TY - JOUR
T1 - Prevention of Missing Data in Clinical Research Studies
AU - Wisniewski, Stephen R.
AU - Leon, Andrew C.
AU - Otto, Michael W.
AU - Trivedi, Madhukar H.
N1 - Funding Information:
This research was supported in part by the following grants: National Institute of Mental Health (MH90003) (SRW); National Institute of Mental Health (MH060447) (ACL); National Institute of Drug Abuse (NIDA DA017904) (MWO); and National Institute of Mental Health (MH90003) (MHT).
PY - 2006/6/1
Y1 - 2006/6/1
N2 - 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.
AB - 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.
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U2 - 10.1016/j.biopsych.2006.01.017
DO - 10.1016/j.biopsych.2006.01.017
M3 - Review article
C2 - 16566901
AN - SCOPUS:33744941826
SN - 0006-3223
VL - 59
SP - 997
EP - 1000
JO - Biological Psychiatry
JF - Biological Psychiatry
IS - 11
ER -