Asymptotic Z and chi-squared tests with auxiliary information

Albertus, Mickael

doi:10.1007/s00184-021-00853-y

Cited by 7 publications

(4 citation statements)

References 18 publications

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“…Some researchers have explored the use of auxiliary information available in the form of known population quantities, see Zenkova and Krainova (2017); Dmitriev et al (2014); others have used auxiliary information available in form of summary statistics such as sample means and standard errors, see Tarima and Pavlov (2006); Tarima et al (2007). Asymptotic relative efficiency of the estimators proposed in Tarima and Pavlov (2006) is derived in Albertus (2022).…”

Section: Use Of Auxiliary Summary Statisticsmentioning

confidence: 99%

Coping with Information Loss and the Use of Auxiliary Sources of Data: A Report from the NISS Ingram Olkin Forum Series on Unplanned Clinical Trial Disruptions

Calderazzo

Tarima

Reid

et al. 2023

Statistics in Biopharmaceutical Research

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Section: Use Of Auxiliary Summary Statisticsmentioning

confidence: 99%

Coping with Information Loss and the Use of Auxiliary Sources of Data: A Report from the NISS Ingram Olkin Forum Series on Unplanned Clinical Trial Disruptions

Calderazzo

Tarima

Reid

et al. 2023

Statistics in Biopharmaceutical Research

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“…For example, if the pandemic impacts the treatment effect and E( θ) = θ, then θ λ0 (0) will be biased. Asymptotic relative efficiency of ( 7) is explored in Albertus (2022).…”

Section: Conflict Of Interestmentioning

confidence: 99%

Estimands and their Estimators for Clinical Trials Impacted by the COVID-19 Pandemic: A Report from the NISS Ingram Olkin Forum Series on Unplanned Clinical Trial Disruptions

Lancker

Tarima

Bartlett

et al. 2022

Statistics in Biopharmaceutical Research

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The COVID-19 pandemic continues to affect the conduct of clinical trials globally. Complications may arise from pandemic-related operational challenges such as site closures, travel limitations and interruptions to the supply chain for the investigational product, or from health-related challenges such as COVID-19 infections. Some of these complications lead to unforeseen intercurrent events in the sense that they affect either the interpretation or the existence of the measurements associated with the clinical question of interest. In this article, we demonstrate how the ICH E9(R1) Addendum on estimands and sensitivity analyses provides a rigorous basis to discuss potential pandemic-related trial disruptions and to embed these disruptions in the context of study objectives and design elements. We introduce several hypothetical estimand strategies and review various causal inference and missing data methods, as well as a statistical method that combines unbiased and possibly biased estimators for estimation. To illustrate, we describe the features of a stylized trial, and how it may have been impacted by the pandemic. This stylized trial will then be re-visited by discussing the changes to the estimand and the estimator to account for pandemic disruptions. Finally, we outline considerations for designing future trials in the context of unforeseen disruptions.

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“…For example, the method based on the auxiliary information in a form of vectors of unbiased estimates in Tarima and Pavlov [9] (T&P (2006)) may be used in the context of this article. The asymptotic properties of the above work are analyzed by Albertus [10]. Tarima and Pavlov used additional information to construct parameter estimation statistics, and completed parameter estimation by adding data sources.…”

Section: Introductionmentioning

confidence: 99%

Empirical Likelihood Ratio Tests for Homogeneity of Multiple Populations in the Presence of Auxiliary Information

Qin

2022

Mathematics

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The empirical likelihood ratio test (ELRT) statistic is constructed for testing the homogeneity of several nonparametric populations in the presence of some auxiliary information. It is shown—under some regularity conditions and under the null hypothesis that all distribution functions of the populations are equal—that the asymptotic distribution of the ELRT is a chi-squared distribution. The proposed ELRT could be more powerful than the Kruskal–Wallis test, as extra information can be efficiently employed by ELRT. The advantage of ELRT over T&P (2006) is that researchers do not need to select approximately normal statistics for inter-group comparisons, and ELRT is more suitable for the multi-population consistency test with a small sample size.

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Asymptotic Z and chi-squared tests with auxiliary information

Cited by 7 publications

References 18 publications

Coping with Information Loss and the Use of Auxiliary Sources of Data: A Report from the NISS Ingram Olkin Forum Series on Unplanned Clinical Trial Disruptions

Coping with Information Loss and the Use of Auxiliary Sources of Data: A Report from the NISS Ingram Olkin Forum Series on Unplanned Clinical Trial Disruptions

Estimands and their Estimators for Clinical Trials Impacted by the COVID-19 Pandemic: A Report from the NISS Ingram Olkin Forum Series on Unplanned Clinical Trial Disruptions

Empirical Likelihood Ratio Tests for Homogeneity of Multiple Populations in the Presence of Auxiliary Information

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