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bbr.bbl
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% $ biblatex auxiliary file $
% $ biblatex bbl format version 2.9 $
% Do not modify the above lines!
%
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%
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{}
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\preamble{%
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\refsection{0}
\datalist[entry]{nty/global//global/global}
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{{hash=6bab575963cae55bdf84d9cd3760fd59}{%
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\field{journaltitle}{The Statistician}
\field{title}{Sample Size Determination: A Review}
\field{volume}{46}
\field{year}{1997}
\field{dateera}{ce}
\field{pages}{261\bibrangedash 283}
\range{pages}{23}
\keyw{sample-size,bayesian-inference,bayes,bayesian-sample-size-estimation,average-coverage-criterion,bayes-factors,mcnemars-test,multinomial-distribution}
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\field{journaltitle}{Stat Med}
\field{title}{Power of Logrank Test and {{Cox}} Regression Model in Clinical Trials with Heterogeneous Samples}
\field{volume}{16}
\field{year}{1997}
\field{dateera}{ce}
\field{pages}{583\bibrangedash 597}
\range{pages}{15}
\keyw{simulation-setup,covariable-adjustment,model-misspecification,baseline-characteristics,loss-of-power-of-unadjusted-tests,model-identification,quantifying-prognostic-spectrum-of-adjustors}
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\field{annotation}{refutation of mic05pre}
\field{journaltitle}{PLoS ONE}
\field{number}{3}
\field{title}{Factors Influencing the Statistical Power of Complex Data Analysis Protocols for Molecular Signature Development from Microarray Data}
\field{volume}{4}
\field{year}{2009}
\field{dateera}{ce}
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\field{journaltitle}{BMJ}
\field{title}{Absence of Evidence Is Not Evidence of Absence}
\field{volume}{311}
\field{year}{1995}
\field{dateera}{ce}
\field{pages}{485}
\range{pages}{1}
\keyw{teaching-mds,statistical-significance,interpretation-of-negative-or-nonsignificant-findings}
\endentry
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{{hash=fe5acb0cb5969eb0c79947f2e4e54c1b}{%
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{{hash=d77ac0af604c52daf07a18aced65b51b}{%
family={Royston},
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\list{publisher}{1}{%
{John Wiley & Sons, Ltd.}%
}
\strng{namehash}{fd62e464b95eaf6b279acb6219abf384}
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\field{abstract}{Prognostic models are designed to predict a clinical outcome in individuals or groups of individuals with a particular disease or condition. To avoid bias many researchers advocate the use of full models developed by prespecifying predictors. Variable selection is not employed and the resulting models may be large and complicated. In practice more parsimonious models that retain most of the prognostic information may be preferred. We investigate the effect on various performance measures, including mean square error and prognostic classification, of three methods for estimating full models (including penalized estimation and Tibshirani's lasso) and consider two methods (backwards elimination and a new proposal called stepdown) for simplifying full models. Simulation studies based on two medical data sets suggest that simplified models can be found that perform nearly as well as, or sometimes even better than, full models. Optimizing the Akaike information criterion appears to be appropriate for choosing the degree of simplification.}
\field{annotation}{ordinary backward stepdown worked well when there was a large fraction of truly irrelevant predictors}
\field{eprinttype}{pmid}
\field{issn}{0277-6715}
\field{journaltitle}{Stat Med}
\field{month}{12}
\field{number}{24}
\field{title}{Simplifying a Prognostic Model: A Simulation Study Based on Clinical Data.}
\field{volume}{21}
\field{year}{2002}
\field{dateera}{ce}
\field{pages}{3803\bibrangedash 3822}
\range{pages}{20}
\verb{doi}
\verb 10.1002/sim.1422
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\verb 12483768
\endverb
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\verb http://dx.doi.org/10.1002/sim.1422
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\verb{url}
\verb http://dx.doi.org/10.1002/sim.1422
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\keyw{variable-selection,aic,roc,penalization,model-approximation,model-simplification,pre-conditioning,prognosis}
\endentry
\entry{amb02sel}{article}{}
\name{author}{2}{}{%
{{hash=19389a924fd1c6b38f98e05cb4427ef8}{%
family={Ambroise},
familyi={A\bibinitperiod},
given={Christophe},
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{{hash=09586cb2d90a2336087ab7943b65700b}{%
family={McLachlan},
familyi={M\bibinitperiod},
given={Geoffrey\bibnamedelima J.},
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}
\list{location}{1}{%
{Laboratoire Heudiasyc, Unit\'{e} Mixte de Recherche/Centre National de la Recherche Scientifique 6599, 60200 Compi\`{e}gne, France.}%
}
\list{publisher}{1}{%
{National Academy of Sciences}%
}
\strng{namehash}{8a7b33c0f4fa81d37dac168fe596a6f5}
