Randomized trials are widely considered as the gold standard for evaluating
the effects of decision policies. Trial data is, however, drawn from a
population which may differ from the intended target population and this raises
a problem of external validity (aka. generalizability). In this paper we seek
to use trial data to draw valid inferences about the outcome of a policy on the
target population. Additional covariate data from the target population is used
to model the sampling of individuals in the trial study. We develop a method
that yields certifiably valid trial-based policy evaluations under any
specified range of model miscalibrations. The method is nonparametric and the
validity is assured even with finite samples. The certified policy evaluations
are illustrated using both simulated and real data.

使用试验数据对目标人群的政策结果进行验证，使用来自目标人群的附加协变量数据模拟试验研究中个体的抽样，开发了一种非参数的方法，确保在任何指定的模型误差范围内得到有效的基于试验的政策评估，通过模拟和真实数据对认证的政策评估进行了展示。

结合试验与观察数据的外部有效政策评估

Externally Valid Policy Evaluation Combining Trial and Observational  Data

PReMiuM is a recently developed R package for Bayesian clustering using a
Dirichlet process mixture model. This model is an alternative to regression
models, non-parametrically linking a response vector to covariate data through
cluster membership. The package allows Bernoulli, Binomial, Poisson, Normal and
categorical response, as well as Normal and discrete covariates. Additionally,
predictions may be made for the response, and missing values for the covariates
are handled. Several samplers and label switching moves are implemented along
with diagnostic tools to assess convergence. A number of R functions for
post-processing of the output are also provided. In addition to fitting
mixtures, it may additionally be of interest to determine which covariates
actively drive the mixture components. This is implemented in the package as
variable selection.

该论文介绍了使用 Dirichlet process 混合模型进行 Bayesian 聚类的 R 软件包 PReMiuM，并实现了对响应向量和协变量数据的非参数链接，同时还提供了一些后处理函数和变量选择的功能。