Email this article Data missing: how to solve and how to escape the problem
- Authors: Tikhova G.P.1
-
Affiliations:
- Petrozavodsk State University named after O.V. Kuusinen
- Issue: Vol 10, No 3 (2016)
- Pages: 205-209
- Section: Articles
- URL: https://journals.rcsi.science/1993-6508/article/view/42843
- DOI: https://doi.org/10.18821/1993-6508-2016-10-3-205-209
- ID: 42843
Cite item
Full Text
Abstract
The article is devoted to the problem of missing data in clinical trials and clinical studies. The author considered three mechanisms of generating of missing data in collected sample. Each mechanism type is reviewed in details in terms of its effects on sample representativeness and the magnitude of result bias. The ways to reduce probability and amount of missing data are pointed in the phase of planning and on the stage of statistical data processing and inference.
Keywords
Full Text
##article.viewOnOriginalSite##About the authors
Galina P. Tikhova
Petrozavodsk State University named after O.V. Kuusinen
Email: tikhovag@gmail.com
senior researcher, Laboratory of clinical epidemiology, Institute of highest biomedical technologies, Petrozavodsk State University 185910, Petrozavodsk
References
- Mirkes E.M., Coats, T.J., Levesley J., Gorban, A.N. Handling missing data in large healthcare dataset: A case study of unknown trauma outcomes. Computers in Biology and Medicine. 2016; 75: 203-16.
- Тихова Г.П. Планируем клиническое исследование. Вопрос 2: Выбор конечных точек. Регионарная анестезия и лечение острой боли. 2014; 10(4): 67-70.
- Enders C.K. Applied Missing Data Analysis. New York: Guilford Press; 2010
- Rubin D.B. Inference and Missing Data. Biometrika.1976; 63(3): 581-92.
