Using the instrumental variables estimator to analyze noninferiority trials with noncompliance

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

The standard intent-to-treat (ITT) approach can lead to erroneous conclusions about treatment efficacy in noninferiority trials with noncompliance. Per-protocol and as-treated analyses are also known to result in biased comparisons of treatment effects. Alternative statistical methods are therefore needed to better address the effects of noncompliance in noninferiority trials. In this paper, we consider the use of the instrumental variables (IV) estimator in a noninferiority trial with a binary outcome and evaluate the performance of this approach in comparison to other conventional analytic methods. Unlike the ITT, per-protocol, and as-treated approaches, the IV method provides an unbiased estimate of the average causal effect of treatment among the subgroup of compliers and maintains the nominal type I error rate, but does increase the sample size requirements of the study as the expected proportion of noncompliers increases. Further development of the IV estimator for more general patterns of noncompliance would be useful and would encourage broader application of this method in noninferiority trials.

Original languageEnglish (US)
Pages (from-to)745-758
Number of pages14
JournalJournal of Biopharmaceutical Statistics
Volume20
Issue number4
DOIs
StatePublished - Jul 2010

Fingerprint

Non-inferiority
Noncompliance
Instrumental Variables
Estimator
Binary Outcomes
Causal Effect
Type I Error Rate
Treatment Effects
Sample Size
Statistical method
Categorical or nominal
Biased
Efficacy
Proportion
Subgroup
Evaluate
Alternatives
Requirements
Estimate

Keywords

  • Instrumental variables estimator
  • Noncompliance
  • Noninferiority trial

ASJC Scopus subject areas

  • Pharmacology (medical)
  • Pharmacology
  • Statistics and Probability

Cite this

Using the instrumental variables estimator to analyze noninferiority trials with noncompliance. / Kim, Mimi.

In: Journal of Biopharmaceutical Statistics, Vol. 20, No. 4, 07.2010, p. 745-758.

Research output: Contribution to journalArticle

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