Computing the Expected Value of Sample Information Efficiently: Practical Guidance and Recommendations for Four Model-Based Methods.
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SUBMITTER: Kunst N
PROVIDER: S-EPMC8183576 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundDecisions 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.MethodsWe 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.ResultsThere 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.ConclusionsWe 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.HighlightsDecisions 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 have developed new methods for computing the EVSI of extending a trial's follow-up, both where a single known survival model is assumed and where we are uncertain about the true survival model. We extend a previously described nonparametric regression-based method for computing EVSI, which we demonstrate in synthetic case studies is fast, straightforward to implement, and scales easily to include any number of candidate survival models in the EVSI calculations.The EVSI methods that we present in this article can quantify the need for collecting additional follow-up data before making an adoption decision given any decision-making context.
| S-EPMC9189722 | biostudies-literature
Project description:The expected value of sample information (EVSI) can be used to prioritize avenues for future research and design studies that support medical decision making and offer value for money spent. EVSI is calculated based on 3 key elements. Two of these, a probabilistic model-based economic evaluation and updating model uncertainty based on simulated data, have been frequently discussed in the literature. By contrast, the third element, simulating data from the proposed studies, has received little attention. This tutorial contributes to bridging this gap by providing a step-by-step guide to simulating study data for EVSI calculations. We discuss a general-purpose algorithm for simulating data and demonstrate its use to simulate 3 different outcome types. We then discuss how to induce correlations in the generated data, how to adjust for common issues in study implementation such as missingness and censoring, and how individual patient data from previous studies can be leveraged to undertake EVSI calculations. For all examples, we provide comprehensive code written in the R language and, where possible, Excel spreadsheets in the supplementary materials. This tutorial facilitates practical EVSI calculations and allows EVSI to be used to prioritize research and design studies.
| S-EPMC8793320 | biostudies-literature
Project description:Background. Investing efficiently in future research to improve policy decisions is an important goal. Expected value of sample information (EVSI) can be used to select the specific design and sample size of a proposed study by assessing the benefit of a range of different studies. Estimating EVSI with the standard nested Monte Carlo algorithm has a notoriously high computational burden, especially when using a complex decision model or when optimizing over study sample sizes and designs. Recently, several more efficient EVSI approximation methods have been developed. However, these approximation methods have not been compared, and therefore their comparative performance across different examples has not been explored. Methods. We compared 4 EVSI methods using 3 previously published health economic models. The examples were chosen to represent a range of real-world contexts, including situations with multiple study outcomes, missing data, and data from an observational rather than a randomized study. The computational speed and accuracy of each method were compared. Results. In each example, the approximation methods took minutes or hours to achieve reasonably accurate EVSI estimates, whereas the traditional Monte Carlo method took weeks. Specific methods are particularly suited to problems where we wish to compare multiple proposed sample sizes, when the proposed sample size is large, or when the health economic model is computationally expensive. Conclusions. As all the evaluated methods gave estimates similar to those given by traditional Monte Carlo, we suggest that EVSI can now be efficiently computed with confidence in realistic examples. No systematically superior EVSI computation method exists as the properties of the different methods depend on the underlying health economic model, data generation process, and user expertise.
| S-EPMC7968749 | biostudies-literature
Project description:IntroductionAdaptive designs allow changes to an ongoing trial based on prespecified early examinations of accrued data. Opportunities are potentially being missed to incorporate health economic considerations into the design of these studies.MethodsWe describe how to estimate the expected value of sample information for group sequential design adaptive trials. We operationalize this approach in a hypothetical case study using data from a pilot trial. We report the expected value of sample information and expected net benefit of sampling results for 5 design options for the future full-scale trial including the fixed-sample-size design and the group sequential design using either the Pocock stopping rule or the O'Brien-Fleming stopping rule with 2 or 5 analyses. We considered 2 scenarios relating to 1) using the cost-effectiveness model with a traditional approach to the health economic analysis and 2) adjusting the cost-effectiveness analysis to incorporate the bias-adjusted maximum likelihood estimates of trial outcomes to account for the bias that can be generated in adaptive trials.ResultsThe case study demonstrated that the methods developed could be successfully applied in practice. The results showed that the O'Brien-Fleming stopping rule with 2 analyses was the most efficient design with the highest expected net benefit of sampling in the case study.ConclusionsCost-effectiveness considerations are unavoidable in budget-constrained, publicly funded health care systems, and adaptive designs can provide an alternative to costly fixed-sample-size designs. We recommend that when planning a clinical trial, expected value of sample information methods be used to compare possible adaptive and nonadaptive trial designs, with appropriate adjustment, to help justify the choice of design characteristics and ensure the cost-effective use of research funding.HighlightsOpportunities are potentially being missed to incorporate health economic considerations into the design of adaptive clinical trials.Existing expected value of sample information analysis methods can be extended to compare possible group sequential and nonadaptive trial designs when planning a clinical trial.We recommend that adjusted analyses be presented to control for the potential impact of the adaptive designs and to maintain the accuracy of the calculations.This approach can help to justify the choice of design characteristics and ensure the cost-effective use of limited research funding.
