Daniele De Martini

University of Genoa

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Co-Authors

S. Valenti

University of Genoa

Lucio De Capitani

University of Milano-Bicocca

Carla Micaela Cuttica

Ente Ospedaliero Ospedali Galliera

Renato Spaziante

University of Salerno

Liliana Bocca

University of Genoa

Luísa M. Seoane

Instituto de Salud Carlos III

Gareth Leng

University of Edinburgh

Christian Broberger

Stockholm University

A Enjalbert

Centre de Recherche en Neurobiologie - Neurophysiologie de Marseille

Michael G. N. Russell

Waitemata District Health Board

Cooperative Institutions

University of Genoa

University of Turin

Centre National de la Recherche Scientifique

Ospedale Policlinico San Martino

Università degli Studi del Piemonte Orientale “Amedeo Avogadro”

Inserm

University of Milan

Centro de Investigación Biomédica en Red

University of Edinburgh

Fondazione Ricerca Molinette

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Copula-Based Models for the Power of Independence Tests

Communication in Statistics- Theory and Methods (2005)

Daniele De Martini Emanuel Vespa

ABSTRACT We consider independence tests and the methods to evaluate their efficiency. First, we observe that many of the most used independence tests are functions of the empirical copula, which is a sufficient statistic. Hence, the power of these tests, such as the tests based on Spearman's ρ, on Kendall's τ, and on Gini's γ, depend solely on the theoretical copula, and not on the marginal distributions. Then, we consider monotone dependence tests and we propose a parametric model to define the power function. Such a model is based on a path of copulas, from the copula of discordance to the copula of concordance, and can be characterized by the copula of the underlying joint distribution. Moreover, we introduce a consistent estimator of the path of copulas. Finally, we provide some examples of applications, and in particular, a bootstrap-plug-in estimator of the power curve, all useful for power comparison.

Statistic

Vine copula

10.1080/03610920500313742

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Citations (3)

Evaluating the Risk in Sample Size Determination

Communications in Statistics - Simulation and Computation (2008)

Daniele De Martini

This article considers experimental costs, besides power evaluation, in order to determine the sample size of an experiment. We focus on the use of standard tools of decision theory in the context of sample size determination. The loss function is defined, from the perspective of an experimenter which adopts the classical frequentist approach, and the risk function is computed. Then, we show the behavior of the risk function in the two-sample t-test, for a small sample experimental setting, with a medium-sized sample, and with large samples. Moreover, an objective criterion for a convenient sample size choice is introduced. Finally, a practical example of sample size determination, which also considers risk computation, is shown.

Sample (material)

10.1080/03610910802296505

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Citations (2)

Success Probability Estimation for Some Widely Used Statistical Tests

Daniele De Martini

10.1002/9781118548516.part2

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Citations (0)

On stochastic orderings of the Wilcoxon Rank Sum test statistic—With applications to reproducibility probability estimation testing

Statistics & Probability Letters (2011)

Lucio De Capitani Daniele De Martini

Statistic

Rank (graph theory)

Location parameter

Stochastic ordering

10.1016/j.spl.2011.04.001

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Citations (26)

Subject Index Vol. 66, 1997

Neuroendocrinology (1997)

Eva Carro Leonor Pinilla Luísa M. Seoane Robert V. Considine E. Aguilar

10.1159/000127271

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Citations (0)

Smoothed bootstrap consistency through the convergence in mallows metric of smooth estimates

Journal of nonparametric statistics (2000)

Daniele De Martini

This article examines smoothed bootstrap consistency of the sample mean, regular functions of the sample mean and multiple regression models. We use Mallows metric convergence of the empirical distribution, plugged-in by the bootstrap method, towards the theoretical distribution of the data. We demonstrate convergence in Mallows metric for kernel and kernel shrunk estimates, and also for histograms with fixed partition. A class of locally adaptive smooth estimators is introduced and its convergence in Mallows metric is proved.

Kernel (algebra)

Uniform convergence

Empirical distribution function

10.1080/10485250008832836

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Citations (7)

Robustness and Corrections in Sample Size Estimation

Daniele De Martini

This chapter discusses conservative sample size estimation. It considers the problems which arise when the effect size of phase II is different from that of phase III. Indeed, broader and more heterogeneous patient populations are often pursued in phase III clinical trials as compared to those in phase II studies. The chapter evaluates the numerical bias of CSSE strategies, and their robustness is compared in different scenarios. Some techniques for correcting the bias are presented, and the changes induced on CSSE strategies are formalized. This chapter adopts clinical relevance launch criterion. Finally, a comparison among the behavior of corrected CSSE strategies is provided. Controlled Vocabulary Terms robustness

Robustness

10.1002/9781118548516.ch4

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Citations (0)

Stability criteria for the outcomes of statistical tests to assess drug effectiveness with a single study

Pharmaceutical Statistics (2012)

Daniele De Martini

At least two adequate and well-controlled clinical studies are usually required to support effectiveness of a certain treatment. In some circumstances, however, a single study providing strong results may be sufficient. Some statistical stability criteria for assessing whether a single study provides very persuasive results are known. A new criterion is introduced, and it is based on the conservative estimation of the reproducibility probability in addition to the possibility of performing statistical tests by referring directly to the reproducibility probability estimate. These stability criteria are compared numerically and conceptually. This work aims to help both regulatory agencies and pharmaceutical companies to decide if the results of a single study may be sufficient to establish effectiveness.

Statistical Analysis

10.1002/pst.1505

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Citations (8)

Phase III Sample Size Based on the 'Power to Detect' Is Logically Wrong and Usually Unethical

SSRN Electronic Journal (2019)

Daniele De Martini

The sample size computation methodology for Phase III, confirmatory, trials is discussed in relation to the probability of success. It is shown that when experimental planning is based on the power to detect a clinically relevant effect: a) the aim of Phase III trials, that is proving clinical relevance, is not accounted for; b) the actual probability of proving clinical relevance is about 41%.Therefore, sample size computations based on the "power to detect" is logically wrong and not ethically sound. The sample size for Phase III trials should be computed on the basis of a likely value of the unknown parameter under testing, and not on a value which is scientifically relevant. Instead, the latter should be embedded in the hypotheses under testing, leading to the so-called "power to prove". Examples from the literature showing errors committed when the "power to detect" is adopted are reported and discussed. A realistic simulation comparing "power to detect" and "power to prove" reports their pros and cons in practice. Then, "power to detect" and "power to prove" are evaluated in relation with ICH9. Finally, results are discussed considering recent literature.

Sample (material)

10.2139/ssrn.3328810

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Citations (0)

Author Index Vol. 66, 1997

Neuroendocrinology (1997)

Eva Carro Leonor Pinilla Luísa M. Seoane Robert V. Considine E. Aguilar

10.1159/000127270

Cite

Citations (0)