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精神医学中的生物统计学(10):进行临床试验时对缺失数据的预防和处理
1.IntroductionThis paper is the second in a 3-part series focusing on missing data.In a clinical study missing data can occur for various reasons, with or without any actual loss of study participants because of drop-outs.Poorly designed assessment schedules or use of inefficient data collection tools can result in missed visits or omissions of questions and, thus, missing data.
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精神医学中的生物统计学(9):从研究设计角度考虑如何避免数据缺失以及由此带来的问题
One of the most common challenges in biomedical and psychosocial research is missing data, which occurs when respondents refuse to provide answers to sensitive questions and when study subjects are lost to follow-up during the repeated assessments of longitudinal trials.This paper is the first in a 3-part series focusing on this important topic; it describes different types of missing data and their differential effects on model estimates, focusing on study design strategies that can be used to prevent or minimize missing data and, thus, maintain the scientific integrity of the research.The second paper in the series will discuss implementation strategies to manage and reduce missing data while conducting the study, and the third paper will discuss analytic strategies for dealing with missing data after completion of data collection.