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@ARTICLE{Brazma2003,
  author = {Brazma, A. and Parkinson, H. and Sarkans, U. and Shojatalab, M. and
	Vilo, J. and Abeygunawardena, N. and Holloway, E. and Kapushesky,
	M. and Kemmeren, P. and Lara, G. G. and Oezcimen, A. and Rocca-Serra,
	P. and Sansone, S. A.},
  title = {Array{E}xpress--a public repository for microarray gene expression
	data at the {EBI}},
  journal = {Nucleic Acids Res},
  year = {2003},
  volume = {31},
  pages = {68--71},
  number = {1},
  note = {1362-4962 (Electronic)
	
	Journal Article
	
	
	},
  abstract = {ArrayExpress is a new public database of microarray gene expression
	data at the EBI, which is a generic gene expression database designed
	to hold data from all microarray platforms. ArrayExpress uses the
	annotation standard Minimum Information About a Microarray Experiment
	(MIAME) and the associated XML data exchange format Microarray Gene
	Expression Markup Language (MAGE-ML) and it is designed to store
	well annotated data in a structured way. The ArrayExpress infrastructure
	consists of the database itself, data submissions in MAGE-ML format
	or via an online submission tool MIAMExpress, online database query
	interface, and the Expression Profiler online analysis tool. ArrayExpress
	accepts three types of submission, arrays, experiments and protocols,
	each of these is assigned an accession number. Help on data submission
	and annotation is provided by the curation team. The database can
	be queried on parameters such as author, laboratory, organism, experiment
	or array types. With an increasing number of organisations adopting
	MAGE-ML standard, the volume of submissions to ArrayExpress is increasing
	rapidly. The database can be accessed at http://www.ebi.ac.uk/arrayexpress.},
  keywords = {Animals
	
	Computational Biology
	
	Data Collection
	
	*Databases, Genetic
	
	Europe
	
	*Gene Expression Profiling
	
	*Oligonucleotide Array Sequence Analysis
	
	Research Support, Non-U.S. Gov't
	
	Software Design},
  owner = {lmarchionni},
  timestamp = {2006.12.17}
}

@ARTICLE{Buyse2006,
  author = {Buyse, M. and Loi, S. and van't Veer, L. and Viale, G. and Delorenzi,
	M. and Glas, A. M. and d'Assignies, M. S. and Bergh, J. and Lidereau,
	R. and Ellis, P. and Harris, A. and Bogaerts, J. and Therasse, P.
	and Floore, A. and Amakrane, M. and Piette, F. and Rutgers, E. and
	Sotiriou, C. and Cardoso, F. and Piccart, M. J.},
  title = {Validation and clinical utility of a 70-gene prognostic signature
	for women with node-negative breast cancer},
  journal = {J Natl Cancer Inst},
  year = {2006},
  volume = {98},
  pages = {1183--92},
  number = {17},
  note = {1460-2105 (Electronic)
	
	Journal Article
	
	Multicenter Study
	
	Research Support, Non-U.S. Gov't
	
	Validation Studies},
  abstract = {BACKGROUND: A 70-gene signature was previously shown to have prognostic
	value in patients with node-negative breast cancer. Our goal was
	to validate the signature in an independent group of patients. METHODS:
	Patients (n = 307, with 137 events after a median follow-up of 13.6
	years) from five European centers were divided into high- and low-risk
	groups based on the gene signature classification and on clinical
	risk classifications. Patients were assigned to the gene signature
	low-risk group if their 5-year distant metastasis-free survival probability
	as estimated by the gene signature was greater than 90%. Patients
	were assigned to the clinicopathologic low-risk group if their 10-year
	survival probability, as estimated by Adjuvant! software, was greater
	than 88% (for estrogen receptor [ER]-positive patients) or 92% (for
	ER-negative patients). Hazard ratios (HRs) were estimated to compare
	time to distant metastases, disease-free survival, and overall survival
	in high- versus low-risk groups. RESULTS: The 70-gene signature outperformed
	the clinicopathologic risk assessment in predicting all endpoints.
	For time to distant metastases, the gene signature yielded HR = 2.32
	(95% confidence interval [CI] = 1.35 to 4.00) without adjustment
	for clinical risk and hazard ratios ranging from 2.13 to 2.15 after
	adjustment for various estimates of clinical risk; clinicopathologic
	risk using Adjuvant! software yielded an unadjusted HR = 1.68 (95%
	CI = 0.92 to 3.07). For overall survival, the gene signature yielded
	an unadjusted HR = 2.79 (95% CI = 1.60 to 4.87) and adjusted hazard
	ratios ranging from 2.63 to 2.89; clinicopathologic risk yielded
	an unadjusted HR = 1.67 (95% CI = 0.93 to 2.98). For patients in
	the gene signature high-risk group, 10-year overall survival was
	0.69 for patients in both the low- and high-clinical risk groups;
	for patients in the gene signature low-risk group, the 10-year survival
	rates were 0.88 and 0.89, respectively. CONCLUSIONS: The 70-gene
	signature adds independent prognostic information to clinicopathologic
	risk assessment for patients with early breast cancer.},
  date = {Sep 6},
  keywords = {Adult
	
