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General-Purpose Methods for Simulating Survival Data for Expected Value of Sample Information Calculations

BACKGROUND: Expected value of sample information (EVSI) quantifies the expected value to a decision maker of reducing uncertainty by collecting additional data. EVSI calculations require simulating plausible data sets, typically achieved by evaluating quantile functions at random uniform numbers usi...

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Autores principales: Vervaart, Mathyn, Aas, Eline, Claxton, Karl P., Strong, Mark, Welton, Nicky J., Wisløff, Torbjørn, Heath, Anna
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10336715/
https://www.ncbi.nlm.nih.gov/pubmed/36971425
http://dx.doi.org/10.1177/0272989X231162069
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author Vervaart, Mathyn
Aas, Eline
Claxton, Karl P.
Strong, Mark
Welton, Nicky J.
Wisløff, Torbjørn
Heath, Anna
author_facet Vervaart, Mathyn
Aas, Eline
Claxton, Karl P.
Strong, Mark
Welton, Nicky J.
Wisløff, Torbjørn
Heath, Anna
author_sort Vervaart, Mathyn
collection PubMed
description BACKGROUND: Expected value of sample information (EVSI) quantifies the expected value to a decision maker of reducing uncertainty by collecting additional data. EVSI calculations require simulating plausible data sets, typically achieved by evaluating quantile functions at random uniform numbers using standard inverse transform sampling (ITS). This is straightforward when closed-form expressions for the quantile function are available, such as for standard parametric survival models, but these are often unavailable when assuming treatment effect waning and for flexible survival models. In these circumstances, the standard ITS method could be implemented by numerically evaluating the quantile functions at each iteration in a probabilistic analysis, but this greatly increases the computational burden. Thus, our study aims to develop general-purpose methods that standardize and reduce the computational burden of the EVSI data-simulation step for survival data. METHODS: We developed a discrete sampling method and an interpolated ITS method for simulating survival data from a probabilistic sample of survival probabilities over discrete time units. We compared the general-purpose and standard ITS methods using an illustrative partitioned survival model with and without adjustment for treatment effect waning. RESULTS: The discrete sampling and interpolated ITS methods agree closely with the standard ITS method, with the added benefit of a greatly reduced computational cost in the scenario with adjustment for treatment effect waning. CONCLUSIONS: We present general-purpose methods for simulating survival data from a probabilistic sample of survival probabilities that greatly reduce the computational burden of the EVSI data-simulation step when we assume treatment effect waning or use flexible survival models. The implementation of our data-simulation methods is identical across all possible survival models and can easily be automated from standard probabilistic decision analyses. HIGHLIGHTS: Expected value of sample information (EVSI) quantifies the expected value to a decision maker of reducing uncertainty through a given data collection exercise, such as a randomized clinical trial. In this article, we address the problem of computing EVSI when we assume treatment effect waning or use flexible survival models, by developing general-purpose methods that standardize and reduce the computational burden of the EVSI data-generation step for survival data. We developed 2 methods for simulating survival data from a probabilistic sample of survival probabilities over discrete time units, a discrete sampling method and an interpolated inverse transform sampling method, which can be combined with a recently proposed nonparametric EVSI method to accurately estimate EVSI for collecting survival data. Our general-purpose data-simulation methods greatly reduce the computational burden of the EVSI data-simulation step when we assume treatment effect waning or use flexible survival models. The implementation of our data-simulation methods is identical across all possible survival models and can therefore easily be automated from standard probabilistic decision analyses.
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spelling pubmed-103367152023-07-13 General-Purpose Methods for Simulating Survival Data for Expected Value of Sample Information Calculations Vervaart, Mathyn Aas, Eline Claxton, Karl P. Strong, Mark Welton, Nicky J. Wisløff, Torbjørn Heath, Anna Med Decis Making Original Research Articles BACKGROUND: Expected value of sample information (EVSI) quantifies the expected value to a decision maker of reducing uncertainty by collecting additional data. EVSI calculations require simulating plausible data sets, typically achieved by evaluating quantile functions at random uniform numbers using standard inverse transform sampling (ITS). This is straightforward when closed-form expressions for the quantile function are available, such as for standard parametric survival models, but these are often unavailable when assuming treatment effect waning and for flexible survival models. In these circumstances, the standard ITS method could be implemented by numerically evaluating the quantile functions at each iteration in a probabilistic analysis, but this greatly increases the computational burden. Thus, our study aims to develop general-purpose methods that standardize and reduce the computational burden of the EVSI data-simulation step for survival data. METHODS: We developed a discrete sampling method and an interpolated ITS method for simulating survival data from a probabilistic sample of survival probabilities over discrete time units. We compared the general-purpose and standard ITS methods using an illustrative partitioned survival model with and without adjustment for treatment effect waning. RESULTS: The discrete sampling and interpolated ITS methods agree closely with the standard ITS method, with the added benefit of a greatly reduced computational cost in the scenario with adjustment for treatment effect waning. CONCLUSIONS: We present general-purpose methods for simulating survival data from a probabilistic sample of survival probabilities that greatly reduce the computational burden of the EVSI data-simulation step when we assume treatment effect waning or use flexible survival models. The implementation of our data-simulation methods is identical across all possible survival models and can easily be automated from standard probabilistic decision analyses. HIGHLIGHTS: Expected value of sample information (EVSI) quantifies the expected value to a decision maker of reducing uncertainty through a given data collection exercise, such as a randomized clinical trial. In this article, we address the problem of computing EVSI when we assume treatment effect waning or use flexible survival models, by developing general-purpose methods that standardize and reduce the computational burden of the EVSI data-generation step for survival data. We developed 2 methods for simulating survival data from a probabilistic sample of survival probabilities over discrete time units, a discrete sampling method and an interpolated inverse transform sampling method, which can be combined with a recently proposed nonparametric EVSI method to accurately estimate EVSI for collecting survival data. Our general-purpose data-simulation methods greatly reduce the computational burden of the EVSI data-simulation step when we assume treatment effect waning or use flexible survival models. The implementation of our data-simulation methods is identical across all possible survival models and can therefore easily be automated from standard probabilistic decision analyses. SAGE Publications 2023-03-27 2023-07 /pmc/articles/PMC10336715/ /pubmed/36971425 http://dx.doi.org/10.1177/0272989X231162069 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Original Research Articles
Vervaart, Mathyn
Aas, Eline
Claxton, Karl P.
Strong, Mark
Welton, Nicky J.
Wisløff, Torbjørn
Heath, Anna
General-Purpose Methods for Simulating Survival Data for Expected Value of Sample Information Calculations
title General-Purpose Methods for Simulating Survival Data for Expected Value of Sample Information Calculations
title_full General-Purpose Methods for Simulating Survival Data for Expected Value of Sample Information Calculations
title_fullStr General-Purpose Methods for Simulating Survival Data for Expected Value of Sample Information Calculations
title_full_unstemmed General-Purpose Methods for Simulating Survival Data for Expected Value of Sample Information Calculations
title_short General-Purpose Methods for Simulating Survival Data for Expected Value of Sample Information Calculations
title_sort general-purpose methods for simulating survival data for expected value of sample information calculations
topic Original Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10336715/
https://www.ncbi.nlm.nih.gov/pubmed/36971425
http://dx.doi.org/10.1177/0272989X231162069
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