Bias Mechanisms in Intention-to-Treat Analysis with Data Subject to Treatment Noncompliance and Missing Outcomes

• Published in: Journal of educational and behavioral statistics Vol. 33; no. 2; pp. 158 - 185
• Main Author: Jo, Booil
• Format: Journal Article
• Language: English
• Published: Thousand Oaks, CA SAGE Publications 01.06.2008; American Educational Research Association
• Subjects: Academic Achievement; African Americans; Analytical estimating; Behavior Modification; Behavior Problems; Bias; Causal Models; Child Health; Children; Comparative Analysis; Compliance (Psychology); Computation; Correlation; Equations (Mathematics); Estimation bias; Estimators; Evaluation Methods; Grade 1; Intention; Intervention; Mental Health; Missing data; Modeling; Parent Participation; Research Methodology; Response rates; Responses; Sample mean; Securities and Exchange Commission regulation; Treatment compliance; intention-to-treat analysis; nonresponse; bias mechanism; missing at random; compound exclusion restriction; missing completely at random; noncompliance; instrumental variable approach
• ISSN: 1076-9986; 1935-1054
• DOI: 10.3102/1076998607302635

An analytical approach was employed to compare sensitivity of causal effect estimates with different assumptions on treatment noncompliance and nonresponse behaviors. The core of this approach is to fully clarify bias mechanisms of considered models and to connect these models based on common parameters. Focusing on intention-to-treat analysis, systematic model comparisons are performed on the basis of explicit bias mechanisms and connectivity between models. The method is applied to the Johns Hopkins school intervention trial, where assessment of the intention-to-treat effect on school children's mental health is likely to be affected by assumptions about intervention noncompliance and nonresponse at follow-up assessments. The example calls attention to the importance of focusing on each case in investigating relative sensitivity of causal effect estimates with different identifying assumptions, instead of pursuing a general conclusion that applies to every occasion.