	Breast Neoplasms/*genetics/pathology
	
	Disease-Free Survival
	
	Europe
	
	Female
	
	Follow-Up Studies
	
	*Gene Expression Profiling
	
	Humans
	
	Lymphatic Metastasis
	
	Middle Aged
	
	Neoplasm Staging
	
	Odds Ratio
	
	Oligonucleotide Array Sequence Analysis
	
	Predictive Value of Tests
	
	Prognosis
	
	ROC Curve
	
	Reproducibility of Results
	
	Risk Assessment
	
	Risk Factors
	
	Sensitivity and Specificity
	
	Survival Analysis
	
	Tumor Markers, Biological/*genetics},
  owner = {lmarchionni},
  timestamp = {2007.12.12},
  url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16954471}
}

@ARTICLE{Geman2004,
  author = {Donald Geman and Christian d'Avignon and Daniel Q Naiman and Raimond
	L Winslow},
  title = {Classifying gene expression profiles from pairwise mRNA comparisons.},
  journal = {Stat Appl Genet Mol Biol},
  year = {2004},
  volume = {3},
  pages = {Article19},
  abstract = {We present a new approach to molecular classification based on mRNA
	comparisons. Our method, referred to as the top-scoring pair(s) (TSP)
	classifier, is motivated by current technical and practical limitations
	in using gene expression microarray data for class prediction, for
	example to detect disease, identify tumors or predict treatment response.
	Accurate statistical inference from such data is difficult due to
	the small number of observations, typically tens, relative to the
	large number of genes, typically thousands. Moreover, conventional
	methods from machine learning lead to decisions which are usually
	very difficult to interpret in simple or biologically meaningful
	terms. In contrast, the TSP classifier provides decision rules which
	i) involve very few genes and only relative expression values (e.g.,
	comparing the mRNA counts within a single pair of genes); ii) are
	both accurate and transparent; and iii) provide specific hypotheses
	for follow-up studies. In particular, the TSP classifier achieves
	prediction rates with standard cancer data that are as high as those
	of previous studies which use considerably more genes and complex
	procedures. Finally, the TSP classifier is parameter-free, thus avoiding
	the type of over-fitting and inflated estimates of performance that
	result when all aspects of learning a predictor are not properly
	cross-validated.},
  doi = {10.2202/1544-6115.1071},
  institution = {Center for Cardiovascular Bioinformatics and Modeling, Whitaker Biomedical
	Engineering Institute and Department of Applied Mathematics and Statistics,
	Johns Hopkins University, USA. ge-man@jhu.edu},
  owner = {marchion},
  pmid = {16646797},
  timestamp = {2009.04.24},
  url = {http://dx.doi.org/10.2202/1544-6115.1071}
}

@ARTICLE{Glas2006,
  author = {Glas, A. M. and Floore, A. and Delahaye, L. J. and Witteveen, A.
	T. and Pover, R. C. and Bakx, N. and Lahti-Domenici, J. S. and Bruinsma,
	T. J. and Warmoes, M. O. and Bernards, R. and Wessels, L. F. and
	Van't Veer, L. J.},
  title = {Converting a breast cancer microarray signature into a high-throughput
	diagnostic test},
  journal = {BMC Genomics},
  year = {2006},
  volume = {7},
  pages = {278},
  note = {1471-2164 (Electronic)
	
	Journal Article
	
	Research Support, Non-U.S. Gov't},
  abstract = {BACKGROUND: A 70-gene tumor expression profile was established as
	a powerful predictor of disease outcome in young breast cancer patients.
	This profile, however, was generated on microarrays containing 25,000
	60-mer oligonucleotides that are not designed for processing of many
	samples on a routine basis. RESULTS: To facilitate its use in a diagnostic
	setting, the 70-gene prognosis profile was translated into a customized
	microarray (MammaPrint) containing a reduced set of 1,900 probes
	suitable for high throughput processing. RNA of 162 patient samples
	from two previous studies was subjected to hybridization to this
	custom array to validate the prognostic value. Classification results
	obtained from the original analysis were then compared to those generated
	using the algorithms based on the custom microarray and showed an
	extremely high correlation of prognosis prediction between the original
	data and those generated using the custom mini-array (p < 0.0001).
	CONCLUSION: In this report we demonstrate for the first time that
	microarray technology can be used as a reliable diagnostic tool.
	The data clearly demonstrate the reproducibility and robustness of
	the small custom-made microarray. The array is therefore an excellent
	tool to predict outcome of disease in breast cancer patients.},
  keywords = {Breast Neoplasms/*diagnosis/genetics
	
	Female
	
	Gene Expression Profiling/*methods
	
	Humans
	
	Kaplan-Meiers Estimate
	
	Middle Aged
	
	Oligonucleotide Array Sequence Analysis/*methods
	
	Prognosis
	
	Reproducibility of Results
	
	Sensitivity and Specificity
	
	Tumor Markers, Biological/genetics},
  owner = {lmarchionni},
  timestamp = {2007.12.12},
  url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=17074082}
}

@ARTICLE{Leek2009,
  author = {Jeffrey T Leek},
  title = {The tspair package for finding top scoring pair classifiers in R.},
  journal = {Bioinformatics},
  year = {2009},
  volume = {25},
  pages = {1203--1204},
  number = {9},
  month = {May},
  abstract = {Top scoring pairs (TSPs) are pairs of genes whose relative rankings
	can be used to accurately classify individuals into one of two classes.
	TSPs have two main advantages over many standard classifiers used
	in gene expression studies: (i) a TSP is based on only two genes,
	which leads to easily interpretable and inexpensive diagnostic tests
	and (ii) TSP classifiers are based on gene rankings, so they are
	more robust to variation in technical factors or normalization than
	classifiers based on expression levels of individual genes. Here
	I describe the R package, tspair, which can be used to quickly identify
	and assess TSP classifiers for gene expression data. Availability:
	The R package tspair is freely available from Bioconductor: http://www.bioconductor.org.},
  doi = {10.1093/bioinformatics/btp126},
  institution = {Department of Oncology, Johns Hopkins School of Medicine, Baltimore,
	MD 21287, USA. jtleek@jhu.edu},
  keywords = {Algorithms; Computational Biology, methods; Gene Expression Profiling,
	methods; Software},
  owner = {marchion},
  pii = {btp126},
  pmid = {19276151},
  timestamp = {2009.08.28},
  url = {http://dx.doi.org/10.1093/bioinformatics/btp126}
}

@ARTICLE{Lin2009,
  author = {Xue Lin and Bahman Asfari and Luigi Marchionni and Leslie Cope and
	Giovanni Parmigiani and Daniel Naiman and Donald Geman},
  title = {The ordering of expression among a few genes can provide simple cancer
	biomarkers and signal BRCA1 mutations.},
  journal = {BMC Bioinformatics},
  year = {2009},
  volume = {10},
  pages = {256},
  number = {1},
  month = {Aug},
  abstract = {ABSTRACT: BACKGROUND: A major challenge in computational biology is
	to extract knowledge about the genetic nature of disease from high-throughput
	data. However, an important obstacle to both biological understanding
	and clinical applications is the "black box" nature of the decision
	rules provided by most machine learning approaches, which usually
	involve many genes combined in a highly complex fashion. Achieving
	biologically relevant results argues for a different strategy. A
	promising alternative is to base prediction entirely upon the relative
	expression ordering of a small number of genes. RESULTS: We present
	a three-gene version of "relative expression analysis" (RXA), a rigorous
	and systematic comparison with earlier approaches in a variety of
	cancer studies, a clinically relevant application to predicting germline
	BRCA1 mutations in breast cancer and a cross-study validation for
	predicting ER status. In the BRCA1 study, RXA yields high accuracy
	with a simple decision rule: in tumors carrying mutations, the expression
	of a "reference gene" falls between the expression of two differentially
	expressed genes, PPP1CB and RNF14. An analysis of the protein-protein
	interactions among the triplet of genes and BRCA1 suggests that the
	classifier has a biological foundation. CONCLUSIONS: RXA has the
	potential to identify genomic "marker interactions" with plausible
	biological interpretation and direct clinical applicability. It provides
	a general framework for understanding the roles of the genes involved
	in decision rules, as illustrated for the difficult and clinically
	relevant problem of identifying BRCA1 mutation carriers.},
  doi = {10.1186/1471-2105-10-256},
  owner = {marchion},
  pii = {1471-2105-10-256},
  pmid = {19695104},
  timestamp = {2009.08.28},
  url = {http://dx.doi.org/10.1186/1471-2105-10-256}
}

@ARTICLE{Marchionni2007b,
  author = {Luigi Marchionni and Renee F Wilson and Spyridon S Marinopoulos and
	Antonio C Wolff and Giovanni Parmigiani and Eric B Bass and Steven
	N Goodman},
  title = {Impact of gene expression profiling tests on breast cancer outcomes.},
  journal = {Evid Rep Technol Assess (Full Rep)},
  year = {2007},
  pages = {1--105},
  number = {160},
  month = {Dec},
  abstract = {OBJECTIVES: To assess the evidence that three marketed gene expression-based
	assays improve prognostic accuracy, treatment choice, and health
	outcomes in women diagnosed with early stage breast cancer. REVIEW
	METHODS: We evaluated the evidence for three gene expression assays
	on the market; Oncotype DX, MammaPrint and the Breast Cancer Profiling
	(BCP or H/I ratio) test, and for gene expression signatures underlying
	the assays. We sought evidence on: analytic performance of tests,
	clinical validity (i.e., prognostic accuracy and discrimination),
	clinical utility (i.e., prediction of treatment benefit), harms,
	impact on clinical decision making and health care costs. RESULTS:
	Few papers were found on the analytic validity of the Oncotype DX
	and MammaPrint tests, but these showed reasonable within-laboratory
	replicability. Pre-analytic issues related to sample storage and
	preparation may play a larger role than within-laboratory variation.
	For clinical validity, studies differed according to whether they
	examined the actual test that is currently being offered to patients
	or the underlying gene signature. Almost all of the Oncotype DX evidence
	was for the marketed test, the strongest validation study being from
	one arm of a randomized controlled trial (NSABP-14) with a clinically
	homogeneous population. This study showed that the test, added in
	a clinically meaningful manner to standard prognostic indices. The
	MammaPrint signature and test itself was examined in studies with
	clinically heterogeneous populations (e.g., mix of ER positivity
	and tamoxifen treatment) and showed a clinically relevant separation
	of patients into risk categories, but it was not clear exactly how
	many predictions would be shifted across decision thresholds if this
	were used in combination with traditional indices. The BCP test itself
	was examined in one study, and the signature was tested in a variety
	of formulations in several studies. One randomized controlled trial
	provided high quality retrospective evidence of the clinical utility
	of Oncotype DX to predict chemotherapy treatment benefit, but evidence
	for clinical utility was not found for MammaPrint or the H/I ratio.
	Three decision analyses examined the cost-effectiveness of breast
	cancer gene expression assays, and overall were inconclusive. CONCLUSIONS:
	Oncotype DX is furthest along the validation pathway, with strong
	retrospective evidence that it predicts distant spread and chemotherapy
	benefit to a clinically relevant extent over standard predictors,
	in a well-defined clinical subgroup with clear treatment implications.
	The evidence for clinical implications of using MammaPrint was not
	as clear as with Oncotype DX, and the ability to predict chemotherapy
	benefit does not yet exist. The H/I ratio test requires further validation.
	For all tests, the relationship of predicted to observed risk in
	different populations still needs further study, as does their incremental
	contribution, optimal implementation, and relevance to patients on
	current therapies.},
  keywords = { Analysis, Adult, Breast Neoplasms, Female, Gene Expression Profiling,
	Humans, Microarray, Middle Aged, Prognosis, Reverse Transcriptase
	Polymerase Chain Reaction, Treatment Outcome, 18457476},
  owner = {lmarchionni},
  pmid = {18457476},
  timestamp = {2008.09.20}
}

