Main effects analysis in clinical research: statistical guidelines for disaggregating treatment groups

Area of Science:

• Statistics in Social Sciences
• Quantitative Research Methods
• Treatment Outcome Analysis

Background:

• Treatment outcome research commonly uses analysis of variance (ANOVA) for comparing treatment effectiveness.
• Existing methods, like Bryk and Raudenbush's (1988) strategy, address heterogeneity of variance for group disaggregation.
• The need to disaggregate treatment groups may exist even when variance assumptions are met.

Purpose of the Study:

• To explore the statistical justification for disaggregating main effects in treatment outcome research.
• To demonstrate how dependent measure reliability influences the significance of disaggregation.
• To provide criteria for undertaking disaggregation to identify significant interactions.

Main Methods:

• Proposed a method to partition residual variance into systematic (individual differences) and random error components.
• Introduced an F test to evaluate the ratio of these variance components.
• Defined conditions based on statistical significance and the proportion of systematic variance for disaggregation.

Main Results:

• The statistical significance of disaggregation is directly related to the reliability of the dependent measure.
• A significant F test, coupled with a substantial proportion of within-cell systematic variance, supports disaggregation.
• This approach allows for the identification of key individual difference or treatment interaction variables.

Conclusions:

• Disaggregation of main effects is a valuable strategy beyond addressing heterogeneity of variance.
• Reliability of the outcome measure is crucial for detecting significant treatment differences through disaggregation.
• Undertaking disaggregation when conditions are met facilitates the discovery of meaningful interactions in treatment research.