A COMPARISON OF IMPUTATION METHODS FOR MISSING DATA IN A MULTI-CENTER RANDOMIZED CLINICAL TRIAL : THE IMPACT STUDY

IMPACT is a multi-center randomized controlled trial of a disease management program for late life depression. Like many longitudinal clinical trials, this study faces problems of item-nonresponse, unit-level non-response and drop out. In this paper, we compare two approaches to handle incomplete data. The first approach is based on hot-deck multiple imputation of missing response, using a modified predicted mean matching method for item-nonresponse (Bell, 1999) and the approximate Bayesian bootstrap for unit-non-response (Lavori, Dawson and Shera 1995). In the second method, we apply multiple imputation based on the multivariate normal model using SAS PROC MI software. The two methods as well as complete-case analysis are compared in a simulation study. Overall both hot-deck multiple imputations performed well with good coverage rates for Monte Carlo means and testing intervention effects. For dichotomous variables, multiple imputation under the multivariate normal model has lower coverage in three variables, which were derived from a highly skewed variable. On the other hand, complete-case analysis showed that 47% varaibles had low coverage in Monte Carlo means, but it had good coverage in the intervention effects because the intervention and the control group have the same direction in bias and biases were cancelled. 1. THE IMPACT STUDY AND MISSING DATA Project IMPACT (Improving Mood: Promoting Access to Collaborative Treatment for Late Life Depression) is a multi-center study to test the effectiveness of a new disease management model for late life depression. 1,801 patients aged 60 and older with major depression or dysthymic disorder were enrolled in the study from 8 organizations. In each clinic, participants are randomly assigned to either the new treatment model or the usual care. Usual care patients could use any primary care or specialty mental health care services available to them as usual. Intervention participants had access to a depression care manager who was supervised by a psychiatrist and a primary care expert for up to 12 months in addition to the usual care component. After 12 months, all study participants continued with their regular primary care provider in care as usual. Telephone surveys were conducted for a two-year period to assess the effects of the new depression treatment model on various health and economic outcomes, as compared to the usual care delivery models in place at each site. This paper uses IMPACT data from baseline, 3 and 6 month follow-ups. Most variables have missing rates of less than 2% at item-level missing data. The unit response rates to the 3 and 6-month follow-up assessments were 90.2% and 87.2% respectively. Missing rates in the control group are higher than in the intervention group (10.1% vs. 8.3% at Month-3 and 12.6% vs. 10.7% at Month-6), while these differences are not significant. The missing data pattern is not monotone in terms that some of patients (2.6%) missed Month-3 assessments and participated at the Month-6 (Table 1). 1521 participants completed both follow-ups with 783 in the intervention group and 738 in the care as usual group. Table 1. Unit Response Pattern Over Waves