Prevention of missing data in clinical research studies.
Missing data is a problem that is ubiquitous to all clinical studies and a source of multiple problems from an analytic point of view (reduced statistical power, increased the type I error, bias) Statistical approaches have been developed to analyze data collected from trials with missing data. Understanding and implementing the appropriate statistical technique is essential but should be differentiated from preventive approaches that are designed to reduce rates of missing data In this article, we draw attention to these preventive efforts. Seven steps to minimizing the amount of missing data are defined as documentation, training, monitoring reports, patient contact, data entry and management, pilot studies, and communication. Although the implementation of these approaches is time consuming and costly, the overall quality of the study is increased. Despite efforts devoted to areas, no study is without missing data. Once the study is completed, it is essential to assess the pattern of missing data and apply the appropriate statistical analysis.
Wisniewski
Stephen R
SR
Epidemiology Data Center, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, USA. wisniew@edc.pitt.edu
Leon
Andrew C
AC
Otto
Michael W
MW
Trivedi
Madhukar H
MH
D015982:Bias; D035843:Biomedical Research / Q000592:standards*; D016494:Computer Security; D003625:Data Collection / Q000379:methods / Q000592:standards*; D003627:Data Interpretation, Statistical; D006801:Humans; D010352:Patient Dropouts / Q000706:statistics & numerical data