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

Details

• Alternative Title:
  Prev Sci
• Personal Author:
  Brincks, Ahnalee ; Montag, Samantha ; Howe, George W. ; Huang, Shi ; Siddique, Juned ; Ahn, Soyeon ; Sandler, Irwin N. ; Pantin, Hilda ; Brown, C. Hendricks
• Description:
  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.
• Subjects:
  Article Harmonization Integrative Data Analysis Participant-level Meta-analysis Synthesis Methodology
• Source:
  Prev Sci. 19(Suppl 1):60-73
• Pubmed ID:
  28434055
• Pubmed Central ID:
  PMC5651214
• Document Type:
  Journal Article
• Funding:
  R01 DA018374/DA/NIDA NIH HHS/United States ; R01-DA13773/DA/NIDA NIH HHS/United States ; R01-DA007029/DA/NIDA NIH HHS/United States ; R01 MH052270/MH/NIMH NIH HHS/United States ; R01 MH064735/MH/NIMH NIH HHS/United States ; K07 CA154862/CA/NCI NIH HHS/United States ; R01-MH048696/MH/NIMH NIH HHS/United States ; R01 DA007031/DA/NIDA NIH HHS/United States ; R01 MH049155/MH/NIMH NIH HHS/United States ; R01 MH040859/MH/NIMH NIH HHS/United States ; K23 MH071320/MH/NIMH NIH HHS/United States ; R01 HL127491/HL/NHLBI NIH HHS/United States ; R01 DA017462/DA/NIDA NIH HHS/United States ; Young Investigator Grant/National Alliance for Research on Schizophrenia and Depression/International ; R03-MH048118/MH/NIMH NIH HHS/United States ; R01 DA025894/DA/NIDA NIH HHS/United States ; R01 MH064503/MH/NIMH NIH HHS/United States ; R01 MH063042/MH/NIMH NIH HHS/United States ; R01 MH064717/MH/NIMH NIH HHS/United States ; UL1 TR000460/TR/NCATS NIH HHS/United States ; K08 MH072918/MH/NIMH NIH HHS/United States ; P30 MH068685/MH/NIMH NIH HHS/United States ; R01 MH064707/MH/NIMH NIH HHS/United States ; U01 PS000671/PS/NCHHSTP CDC HHS/United States ; 961730/William T. Grant Foundation/International
• Volume:
  19
• Collection(s):
  CDC Public Access
• Main Document Checksum:
  urn:sha256:30c63ebaf5aed2886209bc0563ae986d8193695a21b49a1f6723fb9407edfd8b
• Download URL:
  https://stacks.cdc.gov/view/cdc/49316/cdc_49316_DS1.pdf
• File Type:
  [PDF - 526.58 KB ]