| S-EPMC9005835 | biostudies-literature
Project description:Health economic decision-analytic models are used to estimate the expected net benefits of competing decision options. The true values of the input parameters of such models are rarely known with certainty, and it is often useful to quantify the value to the decision maker of reducing uncertainty through collecting new data. In the context of a particular decision problem, the value of a proposed research design can be quantified by its expected value of sample information (EVSI). EVSI is commonly estimated via a 2-level Monte Carlo procedure in which plausible data sets are generated in an outer loop, and then, conditional on these, the parameters of the decision model are updated via Bayes rule and sampled in an inner loop. At each iteration of the inner loop, the decision model is evaluated. This is computationally demanding and may be difficult if the posterior distribution of the model parameters conditional on sampled data is hard to sample from. We describe a fast nonparametric regression-based method for estimating per-patient EVSI that requires only the probabilistic sensitivity analysis sample (i.e., the set of samples drawn from the joint distribution of the parameters and the corresponding net benefits). The method avoids the need to sample from the posterior distributions of the parameters and avoids the need to rerun the model. The only requirement is that sample data sets can be generated. The method is applicable with a model of any complexity and with any specification of model parameter distribution. We demonstrate in a case study the superior efficiency of the regression method over the 2-level Monte Carlo method.
| S-EPMC4471064 | biostudies-literature
Project description:The partial expected value of perfect information (EVPI) quantifies the expected benefit of learning the values of uncertain parameters in a decision model. Partial EVPI is commonly estimated via a 2-level Monte Carlo procedure in which parameters of interest are sampled in an outer loop, and then conditional on these, the remaining parameters are sampled in an inner loop. This is computationally demanding and may be difficult if correlation between input parameters results in conditional distributions that are hard to sample from. We describe a novel nonparametric regression-based method for estimating partial EVPI that requires only the probabilistic sensitivity analysis sample (i.e., the set of samples drawn from the joint distribution of the parameters and the corresponding net benefits). The method is applicable in a model of any complexity and with any specification of input parameter distribution. We describe the implementation of the method via 2 nonparametric regression modeling approaches, the Generalized Additive Model and the Gaussian process. We demonstrate in 2 case studies the superior efficiency of the regression method over the 2-level Monte Carlo method. R code is made available to implement the method.
| S-EPMC4819801 | biostudies-literature
Project description:BackgroundThe expected value of sample information (EVSI) can help prioritize research but its application is hampered by computational infeasibility, especially for complex models. We investigated an approach by Strong and colleagues to estimate EVSI by applying generalized additive models (GAM) to results generated from a probabilistic sensitivity analysis (PSA).MethodsFor 3 potential HIV prevention and treatment strategies, we estimated life expectancy and lifetime costs using the Cost-effectiveness of Preventing AIDS Complications (CEPAC) model, a complex patient-level microsimulation model of HIV progression. We fitted a GAM-a flexible regression model that estimates the functional form as part of the model fitting process-to the incremental net monetary benefits obtained from the CEPAC PSA. For each case study, we calculated the expected value of partial perfect information (EVPPI) using both the conventional nested Monte Carlo approach and the GAM approach. EVSI was calculated using the GAM approach.ResultsFor all 3 case studies, the GAM approach consistently gave similar estimates of EVPPI compared with the conventional approach. The EVSI behaved as expected: it increased and converged to EVPPI for larger sample sizes. For each case study, generating the PSA results for the GAM approach required 3 to 4 days on a shared cluster, after which EVPPI and EVSI across a range of sample sizes were evaluated in minutes. The conventional approach required approximately 5 weeks for the EVPPI calculation alone.ConclusionEstimating EVSI using the GAM approach with results from a PSA dramatically reduced the time required to conduct a computationally intense project, which would otherwise have been impractical. Using the GAM approach, we can efficiently provide policy makers with EVSI estimates, even for complex patient-level microsimulation models.
