Covariate Adjustment in Clinical Trials with Nonignorable Missing Data and Noncompliance

• Activity Number: 295
• Type: Topic Contributed
• Date/Time: Wednesday, August 11, 2004 : 8:30 AM to 10:20 AM
• Sponsor: Section on Health Policy Statistics
• Author(s): Douglas Levy*+ and Alistair J. O'Malley and Sharon-Lise T. Normand
• Companies: Harvard University and Harvard Medical School and Harvard Medical School
• Keywords: covariate adjustment ; instrumental variables ; missing data ; noncompliance ; randomized clinical trials

Abstract

Estimating causal effects in psychiatric clinical trials is often complicated by treatment noncompliance and missing outcomes. While new estimators have recently been proposed to address these problems, they do not allow for inclusion of continuous covariates. We propose estimators that adjust for continuous covariates in addition to noncompliance and missing data. Using simulations, we compare mean squared errors for the new estimators with those of previously established estimators. We then illustrate our findings in a study examining the efficacy of clozapine vs. haloperidol in the treatment of refractory schizophrenia. For data with continuous or binary outcomes in the presence of noncompliance, nonignorable missing data, and a covariate effect, the new estimators generally performed better than the previously established estimators. In the clozapine trial, the new estimators gave point and interval estimates similar to established estimators. We recommend the new estimators as they are unbiased even when outcomes are not missing at random and they are more efficient than established estimators in the presence of covariate effects under the widest array of circumstances.