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\field{abstract}{In the context of cancer diagnosis and treatment, we consider the problem of constructing an accurate prediction rule on the basis of a relatively small number of tumor tissue samples of known type containing the expression data on very many (possibly thousands) genes. Recently, results have been presented in the literature suggesting that it is possible to construct a prediction rule from only a few genes such that it has a negligible prediction error rate. However, in these results the test error or the leave-one-out cross-validated error is calculated without allowance for the selection bias. There is no allowance because the rule is either tested on tissue samples that were used in the first instance to select the genes being used in the rule or because the cross-validation of the rule is not external to the selection process; that is, gene selection is not performed in training the rule at each stage of the cross-validation process. We describe how in practice the selection bias can be assessed and corrected for by either performing a cross-validation or applying the bootstrap external to the selection process. We recommend using 10-fold rather than leave-one-out cross-validation, and concerning the bootstrap, we suggest using the so-called .632+ bootstrap error estimate designed to handle overfitted prediction rules. Using two published data sets, we demonstrate that when correction is made for the selection bias, the cross-validated error is no longer zero for a subset of only a few genes.}
\field{annotation}{Relied on an improper accuracy score (proportion classified correct) so had to use the .632 bootstrap unnecessarily}
\field{eprinttype}{pmid}
\field{issn}{1091-6490}
\field{journaltitle}{PNASs}
\field{month}{5}
\field{number}{10}
\field{title}{Selection Bias in Gene Extraction on the Basis of Microarray Gene-Expression Data}
\field{volume}{99}
\field{year}{2002}
\field{dateera}{ce}
\field{pages}{6562\bibrangedash 6566}
\range{pages}{5}
\verb{doi}
\verb 10.1073/pnas.102102699
\endverb
\verb{eprint}
\verb 11983868
\endverb
\verb{urlraw}
\verb http://dx.doi.org/10.1073/pnas.102102699
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\verb{url}
\verb http://dx.doi.org/10.1073/pnas.102102699
\endverb
\keyw{model-uncertainty,bad-science,bad-biomarker-research,bad-statistics,selection-bias,univariable-screening}
\endentry
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\field{annotation}{"statistics such as the AUC are not especially relevant to someone who must make a decision about a particular x\_c. ... ROC curves lack or obscure several quantities that are necessary for evaluating the operational effectiveness of diagnostic tests. ... ROC curves were first used to check how radio {$<$}i{$>$}receivers{$<$}/i{$>$} (like radar receivers) operated over a range of frequencies. ... This is not how most ROC curves are used now, particularly in medicine. The receiver of a diagnostic measurement ... wants to make a decision based on some x\_c, and is not especially interested in how well he would have done had he used some different cutoff."; in the discussion David Hand states "when integrating to yield the overall AUC measure, it is necessary to decide what weight to give each value in the integration. The AUC implicitly does this using a weighting derived empirically from the data. This is nonsensical. The relative importance of misclassifying a case as a noncase, compared to the reverse, cannot come from the data itself. It must come externally, from considerations of the severity one attaches to the different kinds of misclassifications."; see Lin, Kvam, Lu Stat in Med 28:798-813;2009}
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\field{title}{The Skill Plot: {A} Graphical Technique for Evaluating Continuous Diagnostic Tests (with Discussion)}
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\field{abstract}{To many, the foundations of statistical inference are cryptic and irrelevant to routine statistical practice. The analysis of 2 x 2 contingency tables, omnipresent in the scientific literature, is a case in point. Fisher's exact test is routinely used even though it has been fraught with controversy for over 70 years. The problem, not widely acknowledged, is that several different p-values can be associated with a single table, making scientific inference inconsistent. The root cause of this controversy lies in the table's origins and the manner in which nuisance parameters are eliminated. However, fundamental statistical principles (e.g., sufficiency, ancillarity, conditionality, and likelihood) can shed light on the controversy and guide our approach in using this test. In this paper, we use these fundamental principles to show how much information is lost when the tables origins are ignored and when various approaches are used to eliminate unknown nuisance parameters. We present novel likelihood contours to aid in the visualization of information loss and show that the information loss is often virtually non-existent. We find that problems arising from the discreteness of the sample space are exacerbated by p-value-based inference. Accordingly, methods that are less sensitive to this discreteness - likelihood ratios, posterior probabilities and mid-p-values - lead to more consistent inferences.}
\field{journaltitle}{PLoS ONE}
\field{month}{4}
\field{number}{4}
\field{title}{Elucidating the {{Foundations}} of {{Statistical Inference}} with 2 x 2 {{Tables}}}
\field{volume}{10}
\field{year}{2015}
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\field{pages}{e0121263+}
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\field{note}{C/R 85v39 p238-9}
\field{title}{Graphs in Scientific Publications}
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\field{year}{1984}
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\field{pages}{261\bibrangedash 269}
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\field{volume}{19}
\field{year}{2000}
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\field{pages}{3109\bibrangedash 3125}
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\field{title}{Points to Consider on Adjustment for Baseline Covariates}
\field{volume}{23}
\field{year}{2004}
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\field{pages}{701\bibrangedash 709}
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\field{title}{Multiplicity Considerations in the Design and Analysis of Clinical Trials}
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\field{year}{1996}
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