 Connection
Debashis Ghosh to Data Interpretation, Statistical
This is a "connection" page, showing publications Debashis Ghosh has written about Data Interpretation, Statistical.
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5.085 |
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Ghosh D. Relaxed covariate overlap and margin-based causal effect estimation. Stat Med. 2018 12 10; 37(28):4252-4265.
Score: 0.587
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Daniels M, Frangakis C, Charu V, Ghosh D. University of Pennsylvania 7th annual conference on statistical issues in clinical trials: Current issues regarding the use of biomarkers and surrogate endpoints in clinical trials (morning panel discussion). Clin Trials. 2015 Aug; 12(4):323-32.
Score: 0.469
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Ghosh D. Genomic outlier detection in high-throughput data analysis. Methods Mol Biol. 2013; 972:141-53.
Score: 0.396
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Ghosh D. Incorporating the empirical null hypothesis into the Benjamini-Hochberg procedure. Stat Appl Genet Mol Biol. 2012 Jul 26; 11(4).
Score: 0.385
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Ghosh D. Semiparametric analysis of recurrent events: artificial censoring, truncation, pairwise estimation and inference. Lifetime Data Anal. 2010 Oct; 16(4):509-24.
Score: 0.323
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Ghosh D. On assessing surrogacy in a single trial setting using a semicompeting risks paradigm. Biometrics. 2009 Jun; 65(2):521-9.
Score: 0.309
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Ghosh D, Poisson LM. "Omics" data and levels of evidence for biomarker discovery. Genomics. 2009 Jan; 93(1):13-6.
Score: 0.294
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Liu D, Lin X, Ghosh D. Semiparametric regression of multidimensional genetic pathway data: least-squares kernel machines and linear mixed models. Biometrics. 2007 Dec; 63(4):1079-88.
Score: 0.279
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Ghosh D. Semiparametric inference for surrogate endpoints with bivariate censored data. Biometrics. 2008 Mar; 64(1):149-56.
Score: 0.272
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Ghosh D, Chinnaiyan AM. Empirical Bayes identification [correction of identication] of tumor progression genes from microarray data. Biom J. 2007 Feb; 49(1):68-77.
Score: 0.263
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Ghosh D. Modelling tumour biology-progression relationships in screening trials. Stat Med. 2006 Jun 15; 25(11):1872-84.
Score: 0.252
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Ghosh T, Zhang W, Ghosh D, Kechris K. Predictive Modeling for Metabolomics Data. Methods Mol Biol. 2020; 2104:313-336.
Score: 0.161
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Cho Y, Hu C, Ghosh D. Covariate adjustment using propensity scores for dependent censoring problems in the accelerated failure time model. Stat Med. 2018 02 10; 37(3):390-404.
Score: 0.138
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Hua WY, Ghosh D. Equivalence of kernel machine regression and kernel distance covariance for multidimensional phenotype association studies. Biometrics. 2015 Sep; 71(3):812-20.
Score: 0.116
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Cho Y, Ghosh D. Weighted estimation of the accelerated failure time model in the presence of dependent censoring. PLoS One. 2015; 10(4):e0124381.
Score: 0.116
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Ghosh D, Zhu Y, Coffman DL. Penalized regression procedures for variable selection in the potential outcomes framework. Stat Med. 2015 May 10; 34(10):1645-58.
Score: 0.114
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Hua WY, Nichols TE, Ghosh D. Multiple comparison procedures for neuroimaging genomewide association studies. Biostatistics. 2015 Jan; 16(1):17-30.
Score: 0.110
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Ghosh D. Discrete nonparametric algorithms for outlier detection with genomic data. J Biopharm Stat. 2010 Mar; 20(2):193-208.
Score: 0.081
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Yuan Z, Ghosh D. Combining multiple biomarker models in logistic regression. Biometrics. 2008 Jun; 64(2):431-9.
Score: 0.071
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Ghosh D. On the Plackett distribution with bivariate censored data. Int J Biostat. 2008; 4(1):Article 7.
Score: 0.070
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Choi H, Shen R, Chinnaiyan AM, Ghosh D. A latent variable approach for meta-analysis of gene expression data from multiple microarray experiments. BMC Bioinformatics. 2007 Sep 27; 8:364.
Score: 0.069
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Ghosh D. Proportional hazards regression for cancer studies. Biometrics. 2008 Mar; 64(1):141-8.
Score: 0.067
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Ghosh D. Shrunken p-values for assessing differential expression with applications to genomic data analysis. Biometrics. 2006 Dec; 62(4):1099-106.
Score: 0.065
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Tseng GC, Ghosh D, Feingold E. Comprehensive literature review and statistical considerations for microarray meta-analysis. Nucleic Acids Res. 2012 May; 40(9):3785-99.
Score: 0.023
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Begum F, Ghosh D, Tseng GC, Feingold E. Comprehensive literature review and statistical considerations for GWAS meta-analysis. Nucleic Acids Res. 2012 May; 40(9):3777-84.
Score: 0.023
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Rhodes DR, Kalyana-Sundaram S, Mahavisno V, Varambally R, Yu J, Briggs BB, Barrette TR, Anstet MJ, Kincead-Beal C, Kulkarni P, Varambally S, Ghosh D, Chinnaiyan AM. Oncomine 3.0: genes, pathways, and networks in a collection of 18,000 cancer gene expression profiles. Neoplasia. 2007 Feb; 9(2):166-80.
Score: 0.016
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Shedden K, Chen W, Kuick R, Ghosh D, Macdonald J, Cho KR, Giordano TJ, Gruber SB, Fearon ER, Taylor JM, Hanash S. Comparison of seven methods for producing Affymetrix expression scores based on False Discovery Rates in disease profiling data. BMC Bioinformatics. 2005 Feb 10; 6:26.
Score: 0.014
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Connection Strength
The connection strength for concepts is the sum of the scores for each matching publication.
Publication scores are based on many factors, including how long ago they were written and whether the person is a first or senior author.
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