Clinical randomized controlled trials (RCTs) collect hundreds of measurements
spanning various metric types (e.g., laboratory tests, cognitive/motor
assessments, etc.) across 100s-1000s of subjects to evaluate the effect of a
treatment, but do so at the cost of significant trial expense. To reduce the
number of measurements, trial protocols can be revised to remove metrics
extraneous to the study's objective, but doing so requires additional human
labor and limits the set of hypotheses that can be studied with the collected
data. In contrast, a planned missing design (PMD) can reduce the amount of data
collected without removing any metric by imputing the unsampled data. Standard
PMDs randomly sample data to leverage statistical properties of imputation
algorithms, but are ad hoc, hence suboptimal. Methods that learn PMDs produce
more sample-efficient PMDs, but are not suitable for RCTs because they require
ample prior data (150+ subjects) to model the data distribution. Therefore, we
introduce a framework called Measurement EfficienT Randomized Controlled Trials
using Transformers with Input MasKing (METRIK), which, for the first time,
calculates a PMD specific to the RCT from a modest amount of prior data (e.g.,
60 subjects). Specifically, METRIK models the PMD as a learnable input masking
layer that is optimized with a state-of-the-art imputer based on the
Transformer architecture. METRIK implements a novel sampling and selection
algorithm to generate a PMD that satisfies the trial designer's objective,
i.e., whether to maximize sampling efficiency or imputation performance for a
given sampling budget. Evaluated across five real-world clinical RCT datasets,
METRIK increases the sampling efficiency of and imputation performance under
the generated PMD by leveraging correlations over time and across metrics,
thereby removing the need to manually remove metrics from the RCT.

采用计划失踪设计的研究论文提出了一种名为 METRIK 的框架，该框架能够通过学习输入屏蔽层实现在少量先验数据的基础上计算 RCT 特定的 PMD，从而提高采样效率和填补性能，并将需要手动删除 RCT 中的指标的需求消除。

METRIK: 使用输入遮盖的转换器进行高效测量的随机对照试验

METRIK: Measurement-Efficient Randomized Controlled Trials using  Transformers with Input Masking

We present a corpus of 5,000 richly annotated abstracts of medical articles
describing clinical randomized controlled trials. Annotations include
demarcations of text spans that describe the Patient population enrolled, the
Interventions studied and to what they were Compared, and the Outcomes measured
(the `PICO' elements). These spans are further annotated at a more granular
level, e.g., individual interventions within them are marked and mapped onto a
structured medical vocabulary. We acquired annotations from a diverse set of
workers with varying levels of expertise and cost. We describe our data
collection process and the corpus itself in detail. We then outline a set of
challenging NLP tasks that would aid searching of the medical literature and
the practice of evidence-based medicine.

该研究提供了一份 5000 份医学文章摘要的标注语料库，其中的标注包括描述人口、干预方法及比较、以及测量结果等关键信息，旨在协助检索医学文献并支持循证医学实践。