| S-EPMC5771838 | biostudies-literature
Project description:Expected value of information methods evaluate the potential health benefits that can be obtained from conducting new research to reduce uncertainty in the parameters of a cost-effectiveness analysis model, hence reducing decision uncertainty. Expected value of partial perfect information (EVPPI) provides an upper limit to the health gains that can be obtained from conducting a new study on a subset of parameters in the cost-effectiveness analysis and can therefore be used as a sensitivity analysis to identify parameters that most contribute to decision uncertainty and to help guide decisions around which types of study are of most value to prioritize for funding. A common general approach is to use nested Monte Carlo simulation to obtain an estimate of EVPPI. This approach is computationally intensive, can lead to significant sampling bias if an inadequate number of inner samples are obtained, and incorrect results can be obtained if correlations between parameters are not dealt with appropriately. In this article, we set out a range of methods for estimating EVPPI that avoid the need for nested simulation: reparameterization of the net benefit function, Taylor series approximations, and restricted cubic spline estimation of conditional expectations. For each method, we set out the generalized functional form that net benefit must take for the method to be valid. By specifying this functional form, our methods are able to focus on components of the model in which approximation is required, avoiding the complexities involved in developing statistical approximations for the model as a whole. Our methods also allow for any correlations that might exist between model parameters. We illustrate the methods using an example of fluid resuscitation in African children with severe malaria.
| S-EPMC4948652 | biostudies-literature
Project description:When making decisions, individuals must often compensate for cognitive limitations, particularly in the face of advanced age. Recent findings suggest that age-related variability in striatal activity may increase financial risk-taking mistakes in older adults. In two studies, we sought to further characterize neural contributions to optimal financial risk taking and to determine whether decision aids could improve financial risk taking. In Study 1, neuroimaging analyses revealed that individuals whose mesolimbic activation correlated with the expected value estimates of a rational actor made more optimal financial decisions. In Study 2, presentation of expected value information improved decision making in both younger and older adults, but the addition of a distracting secondary task had little impact on decision quality. Remarkably, provision of expected value information improved the performance of older adults to match that of younger adults at baseline. These findings are consistent with the notion that mesolimbic circuits play a critical role in optimal choice, and imply that providing simplified information about expected value may improve financial risk taking across the adult life span.
| S-EPMC3073388 | biostudies-literature
Project description:Integrated information theory (IIT) has established itself as one of the leading theories for the study of consciousness. IIT essentially proposes that quantitative consciousness is identical to maximally integrated conceptual information, quantified by a measure called Φmax, and that phenomenological experience corresponds to the associated set of maximally irreducible cause-effect repertoires of a physical system being in a certain state. With the current work, we provide a general formulation of the framework, which comprehensively and parsimoniously expresses Φmax in the language of probabilistic models. Here, the stochastic process describing a system under scrutiny corresponds to a first-order time-invariant Markov process, and all necessary mathematical operations for the definition of Φmax are fully specified by a system's joint probability distribution over two adjacent points in discrete time. We present a detailed constructive rule for the decomposition of a system into two disjoint subsystems based on flexible marginalization and factorization of this joint distribution. Furthermore, we show that for a given joint distribution, virtualization is identical to a flexible factorization enforcing independence between variable subsets. We then validate our formulation in a previously established discrete example system, in which we also illustrate the previously unexplored theoretical issue of quale underdetermination due to non-unique maximally irreducible cause-effect repertoires. Moreover, we show that the current definition of Φ entails its sensitivity to the shape of the conceptual structure in qualia space, thus tying together IIT's measures of quantitative and qualitative consciousness, which we suggest be better disentangled. We propose several modifications of the framework in order to address some of these issues.
| S-EPMC6007153 | biostudies-literature