Box 1.

Summary of the Steps and R Code for Computing EVSI Using Gaussian Approximation from a PSA Data Set for a Parameter of Interest θ_I from a New Sample n

1.	Conduct probabilistic sensitivity analysis (PSA) and load the PSA data set.
	library(mgcv); library(matrixStats)
	psa <– read.csv(“example_psa_dataset.csv”)
	n.sim <– nrow(psa) # number of simulations in psa
	theta_I <– psa[, 1] # parameter of interest is column 1
	nmb <– psa[, 5:7] # the strategies’ net monetary benefits are columns 5 through 7
2.	Determine the optimal strategy d*.
	d.star <– which.max(colMeans (nmb))
3.	Compute the opportunity loss L(d, θ).
	loss <– nmb – nmb[, d.star]
4.	Estimate a linear metamodel for the opportunity loss of each d strategy, Ld, by regressing them on the spline basis functions of θI.
	lmm1 <– gam(loss[, 1] ~ s(theta_I)
	lmm2 <– gam(loss[, 2] ~ s(theta_I)
	lmm3 <– gam(loss[, 3] ~ s(theta_I)
5.	Compute EVPPI using the estimated losses for each d strategy, ${\widehat{L}}_d$, and applying equation (20).
	Lhat <– cbind(lmm1$fitted, lmm2$fitted, lmm3$fitted) # estimated losses
	evppi <– mean(rowMaxs(Lhat)) # evppi equation
6.	Load the predict.ga function.
	source(GA_functions.R)
7.	Compute the predicted loss for each d strategy, ${\widehat{L}}_d$, given the prior sample size (n0) and new sample size (n).
	Ltilde1 <– predict.ga(lmm1, n = n, n0 = n0)
	Ltilde2 <– predict.ga(lmm2, n = n, n0 = n0)
	Ltilde3 <– predict.ga(lmm3, n = n, n0 = n0)
	loss.predicted <– cbind(Ltilde1, Ltilde2, Ltilde3)
8.	Compute EVSI using equation (18)
	evsi <– mean(rowMaxs(loss.predicted)) # evsi equation

Appendix B provides example R codes for implementing this approach when there are multiple parameters and unbalanced data collection study designs.