Bias as a threat to the validity of cancer molecular-marker research

By D. Ransohoff. 2005

503 citations recorded by the source corpus.

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In Molecular Biology

Citations in the corpus

Peak selection from MALDI-TOF mass spectra using ant colony optimization (2007, unknown): “Ransohoff (2005) indicated that 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.”
Statistical strategies for avoiding false discoveries in metabolomics and related experiments (2007, background): “…a variety of distinguished analysts, and a summary of cautionary papers that make useful reading include (Ransohoff and Feinstein, 1978; Altman, 2001; Ioannidis et al., 2001; Ein-Dor et al., 2006; Ransohoff, 2004, 2005; Ioannidis, 2005a, b; Ioannidis and Trikalinos , 2005; Wacholder et al., 2004).”
Proposed minimum reporting standards for data analysis in metabolomics (2007, background): “In addition a particularly important issue that can cause confounding or bias (Broadhurst and Kell 2006; Ioannidis 2005; Ransohoff 2005) is the likelihood that 'cases' will be often be taking considerably more pharmaceutical drugs than are the 'matched' 'controls.”
Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK): Explanation and Elaboration (2012, background): “A limitation of internal validation is that there may be biases affecting the entire data set that will not be detected by internal validation because the biases will affect the training and test sets equally [46]; however, if a model has been seriously overfitted to random noise in the training set”
Systems medicine: the future of medical genomics and healthcare (2009, background): “This compromised the usefulness of these platforms, for example in the development of classification and predictive biomarkers [40-42].”