Improving representativeness in non-probability surveys and causal inference with regularized regression and post-stratification

项目摘要/摘要 拟议的项目有一个广泛的目标，即处理日益复杂的调查统计数据，包括 提高响应率。我们特别将重点放在非概率样本(方便样本)上，因为它们 越来越受欢迎，但请注意，这些非概率样本只是概率的极端情况 基于调查，无回复率很高，因此我们的方法有望得到推广。从长远来看，我们的 希望找到安全地调整非概率样本以适应更广泛人群的方法和技术，同时fi 同时开发批评这些估计的方法，以增加研究人员、政策制定者和公众 在这些估计中，fi是正确的。 我们的fic规范致力于开发工具和技术来实现这一点。我们主要专注于 关于正则化回归和事后fi阳离子方法学，已显示出一些成功的非 有代表性的甚至是方便的样品。使用这种方法，我们专注于实现这一点的适应 对公共卫生环境有用的技术。 特别是fi，我们重点关注三管齐下的方法。首先，我们的目标是适应当前的状况 更好地适应公共卫生数据集和问题提出的独特挑战的建模技术弧形。 我们实现这一目标的方法是专注于部分池化，具有更结构化的调整变量，等等 广义地考虑具有连续和非连续分量的高维变量。不仅如此， 但我们也要考虑后期fi阳离子的不确定性，即调整未知变量时 人口数量。在一种补充方法中，我们还旨在通过结合原始调查数据来评估覆盖率，但 假设样本不同。 第二，我们注意到，我们的许多核心方法可以扩展到因果性质的问题。这 与公共卫生挑战特别相关，因为通常需要因果估计。我们的方法是 扩展基于模型的方法，以假设人口子组内影响的异质性。然后 通过使用正则化，估计每个子组内的影响，并将其用于后分层到总体。 接受治疗/未接受治疗的个体相对较少的群体估计会有更大的不确定性，这是一种 创新平衡核算方法。 第三，也是fi，我们注意到正则化回归和预测技术特别依赖于 模型假设。我们的fiNAL目标是考虑测试和验证具有非代表性的模型的方法 数据，以获得更好和更可信的基于人口的估计。

Project Summary/Abstract The proposed project has a broad aim of working with the increasing complexities of survey statistics with de- creasing response rate. We focus specifically on non-probability samples (samples of convenience) due to their increasing popularity, but note that these non-probability samples are simply an extreme case of a probability based survey with high non-response, and so our methods could be expected to generalize. Long term, our hope is to find methods and techniques to safely adjust non-probability samples to a wider population whilst concurrently developing methods of critiquing these estimates to increase researcher, policy maker and public confidence in these estimates. Our specific aims focus in on developing the tools and techniques to make this possible. We focus primarily on a regularized regression and poststratification methodology that has already shown some success with non- representative and even convenience samples. Using this methodology, we focus on adaptions that make this technique useful for public health settings. Specifically we focus on a three pronged approach. Firstly, we aim to make adaptions to the current state of the arc of modelling technique to better suit the unique challenges posed by public health datasets and questions. Our approach to achieve this is to focus on partial pooling with more structured adjustment variables, and more broadly considering high dimensional variables with continuous and non-continuous components. Not only that, but we move to also consider uncertainty in poststratification, namely when adjusting for variables not known in the population. In a complementary approach, we also aim to assess coverage by combining raw survey data but assuming differences in sample. Secondly, we note that many our central methodology could be extended to questions of a causal nature. This is particularly relevant to public health challenges because often causal estimates are desired. Our approach is to extend the model based approach to assume heterogeneity of effect within demographic subgroups. Then by using regularization, the effect within each subgroup is estimated and used to poststratify to the population. Groups with relatively few treated/untreated individuals would be estimated with greater uncertainty, which is an innovative approach to accounting for balance. Thirdly and finally we note that the regularized regression and prediction technique is particularly reliant on model assumptions. Our final aim is to consider methods of testing and validating models with non-representative data in order to obtain better and more trustworthy population based estimates.