ABSTRACT

Returning to baseline (RTB) has been a practical method for handling missing data. Here we consider longitudinal clinical trials with daily patient reported outcomes (PROs), where efficacy endpoints are often defined as the average daily values in a cycle (such as a month or a week). The conventional method treats data at cycle level and ignores daily values. In this paper, we build a two-level constrained longitudinal data analysis (cLDA) model on daily values and propose two-level RTB method to impute daily values. Standard multiple imputation (MI) approach and likelihood-based approach are proposed and evaluated by simulations.

中文翻译：

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使用返回基线策略进行不完整每日患者报告结果的纵向临床试验的两水平方法的估计量和估计量

摘要

返回基线（RTB）是处理缺失数据的实用方法。在这里，我们考虑采用每日患者报告结果 (PRO) 的纵向临床试验，其中疗效终点通常定义为一个周期（例如一个月或一周）的平均每日值。传统方法在周期级别处理数据并忽略每日值。在本文中，我们建立了每日价值的两级约束纵向数据分析（cLDA）模型，并提出了两级 RTB 方法来估算每日价值。提出了标准多重插补（MI）方法和基于可能性的方法并通过模拟进行评估。