Sensitivity analysis of intention-to-treat estimates when withdrawals are related to unobserved compliance status

Agus Salim*, Andrew Mackinnon, Kathleen Griffiths

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    11 Citations (Scopus)

    Abstract

    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.

    Original languageEnglish
    Pages (from-to)1164-1179
    Number of pages16
    JournalStatistics in Medicine
    Volume27
    Issue number8
    DOIs
    Publication statusPublished - 15 Apr 2008

    Fingerprint

    Dive into the research topics of 'Sensitivity analysis of intention-to-treat estimates when withdrawals are related to unobserved compliance status'. Together they form a unique fingerprint.

    Cite this