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

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.

SUBMITTER: Brincks A

PROVIDER: S-EPMC5651214 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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

Brincks Ahnalee A Montag Samantha S Howe George W GW Huang Shi S Siddique Juned J Ahn Soyeon S Sandler Irwin N IN Pantin Hilda H Brown C Hendricks CH

Prevention science : the official journal of the Society for Prevention Research 20180201 Suppl 1

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. H ...[more]

PMID: 28434055

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Project description:BACKGROUND:Lymphatic filariasis (LF) is endemic in Ghana, and the country has implemented the GPELF strategy since 2000 with significant progress made in the control of the disease. However, after several years of mass drug administration (MDA) implementation, there is persistent transmission in 17 of the 98 endemic districts in the country. Current approaches to surveillance are clearly unable to target untreated individuals and new strategies are required to address the endgame challenges to enhance LF elimination as a public health problem in endemic countries. Community registers are used during MDAs to enumerate community members, their age, gender, house numbers, and records of their participation in MDAs. These MDA registers represent an untapped opportunity to identify and characterize non-compliance and inform appropriate programmatic actions. In this study, we analyzed the data presented in the registers to assess the coverage and individuals' compliance in MDA. METHODS:The information in the MDA registers were assessed to verify the reported coverages obtained from the district. The community registers were obtained from the district health offices and the data from each individual record was entered into a database. A simple questionnaire was used to cross-check the participation of study participants in the 2017 MDA. The questionnaire solicited data on: participation in the 2017 MDA, reasons for not taking part in the MDA, adverse events experienced, what was done for the adverse events, and willingness to participate in subsequent MDAs. RESULTS:We found that 40.1% of the population in the registers missed at least one MDA in 3 years (2016-2018) and the majority of them were between 10-30 years of age. The results of the questionnaire assessment indicated that 13.8% of the respondents did not receive treatment in 2017 for various reasons, the most prominent among them being "absence/travel" (37.1%). Data in the registers were used to verify the treatment coverage for the years 2017 and 2018, and reviewed against the reported coverage obtained from the district. Significant differences between the reported and verified coverages were only observed in four communities. However, the assessment also revealed that the reported coverage was only accurate in 33.3% of cases. CONCLUSIONS:The MDA registers allow for the identification of eligible individuals who were not reached during any MDA round. Thus, the MDA registers could be utilized at the community and programme levels to identify missing and untreated individuals, appropriately address their non-compliance to MDA, and thereby improve MDA coverage in each implementation unit and monitor the progress towards elimination of LF. The challenges observed through the review of the registers also offer opportunities to improve the training given to the community drug distributors.

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Dataset Information

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

Publications

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

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