@ARTICLE{Marchionni2008a,
  author = {Luigi Marchionni and Renee F Wilson and Antonio C Wolff and Spyridon
	Marinopoulos and Giovanni Parmigiani and Eric B Bass and Steven N
	Goodman},
  title = {Systematic review: gene expression profiling assays in early-stage
	breast cancer.},
  journal = {Ann Intern Med},
  year = {2008},
  volume = {148},
  pages = {358--369},
  number = {5},
  month = {Mar},
  __markedentry = {[lmarchionni]},
  abstract = {BACKGROUND: Three gene expression-based prognostic breast cancer tests
	have been licensed for use. PURPOSE: To summarize evidence on the
	validity and utility of 3 gene expression-based prognostic breast
	cancer tests: Oncotype DX (Genomic Health, Redwood City, California),
	MammaPrint (Agendia BV, Amsterdam, the Netherlands), and H/I (AvariaDX,
	Carlsbad, California). DATA SOURCES: MEDLINE, EMBASE, and Cochrane
	databases (from 1990 through January 2007), Web sites of test manufacturers,
	and discussion with the manufacturers. STUDY SELECTION: Original
	data studies published in English that addressed prognostic accuracy
	and discrimination or treatment benefit prediction of any of the
	3 tests in women with breast cancer. DATA EXTRACTION: Information
	was extracted about the clinical characteristics of the study population
	(particularly clinical and therapeutic homogeneity), tumor characteristics,
	and whether the marketed test or underlying signature was evaluated.
	DATA SYNTHESIS: The tests are based on distinct gene lists, using
	2 different technologies. Overall, the body of evidence showed that
	this new generation of tests may improve prognostic and therapeutic
	prediction, but the tests are at different stages of development.
	Evidence shows that the tests offer clinically relevant, improved
	risk stratification over standard predictors. Oncotype DX has the
	strongest evidence, closely followed by MammaPrint and H/I (which
	is still maturing). LIMITATIONS: For all tests, the relationship
	of predicted to observed risk in different populations and their
	incremental contribution over conventional predictors, optimal implementation,
	and relevance to patients receiving current therapies need further
	study. CONCLUSION: Gene expression technologies show great promise
	to improve predictions of prognosis and treatment benefit for women
	with early-stage breast cancer. More information is needed on the
	extent of improvement in prediction, characteristics of women in
	whom the tests should be used, and how best to incorporate test results
	into decision making about breast cancer treatment.},
  keywords = {18252678},
  owner = {lmarchionni},
  pii = {0000605-200803040-00208},
  pmid = {18252678},
  timestamp = {2008.03.10}
}

@ARTICLE{vant2002,
  author = {van't Veer, L. J. and Dai, H. and van de Vijver, M. J. and He, Y.
	D. and Hart, A. A. and Mao, M. and Peterse, H. L. and van der Kooy,
	K. and Marton, M. J. and Witteveen, A. T. and Schreiber, G. J. and
	Kerkhoven, R. M. and Roberts, C. and Linsley, P. S. and Bernards,
	R. and Friend, S. H.},
  title = {Gene expression profiling predicts clinical outcome of breast cancer},
  journal = {Nature},
  year = {2002},
  volume = {415},
  pages = {530--6},
  number = {6871},
  note = {0028-0836 (Print)
	
	Journal Article
	
	
	},
  abstract = {Breast cancer patients with the same stage of disease can have markedly
	different treatment responses and overall outcome. The strongest
	predictors for metastases (for example, lymph node status and histological
	grade) fail to classify accurately breast tumours according to their
	clinical behaviour. Chemotherapy or hormonal therapy reduces the
	risk of distant metastases by approximately one-third; however, 70-80%
	of patients receiving this treatment would have survived without
	it. None of the signatures of breast cancer gene expression reported
	to date allow for patient-tailored therapy strategies. Here we used
	DNA microarray analysis on primary breast tumours of 117 young patients,
	and applied supervised classification to identify a gene expression
	signature strongly predictive of a short interval to distant metastases
	('poor prognosis' signature) in patients without tumour cells in
	local lymph nodes at diagnosis (lymph node negative). In addition,
	we established a signature that identifies tumours of BRCA1 carriers.
	The poor prognosis signature consists of genes regulating cell cycle,
	invasion, metastasis and angiogenesis. This gene expression profile
	will outperform all currently used clinical parameters in predicting
	disease outcome. Our findings provide a strategy to select patients
	who would benefit from adjuvant therapy.},
  keywords = {Adult
	
