Structured Abstract

Background

Methods

We published a protocol describing detailed methods for our study.¹² What follows is a summary of our methods.

Effect of Multiple Data Sources on Meta-analyses

Eligibility Criteria for a Trial's Inclusion in Our Study

Our published protocol includes detailed inclusion and exclusion criteria for each case study.¹² Table 1 describes the eligibility criteria for participants, interventions, outcomes, comparisons, and time points.

Table 1

Eligibility Criteria for the Case Studies.

Search Methods for Identification of Trials

We conducted electronic and additional searches to identify trials as described in our study protocol.¹² We searched for the following types of sources, which are listed by their approximate level of expected detail (ie, less detail to more detail):

1. Trial registrations without results posted on trial registries (eg, www.ClinicalTrials.gov)
2. Short reports (eg, conference abstracts, posters)
3. Trial registrations without with summary data posted on trial registries
4. Peer reviewed journal articles
5. Dissertations (eg, Master's or doctoral theses)
6. Unpublished manuscripts and reports (eg, reports to funders, clinical study reports)
7. Information from regulators (ie, FDA medical and statistical reviews)
8. Individual patient data

Selection of Trials

Two investigators independently screened titles and abstracts to determine which were eligible for inclusion in the review, as described in our study protocol.¹² We independently screened full text reports of all potentially relevant trials and resolved disagreements through consensus and by asking a third investigator as needed.

Data Collection

We extracted information about trial design as described in our study protocol.¹² We recorded all outcomes within the prespecified outcome domains, including the 5 elements described in Table 2.^26,27

Table 2

The 5 Elements of an Outcome.

For sources of aggregate data

For trials associated with multiple sources (eg, journal articles, clinical study reports), we extracted data from each source, including trial design, trial quality (ie, risk of bias), outcomes, and results. We developed data collection forms and entered data in the Systematic Review Data Repository (SRDR).^28,29

From single sources about more than 1 trial, we extracted information about each individual trial; we did not extract results from multiple trials when the data had already been combined (ie, we were unable to separate data for an individual trial from the combined data).

We extracted results from figures and tables that included numerical data (eg, counts and means included in graphs), including statistical significance (eg, P value above or below .05). We did not meta-analyze information that was presented only graphically. We extracted text from sources describing results as “statistically significant” or “not significant.”

Two investigators extracted data independently and resolved discrepancies by consensus and through discussion with a third investigator if necessary. We used Stata 14³¹ to reconcile the records and to produce a final data set that we will share publicly through SRDR at the end of the study.

For individual patient data

We received individual participant data for gabapentin trials from Pfizer as Microsoft Access database files. When codebooks were not available, we compared the individual participant data with case report forms (which often showed how and when data were recorded) and statistical analysis plans (which often showed how data were coded and analyzed) to identify the variables contained in the individual participant data.

We did not receive any data sets about quetiapine from AstraZeneca. In 1 quetiapine clinical study report, we identified an appendix with individual participant data, which we extracted using ABBYY FineReader version 11.³²

Results

Manuscripts describing our analyses and findings are currently under review. What follows is a summary of our findings.

Effects of Adding and Replacing Data From Multiple Data Sources on the Results of Meta-analyses

Results of the Search

We identified 21 eligible trials of gabapentin (from 1997 to 2013) and 7 eligible trials of quetiapine (from 2003 to 2014). As Tables 3 and 4 show, most of these trials were associated with more than 1 source (71% and 100%, respectively). We identified 74 (gabapentin) and 50 (quetiapine) reports and extracted information from them. Journal articles and conference abstracts were the most common reports. We identified 1 gabapentin trial only through a conference abstract, and 1 gabapentin trial only on ClinicalTrials.gov (www.clinicaltrials.gov).

Table 3

Multiple Sources of Trials of Gabapentin for Neuropathic Pain.

Table 4

Multiple Sources of Trials of Quetiapine for Bipolar Depression.

FDA reviews included information about 2 trials of gabapentin for postherpetic neuralgia. FDA reviews available online did not include trials of quetiapine for bipolar depression, which was added to the label in a supplemental new drug application.

We identified few registrations for trials of gabapentin on ClinicalTrials.gov and other registries, none of the trials conducted by the manufacturer (Pfizer). In contrast, all eligible trials of quetiapine were registered.

We received clinical study reports and individual participant data for 6 gabapentin trials as a result of the litigation we describe above^7,41; we received no additional information from Pfizer as a result of our requests.

