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Showing 1 to 12 of 19 entries
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Familial Risk for Exceptional Longevity.

North American actuarial journal : NAAJ

Sebastiani P, Andersen SL, McIntosh AI, Nussbaum L, Stevenson MD, Pierce L, Xia S, Salance K, Perls TT.
PMID: 27041978
N Am Actuar J. 2016 Jan 01;20(1):57-64. doi: 10.1080/10920277.2015.1061946. Epub 2015 Oct 07.

One of the most glaring deficiencies in the current assessment of mortality risk is the lack of information concerning the impact of familial longevity. In this work, we update estimates of sibling relative risk of living to extreme ages...

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Sebastiani P, Andersen SL, McIntosh AI, et al. Familial Risk for Exceptional Longevity. N Am Actuar J. 2015;20(1):57-64doi: 10.1080/10920277.2015.1061946.
Sebastiani, P., Andersen, S. L., McIntosh, A. I., Nussbaum, L., Stevenson, M. D., Pierce, L., Xia, S., Salance, K., & Perls, T. T. (2016). Familial Risk for Exceptional Longevity. North American actuarial journal : NAAJ, 20(1), 57-64. https://doi.org/10.1080/10920277.2015.1061946
Sebastiani, Paola, et al. "Familial Risk for Exceptional Longevity." North American actuarial journal : NAAJ vol. 20,1 (2016): 57-64. doi: https://doi.org/10.1080/10920277.2015.1061946
Sebastiani P, Andersen SL, McIntosh AI, Nussbaum L, Stevenson MD, Pierce L, Xia S, Salance K, Perls TT. Familial Risk for Exceptional Longevity. N Am Actuar J. 2016 Jan 01;20(1):57-64. doi: 10.1080/10920277.2015.1061946. Epub 2015 Oct 07. PMID: 27041978; PMCID: PMC4812435.
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Bayesian modeling of factorial time-course data with applications to a bone aging gene expression study.

Journal of applied statistics

Wu J, Gupta M, Hussein AI, Gerstenfeld L.
PMID: 34295011
J Appl Stat. 2021;48(10):1730-1754. doi: 10.1080/02664763.2020.1772733. Epub 2020 Jun 01.

Many scientific studies, especially in the biomedical sciences, generate data measured simultaneously over a multitude of units, over a period of time, and under different conditions or combinations of factors. Often, an important question of interest asked relates to...

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Wu J, Gupta M, Hussein AI, et al. Bayesian modeling of factorial time-course data with applications to a bone aging gene expression study. J Appl Stat. 2020;48(10):1730-1754doi: 10.1080/02664763.2020.1772733.
Wu, J., Gupta, M., Hussein, A. I., & Gerstenfeld, L. (2021). Bayesian modeling of factorial time-course data with applications to a bone aging gene expression study. Journal of applied statistics, 48(10), 1730-1754. https://doi.org/10.1080/02664763.2020.1772733
Wu, Joseph, et al. "Bayesian modeling of factorial time-course data with applications to a bone aging gene expression study." Journal of applied statistics vol. 48,10 (2021): 1730-1754. doi: https://doi.org/10.1080/02664763.2020.1772733
Wu J, Gupta M, Hussein AI, Gerstenfeld L. Bayesian modeling of factorial time-course data with applications to a bone aging gene expression study. J Appl Stat. 2021;48(10):1730-1754. doi: 10.1080/02664763.2020.1772733. Epub 2020 Jun 01. PMID: 34295011; PMCID: PMC8291340.
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What Can Genetic Relatedness Tell Us About Risk Factors for Tuberculosis Transmission?.

Epidemiology (Cambridge, Mass.)

Leavitt SV, Horsburgh CR, Lee RS, Tibbs AM, White LF, Jenkins HE.
PMID: 34847084
Epidemiology. 2022 Jan 01;33(1):55-64. doi: 10.1097/EDE.0000000000001414.

BACKGROUND: To stop tuberculosis (TB), the leading infectious cause of death globally, we need to better understand transmission risk factors. Although many studies have identified associations between individual-level covariates and pathogen genetic relatedness, few have identified characteristics of transmission...

