Mathyn Adrianus Marinus Vervaart

• Johansen, Inger; Moe, Rikke Helene; Eid, Idun; Fossmo, Hanne Ludt; Hole, Anniken Hjellbakk & Rajalahti, Tarja [Show all 10 contributors for this article] (2026). Standardised multidisciplinary rehabilitation improves health-related quality of life and productivity costs in patients with rheumatic, musculoskeletal and other diseases. EULAR Rheumatology Open (ERO). 2(1), p. 271–271. doi: 10.1016/j.ero.2026.02.002. Full text in Research Archive
• Vervaart, Mathyn Adrianus Marinus (2024). Calculating the Expected Net Benefit of Sampling for Survival Data: A Tutorial and Case Study. Medical decision making. ISSN 0272-989X. doi: 10.1177/0272989X241279459. Full text in Research Archive
• Vervaart, Mathyn Adrianus Marinus; Aas, Eline; Claxton, Karl; Strong, Mark; Welton, Nicky J. & Wisløff, Torbjørn [Show all 7 contributors for this article] (2023). General-Purpose Methods for Simulating Survival Data for Expected Value of Sample Information Calculations. Medical decision making. ISSN 0272-989X. 43(5), p. 595–609. doi: 10.1177/0272989X231162069. Full text in Research Archive Show summary

  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.

• Vervaart, Mathyn Adrianus Marinus; Strong, Mark; Claxton, Karl; Welton, Nicky; Wisløff, Torbjørn & Aas, Eline (2021). An Efficient Method for Computing Expected Value of Sample Information for Survival Data from an Ongoing Trial. Medical decision making. ISSN 0272-989X. doi: 10.1177/0272989X211068019. Full text in Research Archive Show summary

  Background - Decisions about new health technologies are increasingly being made while trials are still in an early stage, which may result in substantial uncertainty around key decision drivers such as estimates of life expectancy and time to disease progression. Additional data collection can reduce uncertainty, and its value can be quantified by computing the expected value of sample information (EVSI), which has typically been described in the context of designing a future trial. In this article, we develop new methods for computing the EVSI of extending an existing trial’s follow-up, first for an assumed survival model and then extending to capture uncertainty about the true survival model.<p> <p>Methods - We developed a nested Markov Chain Monte Carlo procedure and a nonparametric regression-based method. We compared the methods by computing single-model and model-averaged EVSI for collecting additional follow-up data in 2 synthetic case studies.<p> <p>Results - There was good agreement between the 2 methods. The regression-based method was fast and straightforward to implement, and scales easily to include any number of candidate survival models in the model uncertainty case. The nested Monte Carlo procedure, on the other hand, was extremely computationally demanding when we included model uncertainty.<p> <p>Conclusions - We present a straightforward regression-based method for computing the EVSI of extending an existing trial’s follow-up, both where a single known survival model is assumed and where we are uncertain about the true survival model. EVSI for ongoing trials can help decision makers determine whether early patient access to a new technology can be justified on the basis of the current evidence or whether more mature evidence is needed.

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• Aas, Eline; Kinge, Jonas Minet; Burger, Emily Annika; Pedersen, Kine; Iversen, Tor & Godager, Geir [Show all 14 contributors for this article] (2025). Feil tallgrunnlag kan koste liv i helsetjenesten. Full text in Research Archive
• Kleve, Guro; Santa, Chiara Dalla; Kristiansen, Felicia Iselin Svensson; Tegnander, Marita; Buch, Victoria & Hagemann, Cecilie Steen [Show all 29 contributors for this article] (2025). Digital home-based multimodal prehabilitation of colorectal cancer patients prior to surgery (the dHOPE study): a non-inferiority clinical trial protocol. doi: 10.3389/fdgth.2025.1609678. Full text in Research Archive Show summary

  Cancer surgery is associated with risk of complications and loss of function. Vulnerability factors, such as advanced age, malnutrition, smoking, comorbidity, frailty, and low socioeconomic status increase the risk. Lifestyle intervention prior to surgery, known as prehabilitation, often include physical activity, nutritional support, psychological coaching, and smoking cessation, increase functional reserves and reduce postoperative complications. Most importantly, it prevents loss of functional capacity and dependence. Methods The dHOPE study is a three-armed, open-labelled, parallel-group randomized controlled trial (RCT) with non-inferiority design to compare a digital home-based prehabilitation program with a hospital-based program or no organized prehabilitation. In addition, the dHOPE study aims to identify measurable parameters reflecting the effect of prehabilitation, thus preparing for future personalization of the prehabilitation programs. Discussion The feasibility of multimodal prehabilitation is threatened by low compliance to hospital-based programs due to burdensome commuting, even in central and metropolitan areas. In sparsely populated countries, this challenge is even more pronounced. To ensure equal healthcare to all citizens regardless of address or economic situation, there is a need to transfer the prehabilitation program to the patients’ homes. Thus, the primary hypothesis of dHOPE is that a digital home-based program is not inferior to a hospital-based program. Moreover, given the patient diversity, prehabilitation must be personalized to meet individual profiles or needs. An exploratory subtask of dHOPE is to confirm the utility of clinical, genetic, and molecular factors in evaluating prehabilitation response ultimately to identify new biomarkers and develop medical software for individual risk stratification and development of personalized prehabilitation programs. Clinical Trial registration https://clinicaltrials.gov/ , identifier (NCT06231576).

• Vervaart, Mathyn Adrianus Marinus (2023). Managing uncertainty in economic evaluations of health technologies with immature survival data. Full text in Research Archive
• Vervaart, Mathyn Adrianus Marinus (2022). General-Purpose Methods for Simulating Survival Data for Expected Value of Sample Information Calculations. Full text in Research Archive
• Vervaart, Mathyn Adrianus Marinus (2022). General-Purpose Methods for Simulating Survival Data for Expected Value of Sample Information Calculations. Full text in Research Archive
• Vervaart, Mathyn Adrianus Marinus (2021). Seminar on uncertainty in assessing advanced therapies at BI Norwegian Business School. Full text in Research Archive
• Vervaart, Mathyn Adrianus Marinus; Aas, Eline; Asphaug, Lars; Bjørnelv, Gudrun Maria Waaler; Burger, Emily Annika & Wisløff, Torbjørn (2021). Undervurdering av en kompleks problemstilling. Dagens medisin. ISSN 1501-4290. Full text in Research Archive
• Vervaart, Mathyn Adrianus Marinus (2021). Dealing with Immature Trial Evidence in the Economic Evaluation of Health Care Technologies. Full text in Research Archive
• Vervaart, Mathyn Adrianus Marinus; Aas, Eline; Asphaug, Lars; Bjørnelv, Gudrun Maria Waaler; Burger, Emily Annika & Wisløff, Torbjørn (2021). I strid med etablert kunnskap. Dagens medisin. ISSN 1501-4290. Full text in Research Archive
• Vervaart, Mathyn Adrianus Marinus (2020). Expected Value of Sample Information for Survival Data from Ongoing Trials. Full text in Research Archive
• Aas, Eline & Vervaart, Mathyn Adrianus Marinus (2019). Skal kalkulere legemiddelusikkerhet. [Journal]. Dagens Medisin. Full text in Research Archive
• Vervaart, Mathyn Adrianus Marinus (2019). Expected value of sample information for survival data from ongoing trials. Full text in Research Archive
• Vervaart, Mathyn Adrianus Marinus (2019). Dealing with Immature Trial Evidence in the Economic Evaluation of Health Care Technologies. Full text in Research Archive
• Vervaart, Mathyn Adrianus Marinus (2019). PRECISE. Full text in Research Archive

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