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Sensitivity analysis for direct and indirect effects in the presence of exposure-induced mediator-outcome confounders.

Epidemiology, biostatistics and public health

VanderWeele TJ, Chiba Y.
PMID: 25580387
Epidemiol Biostat Public Health. 2014;11(2). doi: 10.2427/9027.

Questions of mediation are often of interest in reasoning about mechanisms, and methods have been developed to address these questions. However, these methods make strong assumptions about the absence of confounding. Even if exposure is randomized, there may be...

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VanderWeele TJ, Chiba Y. Sensitivity analysis for direct and indirect effects in the presence of exposure-induced mediator-outcome confounders. Epidemiol Biostat Public Health. 2014;11(2)doi: 10.2427/9027.
VanderWeele, T. J., & Chiba, Y. (2014). Sensitivity analysis for direct and indirect effects in the presence of exposure-induced mediator-outcome confounders. Epidemiology, biostatistics and public health, 11(2), . https://doi.org/10.2427/9027
VanderWeele, Tyler J, and Chiba, Yasutaka. "Sensitivity analysis for direct and indirect effects in the presence of exposure-induced mediator-outcome confounders." Epidemiology, biostatistics and public health vol. 11,2 (2014). doi: https://doi.org/10.2427/9027
VanderWeele TJ, Chiba Y. Sensitivity analysis for direct and indirect effects in the presence of exposure-induced mediator-outcome confounders. Epidemiol Biostat Public Health. 2014;11(2). doi: 10.2427/9027. PMID: 25580387; PMCID: PMC4287391.
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Estimating gene regulatory networks with pandaR.

Bioinformatics (Oxford, England)

Schlauch D, Paulson JN, Young A, Glass K, Quackenbush J.
PMID: 28334344
Bioinformatics. 2017 Jul 15;33(14):2232-2234. doi: 10.1093/bioinformatics/btx139.

CONTACT: [email protected] or [email protected] AND IMPLEMENTATION: PandaR is provided as a Bioconductor R Package and is available at bioconductor.org/packages/pandaR.

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Schlauch D, Paulson JN, Young A, et al. Estimating gene regulatory networks with pandaR. Bioinformatics. 2017;33(14):2232-2234doi: 10.1093/bioinformatics/btx139.
Schlauch, D., Paulson, J. N., Young, A., Glass, K., & Quackenbush, J. (2017). Estimating gene regulatory networks with pandaR. Bioinformatics (Oxford, England), 33(14), 2232-2234. https://doi.org/10.1093/bioinformatics/btx139
Schlauch, Daniel, et al. "Estimating gene regulatory networks with pandaR." Bioinformatics (Oxford, England) vol. 33,14 (2017): 2232-2234. doi: https://doi.org/10.1093/bioinformatics/btx139
Schlauch D, Paulson JN, Young A, Glass K, Quackenbush J. Estimating gene regulatory networks with pandaR. Bioinformatics. 2017 Jul 15;33(14):2232-2234. doi: 10.1093/bioinformatics/btx139. PMID: 28334344; PMCID: PMC5870629.
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Hierarchical models for semi-competing risks data with application to quality of end-of-life care for pancreatic cancer.

Journal of the American Statistical Association

Lee KH, Dominici F, Schrag D, Haneuse S.
PMID: 28303074
J Am Stat Assoc. 2016;111(515):1075-1095. doi: 10.1080/01621459.2016.1164052. Epub 2016 Oct 18.

Readmission following discharge from an initial hospitalization is a key marker of quality of health care in the United States. For the most part, readmission has been studied among patients with 'acute' health conditions, such as pneumonia and heart...

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Lee KH, Dominici F, Schrag D, et al. Hierarchical models for semi-competing risks data with application to quality of end-of-life care for pancreatic cancer. J Am Stat Assoc. 2016;111(515):1075-1095doi: 10.1080/01621459.2016.1164052.
Lee, K. H., Dominici, F., Schrag, D., & Haneuse, S. (2016). Hierarchical models for semi-competing risks data with application to quality of end-of-life care for pancreatic cancer. Journal of the American Statistical Association, 111(515), 1075-1095. https://doi.org/10.1080/01621459.2016.1164052
Lee, Kyu Ha, et al. "Hierarchical models for semi-competing risks data with application to quality of end-of-life care for pancreatic cancer." Journal of the American Statistical Association vol. 111,515 (2016): 1075-1095. doi: https://doi.org/10.1080/01621459.2016.1164052
Lee KH, Dominici F, Schrag D, Haneuse S. Hierarchical models for semi-competing risks data with application to quality of end-of-life care for pancreatic cancer. J Am Stat Assoc. 2016;111(515):1075-1095. doi: 10.1080/01621459.2016.1164052. Epub 2016 Oct 18. PMID: 28303074; PMCID: PMC5347153.
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Sharp sensitivity bounds for mediation under unmeasured mediator-outcome confounding.