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Leavitt SV, Horsburgh CR, Lee RS, et al. What Can Genetic Relatedness Tell Us About Risk Factors for Tuberculosis Transmission?. Epidemiology. 2022;33(1):55-64doi: 10.1097/EDE.0000000000001414.
Leavitt, S. V., Horsburgh, C. R., Lee, R. S., Tibbs, A. M., White, L. F., & Jenkins, H. E. (2022). What Can Genetic Relatedness Tell Us About Risk Factors for Tuberculosis Transmission?. Epidemiology (Cambridge, Mass.), 33(1), 55-64. https://doi.org/10.1097/EDE.0000000000001414
Leavitt, Sarah V, et al. "What Can Genetic Relatedness Tell Us About Risk Factors for Tuberculosis Transmission?." Epidemiology (Cambridge, Mass.) vol. 33,1 (2022): 55-64. doi: https://doi.org/10.1097/EDE.0000000000001414
Leavitt SV, Horsburgh CR, Lee RS, Tibbs AM, White LF, Jenkins HE. What Can Genetic Relatedness Tell Us About Risk Factors for Tuberculosis Transmission?. Epidemiology. 2022 Jan 01;33(1):55-64. doi: 10.1097/EDE.0000000000001414. PMID: 34847084; PMCID: PMC8638913.
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Use of the likelihood reduction factor in a path analysis framework to quantify surrogacy in clinical trials.

Statistics in medicine

Bloore K, Song Y, Cabral H, Massaro J, LaValley M.
PMID: 34545969
Stat Med. 2021 Dec 10;40(28):6373-6386. doi: 10.1002/sim.9188. Epub 2021 Sep 21.

In clinical trials, surrogate endpoints are useful when the endpoint of interest is difficult to measure or requires a long follow-up time. Current methodology for validating surrogate endpoints encounters challenges in the presence of collinearity between the treatment and...

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Bloore K, Song Y, Cabral H, et al. Use of the likelihood reduction factor in a path analysis framework to quantify surrogacy in clinical trials. Stat Med. 2021;40(28):6373-6386doi: 10.1002/sim.9188.
Bloore, K., Song, Y., Cabral, H., Massaro, J., & LaValley, M. (2021). Use of the likelihood reduction factor in a path analysis framework to quantify surrogacy in clinical trials. Statistics in medicine, 40(28), 6373-6386. https://doi.org/10.1002/sim.9188
Bloore, Katherine, et al. "Use of the likelihood reduction factor in a path analysis framework to quantify surrogacy in clinical trials." Statistics in medicine vol. 40,28 (2021): 6373-6386. doi: https://doi.org/10.1002/sim.9188
Bloore K, Song Y, Cabral H, Massaro J, LaValley M. Use of the likelihood reduction factor in a path analysis framework to quantify surrogacy in clinical trials. Stat Med. 2021 Dec 10;40(28):6373-6386. doi: 10.1002/sim.9188. Epub 2021 Sep 21. PMID: 34545969; PMCID: PMC8595763.
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Geographic Barriers Affect Follow-Up Care in Head and Neck Cancer.

The Laryngoscope

Alwani T, Shehan JN, LeClair J, Mahoney TF, Agarwal P, Chaudhry ST, Wang JJ, Noordzij JP, Tracy LF, Edwards HA, Grillone G, Salama AR, Jalisi SM, Devaiah AK.
PMID: 34762300
Laryngoscope. 2021 Nov 11; doi: 10.1002/lary.29934. Epub 2021 Nov 11.

OBJECTIVES/HYPOTHESIS: Follow-up care in head and neck cancers (HNC) is critical in managing patient health. However, social determinants of health (SDOH) can create difficulties in maintaining follow-up care. The study goal is to explore how SDOH impacts maintenance of...

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Alwani T, Shehan JN, LeClair J, et al. Geographic Barriers Affect Follow-Up Care in Head and Neck Cancer. Laryngoscope. 2021;doi: 10.1002/lary.29934.
Alwani, T., Shehan, J. N., LeClair, J., Mahoney, T. F., Agarwal, P., Chaudhry, S. T., Wang, J. J., Noordzij, J. P., Tracy, L. F., Edwards, H. A., Grillone, G., Salama, A. R., Jalisi, S. M., & Devaiah, A. K. (2021). Geographic Barriers Affect Follow-Up Care in Head and Neck Cancer. The Laryngoscope, . https://doi.org/10.1002/lary.29934
Alwani, Tooba, et al. "Geographic Barriers Affect Follow-Up Care in Head and Neck Cancer." The Laryngoscope vol. (2021). doi: https://doi.org/10.1002/lary.29934
Alwani T, Shehan JN, LeClair J, Mahoney TF, Agarwal P, Chaudhry ST, Wang JJ, Noordzij JP, Tracy LF, Edwards HA, Grillone G, Salama AR, Jalisi SM, Devaiah AK. Geographic Barriers Affect Follow-Up Care in Head and Neck Cancer. Laryngoscope. 2021 Nov 11; doi: 10.1002/lary.29934. Epub 2021 Nov 11. PMID: 34762300.
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Bayesian Polynomial Regression Models to Fit Multiple Genetic Models for Quantitative Traits.

Bayesian analysis

Bae H, Perls T, Steinberg M, Sebastiani P.
PMID: 26029316
Bayesian Anal. 2015 Mar;10(1):53-74. doi: 10.1214/14-BA880.

