56 research outputs found
Simulated Clinical Trias: some design issues
Simulation is widely used to investigate real-world systems in a large number of fields, including clinical trials for drug development, since real trials are costly, frequently fail and may lead to serious side effects. This paper is a survey of the statistical issues arising in these simulated trials. We illustrate the broad applicability of this investigation tool by means of examples selected from the literature. We discuss the aims and the peculiarities of the simulation models used in this context, including a brief mention of the use of metamodels. Of special interest is the topic of the design of the virtual experiments, stressing similarities and differences with the design of real life trials. Since it is important for a computerized model to possess a satisfactory range of accuracy consistent with its intended application, real data provided by physical experiments are used to confirm the simulator : we illustrate validating techniques through a number of examples. We end the paper with some challenging questions on the scientificity, ethics and effectiveness of simulation in the clinical research, and the interesting research problem of how to integrate simulated and physical experiments in a clinical context.Simulation models; pharmacokinetics; pharmacodynamics; model validation; experimental design, ethics. Modelli di simulazione; farmacocinetica; farmacodinamica; validazione; disegno degli esperimenti; etica.
Multi-objective optimal designs in comparative clinical trials with covariates: The reinforced doubly adaptive biased coin design
The present paper deals with the problem of allocating patients to two
competing treatments in the presence of covariates or prognostic factors in
order to achieve a good trade-off among ethical concerns, inferential precision
and randomness in the treatment allocations. In particular we suggest a
multipurpose design methodology that combines efficiency and ethical gain when
the linear homoscedastic model with both treatment/covariate interactions and
interactions among covariates is adopted. The ensuing compound optimal
allocations of the treatments depend on the covariates and their distribution
on the population of interest, as well as on the unknown parameters of the
model. Therefore, we introduce the reinforced doubly adaptive biased coin
design, namely a general class of covariate-adjusted response-adaptive
procedures that includes both continuous and discontinuous randomization
functions, aimed to target any desired allocation proportion. The properties of
this proposal are described both theoretically and through simulations.Comment: Published in at http://dx.doi.org/10.1214/12-AOS1007 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
On the almost sure convergence of adaptive allocation procedures
In this paper, we provide some general convergence results for adaptive
designs for treatment comparison, both in the absence and presence of
covariates. In particular, we demonstrate the almost sure convergence of the
treatment allocation proportion for a vast class of adaptive procedures, also
including designs that have not been formally investigated but mainly explored
through simulations, such as Atkinson's optimum biased coin design, Pocock and
Simon's minimization method and some of its generalizations. Even if the large
majority of the proposals in the literature rely on continuous allocation
rules, our results allow to prove via a unique mathematical framework the
convergence of adaptive allocation methods based on both continuous and
discontinuous randomization functions. Although several examples of earlier
works are included in order to enhance the applicability, our approach provides
substantial insight for future suggestions, especially in the absence of a
prefixed target and for designs characterized by sequences of allocation rules.Comment: Published at http://dx.doi.org/10.3150/13-BEJ591 in the Bernoulli
(http://isi.cbs.nl/bernoulli/) by the International Statistical
Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm
Simulated Clinical Trias: some design issues
Simulation is widely used to investigate real-world systems in a large number of fields, including clinical trials for drug development, since real trials are costly, frequently fail and may lead to serious side effects. This paper is a survey of the statistical issues arising in these simulated trials. We illustrate the broad applicability of this investigation tool by means of examples selected from the literature. We discuss the aims and the peculiarities of the simulation models used in this context, including a brief mention of the use of metamodels. Of special interest is the topic of the design of the virtual experiments, stressing similarities and differences with the design of real life trials.
Since it is important for a computerized model to possess a satisfactory range of accuracy consistent with its intended application, real data provided by physical experiments are used to confirm the simulator: we illustrate validating techniques through a number of examples. We end the paper with some challenging questions on the scientificity, ethics and effectiveness of simulation in the clinical research, and the interesting research problem of how to integrate simulated and physical experiments in a clinical context
A simple solution to the inadequacy of asymptotic likelihood-based inference for response-adaptive clinical trials
The 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
Simulated annealing for balancing covariates
Covariate 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
New insights into adaptive enrichment designs
The transition towards personalized medicine is happening and the new experimental framework is raising several challenges, from a clinical, ethical, logistical, regulatory, and statistical perspective. To face these challenges, innovative study designs with increasing complexity have been proposed. In particular, adaptive enrichment designs are becoming more attractive for their flexibility. However, these procedures rely on an increasing number of parameters that are unknown at the planning stage of the clinical trial, so the study design requires particular care. This review is dedicated to adaptive enrichment studies with a focus on design aspects. While many papers deal with methods for the analysis, the sample size determination and the optimal allocation problem have been overlooked. We discuss the multiple aspects involved in adaptive enrichment
designs that contribute to their advantages and disadvantages. The decision-making process of whether or not it is worth enriching should be driven by
clinical and ethical considerations as well as scientific and statistical concerns
Choosing a Covariate-Adaptive randomization procedure in practice
Pocock 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
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