Biometrika

Ding P, Vanderweele TJ.
PMID: 27279672
Biometrika. 2016 Jun;103(2):483-490. doi: 10.1093/biomet/asw012. Epub 2016 Apr 30.

It is often of interest to decompose the total effect of an exposure into a component that acts on the outcome through some mediator and a component that acts independently through other pathways. Said another way, we are interested...

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Ding P, Vanderweele TJ. Sharp sensitivity bounds for mediation under unmeasured mediator-outcome confounding. Biometrika. 2016;103(2):483-490doi: 10.1093/biomet/asw012.
Ding, P., & Vanderweele, T. J. (2016). Sharp sensitivity bounds for mediation under unmeasured mediator-outcome confounding. Biometrika, 103(2), 483-490. https://doi.org/10.1093/biomet/asw012
Ding, Peng, and Vanderweele, Tyler J. "Sharp sensitivity bounds for mediation under unmeasured mediator-outcome confounding." Biometrika vol. 103,2 (2016): 483-490. doi: https://doi.org/10.1093/biomet/asw012
Ding P, Vanderweele TJ. Sharp sensitivity bounds for mediation under unmeasured mediator-outcome confounding. Biometrika. 2016 Jun;103(2):483-490. doi: 10.1093/biomet/asw012. Epub 2016 Apr 30. PMID: 27279672; PMCID: PMC4890130.
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Estimating Causal Effects in Trials Involving Multi-Treatment Arms Subject to Non-compliance: A Bayesian framework.

Journal of the Royal Statistical Society. Series C, Applied statistics

Long Q, Little RJ, Lin X.
PMID: 21637737
J R Stat Soc Ser C Appl Stat. 2010 May;59(3):513-531. doi: 10.1111/j.1467-9876.2009.00709.x.

Data analysis for randomized trials including multi-treatment arms is often complicated by subjects who do not comply with their treatment assignment. We discuss here methods of estimating treatment efficacy for randomized trials involving multi-treatment arms subject to non-compliance. One...

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Long Q, Little RJ, Lin X. Estimating Causal Effects in Trials Involving Multi-Treatment Arms Subject to Non-compliance: A Bayesian framework. J R Stat Soc Ser C Appl Stat. 2010;59(3):513-531doi: 10.1111/j.1467-9876.2009.00709.x.
Long, Q., Little, R. J., & Lin, X. (2010). Estimating Causal Effects in Trials Involving Multi-Treatment Arms Subject to Non-compliance: A Bayesian framework. Journal of the Royal Statistical Society. Series C, Applied statistics, 59(3), 513-531. https://doi.org/10.1111/j.1467-9876.2009.00709.x
Long, Qi, et al. "Estimating Causal Effects in Trials Involving Multi-Treatment Arms Subject to Non-compliance: A Bayesian framework." Journal of the Royal Statistical Society. Series C, Applied statistics vol. 59,3 (2010): 513-531. doi: https://doi.org/10.1111/j.1467-9876.2009.00709.x
Long Q, Little RJ, Lin X. Estimating Causal Effects in Trials Involving Multi-Treatment Arms Subject to Non-compliance: A Bayesian framework. J R Stat Soc Ser C Appl Stat. 2010 May;59(3):513-531. doi: 10.1111/j.1467-9876.2009.00709.x. PMID: 21637737; PMCID: PMC3104736.
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Landmark Estimation of Survival and Treatment Effect in a Randomized Clinical Trial.

Journal of the American Statistical Association

Parast L, Tian L, Cai T.
PMID: 24659838
J Am Stat Assoc. 2014 Jan 01;109(505):384-394. doi: 10.1080/01621459.2013.842488.

