1,721,026 research outputs found
Choosing a Covariate-Adaptive randomization procedure in practice
No full textPocock and Simon's minimization method is a very popular covariate-adaptive randomization procedure intended to balance the allocations of two treatments across a set of covariates without compromising randomness. Additional covariate-adaptive schemes have been proposed in the literature, such as Atkinson's DA-optimum Biased Coin Design and the Covariate-Adaptive Biased Coin Design (CA-BCD), and their properties were analyzed and compared in terms of imbalance and predictability. The aim of this paper is to push forward these comparisons by also taking into account other randomization methods, such as the Permuted Block Design, the Big Stick Design, a generalization of the CA-BCD that can be implemented when the covariate distribution is unknown, and the Covariate-Adaptive Dominant Biased Coin Design, which is a new class of stratified randomization methods that forces the balance increasingly as the joint imbalance grows and improves the degree of randomness as the size of every stratum increases. The performance of covariate-adaptive procedures is strictly related to the considered factors and the number of patients in the trial as well, which makes it hard to find a dominant rule, namely a design that is more balanced and less predictable with respect to other schemes. In general, stratified randomization methods perform very well when the number of strata is small, showing also some dominance structure with respect to the other designs. Nevertheless, the evolution and the performance of stratified designs are strictly related to the random entries of the subjects. Thus, these rules become less efficient in the case of both (i) limited samples and (ii) large number of factors/levels
Unsuitability of likelihood-based asymptotic confidence intervals for Response-Adaptive designs in normal homoscedastic trials
No full textA new inferential approach for response-adaptive clinical trials: the variance-stabilized bootstrap
Full text linkThis paper discusses disadvantages and limitations of the available inferential
approaches in sequential clinical trials for treatment comparisons managed via
response-adaptive randomization. Then, we propose an inferential methodology for
response-adaptive designs which, by exploiting a variance stabilizing transformation
into a bootstrap framework, is able to overcome the above-mentioned drawbacks,
regardless of the chosen allocation procedure aswell as the desired target.We derive the
theoretical properties of the suggested proposal, showing its superiority with respect
to likelihood, randomization and design-based inferential approaches. Several illustrative
examples and simulation studies are provided in order to confirm the relevance
of our results
A simple solution to the inadequacy of asymptotic likelihood-based inference for response-adaptive clinical trials
Full text linkThe present paper discusses drawbacks and limitations of likelihood-based inference
in sequential clinical trials for treatment comparisons managed viaResponse-Adaptive
Randomization. Taking into account the most common statistical models for the primary
outcome—namely binary, Poisson, exponential and normal data—we derive the
conditions under which (i) the classical confidence intervals degenerate and (ii) the
Wald test becomes inconsistent and strongly affected by the nuisance parameters, also
displaying a non monotonic power. To overcome these drawbacks, we provide a very
simple solution that could preserve the fundamental properties of likelihood-based
inference. Several illustrative examples and simulation studies are presented in order
to confirm the relevance of our results and provide some practical recommendations
Estimation accuracy under covariate-adaptive randomization procedures
Full text linkIn this paper we provide some general asymptotic properties of covariate-adaptive (CA) randomized designs aimed at balancing the allocations of two treatments across a set of chosen covariates. In particular, we establish the central limit theorem for a vast class of covariate-adaptive procedures characterized by i) a different allocation function for each covariate profile and ii) sequences of allocation rules instead of a pre-fixed one. This result allows one to derive theoretically the asymptotic expressions of the loss of information induced by imbalance and the selection bias due to the lack of randomness, that are the fundamental properties for estimation of every CA rule, widely used in order to compare different CA procedures. Besides providing the proofs of unsolved conjectures about some CA designs suggested in the literature, explored up to now almost exclusively through simulations, our results provide substantial insight for future suggestions and represent an accurate tool for the large sample comparisons between CA designs. A numerical study is also performed to assess the validity of the suggested approach
Is the classical Wald test always suitable under response-adaptive randomization?
Full text linkThe aim of this paper is to analyze the impact of response-adaptive randomization rules for normal response trials
intended to test the superiority of one of two available treatments. Taking into account the classical Wald test, we show
how response-adaptive methodology could induce a consistent loss of inferential precision. Then, we suggest a modified
version of theWald test which, by using the current allocation proportion to the treatments as a consistent estimator of
the target, avoids some degenerate scenarios and so it should be preferable to the classical test. Furthermore, we show
both analytically and via simulations how some target allocations may induce a locally decreasing power function. Thus,
we derive the conditions on the target guaranteeing its monotonicity and we show how a correct choice of the initial
sample size allows one to overcome this drawback regardless of the adopted target
Optimal designs for testing the efficacy of heterogeneous experimental groups
Full text linkThis paper develops a unified framework for deriving optimal
designs for hypothesis testing in the presence of several heteroscedastic
groups. In particular, the obtained optimal designs are generalized Neyman
allocations involving only two experimental groups. In order to account for
the ordering among the treatments, particularly relevant in the clinical
context for ethical reasons, we provide the optimal design for testing under
constraints reflecting their effectiveness. The advantages of the suggested
allocations are illustrated both theoretically and through several numerical
examples, also compared with other designs proposed in the literature,
showing a substantial gain in terms of both power and ethics
Simulated annealing for balancing covariates
Full text linkCovariate balance is one of the fundamental issues in designing experiments for
treatment comparisons, especially in randomized clinical trials. In this article,
we introduce a new class of covariate-adaptive procedures based on the Simulated
Annealing algorithm aimed at balancing the allocations of two competing
treatments across a set of pre-specified covariates. Due to the nature of the simulated
annealing, these designs are intrinsically randomized, thus completely
unpredictable, and very flexible: they can manage both quantitative and qualitative
factors and be implemented in a static version as well as sequentially.
The properties of the suggested proposal are described, showing a significant
improvement in terms of covariate balance and inferential accuracy with respect
to all the other procedures proposed in the literature. An illustrative example
based on real data is also discussed
Compound optimal allocations for survival clinical trials
Full text linkThe aim of the present paper is to provide optimal allocations for comparative
clinical trials with survival outcomes. The suggested targets are derived adopting
a compound optimization strategy based on a subjective weighting of the
relative importance of inferential demands and ethical concerns. The ensuing
compound optimal targets are continuous functions of the treatment effects, so
we provide the conditions under which they can be approached by standard
response-adaptive randomization procedures, also guaranteeing the applicability
of the classical asymptotic inference. The operating characteristics of the suggested
methodology are verified both theoretically and by simulation, including
the robustness to model misspecification.With respect to the other available proposals,
our strategy always assigns more patients to the best treatment without
compromising inference, taking into account estimation efficiency and power as
well.We illustrate our procedure by redesigning two real oncological trials
- …
