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Sample size calculation using Markov chains for a one-arm study of heroin administration routes


Grischott, Thomas; Valeri, Fabio; Falcato, Luis (2021). Sample size calculation using Markov chains for a one-arm study of heroin administration routes. Journal of Biopharmaceutical Statistics:Epub ahead of print.

Abstract

Sample size calculations for trials with time-to-event outcomes are usually based on the assumption that an event – prototypically death in survival analysis – occurs only once per sample unit. However, events like changes in disease status or switches between treatment modalities may repeat over time. In trials with such outcomes, standard sample size formulae derived from the classical survival time models are not applicable. Instead, modeling the repeating transition events must precede the actual sample size calculation. Markov chains are an obvious choice to model transitions. Accordingly, in order to determine the sample size for a one-arm feasibility and acceptability study of a new drug intake route, we model switches of administration routes by a homogeneous finite-state, higher-order Markov chain. Assumptions about its transition matrix translate into multinomial distributions of the preferred administration routes at given points in time. From these distributions, the required sample size can then be calculated according to the study’s specific question. In this manuscript, we first introduce the method for the case of drug intake preferences, before we briefly discuss how the proposed method can also be used for power-based sample size calculation in multi-arm trials.

Abstract

Sample size calculations for trials with time-to-event outcomes are usually based on the assumption that an event – prototypically death in survival analysis – occurs only once per sample unit. However, events like changes in disease status or switches between treatment modalities may repeat over time. In trials with such outcomes, standard sample size formulae derived from the classical survival time models are not applicable. Instead, modeling the repeating transition events must precede the actual sample size calculation. Markov chains are an obvious choice to model transitions. Accordingly, in order to determine the sample size for a one-arm feasibility and acceptability study of a new drug intake route, we model switches of administration routes by a homogeneous finite-state, higher-order Markov chain. Assumptions about its transition matrix translate into multinomial distributions of the preferred administration routes at given points in time. From these distributions, the required sample size can then be calculated according to the study’s specific question. In this manuscript, we first introduce the method for the case of drug intake preferences, before we briefly discuss how the proposed method can also be used for power-based sample size calculation in multi-arm trials.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > University Hospital Zurich > Institute of General Practice
Dewey Decimal Classification:610 Medicine & health
Uncontrolled Keywords:Statistics and Probability, Pharmacology (medical), Pharmacology
Language:English
Date:21 January 2021
Deposited On:28 Jan 2021 07:56
Last Modified:14 Feb 2021 08:57
Publisher:Taylor & Francis
ISSN:1054-3406
OA Status:Closed
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1080/10543406.2020.1852249
PubMed ID:33476221

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