  Objectives: Value of information (VOI) analyses can help policy-makers make
informed decisions about whether to conduct and how to design future studies.
Historically, a computationally expensive method to compute the Expected Value
of Sample Information (EVSI) restricted the use of VOI to simple decision
models and study designs. Recently, four EVSI approximation methods have made
such analyses more feasible and accessible. We provide practical
recommendations for analysts computing EVSI by evaluating these novel methods.
Methods: Members of the Collaborative Network for Value of Information (ConVOI)
compared the inputs, analyst's expertise and skills, and software required for
four recently developed approximation methods. Information was also collected
on the strengths and limitations of each approximation method. Results: All
four EVSI methods require a decision-analytic model's probabilistic sensitivity
analysis (PSA) output. One of the methods also requires the model to be re-run
to obtain new PSA outputs for each EVSI estimation. To compute EVSI, analysts
must be familiar with at least one of the following skills: advanced regression
modeling, likelihood specification, and Bayesian modeling. All methods have
different strengths and limitations, e.g., some methods handle evaluation of
study designs with more outcomes more efficiently while others quantify
uncertainty in EVSI estimates. All methods are programmed in the statistical
language R and two of the methods provide online applications. Conclusion: Our
paper helps to inform the choice between four efficient EVSI estimation
methods, enabling analysts to assess the methods' strengths and limitations and
select the most appropriate EVSI method given their situation and skills.
