Sensitivity analysis of intention-to-treat estimates when withdrawals are related to unobserved compliance status.
In the presence of dropout, intent(ion)-to-treat analysis is usually carried out using methods that assume a missing-at-random (MAR) dropout mechanism. We investigate the potential bias caused by assuming MAR when the dropout is related to unobserved compliance status. A framework to assess the magnitude of bias in the context of pre- and post-test design (PPD) with two treatment arms is presented. Scenarios with all-or-none and partial compliance level are investigated. Using two simulated data sets and actual data from an e-mental health trial, we demonstrate the utility of sensitivity analyses to assess the bias magnitude and show that they are plausible options when some knowledge of compliance behaviour in the dropout exists. We recommend that our approach be used in conjunction with methods of analysis which assume MAR in estimating the ITT effect.
Salim
Agus
A
Centre for Mental Health Research, Australian National University, Canberra, ACT, Australia. agus.salim@anu.edu.au
Mackinnon
Andrew
A
Griffiths
Kathleen
K
D015982:Bias; D003198:Computer Simulation; D006801:Humans; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method; D010349:Patient Compliance / Q000706:statistics & numerical data*; D010352:Patient Dropouts; D016032:Randomized Controlled Trials as Topic / Q000379:methods*; D045808:Refusal to Participate; D012107:Research Design; D012680:Sensitivity and Specificity; D013315:Stress, Psychological / Q000628:therapy; D013810:Therapeutic Equivalency; D016896:Treatment Outcome