In many studies with a survival outcome, it is often not feasible to fully observe the primary event of interest. This often leads to heavy censoring and thus, difficulty in efficiently estimating survival or comparing survival rates between two...

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Parast L, Tian L, Cai T. Landmark Estimation of Survival and Treatment Effect in a Randomized Clinical Trial. J Am Stat Assoc. 2014;109(505):384-394doi: 10.1080/01621459.2013.842488.
Parast, L., Tian, L., & Cai, T. (2014). Landmark Estimation of Survival and Treatment Effect in a Randomized Clinical Trial. Journal of the American Statistical Association, 109(505), 384-394. https://doi.org/10.1080/01621459.2013.842488
Parast, Layla, et al. "Landmark Estimation of Survival and Treatment Effect in a Randomized Clinical Trial." Journal of the American Statistical Association vol. 109,505 (2014): 384-394. doi: https://doi.org/10.1080/01621459.2013.842488
Parast L, Tian L, Cai T. Landmark Estimation of Survival and Treatment Effect in a Randomized Clinical Trial. J Am Stat Assoc. 2014 Jan 01;109(505):384-394. doi: 10.1080/01621459.2013.842488. PMID: 24659838; PMCID: PMC3960087.
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Detecting phenotype-driven transitions in regulatory network structure.

NPJ systems biology and applications

Padi M, Quackenbush J.
PMID: 29707235
NPJ Syst Biol Appl. 2018 Apr 19;4:16. doi: 10.1038/s41540-018-0052-5. eCollection 2018.

Complex traits and diseases like human height or cancer are often not caused by a single mutation or genetic variant, but instead arise from functional changes in the underlying molecular network. Biological networks are known to be highly modular...

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Padi M, Quackenbush J. Detecting phenotype-driven transitions in regulatory network structure. NPJ Syst Biol Appl. 2018;4:16doi: 10.1038/s41540-018-0052-5.
Padi, M., & Quackenbush, J. (2018). Detecting phenotype-driven transitions in regulatory network structure. NPJ systems biology and applications, 416. https://doi.org/10.1038/s41540-018-0052-5
Padi, Megha, and Quackenbush, John. "Detecting phenotype-driven transitions in regulatory network structure." NPJ systems biology and applications vol. 4 (2018): 16. doi: https://doi.org/10.1038/s41540-018-0052-5
Padi M, Quackenbush J. Detecting phenotype-driven transitions in regulatory network structure. NPJ Syst Biol Appl. 2018 Apr 19;4:16. doi: 10.1038/s41540-018-0052-5. eCollection 2018. PMID: 29707235; PMCID: PMC5908977.
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FDR control of detected regions by multiscale matched filtering.

Communications in statistics: Simulation and computation

Kachouie NN, Lin X, Schwartzman A.
PMID: 31501637
Commun Stat Simul Comput. 2017;46(1):127-144. doi: 10.1080/03610918.2014.957842. Epub 2014 Dec 23.

Feature extraction from observed noisy samples is a common important problem in statistics and engineering. This paper presents a novel general statistical approach to the region detection problem in long data sequences. The proposed technique is a multi-scale kernel...

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Kachouie NN, Lin X, Schwartzman A. FDR control of detected regions by multiscale matched filtering. Commun Stat Simul Comput. 2014;46(1):127-144doi: 10.1080/03610918.2014.957842.
Kachouie, N. N., Lin, X., & Schwartzman, A. (2017). FDR control of detected regions by multiscale matched filtering. Communications in statistics: Simulation and computation, 46(1), 127-144. https://doi.org/10.1080/03610918.2014.957842
Kachouie, Nezamoddin N, et al. "FDR control of detected regions by multiscale matched filtering." Communications in statistics: Simulation and computation vol. 46,1 (2017): 127-144. doi: https://doi.org/10.1080/03610918.2014.957842
Kachouie NN, Lin X, Schwartzman A. FDR control of detected regions by multiscale matched filtering. Commun Stat Simul Comput. 2017;46(1):127-144. doi: 10.1080/03610918.2014.957842. Epub 2014 Dec 23. PMID: 31501637; PMCID: PMC6733272.
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Genome-wide association study of INDELs identified four novel susceptibility loci associated with lung cancer risk.

