Abstract

Randomized trials of diverse behavioral interventions routinely observe declines in problem behavior among control subjects that cannot be attributed to flawed experimental design. Explanations for this pattern of effects generally focus on assessment reactivity. The research activities and procedures themselves may constitute an intervention of sorts, and control subjects may be better characterized as an "intervention lite" group rather than an untreated control group. Such conditions may lead to serious underestimates of the efficacy of behavioral interventions. One possible remedy for this problem is the use of "designed missingness" for collecting baseline or pretest data. This strategy intentionally collects data on only a subset of indicators and uses imputation techniques to address the resulting structured missingness. In this study, pretest questionnaires used in the evaluation of a large suicide prevention intervention were modified to reduce the amount of behavioral information collected at baseline among control subjects. Four versions of the pretest questionnaire were used: one full version and three truncated versions, each of which included a different subset of items from the full version. The completion rate increased from 12% for pretests to 19% for posttests. Additionally, treatment effects after imputation were larger for subjects who were assigned the truncated versions of the pretest than for subjects who were assigned the nontruncated questionnaire at pretest. Although more research is needed on this subject to establish optimal questionnaire configurations and study designs, "designed missingness" methods have the potential to improve the assessment of treatment effects in a broad range of efficacy studies.

MeSH Keywords: Questionnaires, statistical models

Keywords: Missing data, multiple imputation, questionnaire design, statistical models, suicide, randomized controlled trial