Improving traditional intention-to-treat analyses: a new approachPresented in part at the 15th International Conference on Mental Health Services Research organized by the National Institute of Mental Health, Washington, DC, USA, 2 April 2002. This work was completed while Dr Horvitz-Lennon was Instructor, Department of Psychiatry, The Cambridge Hospital, Cambridge, MA, and Harvard Medical School, Boston, MA, USA.
HORVITZ-LENNON, MARCELA; JAMES OʼMALLEY, A.; FRANK, RICHARD G.; NORMAND, SHARON-LISE T.; HORVITZ-LENNON MARCELA; University of Pittsburgh; University of Pittsburgh Medical School; JAMES OʼMALLEY A.; Harvard Medical School; FRANK RICHARD G.; Harvard Medical School; NORMAND SHARON-LISE T.; Harvard Medical School; Harvard School of Public Health
Журнал:
Psychological Medicine
Дата:
2005
Аннотация:
Background. Drop-out, often accompanied by treatment non-compliance, is common in psychiatric trials. Methodologists have criticized the use of a traditional intention-to-treat (ITT) approach in such cases, and have proposed alternative methods. We set out to describe and assess methods for estimation of a treatment effect when the trial is ‘broken’.Method. We describe a stratified method of moments (SMOM) estimator that assesses treatment effects on subjects who are willing to comply with all the treatments under study. A simulation study and a re-analysis of data from an antipsychotics trial are used to compare SMOM to ITT, as-treated, and adequate estimators.Results. The new estimator retains good statistical properties under different levels of non-compliance and drop-out mechanisms. The re-analysis indicates that SMOM yields more precise results.Conclusions. Although the traditional ITT approach provides a valid method to estimate treatment effects, it can be biased in the presence of treatment non-compliance and drop-out. It is critical that researchers move beyond traditional approaches when trials are broken. A key first step is to consider non-compliance and drop-out as two independent phenomena, tracking and reporting rates separately.
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