariate imbalance, limiting the randomization space to a pre-specified percentage of candidate allocations and randomly selecting one scheme to implement. When the outcome is binary, a number of statistical issues arise regarding the potential advantages of such designs in making inference. In particular, properties found for continuous outcomes may not directly apply, and additional variations on statistical tests are available. Motivated by two recent trials, we conduct a series of Monte Carlo simulations to evaluate the statistical properties of model-based and randomization-based tests under both simple and constrained randomization designs, with varying degrees of analysis-based covariate adjustment. Our results indicate that constrained randomization improves the power of the linearization F-test, the KC-corrected GEE t-test (Kauermann and Carroll, 2001, Journal of the American Statistical Association 96, 1387–1396) and two permutation tests when the prognostic group-level variables are controlled for in the analysis and the size of randomization space is reasonably small. We also demonstrate that constrained randomization reduces power loss from redundant analysis-based adjustment for non-prognostic covariates. Design considerations such as the choice of the balance metric and the size of randomization space are discussed.

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The “best balance” allocation led to optimal balance in cluster-controlled trials

Abstract: BB results in a better balance of prognostic factors than randomization, minimization, stratification, and matching in most situations. Furthermore, BB cannot result in a worse balance of prognostic factors than the other methods.

Cited by 35 publications

References 13 publications

A consultation training program for physicians for communication about complementary medicine with breast cancer patients: a prospective, multi-center, cluster-randomized, mixed-method pilot study

A consultation training program for physicians for communication about complementary medicine with breast cancer patients: a prospective, multi-center, cluster-randomized, mixed-method pilot study

An evaluation of constrained randomization for the design and analysis of group‐randomized trials

An evaluation of constrained randomization for the design and analysis of group‐randomized trials with binary outcomes

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