Group sequential designs for negative binomial outcomes.
Asthma
/ physiopathology
Binomial Distribution
Chronic Disease
Clinical Trials as Topic
Heart Failure
/ therapy
Hospitalization
/ statistics & numerical data
Humans
Likelihood Functions
Magnetic Resonance Imaging
Multiple Sclerosis, Relapsing-Remitting
/ diagnostic imaging
Pulmonary Disease, Chronic Obstructive
/ physiopathology
Research Design
Sample Size
Group sequential
heart failure
interim analysis
multiple sclerosis
negative binomial
recurrent events
Journal
Statistical methods in medical research
ISSN: 1477-0334
Titre abrégé: Stat Methods Med Res
Pays: England
ID NLM: 9212457
Informations de publication
Date de publication:
08 2019
08 2019
Historique:
pubmed:
18
5
2018
medline:
28
10
2020
entrez:
18
5
2018
Statut:
ppublish
Résumé
Count data and recurrent events in clinical trials, such as the number of lesions in magnetic resonance imaging in multiple sclerosis, the number of relapses in multiple sclerosis, the number of hospitalizations in heart failure, and the number of exacerbations in asthma or in chronic obstructive pulmonary disease (COPD) are often modeled by negative binomial distributions. In this manuscript, we study planning and analyzing clinical trials with group sequential designs for negative binomial outcomes. We propose a group sequential testing procedure for negative binomial outcomes based on Wald statistics using maximum likelihood estimators. The asymptotic distribution of the proposed group sequential test statistics is derived. The finite sample size properties of the proposed group sequential test for negative binomial outcomes and the methods for planning the respective clinical trials are assessed in a simulation study. The simulation scenarios are motivated by clinical trials in chronic heart failure and relapsing multiple sclerosis, which cover a wide range of practically relevant settings. Our research assures that the asymptotic normal theory of group sequential designs can be applied to negative binomial outcomes when the hypotheses are tested using Wald statistics and maximum likelihood estimators. We also propose two methods, one based on Student's
Identifiants
pubmed: 29770729
doi: 10.1177/0962280218773115
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM