Sequential Allocation to Balance Prognostic Factors in a Psychiatric Clinical Trial

Fossaluza, Victor; Diniz, Juliana Belo; Pereira, Basílio de Bragança; Miguel, Eurí­pedes Constantino; Pereira, Carlos Alberto

doi:10.1590/s1807-59322009000600005

Cited by 30 publications

(34 citation statements)

References 19 publications

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“…In 2009, Fossaluza et al 12 described a method of intentional allocation based on compositional data balance. 17 This procedure allocates every new patient to the arm that …”

Section: Evaluating Sequential Methods As Alternatives To Randomizationmentioning

confidence: 99%

“…10 The attempts to control for the imbalances that may be produced by randomization are also known as minimization methods. For a complete review on these methodologies, please see Scott et al 11 The alternative methods of allocation including minimization procedures that can avoid randomization shortcomings [12][13][14] are, however, rarely used. The infrequent application of such methods in current clinical trials may be related to the misconceptions regarding the risks of abandoning randomization.…”

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confidence: 99%

“…18,19 To evaluate the performance of this intentional allocation procedure in comparison with simple randomization controlled exclusively for group sizes, we will use the information from 50 patients who participated in a different trial (unpublished) that required allocation into two alternative arms, that is our real case example. According to the method developed by Fossaluza et al, 12 given only two possibilities of intervention, when a new patient arrives, the mathematical suitable distance between the two arms is calculated in both alternative provisional situations: new patient in Arm 1 and, alternatively, new patient in Arm 2 . The situation in which the distance is smallest is entailed, that is, the new patient is actually allocated to the arm that produces the smallest distance between arms.…”

mentioning

confidence: 99%

“…Operational and financial reasons can, however, point to an experiment with (very) unequal sample sizes. In general, the method by Fossaluza et al 12 may conduct the final group sizes to given values chosen by researchers, by treating relative sample sizes as a (pseudo) covariate having weight 2 (which forces the assignment of weights for true covariates to be even more careful as relative sample sizes and clinical prognostic factors are then elicited in a same scale. equal to the number of patients allocated to the first group after the ith arrival and n(i) equal to the number of patients allocated to the second group after the ith arrival; i=1, 2, 3, …, (m + n), where m and n are the two arms' final sizes.…”

mentioning

confidence: 99%

“…equal to the number of patients allocated to the first group after the ith arrival and n(i) equal to the number of patients allocated to the second group after the ith arrival; i=1, 2, 3, …, (m + n), where m and n are the two arms' final sizes. The two last columns of Table 1 shows the allocation results based on two different situations: intentional sequential allocation (ISA) developed by Fossaluza et al, 12 in which every new patient is allocated as it arrives and there is no information about the next patients in line; or optimal global allocation (OGA), which means the whole sample is known a priori and the optimal allocation for the final sample can be calculated by purposely dividing patients in the same stratum. In our case, the OGA method is the equivalent of minimization procedures.…”

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confidence: 99%

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Rain dance: the role of randomization in clinical trials

Diniz¹,

Fossaluza²,

Pereira³

et al. 2016

OAJCT

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Abstract:Randomized clinical trials are the gold standard for testing efficacy of treatment interventions. However, although randomization protects against deliberately biased samples, it does not guarantee random imbalances will not occur. Methods of intentional allocation that can overcome such deficiency of randomization have been developed, but are less frequently applied than randomization. Initially, we introduce a fictitious case example to revise and discuss the reasons of researchers' resistance to intentionally allocate instead of simply randomizing. We then introduce a real case example to evaluate the performance of an intentional protocol for allocation based on compositional data balance. A real case of allocation of 50 patients in two arms was compared with an optimal allocation of global instead of sequential arrivals. Performance was measured by a weighted average of Aitchison distances, between arms, of prognostic factors. To compare the intentional allocation with simple random allocation, 50,000 arrival orderings of 50 patients were simulated. To each one of the orders, both kinds of allocations into two arms were considered. Intentional allocation performed as well as optimal allocation in the case considered. In addition, out of the 50,000 simulated orders, 61% of them performed better with intentional allocation than random allocation. Hence, we conclude that intentional allocation should be encouraged in the design of future interventional clinical trials as a way to prevent unbalanced samples. Our sequential method is a viable alternative to overcome technical difficulties for study designs that require sequential inclusion of patients as it does not require prior knowledge about the total sample composition.

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“…In 2009, Fossaluza et al 12 described a method of intentional allocation based on compositional data balance. 17 This procedure allocates every new patient to the arm that …”

Section: Evaluating Sequential Methods As Alternatives To Randomizationmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Rain dance: the role of randomization in clinical trials

Diniz¹,

Fossaluza²,

Pereira³

et al. 2016

OAJCT

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Clinical Predictors of Long-Term Outcome in Obsessive-Compulsive Disorder

et al. 2012

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Combining Optimization and Randomization Approaches for the Design of Clinical Trials

Fossaluza

Lauretto²,

Pereira

et al. 2015

Springer Proceedings in Mathematics &Amp; Statistics

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Intentional sampling methods are non-randomized procedures that select a group of individuals for a sample with the purpose of meeting specific prescribed criteria. In this paper we extend previous works related to intentional sampling, and address the problem of sequential allocation for clinical trials with few patients. Roughly speaking, patients are enrolled sequentially, according to the order in which they start the treatment at the clinic or hospital. The allocation problem consists in assigning each new patient to one, and only one, of the alternative treatment arms. The main requisite is that the profiles in the alternative arms remain similar with respect to some relevant patients' attributes (age, gender, disease, symptom severity and others). We perform numerical experiments based on a real case study and discuss how to conveniently set up perturbation parameters, in order to yield a suitable balance between optimality -the similarity among the relative frequencies of patients in the several categories for both arms, and decoupling -the absence of a tendency to allocate each pair of patients consistently to the same arm.We describe a possible allocation that the experimenter judges to be free of covariate interference as haphazard. Randomization may be a convenient way of producing a haphazard design. We argue that it is the haphazard nature, and not the randomization, that is important. It seems therefore that a reasonable approximation to an optimal design would be to select a haphazard design. ... a detailed Bayesian consideration of possible covariates would almost certainly not be robust in that the analysis might be sensitive to small changes in judgments about covariates. Lindley (1982, p.438-439) -The Role of Randomization in Inference.

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Sequential Allocation to Balance Prognostic Factors in a Psychiatric Clinical Trial

Cited by 30 publications

References 19 publications

Rain dance: the role of randomization in clinical trials

Rain dance: the role of randomization in clinical trials

Clinical Predictors of Long-Term Outcome in Obsessive-Compulsive Disorder

Combining Optimization and Randomization Approaches for the Design of Clinical Trials

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