Reuse of data sources to evaluate drug safety signals: When is it appropriate?

Wang, Shirley; Kulldorff, Martin; Glynn, Robert J.; Gagne, Joshua J.; Pottegård, Anton; Rothman, Kenneth J.; Schneeweiß, Sebastian; Walker, Alexander M.

doi:10.1002/pds.4442

Cited by 14 publications

(16 citation statements)

References 14 publications

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“…We acknowledge that validating an algorithm in the same database that was used to develop it may be questionable. The suitability of using an unique data source to generate and evaluate a hypothesis has been previously discussed in the scientific literature, even if the scope was slightly different [29][30][31][32]. Walker AM., and Wang SV.…”

Section: Diagnostic Performance Of the Ms Relapse Algorithmmentioning

confidence: 99%

Intra-database Validation of Case-identifying Algorithms Using Reconstituted Electronic Health Records From Healthcare Claims Data

Thurin

Bosco‐Lévy

Blin

et al. 2021

Preprint

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Background: Diagnosis performances of case-identifying algorithms developed in healthcare database are usually assessed by comparing identified cases with an external data source. When this is not feasible, intra-database validation can present an appropriate alternative.Objectives: To illustrate through two practical examples how to perform intra-database validations of case-identifying algorithms using reconstituted Electronic Health Records (rEHRs).Methods: Patients with 1) multiple sclerosis (MS) relapses and 2) metastatic castration-resistant prostate cancer (mCRPC) were identified in the French nationwide healthcare database (SNDS) using two case-identifying algorithms. A validation study was then conduct to estimate diagnostic performances of these algorithms through the calculation of their positive predictive value (PPV) and negative predictive value (NPV). To that end, anonymized rEHRs were generated based on the overall information captured in the SNDS over time (e.g. procedure, hospital stays, drug dispensing, medical visits) for a random selection of patients identified as cases or non-cases according to the predefined algorithms. For each diseases, an independent validation committee reviewed the rEHRs of 100 cases and 100 non-cases in order to adjudicate on the status of the selected patients (true case/ true non-case), blinded with respect to the result of the corresponding algorithm. Results: Algorithm for relapses identification in MS showed a 95% PPV and 100% NPV and for mCRPC identification, a 97% PPV and 99% NPV. Conclusion: The use of rEHRs to conduct an intra-database validation appears to be a valuable tool to estimate the performances of a case-identifying algorithm and assess its validity, in the absence of alternative.

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Section: Diagnostic Performance Of the Ms Relapse Algorithmmentioning

confidence: 99%

Intra-database Validation of Case-identifying Algorithms Using Reconstituted Electronic Health Records From Healthcare Claims Data

Thurin

Bosco‐Lévy

Blin

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…near-independence between the two) and that this type of data reuse may contribute to better understanding a hypothesis and is distinct from re-using data with the same methods. 55 An alternative, given the significant power one often has access to for a given drug-AE pair, might be that, for a given analysis, a database would be partitioned, one part to be used for signal detection and the other for hypothesis testing studies. 56 More work is needed to assess how either approach could be used in practice.…”

Section: Challenges To Implementing Signal Detection In Rwdmentioning

confidence: 99%

Hypothesis-free signal detection in healthcare databases: finding its value for pharmacovigilance

Bate

Hornbuckle

Juhaeri

et al. 2019

Therapeutic Advances in Drug Safety

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“…To clarify this, the convened initiative reiterated the distinction between exploratory hypothesis generation studies and HETE studies based on a priori hypotheses and analysis planning that was described in the ISPOR/ISPE Special Task Force report [7]. In the transition from using RWD for hypothesis generation to hypothesis evaluation, there will often be a need to refine and/or replicate the results using different methods, evaluation of orthogonal hypotheses, or use of independent data [31]. Earlier exploratory studies may be used to inform analysis planning for independent HETE studies and are not the subject of these recommendations.…”

Section: Defining the Spectrum Of Studies Definition Of "Pre-looks"mentioning

confidence: 99%

Decision letter for "Transparency in real-world evidence (RWE) studies to build confidence for decision making: Reporting RWE research in diabetes"

2019

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Reuse of data sources to evaluate drug safety signals: When is it appropriate?

Cited by 14 publications

References 14 publications

Intra-database Validation of Case-identifying Algorithms Using Reconstituted Electronic Health Records From Healthcare Claims Data

Intra-database Validation of Case-identifying Algorithms Using Reconstituted Electronic Health Records From Healthcare Claims Data

Hypothesis-free signal detection in healthcare databases: finding its value for pharmacovigilance

Decision letter for "Transparency in real-world evidence (RWE) studies to build confidence for decision making: Reporting RWE research in diabetes"

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