Global Cumulative Treatment Analysis (GCTA) is a novel clinical research model combining expert knowledge, and treatment coordination based upon global information-gain, to treat every patient optimally while efficiently searching the vast space that is the realm of cancer research.
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4.
“Treatments” are, in reality, usually complex treatment plans, including activities such as cycles of drug infusion, monitoring, additional tests, and so on. Here we will simply call these collectively “treatments.”
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7.
Two senses of the term “big” are often conflated. The sense of “big data” where, for example, Google and Facebook have enormous datasets, might more accurately be called “tall, narrow data,” wherein there are many independent observations of a few features. Put in terms of dimensionality and sample size, from earlier in the paper, there are many independent samples (“n”) for relatively few dimensions of feature space. This “tall narrow data” might be more correctly called “large n, low (or moderate) dimensionality data.” Medical data, especially at the molecular level, might better be called “short wide data,” with relatively few independent observations over a very large number of features, or, in the above parlance: “small n, high dimensionality data.” The “big data” problems that have seen success are the tall narrow ones. The short wide ones remain out of reach of current technology.
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P. D.Karp, R.Billington, R.Caspi, C. A.Fulcher, M.Latendresse, A.Kothari, I. M.Keseler, M.Krummenacker, P. E.Midford, Q.Ong, W. K.Ong, S. M.Paley, and P.Subhraveti, “The BioCyc Collection of Microbial Genomes and Metabolic Pathways,”Briefings in Bioinformatics (2017): bbx085, doi: 10.1093/bib/bbx085.
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Sweetnam et al. note that TRs should be captured not only for the recommended treatments (tests, etc.), but also for those actions that are considered but rejected, because they are incorrect, undesirable, or infeasible. These “contra treatment rationales” (“contra-TRs”) can carry as much, or in some cases more, information than the rationale supporting the final recommendation; often the final recommendation is a safe or possible choice, whereas physicians might like to do something that may be more effective if practical barriers, such as cost or side effects, could be surmounted. Contra TRs may also represent new treatment hypotheses, possibly worthy of testing.
12.
Even though reasoning based on these partial explanations would likely be imperfect, these can serve as hypothetical “creases” where the space could fold, as depicted in Figure 2. (Unless they are analytic [for example, based upon mathematical formulae], such hypothetical folds need to be treated with statistical care regarding Type I Error [False Discovery]). The present hypothesis is that such hints do more to reduce the dimensionality of the problem than they do to increase the false discovery rate.
13.
S.P.Hey and A. S.Kesselheim, “Countering Imprecision in Precision Medicine,”Science, July29, 2016.
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J.Shrager, “Precision Medicine: Fantasy Meets Reality,”Letters, Science353, no. 6305 (2016).
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16.
J.Shrager, “Theoretical Issues for Global Cumulative Treatment Analysis (GCTA),” arXiv: 1308.1066v1 [stat.AP] (2013); J.Shrager and J. M.Tenenbaum, “Rapid Learning Precision Oncology,”Nature Reviews Clinical Oncology11 (2014): 109-118.
