Blinded sample size reestimation for negative binomial regression with baseline adjustment

Antonia Zapf; Thomas Asendorf; Christoph Anten; Tobias Mütze; Tim Friede

doi:10.1002/sim.8525

Blinded sample size reestimation for negative binomial regression with baseline adjustment

Standard

Blinded sample size reestimation for negative binomial regression with baseline adjustment. / Zapf, Antonia; Asendorf, Thomas; Anten, Christoph; Mütze, Tobias; Friede, Tim.

in: STAT MED, Jahrgang 39, Nr. 14, 30.06.2020, S. 1980-1998.

Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung

Harvard

Zapf, A, Asendorf, T, Anten, C, Mütze, T & Friede, T 2020, 'Blinded sample size reestimation for negative binomial regression with baseline adjustment', STAT MED, Jg. 39, Nr. 14, S. 1980-1998. https://doi.org/10.1002/sim.8525

APA

Zapf, A., Asendorf, T., Anten, C., Mütze, T., & Friede, T. (2020). Blinded sample size reestimation for negative binomial regression with baseline adjustment. STAT MED, 39(14), 1980-1998. https://doi.org/10.1002/sim.8525

Vancouver

Zapf A, Asendorf T, Anten C, Mütze T, Friede T. Blinded sample size reestimation for negative binomial regression with baseline adjustment. STAT MED. 2020 Jun 30;39(14):1980-1998. https://doi.org/10.1002/sim.8525

Bibtex

@article{048cc27475af4c8f86f1510990c1a32c,

title = "Blinded sample size reestimation for negative binomial regression with baseline adjustment",

abstract = "In randomized clinical trials, it is standard to include baseline variables in the primary analysis as covariates, as it is recommended by international guidelines. For the study design to be consistent with the analysis, these variables should also be taken into account when calculating the sample size to appropriately power the trial. Because assumptions made in the sample size calculation are always subject to some degree of uncertainty, a blinded sample size reestimation (BSSR) is recommended to adjust the sample size when necessary. In this article, we introduce a BSSR approach for count data outcomes with baseline covariates. Count outcomes are common in clinical trials and examples include the number of exacerbations in asthma and chronic obstructive pulmonary disease, relapses, and scan lesions in multiple sclerosis and seizures in epilepsy. The introduced methods are based on Wald and likelihood ratio test statistics. The approaches are illustrated by a clinical trial in epilepsy. The BSSR procedures proposed are compared in a Monte Carlo simulation study and shown to yield power values close to the target while not inflating the type I error rate.",

author = "Antonia Zapf and Thomas Asendorf and Christoph Anten and Tobias M{\"u}tze and Tim Friede",

note = "{\textcopyright} 2020 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd.",

year = "2020",

month = jun,

day = "30",

doi = "10.1002/sim.8525",

language = "English",

volume = "39",

pages = "1980--1998",

journal = "STAT MED",

issn = "0277-6715",

publisher = "John Wiley and Sons Ltd",

number = "14",

}

RIS

TY - JOUR

T1 - Blinded sample size reestimation for negative binomial regression with baseline adjustment

AU - Zapf, Antonia

AU - Asendorf, Thomas

AU - Anten, Christoph

AU - Mütze, Tobias

AU - Friede, Tim

PY - 2020/6/30

Y1 - 2020/6/30

N2 - In randomized clinical trials, it is standard to include baseline variables in the primary analysis as covariates, as it is recommended by international guidelines. For the study design to be consistent with the analysis, these variables should also be taken into account when calculating the sample size to appropriately power the trial. Because assumptions made in the sample size calculation are always subject to some degree of uncertainty, a blinded sample size reestimation (BSSR) is recommended to adjust the sample size when necessary. In this article, we introduce a BSSR approach for count data outcomes with baseline covariates. Count outcomes are common in clinical trials and examples include the number of exacerbations in asthma and chronic obstructive pulmonary disease, relapses, and scan lesions in multiple sclerosis and seizures in epilepsy. The introduced methods are based on Wald and likelihood ratio test statistics. The approaches are illustrated by a clinical trial in epilepsy. The BSSR procedures proposed are compared in a Monte Carlo simulation study and shown to yield power values close to the target while not inflating the type I error rate.

AB - In randomized clinical trials, it is standard to include baseline variables in the primary analysis as covariates, as it is recommended by international guidelines. For the study design to be consistent with the analysis, these variables should also be taken into account when calculating the sample size to appropriately power the trial. Because assumptions made in the sample size calculation are always subject to some degree of uncertainty, a blinded sample size reestimation (BSSR) is recommended to adjust the sample size when necessary. In this article, we introduce a BSSR approach for count data outcomes with baseline covariates. Count outcomes are common in clinical trials and examples include the number of exacerbations in asthma and chronic obstructive pulmonary disease, relapses, and scan lesions in multiple sclerosis and seizures in epilepsy. The introduced methods are based on Wald and likelihood ratio test statistics. The approaches are illustrated by a clinical trial in epilepsy. The BSSR procedures proposed are compared in a Monte Carlo simulation study and shown to yield power values close to the target while not inflating the type I error rate.

U2 - 10.1002/sim.8525

DO - 10.1002/sim.8525

M3 - SCORING: Journal article

C2 - 32207171

VL - 39

SP - 1980

EP - 1998

JO - STAT MED

JF - STAT MED

SN - 0277-6715

IS - 14

ER -