Dataset Information

Improving the quality of biomarker discovery research: the right samples and enough of them.

ABSTRACT: Biomarker discovery research has yielded few biomarkers that validate for clinical use. A contributing factor may be poor study designs.The goal in discovery research is to identify a subset of potentially useful markers from a large set of candidates assayed on case and control samples. We recommend the PRoBE design for selecting samples. We propose sample size calculations that require specifying: (i) a definition for biomarker performance; (ii) the proportion of useful markers the study should identify (Discovery Power); and (iii) the tolerable number of useless markers amongst those identified (False Leads Expected, FLE).We apply the methodology to a study of 9,000 candidate biomarkers for risk of colon cancer recurrence where a useful biomarker has positive predictive value ? 30%. We find that 40 patients with recurrence and 160 without recurrence suffice to filter out 98% of useless markers (2% FLE) while identifying 95% of useful biomarkers (95% Discovery Power). Alternative methods for sample size calculation required more assumptions.Biomarker discovery research should utilize quality biospecimen repositories and include sample sizes that enable markers meeting prespecified performance characteristics for well-defined clinical applications to be identified.The scientific rigor of discovery research should be improved.

SUBMITTER: Pepe MS

PROVIDER: S-EPMC4452419 | biostudies-literature | 2015 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Improving the quality of biomarker discovery research: the right samples and enough of them.

Pepe Margaret S MS Li Christopher I CI Feng Ziding Z

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology 20150402 6

<h4>Background</h4>Biomarker discovery research has yielded few biomarkers that validate for clinical use. A contributing factor may be poor study designs.<h4>Methods</h4>The goal in discovery research is to identify a subset of potentially useful markers from a large set of candidates assayed on case and control samples. We recommend the PRoBE design for selecting samples. We propose sample size calculations that require specifying: (i) a definition for biomarker performance; (ii) the proportio ...[more]

PMID: 25837819

Similar Datasets

Project description:BackgroundBetween-center variation in outcome may offer opportunities to identify variation in quality of care. By intervening on these quality differences, patient outcomes may be improved. However, whether observed differences in outcome reflect the true quality improvement potential is not known for many diseases. Therefore, we aimed to analyze the effect of differences in performance on structure and processes of care, and case-mix on between-center differences in outcome after endovascular treatment (EVT) for ischemic stroke.MethodsIn this observational cohort study, ischemic stroke patients who received EVT between 2014 and 2017 in all 17 Dutch EVT-centers were included. Primary outcome was the modified Rankin Scale, ranging from 0 (no symptoms) to 6 (death), at 90 days. We used random effect proportional odds regression modelling, to analyze the effect of differences in structure indicators (center volume and year of admission), process indicators (time to treatment and use of general anesthesia) and case-mix, by tracking changes in tau2, which represents the amount of between-center variation in outcome.ResultsThree thousand two hundred seventy-nine patients were included. Performance on structure and process indicators varied significantly between EVT-centers (P < 0.001). Predicted probability of good functional outcome (modified Rankin Scale 0-2 at 90 days), which can be interpreted as an overall measure of a center's case-mix, varied significantly between 17 and 50% across centers. The amount of between-center variation (tau2) was estimated at 0.040 in a model only accounting for random variation. This estimate more than doubled after adding case-mix variables (tau2: 0.086) to the model, while a small amount of between-center variation was explained by variation in performance on structure and process indicators (tau2: 0.081 and 0.089, respectively). This indicates that variation in case-mix affects the differences in outcome to a much larger extent.ConclusionsBetween-center variation in outcome of ischemic stroke patients mostly reflects differences in case-mix, rather than differences in structure or process of care. Since the latter two capture the real quality improvement potential, these should be used as indicators for comparing center performance. Especially when a strong association exists between those indicators and outcome, as is the case for time to treatment in ischemic stroke.

Project description:BackgroundFor heterogeneous tissues, such as blood, measurements of gene expression are confounded by relative proportions of cell types involved. Conclusions have to rely on estimation of gene expression signals for homogeneous cell populations, e.g. by applying micro-dissection, fluorescence activated cell sorting, or in-silico deconfounding. We studied feasibility and validity of a non-negative matrix decomposition algorithm using experimental gene expression data for blood and sorted cells from the same donor samples. Our objective was to optimize the algorithm regarding detection of differentially expressed genes and to enable its use for classification in the difficult scenario of reversely regulated genes. This would be of importance for the identification of candidate biomarkers in heterogeneous tissues.ResultsExperimental data and simulation studies involving noise parameters estimated from these data revealed that for valid detection of differential gene expression, quantile normalization and use of non-log data are optimal. We demonstrate the feasibility of predicting proportions of constituting cell types from gene expression data of single samples, as a prerequisite for a deconfounding-based classification approach.Classification cross-validation errors with and without using deconfounding results are reported as well as sample-size dependencies. Implementation of the algorithm, simulation and analysis scripts are available.ConclusionsThe deconfounding algorithm without decorrelation using quantile normalization on non-log data is proposed for biomarkers that are difficult to detect, and for cases where confounding by varying proportions of cell types is the suspected reason. In this case, a deconfounding ranking approach can be used as a powerful alternative to, or complement of, other statistical learning approaches to define candidate biomarkers for molecular diagnosis and prediction in biomedicine, in realistically noisy conditions and with moderate sample sizes.

Project description:How much can we rely on whether what was reported in a study was actually done? Systematic and independent examination of records, documents and processes through audits are a central element of quality management systems. In the context of current concerns about the robustness and reproducibility of experimental biomedical research audits have been suggested as a remedy a number of times. However, audits are resource intense and time consuming, and due to their very nature may be perceived as inquisition. Consequently, there is very little experience or literature on auditing and assessments in the complex preclinical biomedical research environment. To gain some insight into which audit approaches might best suit biomedical research in academia, in this study we have applied a number of them in a typical academic neuroscience environment consisting of twelve research groups with about 100 researchers, students and technicians, utilizing the full gamut of state-of-the-art methodology. Several types of assessments and internal as well as external audits (including the novel format of a peer audit) were systematically explored by a team of quality management specialists. An experimental design template was developed (and is provided here) that takes into account and mitigates difficulties, risks and systematic errors that may occur during the course of a study. All audits were performed according to a pre-defined workflow developed by us. Outcomes were assessed qualitatively. We asked for feedback from participating employees in every final discussion of an audit and documented this in the audit reports. Based on these reports follow-up audits were improved. We conclude that several realistic options for auditing exist which have the potential to improve preclinical biomedical research in academia, and have listed specific recommendations regarding their benefits and provided practical resources for their implementation (e.g. study design and audit templates, audit workflow).

Dataset Information

Improving the quality of biomarker discovery research: the right samples and enough of them.

Publications

Improving the quality of biomarker discovery research: the right samples and enough of them.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets