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Independent validation test of the vote-counting strategy used to rank biomarkers from published studies.

ABSTRACT: Vote counting is frequently used in meta-analyses to rank biomarker candidates, but to our knowledge, there have been no independent assessments of its validity. Here, we used predictions from a recent meta-analysis to determine how well number of supporting studies, combined sample size and mean fold change performed as vote-counting strategy criteria.Fifty miRNAs previously ranked for their ability to distinguish lung cancer tissue from normal were assayed by RT-qPCR using 45 paired tumor-normal samples.Number of supporting studies predicted biomarker performance (p = 0.0006; r = 0.44), but sample size and fold change did not (p > 0.2).Despite limitations, counting the number supporting studies appears to be an effective criterion for ranking biomarkers. Predictions based on sample size and fold change provided little added value. External validation studies should be conducted to establish the performance characteristics of strategies used to rank biomarkers.

SUBMITTER: Rikke BA

PROVIDER: S-EPMC4770796 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Independent validation test of the vote-counting strategy used to rank biomarkers from published studies.

Rikke Brad A BA Wynes Murry W MW Rozeboom Leslie M LM Barón Anna E AE Hirsch Fred R FR

Biomarkers in medicine 20150730 8

<h4>Aim</h4>Vote counting is frequently used in meta-analyses to rank biomarker candidates, but to our knowledge, there have been no independent assessments of its validity. Here, we used predictions from a recent meta-analysis to determine how well number of supporting studies, combined sample size and mean fold change performed as vote-counting strategy criteria.<h4>Materials & methods</h4>Fifty miRNAs previously ranked for their ability to distinguish lung cancer tissue from normal were assay ...[more]

PMID: 26223535

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Project description:BackgroundPatient data is considered particularly sensitive personal data. Privacy regulations strictly govern the use of patient data and restrict their exchange. However, medical research can benefit from multicentric studies in which patient data from different institutions are pooled and evaluated together. Thus, the goals of data utilization and data protection are in conflict. Secure multiparty computation (SMPC) solves this conflict because it allows direct computation on distributed proprietary data-held by different data owners-in a secure way without exchanging private data.ObjectiveThe objective of this work was to provide a proof-of-principle of secure and privacy-preserving multicentric computation by SMPC with real-patient data over the free internet. A privacy-preserving log-rank test for the Kaplan-Meier estimator was implemented and tested in both an experimental setting and a real-world setting between two university hospitals.MethodsThe domain of survival analysis is particularly relevant in clinical research. For the Kaplan-Meier estimator, we provided a secure version of the log-rank test. It was based on the SMPC realization SPDZ and implemented via the FRESCO framework in Java. The complexity of the algorithm was explored both for synthetic data and for real-patient data in a proof-of-principle over the internet between two clinical institutions located in Munich and Berlin, Germany.ResultsWe obtained a functional realization of an SMPC-based log-rank evaluation. This implementation was assessed with respect to performance and scaling behavior. We showed that network latency strongly influences execution time of our solution. Furthermore, we identified a lower bound of 2 Mbit/s for the transmission rate that has to be fulfilled for unimpeded communication. In contrast, performance of the participating parties have comparatively low influence on execution speed, since the peer-side processing is parallelized and the computational time only constitutes 30% to 50% even with optimal network settings. In the real-world setting, our computation between three parties over the internet, processing 100 items each, took approximately 20 minutes.ConclusionsWe showed that SMPC is applicable in the medical domain. A secure version of commonly used evaluation methods for clinical studies is possible with current implementations of SMPC. Furthermore, we infer that its application is practically feasible in terms of execution time.

Dataset Information

Independent validation test of the vote-counting strategy used to rank biomarkers from published studies.

Publications

Independent validation test of the vote-counting strategy used to rank biomarkers from published studies.

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