	Breast Neoplasms/*genetics/physiopathology/therapy
	
	Chemotherapy, Adjuvant
	
	Cluster Analysis
	
	DNA, Neoplasm
	
	Female
	
	*Gene Expression Profiling
	
	Genes, BRCA1
	
	Genes, BRCA2
	
	Humans
	
	Lymphatic Metastasis
	
	Oligonucleotide Array Sequence Analysis
	
	Patient Selection
	
	Predictive Value of Tests
	
	Prognosis
	
	Research Support, Non-U.S. Gov't
	
	Treatment Outcome},
  owner = {lmarchionni},
  timestamp = {2006.12.17}
}

@ARTICLE{Vijver2002,
  author = {van de Vijver, M. J. and He, Y. D. and van't Veer, L. J. and Dai,
	H. and Hart, A. A. and Voskuil, D. W. and Schreiber, G. J. and Peterse,
	J. L. and Roberts, C. and Marton, M. J. and Parrish, M. and Atsma,
	D. and Witteveen, A. and Glas, A. and Delahaye, L. and van der Velde,
	T. and Bartelink, H. and Rodenhuis, S. and Rutgers, E. T. and Friend,
	S. H. and Bernards, R.},
  title = {A gene-expression signature as a predictor of survival in breast
	cancer},
  journal = {N Engl J Med},
  year = {2002},
  volume = {347},
  pages = {1999--2009},
  number = {25},
  note = {1533-4406 (Electronic)
	
	Evaluation Studies
	
	Journal Article
	
	
	},
  abstract = {BACKGROUND: A more accurate means of prognostication in breast cancer
	will improve the selection of patients for adjuvant systemic therapy.
	METHODS: Using microarray analysis to evaluate our previously established
	70-gene prognosis profile, we classified a series of 295 consecutive
	patients with primary breast carcinomas as having a gene-expression
	signature associated with either a poor prognosis or a good prognosis.
	All patients had stage I or II breast cancer and were younger than
	53 years old; 151 had lymph-node-negative disease, and 144 had lymph-node-positive
	disease. We evaluated the predictive power of the prognosis profile
	using univariable and multivariable statistical analyses. RESULTS:
	Among the 295 patients, 180 had a poor-prognosis signature and 115
	had a good-prognosis signature, and the mean (+/-SE) overall 10-year
	survival rates were 54.6+/-4.4 percent and 94.5+/-2.6 percent, respectively.
	At 10 years, the probability of remaining free of distant metastases
	was 50.6+/-4.5 percent in the group with a poor-prognosis signature
	and 85.2+/-4.3 percent in the group with a good-prognosis signature.
	The estimated hazard ratio for distant metastases in the group with
	a poor-prognosis signature, as compared with the group with the good-prognosis
	signature, was 5.1 (95 percent confidence interval, 2.9 to 9.0; P<0.001).
	This ratio remained significant when the groups were analyzed according
	to lymph-node status. Multivariable Cox regression analysis showed
	that the prognosis profile was a strong independent factor in predicting
	disease outcome. CONCLUSIONS: The gene-expression profile we studied
	is a more powerful predictor of the outcome of disease in young patients
	with breast cancer than standard systems based on clinical and histologic
	criteria.},
  keywords = {Adult
	
	Age Factors
	
	Breast Neoplasms/*genetics/mortality/pathology
	
	Cohort Studies
	
	Female
	
	*Gene Expression Profiling
	
	Humans
	
	Lymphatic Metastasis
	
	Middle Aged
	
	Multivariate Analysis
	
	Neoplasm Metastasis
	
	Oligonucleotide Array Sequence Analysis
	
	Patient Selection
	
	Prognosis
	
	Proportional Hazards Models
	
	Research Support, Non-U.S. Gov't
	
	Survival Analysis},
  owner = {lmarchionni},
  timestamp = {2006.12.17}
}

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