Open AccessData missing: how to solve and how to escape the problem
Author(s) -
G.P. Tikhova
Publication year - 2016
Publication title -
regionarnaâ anesteziâ i lečenie ostroj boli
Language(s) - English
Resource type - Journals
eISSN - 2687-1394
pISSN - 1993-6508
DOI - 10.18821/1993-6508-2016-10-3-205-209
Subject(s) - missing data , representativeness heuristic , sample (material) , computer science , inference , statistical inference , sample size determination , data mining , statistics , artificial intelligence , machine learning , mathematics , chemistry , chromatography
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.