Volume 39 · Number 7
JULY 2009
The Role of ANCOVA in Analyzing Experimental Data
By Thomas R. Belin, PhD; Sharon-Lise T. Normand, PhD
The randomized controlled trial (RCT) is generally viewed as the gold standard for health science research. However, an implication of random treatment assignment is that there will occasionally be substantial discrepancies in distributions of background characteristics across treatment arms. When these background characteristics are related to research outcomes, imbalances in the distribution of characteristics across treatment arms can mislead investigators and can induce erroneous inferences about connections between treatments and outcomes. Such imbalances can induce spurious association or can mask actual association. The analysis of covariance (ANCOVA) is a technique that can be used to help answer scientific questions that arise in this context.
Design and Analysis of Longitudinal Studies, Part 2
Of the Terrible Doubt of Appearances
Jan Fawcett, MD
Design and Analysis of Longitudinal Studies, Part 2
Robert D. Gibbons, PhD
A 54-year-old Man with History of PTSD
Violeta O. Tan, MD;
Natalie L. Rasgon, PhD
Where Do We Go Wrong in Assessing Risk Factors, Diagnostic and Prognostic Tests? The Problems of Two-by-two Association
Helena Chmura Kraemer, PhD;
Robert D. Gibbons, PhD
Using Non-experimental Data to Estimate Treatment Effects
Elizabeth A. Stuart, PhD;
Sue M. Marcus, PhD;
Marcela V. Horvitz-Lennon, MD;
Robert D. Gibbons, PhD;
Sharon-Lise T. Normand, PhD;
C. Hendricks Brown, PhD
Statistical Approaches to Modeling Multiple Outcomes in Psychiatric Studies
Armando Teixeira-Pinto, PhD;
Juned Siddique, DrPH;
Robert D. Gibbons, PhD;
Sharon-Lise T. Normand, PhD
Why Does the Randomized Clinical Trial Methodology So Often Mislead Clinical Decision Making? Focus on Moderators and Mediators of Treatment
Helena Chmura Kraemer, PhD;
Robert D. Gibbons, PhD