By searching online, we identified clinical study reports for 2 quetiapine trials that were released during litigation (http://psychrights.org/); 1 of the clinical study reports included individual participant data in an appendix. AstraZeneca declined our request for more information about quetiapine trials, and our correspondence with AstraZenenca has been published.⁴² Thus, in our study, all nonpublic sources were previously nonpublic, since they were available to us online from litigation sources (ie, they were disclosed during patient lawsuits), and we received no additional sources from the trials' sponsors.

In summary, we found multiple sources for most trials, the largest contributors to multiple reports being journal articles and conference abstracts.

Completeness of Information

Trial design (eg, participant, intervention characteristics) was best described in clinical study reports and journal articles. For example, conference abstracts were incomplete and sometimes contradicted other sources. Five trial registrations submitted to ClinicalTrials.gov by AstraZeneca described a single intervention, “quetiapine fumarate,” while other sources indicated that 2 groups received quetiapine at different doses (ie, 300 mg and 600 mg).

Trial quality (defined as our assessment of the risk of bias for each trial) was often unclear in the sources we identified (see Tables 5 and 6). When we located enough information to assess risk of bias (usually in a journal article or clinical study report), that information almost always led from an unclear to a low risk of bias rating. The only exception was information about missing data, where identification of additional information led from an unclear to a high risk of bias rating for trials with large amounts of missing data.

Table 5

Trial Quality (Risk of Bias) for Sources of Gabapentin for Neuropathic Pain Trials.

Table 6

Trial Quality (Risk of Bias) by Source of Quetiapine for Bipolar Depression Trials.

Statistical information about many outcomes described in journal articles and short reports did not include sufficient detail for results to be included in meta-analysis. In contrast, data in clinical study reports were usually complete and could almost always be used for analysis.

For these reasons, clinical study reports—when they were available—were the most complete sources of information about methods and results.

Locating Patient-Centered Outcomes in Multiple Data Sources

Across all sources for all gabapentin trials, we located 4 of 5 prespecified outcome domains as described in section “Patient-centered outcomes identified through existing sources and stakeholder coinvestigators” (Tables 7 and 8). Across all sources for all quetiapine trials, we identified 7 of 8 prespecified domains. Quetiapine sources included about 6 (median) outcome domains, and they were also highly variable (range, 2-6 domains per source). To our knowledge, none of the trials measured the prespecified outcome domains for pain interference (gabapentin) or psychiatric hospitalization (quetiapine).

Table 7

Number of Prespecified Outcome Domains in Each Source of Gabapentin for Neuropathic Pain Trials.

Table 8

Number of Prespecified Outcome Domains in Each Source of Quetiapine for Bipolar Depression Pain Trials.

Conference abstracts and other short reports included little useful information about PCOs. Journal articles contained more PCOs than did other public sources, although clinical study reports were consistently the best source of aggregate information about PCOs. No outcome measures appeared in public sources that did not also appear in corresponding clinical study reports; however, we identified methods of aggregation in public sources (ie, journal articles and an FDA review) for gabapentin trials that were not included in clinical study reports.

In addition to the prespecified outcomes related to treatment benefits, clinical study reports and individual participant data described hundreds of harms that were not included in public sources.¹⁶⁷

Multiple Outcome Definitions

As noted earlier, it is also possible for a single prespecified outcome domain to be assessed in multiple ways (ie, by varying the measure, metric, and method of aggregation). For this reason, an outcome might appear to be reported as described in the protocol when in fact there were multiple opportunities for changing the associated results (Figure 1).

Figure 1

Number of Outcomes in a Trial Is a Function of the Number of Definitions of Each of the 5 Elements.

For each case study, we identified hundreds of outcomes for the prespecified domains (Figure 2). By varying 4 elements, gabapentin RCTs reported 214 outcomes and quetiapine RCTs reported 81 outcomes. For pain intensity and depression, the most common outcome domains for gabapentin and quetiapine RCTs, we found 119 and 44 outcomes, respectively. Multiple methods of analysis also contributed to multiple results for the same defined outcome. For these reasons, many meta-analyses were possible for each outcome domain using the same included trials.¹⁶⁷

Figure 2

Number of Outcomes Observed Increased as a Consequence of Multiple Definitions of the Elements.

Impact of Adding or Replacing Multiple Data Sources on Pooled Effect Sizes and Precision

For each case study, we conducted a series of meta-analyses in which we added or replaced data from each of the multiple sources for a single outcome domain, measure, and time point to evaluate the effect of adding or replacing data. In the presence of reporting bias, whereby negative or null results are reported in nonpublic sources but not in public sources, we would expect the addition of nonpublic sources to change the average effects estimated in meta-analyses. This change would lead to a meta-analytic effect estimate closer to the null.

