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Incorporation of real-world data to a clinical trial: use of external controls.

ABSTRACT: As real-world data (RWD) becomes more available and the methodology for handling RWD evolves, the use of RWD in drug development and drug approval is drawing interest. One of the ways RWD can be applied to a clinical trial is using an external control, a cohort of patients established separately serving as a control group for the clinical trial's treatment group. Although external controls have the possibility of bias as a result of differences in baseline characteristics between the external control and experimental groups, selecting an appropriate data source and ensuring comparability through proper handling of the data can increase the utility of external controls, raising the efficiency of drug development. This article discusses several topics relevant to using external controls in clinical trials, including the definition of external control, the selection of data sources, the strategy ensuring comparability, current regulatory circumstances, and future directions.

SUBMITTER: Kim TE

PROVIDER: S-EPMC9532857 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature

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Incorporation of real-world data to a clinical trial: use of external controls.

Kim Tae-Eun TE Park Sang-In SI Shin Kwang-Hee KH

Translational and clinical pharmacology 20220915 3

As real-world data (RWD) becomes more available and the methodology for handling RWD evolves, the use of RWD in drug development and drug approval is drawing interest. One of the ways RWD can be applied to a clinical trial is using an external control, a cohort of patients established separately serving as a control group for the clinical trial's treatment group. Although external controls have the possibility of bias as a result of differences in baseline characteristics between the external co ...[more]

PMID: 36247745

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Project description:ImportanceAlthough randomized clinical trials are considered to be the criterion standard for generating clinical evidence, the use of real-world evidence to evaluate the efficacy and safety of medical interventions is gaining interest. Whether observational data can be used to address the same clinical questions being answered by traditional clinical trials is still unclear.ObjectiveTo identify the number of clinical trials published in high-impact journals in 2017 that could be feasibly replicated using observational data from insurance claims and/or electronic health records (EHRs).Design, setting, and participantsIn this cross-sectional analysis, PubMed was searched to identify all US-based clinical trials, regardless of randomization, published between January 1, 2017, and December 31, 2017, in the top 7 highest-impact general medical journals of 2017. Trials were excluded if they did not involve human participants, did not use end points that represented clinical outcomes among patients, were not characterized as clinical trials, and had no recruitment sites in the United States.Main outcomes and measuresThe primary outcomes were the number and percentage of trials for which the intervention, indication, trial inclusion and exclusion criteria, and primary end points could be ascertained from insurance claims and/or EHR data.ResultsOf the 220 US-based trials analyzed, 33 (15.0%) could be replicated using observational data because their intervention, indication, inclusion and exclusion criteria, and primary end points could be routinely ascertained from insurance claims and/or EHR data. Of the 220 trials, 86 (39.1%) had an intervention that could be ascertained from insurance claims and/or EHR data. Among the 86 trials, 62 (72.1%) had an indication that could be ascertained. Forty-five (72.6%) of 62 trials had at least 80% of inclusion and exclusion criteria data that could be ascertained. Of these 45 studies, 33 (73.3%) had at least 1 primary end point that could be ascertained.Conclusions and relevanceThis study found that only 15% of the US-based clinical trials published in high-impact journals in 2017 could be feasibly replicated through analysis of administrative claims or EHR data. This finding suggests the potential for real-world evidence to complement clinical trials, both by examining the concordance between randomized experiments and observational studies and by comparing the generalizability of the trial population with the real-world population of interest.

Project description:IntroductionThe first cardiovascular safety trial in the sodium-glucose co-transporter-2 (SGLT2) inhibitor drug class, the Empagliflozin Cardiovascular Outcomes and Mortality in Type 2 Diabetes (EMPA-REG OUTCOME) trial, demonstrated significant cardiovascular risk reduction with empagliflozin. It is currently not clear what proportions of people with type 2 diabetes (T2DM) have the same high cardiovascular risk as those included in the trial, and will therefore be likely to experience the same cardiovascular benefit. We aimed to identify and describe the proportion of people with T2DM from a representative English national population who have the comparable high cardiovascular risk to those included in the EMPA-REG trial.MethodA cross-sectional analysis of cardiovascular risk in people with T2DM and a subgroup prescribed SGLT2 inhibitors. Patients were identified from the Royal College of General Practitioners Research and Surveillance Centre database. Cardiovascular risk factors were identified from electronic patient records.ResultsFrom 1,238,909 patients at 128 GP practices, we identified 60,327 adults with T2DM (mean age 66.1 years, SD 13.9) of whom 55.6% were male. From these 1642 (2.7%) people had been initiated on an SGLT2 inhibitor (mean age 58.1 years, SD 10.4; 58.8% male). In the complete T2DM group only 15.7% (95% CI 15.5-16.0%) had the same high cardiovascular risk as those included in the EMPA-REG trial. In those already initiated on SGLT2 inhibitors this proportion was 11.1% (95% CI 9.8-12.4%). Whilst the proportion was higher in the oldest age groups, in those 70+ years old less than a quarter met the EMPA-REG trial high cardiovascular risk criteria.ConclusionsThe EMPA-REG trial results are applicable only to a small proportion of people with T2DM and a smaller proportion of those currently treated with SGLT2 inhibitors. Additional data are required to identify any cardiovascular benefit in people with lower cardiovascular risk.FundingAstraZeneca.

Project description:Procedural coding presents a taxing challenge for clinicians. However, recent advances in natural language processing offer a promising avenue for developing applications that assist clinicians, thereby alleviating their administrative burdens. This study seeks to create an application capable of predicting procedure codes by analysing clinicians' operative notes, aiming to streamline their workflow and enhance efficiency. We downstreamed an existing and a native German medical BERT model in a secondary use scenario, utilizing already coded surgery notes to model the coding procedure as a multi-label classification task. In comparison to the transformer-based architecture, we were levering the non-contextual model fastText, a convolutional neural network, a support vector machine and logistic regression for a comparative analysis of possible coding performance. About 350,000 notes were used for model adaption. By considering the top five suggested procedure codes from medBERT.de, surgeryBERT.at, fastText, a convolutional neural network, a support vector machine and a logistic regression, the mean average precision achieved was 0.880, 0.867, 0.870, 0.851, 0.870 and 0.805 respectively. Support vector machines performed better for surgery reports with a sequence length greater than 512, achieving a mean average precision of 0.872 in comparison to 0.840 for fastText, 0.837 for medBERT.de and 0.820 for surgeryBERT.at. A prototypical front-end application for coding support was additionally implemented. The problem of predicting procedure codes from a given operative report can be successfully modelled as a multi-label classification task, with a promising performance. Support vector machines as a classical machine learning method outperformed the non-contextual fastText approach. FastText with less demanding hardware resources has reached a similar performance to BERT-based models and has shown to be more suitable for explaining the predictions efficiently.

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Incorporation of real-world data to a clinical trial: use of external controls.

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Incorporation of real-world data to a clinical trial: use of external controls.

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