S G Baker

Summary

Affiliation: National Institutes of Health
Country: USA

Publications

  1. Baker S, Kramer B. Systems biology and cancer: promises and perils. Prog Biophys Mol Biol. 2011;106:410-3 pubmed publisher
    ..Using illustrative examples we discuss these threads in the context of cancer research. ..
  2. Baker S. Causal inference, probability theory, and graphical insights. Stat Med. 2013;32:4319-30 pubmed publisher
  3. Baker S, Kramer B. Evaluating surrogate endpoints, prognostic markers, and predictive markers: Some simple themes. Clin Trials. 2015;12:299-308 pubmed publisher
    ..The simple themes provide a general guideline for evaluation of surrogate endpoints, prognostic markers, and predictive markers. ..
  4. Baker S. Estimation and inference for the causal effect of receiving treatment on a multinomial outcome: an alternative approach. Biometrics. 2011;67:319-23; discussion 323-5 pubmed publisher
    ..We believe this approach is easier to implement, which would facilitate the reproduction of calculations. ..
  5. Baker S, Sargent D, Buyse M, Burzykowski T. Predicting treatment effect from surrogate endpoints and historical trials: an extrapolation involving probabilities of a binary outcome or survival to a specific time. Biometrics. 2012;68:248-57 pubmed publisher
    ..To summarize the additional uncertainty from using a predicted instead of true result for the estimated treatment effect, we compute its multiplier of standard error. Software is available for download. ..
  6. Baker S. The latent class twin method. Biometrics. 2016;72:827-34 pubmed publisher
    ..Applying the latent class twin method to data on breast cancer among Nordic twins, we estimated a genetic prevalence of 1%, a result with important implications for breast cancer prevention research. ..
  7. Baker S, Kramer B. Identifying genes that contribute most to good classification in microarrays. BMC Bioinformatics. 2006;7:407 pubmed
    ..Using multiple random validation, investigators should look for classification rules that perform well with few genes and select, for further study, genes with relatively high frequencies of occurrence in these classification rules. ..
  8. Baker S, Kramer B. Paradoxes in carcinogenesis: new opportunities for research directions. BMC Cancer. 2007;7:151 pubmed
    ..Now we have some hope of making progress." The same viewpoint should apply to cancer research. It is easy to ignore this piece of wisdom about the means to advance knowledge, but we do so at our peril. ..
  9. Baker S, Soto A, Sonnenschein C, Cappuccio A, Potter J, Kramer B. Plausibility of stromal initiation of epithelial cancers without a mutation in the epithelium: a computer simulation of morphostats. BMC Cancer. 2009;9:89 pubmed publisher
    ..Because the model is biologically plausible, we hope that these results will stimulate further experiments. ..

More Information

Publications13

  1. Baker S. Simple and flexible classification of gene expression microarrays via Swirls and Ripples. BMC Bioinformatics. 2010;11:452 pubmed publisher
    ..The parsimonious selection of classifiers coupled with the selection of either Swirls or Ripples provides a good basis for formulating a simple, yet flexible, classification rule. Open source software is available for download. ..
  2. Baker S, Kramer B. Surrogate endpoint analysis: an exercise in extrapolation. J Natl Cancer Inst. 2013;105:316-20 pubmed publisher
    ..In summary, when using surrogate endpoint analyses, an appreciation of the problems of extrapolation is crucial. ..
  3. Baker S, Bonetti M. Evaluating Markers for Guiding Treatment. J Natl Cancer Inst. 2016;108: pubmed publisher
    ..Because the method has desirable decision-analytic properties and yields an informative plot, it is worth applying to randomized trials on the chance there is a large treatment effect in a subgroup determined by the predictive markers. ..
  4. Baker S. Comparative Analysis of Biologically Relevant Response Curves in Gene Expression Experiments: Heteromorphy, Heterochrony, and Heterometry. Microarrays (Basel). 2014;3:39-51 pubmed publisher
    ..We illustrated the algorithm using data on gene expression at 14 times in the embryonic development in two species of frogs. Software written in Mathematica is freely available. ..