| Title |
Addressing Methodologic Challenges and Minimizing Threats to Validity in Synthesizing Findings from Individual-Level Data Across Longitudinal Randomized Trials
|
|---|---|
| Published in |
Prevention Science, April 2017
|
| DOI | 10.1007/s11121-017-0769-1 |
| Pubmed ID | |
| Authors |
Ahnalee Brincks, Samantha Montag, George W. Howe, Shi Huang, Juned Siddique, Soyeon Ahn, Irwin N. Sandler, Hilda Pantin, C. Hendricks Brown |
| 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. |
X Demographics
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 62 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 62 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 13 | 21% |
| Student > Ph. D. Student | 11 | 18% |
| Student > Doctoral Student | 7 | 11% |
| Professor | 5 | 8% |
| Student > Master | 4 | 6% |
| Other | 11 | 18% |
| Unknown | 11 | 18% |
| Readers by discipline | Count | As % |
|---|---|---|
| Psychology | 19 | 31% |
| Social Sciences | 10 | 16% |
| Medicine and Dentistry | 5 | 8% |
| Mathematics | 2 | 3% |
| Nursing and Health Professions | 2 | 3% |
| Other | 8 | 13% |
| Unknown | 16 | 26% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 22 April 2017.
All research outputs
#18,805,293
of 23,305,591 outputs
Outputs from Prevention Science
#937
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#236,034
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Outputs of similar age from Prevention Science
#22
of 23 outputs
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