Expected value of sample information for Weibull survival data
研究了五种计算威布尔生存模型后验期望净收益的方法，比较了精度和计算时间，发现Brennan & Kharroubi方法最接近MCMC且速度快12倍，可用于经济模型中的样本信息期望值计算。
Expected value of sample information (EVSI) involves simulating data collection, Bayesian updating, and re-examining decisions. Bayesian updating in Weibull models typically requires Markov chain Monte Carlo (MCMC). We examine five methods for calculating posterior expected net benefits: two heuristic methods (data lumping and pseudo-normal); two Bayesian approximation methods (Tierney & Kadane, Brennan & Kharroubi); and the gold standard MCMC. A case study computes EVSI for 25 study options. We compare accuracy, computation time and trade-offs of EVSI versus study costs. Brennan & Kharroubi (B&K) approximates expected net benefits to within +/-1% of MCMC. Other methods, data lumping (+54%), pseudo-normal (-5%) and Tierney & Kadane (+11%) are less accurate. B&K also produces the most accurate EVSI approximation. Pseudo-normal is also reasonably accurate, whilst Tierney & Kadane consistently underestimates and data lumping exhibits large variance. B&K computation is 12 times faster than the MCMC method in our case study. Though not always faster, B&K provides most computational efficiency when net benefits require appreciable computation time and when many MCMC samples are needed. The methods enable EVSI computation for economic models with Weibull survival parameters. The approach can generalize to complex multi-state models and to survival analyses using other smooth parametric distributions.