Abstract
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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1-14 |
| Number of pages | 14 |
| Journal | Prevention Science |
| DOIs | |
| State | Accepted/In press - Apr 22 2017 |
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Keywords
- Harmonization
- Integrative data analysis
- Participant-level meta-analysis
- Synthesis methodology
ASJC Scopus subject areas
- Public Health, Environmental and Occupational Health
Cite this
Addressing Methodologic Challenges and Minimizing Threats to Validity in Synthesizing Findings from Individual-Level Data Across Longitudinal Randomized Trials. / Brincks, Ahnalee; Montag, Samantha; Howe, George W.; Huang, Shi; Siddique, Juned; Ahn, Soyeon; Sandler, Irwin N.; Pantin, Hilda; Brown, C. Hendricks.
In: Prevention Science, 22.04.2017, p. 1-14.Research output: Contribution to journal › Article
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TY - JOUR
T1 - Addressing Methodologic Challenges and Minimizing Threats to Validity in Synthesizing Findings from Individual-Level Data Across Longitudinal Randomized Trials
AU - Brincks, Ahnalee
AU - Montag, Samantha
AU - Howe, George W.
AU - Huang, Shi
AU - Siddique, Juned
AU - Ahn, Soyeon
AU - Sandler, Irwin N.
AU - Pantin, Hilda
AU - Brown, C. Hendricks
PY - 2017/4/22
Y1 - 2017/4/22
N2 - 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.
AB - 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.
KW - Harmonization
KW - Integrative data analysis
KW - Participant-level meta-analysis
KW - Synthesis methodology
UR - http://www.scopus.com/inward/record.url?scp=85018848570&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85018848570&partnerID=8YFLogxK
U2 - 10.1007/s11121-017-0769-1
DO - 10.1007/s11121-017-0769-1
M3 - Article
C2 - 28434055
AN - SCOPUS:85018848570
SP - 1
EP - 14
JO - Prevention Science
JF - Prevention Science
SN - 1389-4986
ER -