Omics and Biomarker Study Design
Smarter Studies: Although a careful consideration of study design upfront is much cheaper than subsequent failed or suboptimal experiments, it is probably the most important neglected factor in numerous post-genomic studies.
"To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: He may be able to say what the experiment died of."
"As new ‘-omics’ fields are explored to assess molecular markers for cancer, bias will increasingly be recognized as the most important ‘threat to validity’ that must be addressed in the design, conduct and interpretation of such research."
Clear guidelines only begin to emerge, but there is a wealth of prior knowledge from traditional marker studies or when considering different omics fields jointly. Numerous guidelines and standards are given here as a help for considering the crucial factors for your own experiments. Regardless if developing companion diagnostics in a regulatory setting or performing an academic research study, issues like (pre-)analytical validity, randomisation, bias and confounding factors should be essential considerations before beginning any study.
Introductions / Tutorials
- Bias as a threat to the validity of cancer molecular-marker research by D.F. Ransohoff
- Sources of Bias in Specimens for Research About Molecular Markers for Cancer by D.F. Ransohoff and M.L. Gourlay
- Do's and Don'ts in Microarray Research Article by Richard Simon
- A Perspective on Challenges and Issues in Biomarker Development and Drug and Biomarker Codevelopment by S.E. Taube et al.
- Evolution of Translational Omics: Lessons Learned and the Path Forward from Institute of Medicine
- Protein-Based Multiplex Assays: A Workshop Report by the NCI-FDA Interagency Oncology Task Force
- Statistical Design of Quantitative Mass Spectrometry-Based Proteomic Experiments by A.L. Oberg and O. Vitek
- Smarter Studies: Designing Efficient and Rigorous Molecular Diagnostics and Biomarker Studies Course DVD by T.P. Speed, J. von Frese and M. Palmer
Phases of Biomarker Research
- Phases of Biomarker Development for Early Detection of Cancer by M.S. Pepe et al.
- Roles of Phases, Guidelines, and Study Design by D.F. Ransohoff
- Proposals for a Phased Evaluation of Medical Tests by J.G. Lijmer et al.
Negative Examples
- Cancer Biomarkers: Can We Turn Recent Failures into Success? by E.P. Diamandis
- Lessons From Controversy: Ovarian Cancer Screening and Serum Proteomics by D.F. Ransohoff
- Reproducibility of SELDI-TOF protein patterns in serum: comparing datasets from different experiments by K.A. Baggerly et al.
- SELDI-TOF MS Whole Serum Proteomic Profiling with IMAC Surface Does Not Reliably Detect Prostate Cancer by the Early Detection Research Network
- Confounding Effects in "A Six-Gene Signature Predicting Breast Cancer Lung Metastasis" by A.C. Culhane and J. Quackenbush
- Stop Ignoring Experimental Design (in GWAS studies) by C. Lambert
Observational Studies / Clinical Epidemiology
- Design of Observational Studies Textbook by Paul R. Rosenbaum
- Epidemiology, cancer genetics and microarrays: making correct inferences, using appropriate designs by J.D. Potter
- Matching methods for observational microarray studies by R. Heller et al.
- An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies by P.C. Austin
Bias / Confounding Factors: Examples
- Sources of variation in baseline gene expression levels from toxicogenomics study control animals by M.J. Boedigheimer et al.
- Assessment of Analytical Reproducibility of 1H NMR Spectroscopy Based Metabonomics by M.E. Dumas et al.
- Impact of Analytical Bias in Metabonomic Studies of Human Blood Serum and Plasma by O. Teahan et al.
- Analytical Validation of Serum Proteomic Profiling for Diagnosis of Prostate Cancer: Sources of Sample Bias by the Early Detection Research Network
- Patient characteristics and stratification for metastatic colorectal cancer by H. Sorby et al.
- Bias in Analytical Research by D.L. Sackett
Bias Correction / Removal of Batch Effects
- Adjusting for Covariates in Studies of Diagnostic, Screening, or Prognostic Markers by H. Janes and M.S. Pepe
- GlobalANCOVA: exploration and assessment of gene group effects by M. Hummel, R. Meister and U. Mansmann
- Capturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis by J.T. Leek and J.D. Storey
- Adjusting batch effects in microarray expression data using empirical Bayes methods by W. Evan Johnson, C. Li and A. Rabinovic with R code
- Using Control Genes to Correct for Unwanted Variation in Microarray Data by J.A. Gagnon-Bartsch and T.P. Speed
- A comparison of batch effect removal methods within the MAQC2 project
Biomarkers in Clinical Studies
- Clinical trial designs for evaluating the medical utility of prognostic and predictive biomarkers in oncology by R. Simon
- Clinical Trial Designs for Predictive Biomarker Validation by Sumithra J. Mandrekar and Daniel J. Sargent
- Predictive biomarker validation in practice: lessons from real trials by Sumithra J. Mandrekar and Daniel J. Sargent
- Randomized Clinical Trials With Biomarkers: Design Issues by B. Freidlin, L.M. McShane and E.L. Korn
- Designing prospective clinical pharmacogenomic (PG) trials (meeting report) by W.L. Trepicchio et al.