International journal of cancer

Dai J, Huang M, Amos CI, Hung RJ, Tardon A, Andrew A, Chen C, Christiani DC, Albanes D, Rennert G, Fan J, Goodman G, Liu G, Field JK, Grankvist K, Kiemeney LA, Le Marchand L, Schabath MB, Johansson M, Aldrich MC, Johansson M, Caporaso N, Lazarus P, Lam S, Bojesen SE, Arnold S, Landi MT, Risch A, Wichmann HE, Bickeboller H, Brennan P, Shete S, Melander O, Brunnstrom H, Zienolddiny S, Woll P, Stevens V, Hu Z, Shen H.
PMID: 31577861
Int J Cancer. 2020 May 15;146(10):2855-2864. doi: 10.1002/ijc.32698. Epub 2019 Oct 31.

Genome-wide association studies (GWAS) have identified 45 susceptibility loci associated with lung cancer. Only less than SNPs, small insertions and deletions (INDELs) are the second most abundant genetic polymorphisms in the human genome. INDELs are highly associated with multiple...

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Dai J, Huang M, Amos CI, et al. Genome-wide association study of INDELs identified four novel susceptibility loci associated with lung cancer risk. Int J Cancer. 2019;146(10):2855-2864doi: 10.1002/ijc.32698.
Dai, J., Huang, M., Amos, C. I., Hung, R. J., Tardon, A., Andrew, A., Chen, C., Christiani, D. C., Albanes, D., Rennert, G., Fan, J., Goodman, G., Liu, G., Field, J. K., Grankvist, K., Kiemeney, L. A., Le Marchand, L., Schabath, M. B., Johansson, M., Aldrich, M. C., Johansson, M., Caporaso, N., Lazarus, P., Lam, S., Bojesen, S. E., Arnold, S., Landi, M. T., Risch, A., Wichmann, H. E., Bickeboller, H., Brennan, P., Shete, S., Melander, O., Brunnstrom, H., Zienolddiny, S., Woll, P., Stevens, V., Hu, Z., & Shen, H. (2020). Genome-wide association study of INDELs identified four novel susceptibility loci associated with lung cancer risk. International journal of cancer, 146(10), 2855-2864. https://doi.org/10.1002/ijc.32698
Dai, Juncheng, et al. "Genome-wide association study of INDELs identified four novel susceptibility loci associated with lung cancer risk." International journal of cancer vol. 146,10 (2020): 2855-2864. doi: https://doi.org/10.1002/ijc.32698
Dai J, Huang M, Amos CI, Hung RJ, Tardon A, Andrew A, Chen C, Christiani DC, Albanes D, Rennert G, Fan J, Goodman G, Liu G, Field JK, Grankvist K, Kiemeney LA, Le Marchand L, Schabath MB, Johansson M, Aldrich MC, Johansson M, Caporaso N, Lazarus P, Lam S, Bojesen SE, Arnold S, Landi MT, Risch A, Wichmann HE, Bickeboller H, Brennan P, Shete S, Melander O, Brunnstrom H, Zienolddiny S, Woll P, Stevens V, Hu Z, Shen H. Genome-wide association study of INDELs identified four novel susceptibility loci associated with lung cancer risk. Int J Cancer. 2020 May 15;146(10):2855-2864. doi: 10.1002/ijc.32698. Epub 2019 Oct 31. PMID: 31577861; PMCID: PMC7101262.
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Mutant-allele fraction heterogeneity is associated with non-small cell lung cancer patient survival.

Oncology letters

Shen S, Wei Y, Zhang R, Du M, Duan W, Yang S, Zhao Y, Christiani DC, Chen F.
PMID: 29399148
Oncol Lett. 2018 Jan;15(1):795-802. doi: 10.3892/ol.2017.7428. Epub 2017 Nov 15.

Genetic intratumor heterogeneity is associated with tumor occurrence, development and overall outcome. The present study aims to explore the association between mutant-allele fraction (MAF) heterogeneity and patient overall survival in lung cancer. Somatic mutation data of 939 non-small cell...

