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. 2019 Oct:114:84-94.
doi: 10.1016/j.jclinepi.2019.06.010. Epub 2019 Jun 18.

Rapid network meta-analysis using data from Food and Drug Administration approval packages is feasible but with limitations

Lin Wang  1 , Benjamin Rouse  1 , Arielle Marks-Anglin  2 , Rui Duan  2 , Qiyuan Shi  1 , Kevin Quach  1 , Yong Chen  2 , Christopher Cameron  3 , Christopher H Schmid  4 , Tianjing Li  5
Affiliations

Rapid network meta-analysis using data from Food and Drug Administration approval packages is feasible but with limitations

Lin Wang et al. J Clin Epidemiol. 2019 Oct.

Abstract

Objective: To test rapid approaches that use Drugs@FDA (a public database of approved drugs) and ClinicalTrials.gov to identify trials and to compare these two sources with bibliographic databases as an evidence base for a systematic review and network meta-analysis (NMA).

Study design and setting: We searched bibliographic databases, Drugs@FDA, and ClinicalTrials.gov for eligible trials on first-line glaucoma medications. We extracted data, assessed risk of bias, and examined the completeness and consistency of information provided by different sources. We fitted random-effects NMA models separately for trials identified from each source and for all unique trials from three sources.

Results: We identified 138 unique trials including 29,394 participants on 15 first-line glaucoma medications. For a given trial, information reported was sometimes inconsistent across data sources. Journal articles provided the most information needed for a systematic review; trial registrations provided the least. Compared to an NMA including all unique trials, we were able to generate reasonably precise effect estimates and similar relative rankings for available interventions using trials from Drugs@FDA alone (but not ClinicalTrials.gov).

Conclusions: A rapid NMA approach using data from Drugs@FDA is feasible but has its own limitations. Reporting of trial design and results can be improved in both the drug approval packages and on ClinicalTrials.gov.

Keywords: Clinical trial; ClinicalTrials.gov; Comparative-effectiveness research; Drugs@FDA; Network meta-analysis; Rapid systematic review.

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Conflict of interest statement

Declaration of interest: None

Figures

Fig. 1.
Fig. 1.. Identification of trials
(A)Identification of trials from bibliographic database. (B) Identification of trials from Drugs@FDA. (C) Identification of trials from ClinicalTrials.gov.
Fig. 1.
Fig. 1.. Identification of trials
(A)Identification of trials from bibliographic database. (B) Identification of trials from Drugs@FDA. (C) Identification of trials from ClinicalTrials.gov.
Fig. 2.
Fig. 2.. The extent of overlap of trials among bibliographic database, Drugs@FDA, and ClinicalTrials.gov
(A) All included trials. (B) Trials with sufficient data for meta-analysis. n, number of trials. N, number of participants.
Fig. 2.
Fig. 2.. The extent of overlap of trials among bibliographic database, Drugs@FDA, and ClinicalTrials.gov
(A) All included trials. (B) Trials with sufficient data for meta-analysis. n, number of trials. N, number of participants.
Fig. 3.
Fig. 3.. Network graphs
(A) All unique trials. (B) Published trials. (C) FDA trials. (D) ClinicalTrials.gov trials. (E) Published trials without overlaps. Across graphs, the node size is scaled by the percentage of total participants in a network, the edge width is scaled by the percentage of total trials in a network. Within a graph, the node size is proportional to the number of participants randomized to the medication, the line thickness is proportional to the number of trials directly comparing the connected two medications.
Fig. 3.
Fig. 3.. Network graphs
(A) All unique trials. (B) Published trials. (C) FDA trials. (D) ClinicalTrials.gov trials. (E) Published trials without overlaps. Across graphs, the node size is scaled by the percentage of total participants in a network, the edge width is scaled by the percentage of total trials in a network. Within a graph, the node size is proportional to the number of participants randomized to the medication, the line thickness is proportional to the number of trials directly comparing the connected two medications.
Fig. 3.
Fig. 3.. Network graphs
(A) All unique trials. (B) Published trials. (C) FDA trials. (D) ClinicalTrials.gov trials. (E) Published trials without overlaps. Across graphs, the node size is scaled by the percentage of total participants in a network, the edge width is scaled by the percentage of total trials in a network. Within a graph, the node size is proportional to the number of participants randomized to the medication, the line thickness is proportional to the number of trials directly comparing the connected two medications.
Fig. 4.
Fig. 4.. Estimated mean difference in IOP at 3 months derived from network meta-analyses (relative to timolol)
Abbreviations: IOP, intraocular pressure. Medications with effect estimates (markers) and confidence intervals (lines) on the right are better than timolol in reducing IOP at 3 months. Medications with effect estimates (markers) and confidence intervals (lines) on the left are worse than timolol in reducing IOP at 3 months.
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