- Use of Archived Specimens in Evaluation of Prognostic and Predictive Biomarkers by R. Simon, S. Paik and D.F. Hayes
- Drug and Pharmacodiagnostic Co-Development - Statistical Considerations by R. Simon
- Evaluating the Efficiency of Targeted Designs for Randomized Clinical Trials by R. Simon and A. Maltournam
- Biomarker-Adaptive Threshold Design by W. Jiang, B. Freidlin and R. Simon
- The Cross-Validated Adaptive Signature Design by B. Freidlin, W. Jiang and R. Simon
FDA Guidances
- IVD Regulation
- Statistical Guidance on Reporting Results from Studies Evaluating Diagnostic Tests (2007)
- Class II Special Controls Guidance Document: Instrumentation for Clinical Multiplex Test Systems (2005)
- Class II Special Controls Guidance Document: Gene Expression Profiling Test System for Breast Cancer Prognosis (2007)
- E16 Biomarkers Related to Drug or Biotechnology Product Development: Context, Structure, and Format of Qualification Submissions (2011)
- Pharmacogenomic Data Submissions (2005)
- Pharmacogenetic Tests and Genetic Tests for Heritable Markers (2007)
- In Vitro Diagnostics: The Complete Regulatory Guide by the Food and Drug Law Institute (2010)
FDA Draft Guidances
- In Vitro Diagnostic Multivariate Index Assays (2007)
- In Vitro Companion Diagnostic Devices (2011)
- Clinical Pharmacogenomics: Premarketing Evaluation in Early Phase Clinical Studies (2011)
- Qualification Process for Drug Development Tools (2010)
- Pharmacogenomic Data Submissions - Companion Guidance (2007)
- Drug-Diagnostic Co-Development Concept Paper (2005)
- Assay Migration Studies for In Vitro Diagnostic Devices (2009)
- Commercially Distributed In Vitro Diagnostic Products Labeled for Research Use Only or Investigational Use Only: FAQ (2011)
FDA Staff: Articles & Presentations
- US FDA and personalized medicine: in vitro diagnostic regulatory perspective
- Integration and use of biomarkers in drug development, regulation and clinical practice
- Drug-Diagnostic Codevelopment Strategies: 4th FDA/DIA/PhRMA/PWG/BIO Pharmacogenomics Workshop
- Protein-Based Multiplex Assays: Mock Presubmissions to the US Food and Drug Administration
- CDRH Learn
Standards and Recommendations
- Database of FDA recognized Consensus Standards for Medical Devices
- College of American Pathologists
- Guidelines for the Development and Incorporation of Biomarker Studies in Early Clinical Trials of Novel Agents by the Biomarker Taskforce (NCI)
- EQUATOR Network: Library for health research reporting
- Consolidated Standards of Reporting Trials (CONSORT) 2010 Statement and website
- STAndards for the Reporting of Diagnostic accuracy studies (STARD) and website
- QUality Assessment of Diagnostic Accuracy Studies (QUADAS)
- Pivotal Evaluation of the Accuracy of a Biomarker Used for Classification or Prediction: Standards for Study Design (PRoBE)
- STrengthening the Reporting of OBservational Studies in Epidemiology (STROBE) and website
- STrengthening the Reporting of OBservational studies in Molecular Epidemiology (STROBE-ME)
- STrengthening the REporting of Genetic Association studies (STREGA)
- Biospecimen Reporting for Improved Study Quality (BRISQ)
- ACCE Model Process for Evaluating Genetic Tests from CDC
- Use of Tumor Markers in Clinical Practice (NACB Practice Guideline)
- REporting Recommendations for Tumor MARKer Prognostic Studies (REMARK)
- Tumor Marker Utility Grading System
CLSI Standards
- Evaluation of Precision of Quantitative Measurement Procedures EP05-A3 *
- Evaluation of the Linearity of Quantitative Measurement Procedures EP06 2nd ed. *
- Interference Testing in Clinical Chemistry EP07 3rd ed. *
- User Protocol for Evaluation of Qualitative Test Performance EP12 2nd ed.-A2 *
- Evaluation of Commutability of Processed Samples EP14 4th ed. *
- Evaluation of Detection Capability for Clinical Laboratory Measurement Procedures EP17-A2 *
- Assessment of the Diagnostic Accuracy of Laboratory Tests Using Receiver Operating Characteristic Curves EP24-A2 *
- Statistical Quality Control for Quantitative Measurement Procedures C24 4th ed. *
- Molecular Methods for Clinical Genetics and Oncology Testing MM01-A3 *
- Diagnostic Nucleic Acid Microarrays MM12-A
- Collection, Transport, Preparation, and Storage of Specimens for Molecular Methods MM13 2nd ed. *
- Design of Molecular Proficiency Testing/External Quality Assessment MM14-A2
- Validation and Verification of Multiplex Nucleic Acid Assays MM17 2nd ed. *
* Marked standards are recognized by the FDA (see Recognized Consensus Standards)
Software
- G*Power 3 Free Sample Size and Power Analysis Software
- nQuery Commercial Sample Size and Power Analysis Software
- Sample size for Phase III from Richard Simon
- Sample size for Classifier Development from Richard Simon
- R Task View: Clinical Trial Design, Monitoring, and Analysis
- R Task View: Design of Experiments (DoE) & Analysis of Experimental Data
- Propensity Score Software Overview by E.A. Stuart
- R Matching package
Epidemiological Data
- SEER U.S. Cancer Data
- CancerMondial Collection from the International Agency for Research on Cancer (IARC)
- EUROCARE European Cancer Survival Data
- CancerStats Cancer Statistics for the UK
- German Cancer Registries
Contact
Dr. Juergen von Frese
Data Analysis Solutions S.L.
Telephone: +34 871 811 605
E-Mail: jvf@da-sol.com
Disclaimer
This page has been compiled for your convenience. The author reserves the right not to be responsible for the correctness, completeness or quality of the information provided and shall in no event be liable for any direct, indirect, incidental, consequential, or exemplary damages. The author is not responsible for any contents linked or referred to from his pages.