缺少数据
估计员
插补(统计学)
纵向数据
基线(sea)
统计
临床试验
纵向研究
计算机科学
计量经济学
医学
数学
数据挖掘
内科学
海洋学
地质学
标识
DOI:10.1080/10543406.2021.1934855
摘要
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.
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