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. 2018 Feb;19(Suppl 1):60-73.
doi: 10.1007/s11121-017-0769-1.

Addressing Methodologic Challenges and Minimizing Threats to Validity in Synthesizing Findings From Individual-Level Data Across Longitudinal Randomized Trials

Free PMC article

Addressing Methodologic Challenges and Minimizing Threats to Validity in Synthesizing Findings From Individual-Level Data Across Longitudinal Randomized Trials

Ahnalee Brincks et al. Prev Sci. .
Free PMC article


Integrative Data Analysis (IDA) encompasses a collection of methods for data synthesis that pools participant-level data across multiple studies. Compared with single-study analyses, IDA provides larger sample sizes, better representation of participant characteristics, and often increased statistical power. Many of the methods currently available for IDA have focused on examining developmental changes using longitudinal observational studies employing different measures across time and study. However, IDA can also be useful in synthesizing across multiple randomized clinical trials to improve our understanding of the comprehensive effectiveness of interventions, as well as mediators and moderators of those effects. The pooling of data from randomized clinical trials presents a number of methodological challenges, and we discuss ways to examine potential threats to internal and external validity. Using as an illustration a synthesis of 19 randomized clinical trials on the prevention of adolescent depression, we articulate IDA methods that can be used to minimize threats to internal validity, including (1) heterogeneity in the outcome measures across trials, (2) heterogeneity in the follow-up assessments across trials, (3) heterogeneity in the sample characteristics across trials, (4) heterogeneity in the comparison conditions across trials, and (5) heterogeneity in the impact trajectories. We also demonstrate a technique for minimizing threats to external validity in synthesis analysis that may result from non-availability of some trial datasets. The proposed methods rely heavily on latent variable modeling extensions of the latent growth curve model, as well as missing data procedures. The goal is to provide strategies for researchers considering IDA.

Keywords: Harmonization; Integrative data analysis; Participant-level meta-analysis; Synthesis methodology.

Conflict of interest statement

Disclosure of Potential Conflict of Interest

CHB was supported as a consultant on one of these projects (New Beginnings) and received funding support on the Familias Unidas trials as did Brincks, Huang, and Pantin who developed and directed much of research on this intervention. Sandler developed and directed much of the work on two trials in this synthesis.


Figure 1
Figure 1. Second-Order Latent Growth Curve Model
Footnote: Though not pictured, the observed depression measures are indicators of the latent internalizing construct at each time point.
Figure 2
Figure 2. Forest Plot of Effect Sizes for Our 19 Trials and the Greater Universe of Trials
Footnotes: *Effect size where negative effect represents beneficial intervention. **These trials did not specifically target depression with their intervention.

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