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The problem of measurement error in modelling the effect of compliance in a randomized trial
Author(s) -
Dunn Graham
Publication year - 1999
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/(sici)1097-0258(19991115)18:21<2863::aid-sim238>3.0.co;2-i
Subject(s) - compliance (psychology) , observational error , computer science , covariance , analysis of covariance , econometrics , data collection , statistics , errors in variables models , mathematics , psychology , machine learning , social psychology
This paper explores the implications of measurement error in the analysis of compliance–response relationships in data from randomized trials. Given that compliance measures are rarely, if ever, error‐free indicators of exposure it is argued that both the designs for the collection of compliance data and the statistical models for their resulting analysis should be changed to take the possibility of measurement error into account. An analysis which ignores measurement error in the compliance measurements will provide biased estimates of compliance–response relationships. Provided that one has two or more indicators of compliance for each subject, more appropriate models can be fitted using covariance structure modelling software. If one wishes to explore interactions from repeated measures data on both compliance and response then it is also important that one recognizes that the response measures are also error‐prone and that they too are dealt with appropriately. Copyright © 1999 John Wiley & Sons, Ltd.