Estimating treatment effects from randomized clinical trials with noncompliance and loss to follow-up: The role of instrumental variable methods

Graham Dunn, Mohammad Maracy, Barbara Tomenson

    Research output: Contribution to journalArticlepeer-review

    Abstract

    Perfectly implemented randomized clinical trials, particularly of complex interventions, are extremely rare. Almost always they are characterized by imperfect adherence to the randomly allocated treatment and variable amounts of missing outcome data. Here we start by describing a wide variety of examples and then introduce instrumental variable methods for the analysis of such trials. We concentrate mainly on situations in which compliance is all or nothing (either the patient receives the allocated treatment or they do not - in the latter case they may receive no treatment or a treatment other than the one allocated). The main purpose of the review is to illustrate the use of latent class (finite mixture) models, using maximum likelihood, for complier-average causal effect estimation under varying assumptions concerning the mechanism of the missing outcome data. © 2005 Edward Arnold (Publishers) Ltd.
    Original languageEnglish
    Pages (from-to)369-395
    Number of pages26
    JournalStatistical Methods in Medical Research
    Volume14
    Issue number4
    DOIs
    Publication statusPublished - Aug 2005

    Fingerprint

    Dive into the research topics of 'Estimating treatment effects from randomized clinical trials with noncompliance and loss to follow-up: The role of instrumental variable methods'. Together they form a unique fingerprint.

    Cite this