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Shen S, Wei Y, Zhang R, et al. Mutant-allele fraction heterogeneity is associated with non-small cell lung cancer patient survival. Oncol Lett. 2017;15(1):795-802doi: 10.3892/ol.2017.7428.
Shen, S., Wei, Y., Zhang, R., Du, M., Duan, W., Yang, S., Zhao, Y., Christiani, D. C., & Chen, F. (2018). Mutant-allele fraction heterogeneity is associated with non-small cell lung cancer patient survival. Oncology letters, 15(1), 795-802. https://doi.org/10.3892/ol.2017.7428
Shen, Sipeng, et al. "Mutant-allele fraction heterogeneity is associated with non-small cell lung cancer patient survival." Oncology letters vol. 15,1 (2018): 795-802. doi: https://doi.org/10.3892/ol.2017.7428
Shen S, Wei Y, Zhang R, Du M, Duan W, Yang S, Zhao Y, Christiani DC, Chen F. Mutant-allele fraction heterogeneity is associated with non-small cell lung cancer patient survival. Oncol Lett. 2018 Jan;15(1):795-802. doi: 10.3892/ol.2017.7428. Epub 2017 Nov 15. PMID: 29399148; PMCID: PMC5772758.
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Multiple Testing of Submatrices of a Precision Matrix with Applications to Identification of Between Pathway Interactions.

Journal of the American Statistical Association

Xia Y, Cai T, Cai TT.
PMID: 29881130
J Am Stat Assoc. 2018;113(521):328-339. doi: 10.1080/01621459.2016.1251930. Epub 2017 Sep 26.

Making accurate inference for gene regulatory networks, including inferring about pathway by pathway interactions, is an important and difficult task. Motivated by such genomic applications, we consider multiple testing for conditional dependence between subgroups of variables. Under a Gaussian...

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Xia Y, Cai T, Cai TT. Multiple Testing of Submatrices of a Precision Matrix with Applications to Identification of Between Pathway Interactions. J Am Stat Assoc. 2017;113(521):328-339doi: 10.1080/01621459.2016.1251930.
Xia, Y., Cai, T., & Cai, T. T. (2018). Multiple Testing of Submatrices of a Precision Matrix with Applications to Identification of Between Pathway Interactions. Journal of the American Statistical Association, 113(521), 328-339. https://doi.org/10.1080/01621459.2016.1251930
Xia, Yin, et al. "Multiple Testing of Submatrices of a Precision Matrix with Applications to Identification of Between Pathway Interactions." Journal of the American Statistical Association vol. 113,521 (2018): 328-339. doi: https://doi.org/10.1080/01621459.2016.1251930
Xia Y, Cai T, Cai TT. Multiple Testing of Submatrices of a Precision Matrix with Applications to Identification of Between Pathway Interactions. J Am Stat Assoc. 2018;113(521):328-339. doi: 10.1080/01621459.2016.1251930. Epub 2017 Sep 26. PMID: 29881130; PMCID: PMC5988269.
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Large-Scale Simultaneous Testing of Cross-Covariance Matrices with Applications to PheWAS.

Statistica Sinica

Cai T, Cai TT, Liao K, Liu W.
PMID: 31889766
Stat Sin. 2019 Apr;29(2):983-1005. doi: 10.5705/ss.202017.0189.

Motivated by applications in phenome-wide association studies (PheWAS), we consider in this paper simultaneous testing of columns of high-dimensional cross-covariance matrices and develop a multiple testing procedure with theoretical guarantees. It is shown that the proposed testing procedure maintains...

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Cai T, Cai TT, Liao K, et al. Large-Scale Simultaneous Testing of Cross-Covariance Matrices with Applications to PheWAS. Stat Sin. 2019;29(2):983-1005doi: 10.5705/ss.202017.0189.
Cai, T., Cai, T. T., Liao, K., & Liu, W. (2019). Large-Scale Simultaneous Testing of Cross-Covariance Matrices with Applications to PheWAS. Statistica Sinica, 29(2), 983-1005. https://doi.org/10.5705/ss.202017.0189
Cai, Tianxi, et al. "Large-Scale Simultaneous Testing of Cross-Covariance Matrices with Applications to PheWAS." Statistica Sinica vol. 29,2 (2019): 983-1005. doi: https://doi.org/10.5705/ss.202017.0189
Cai T, Cai TT, Liao K, Liu W. Large-Scale Simultaneous Testing of Cross-Covariance Matrices with Applications to PheWAS. Stat Sin. 2019 Apr;29(2):983-1005. doi: 10.5705/ss.202017.0189. PMID: 31889766; PMCID: PMC6937133.
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Showing 1 to 12 of 57 entries