Not all trials could contribute data to a meta-analysis (Tables 9 and 10). Of the 21 gabapentin and 7 quetiapine trials, 9 (43%) and 4 (57%), respectively, could not be included in an analysis of pain intensity or depression using continuous data (eg, the mean) because no source for those trials included meta-analyzable data (see “Series of Meta-analyses”).

Table 9

Series of Meta-analyses and the Effect of Adding or Replacing Data Sources on the Pooled Estimate of Pain Intensity in Trials of Gabapentin for Neuropathic Pain (SMD [95% CI]).

Table 10

Series of Meta-analyses and the Effect of Adding or Replacing Data Sources on the Pooled Estimate of Depression in Trials of Quetiapine Bipolar Depression (SMD [95% CI]).

When restricted to the most common outcome defined as the same outcome domain, measure, and time point, results in both case studies were largely consistent across sources. For gabapentin, including data from previously nonpublic sources (ie, clinical study reports and individual participant data) reduced the magnitude of the effect estimate; adding additional sources did not affect the statistical significance of the treatment effect. In our series of meta-analyses, the effect of gabapentin on pain intensity ranged from an SMD of −0.46 (95% CI −0.64 to −0.28) to an SMD of −0.31 (95% CI −0.47 to −0.15). For quetiapine, there were no meaningful differences as a result of adding and replacing data from multiple sources. The effect of quetiapine on depression ranged from an SMD of −0.42 (95% CI −0.62 to −0.22) to an SMD of −0.44 (95% CI −0.63 to −0.26).

Resources Required to Use Multiple Data Sources in Systematic Reviews and Meta-analyses

Available guidance for performing systematic reviews (eg, from the Cochrane Collaboration and the National Academy of Medicine [formerly the Institute of Medicine]) suggests that reviewers should use all data sources for clinical trials.^3,4 Identifying and extracting data from multiple data sources was resource intensive and required many skills, and our findings suggest that existing guidance could be improved to help systematic reviewers use multiple data sources efficiently (Table 11).

Table 11

Main Findings From MUDS and our Recommendations for Application of the Findings.

In general, we were able to extract information from conference abstracts quickly; journal articles took several hours to extract.

Clinical study reports, which included thousands of pages of appendices and hundreds of outcomes and analyses, required dozens of hours. Two extractors each spent at least 5 8-hour days extracting data from each clinical study report.

Because we did not receive codebooks for individual participant data, each data set was a unique puzzle we had to solve even before we could synthesize the data. Members of our team spent months developing codebooks and creating a common data format to combine data across trials. Even with this investment, there were some variables we could not identify with certainty.

Guidance for Using Multiple Data Sources in Systematic Reviews

As a final step in our investigation, we developed guidance for systematic reviewers (Table 12). For the open science movement to succeed, systematic reviewers need to make efficient use of evidence from clinical trials, including previously nonpublic information. Although it would have been desirable to identify a best source of information for clinical trials, we did not find a single source or combination of sources that provided complete information about trial design, trial quality, outcomes, and results. Our recommendations incorporate the main findings for each step in the review process.

Table 12

Recommendations for Systematic Reviews Using Multiple Data Sources.

Discussion

Conclusions

For systematic reviewers and meta-analysts, using multiple data sources for the same trial is time consuming and comes with both rewards (eg, more complete data) and challenges (eg, the need to resolve differences among contradictory sources). We found no advantage in using conference abstracts except for identifying the existence of unregistered and otherwise unpublished trials. Trial registrations in these case studies were relatively old (eg, we only found information in ClinicalTrials.gov, not other registries), and they provided less information than users might find in more recent registrations. Systematic reviewers should anticipate the vast amount of information that could be extracted from nonpublic sources and plan accordingly. Further research is needed to determine if the results of these case studies are representative of all trials and to determine how multiple data sources might affect meta-analyses of potential harms.

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Acknowledgments

Suggested citation:

Dickersin K, Mayo-Wilson E, Li T. (2018). The Benefits and Challenges of Using Multiple Sources of Information about Clinical Trials. Patient-Centered Outcomes Research Institute (PCORI). https://doi.org/10.25302/3.2018.ME.13035785

Disclaimer

The [views, statements, opinions] presented in this report are solely the responsibility of the author(s) and do not necessarily represent the views of the Patient-Centered Outcomes Research Institute® (PCORI®), its Board of Governors or Methodology Committee.