We present a coherent Bayesian framework for selection of the most likely model from the five genetic models (genotypic, additive, dominant, co-dominant, and recessive) commonly used in genetic association studies. The approach uses a polynomial parameterization of genetic data...

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Bae H, Perls T, Steinberg M, et al. Bayesian Polynomial Regression Models to Fit Multiple Genetic Models for Quantitative Traits. Bayesian Anal. 2015;10(1):53-74doi: 10.1214/14-BA880.
Bae, H., Perls, T., Steinberg, M., & Sebastiani, P. (2015). Bayesian Polynomial Regression Models to Fit Multiple Genetic Models for Quantitative Traits. Bayesian analysis, 10(1), 53-74. https://doi.org/10.1214/14-BA880
Bae, Harold, et al. "Bayesian Polynomial Regression Models to Fit Multiple Genetic Models for Quantitative Traits." Bayesian analysis vol. 10,1 (2015): 53-74. doi: https://doi.org/10.1214/14-BA880
Bae H, Perls T, Steinberg M, Sebastiani P. Bayesian Polynomial Regression Models to Fit Multiple Genetic Models for Quantitative Traits. Bayesian Anal. 2015 Mar;10(1):53-74. doi: 10.1214/14-BA880. PMID: 26029316; PMCID: PMC4446790.
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Erratum: Burden of disease variants in participants of the long life family study.

Aging

[No authors listed]
PMID: 26175384
Aging (Albany NY). 2015 Jun;7(6):450. doi: 10.18632/aging.100761.

No abstract available.

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Erratum: Burden of disease variants in participants of the long life family study. Aging (Albany NY). 2015;7(6):450doi: 10.18632/aging.100761.
(2015). Erratum: Burden of disease variants in participants of the long life family study. Aging, 7(6), 450. https://doi.org/10.18632/aging.100761
"Erratum: Burden of disease variants in participants of the long life family study." Aging vol. 7,6 (2015): 450. doi: https://doi.org/10.18632/aging.100761
Erratum: Burden of disease variants in participants of the long life family study. Aging (Albany NY). 2015 Jun;7(6):450. doi: 10.18632/aging.100761. PMID: 26175384; PMCID: PMC4505170.
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Counterfactual mediation analysis in the multistate model framework for surrogate and clinical time-to-event outcomes in randomized controlled trials.

Pharmaceutical statistics

Weir IR, Rider JR, Trinquart L.
PMID: 34346173
Pharm Stat. 2021 Aug 04; doi: 10.1002/pst.2159. Epub 2021 Aug 04.

In cancer randomized controlled trials, surrogate endpoints are frequently time-to-event endpoints, subject to the competing risk from the time-to-event clinical outcome. In this context, we introduce a counterfactual-based mediation analysis for a causal assessment of surrogacy. We use a...

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Weir IR, Rider JR, Trinquart L. Counterfactual mediation analysis in the multistate model framework for surrogate and clinical time-to-event outcomes in randomized controlled trials. Pharm Stat. 2021;doi: 10.1002/pst.2159.
Weir, I. R., Rider, J. R., & Trinquart, L. (2021). Counterfactual mediation analysis in the multistate model framework for surrogate and clinical time-to-event outcomes in randomized controlled trials. Pharmaceutical statistics, . https://doi.org/10.1002/pst.2159
Weir, Isabelle R, et al. "Counterfactual mediation analysis in the multistate model framework for surrogate and clinical time-to-event outcomes in randomized controlled trials." Pharmaceutical statistics vol. (2021). doi: https://doi.org/10.1002/pst.2159
Weir IR, Rider JR, Trinquart L. Counterfactual mediation analysis in the multistate model framework for surrogate and clinical time-to-event outcomes in randomized controlled trials. Pharm Stat. 2021 Aug 04; doi: 10.1002/pst.2159. Epub 2021 Aug 04. PMID: 34346173.
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Detecting differentially methylated regions with multiple distinct associations.

Epigenomics

Lent S, Cardenas A, Rifas-Shiman SL, Perron P, Bouchard L, Liu CT, Hivert MF, Dupuis J.
PMID: 33641349
Epigenomics. 2021 Mar;13(6):451-464. doi: 10.2217/epi-2020-0344. Epub 2021 Mar 01.

No abstract available.

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Lent S, Cardenas A, Rifas-Shiman SL, et al. Detecting differentially methylated regions with multiple distinct associations. Epigenomics. 2021;13(6):451-464doi: 10.2217/epi-2020-0344.
Lent, S., Cardenas, A., Rifas-Shiman, S. L., Perron, P., Bouchard, L., Liu, C. T., Hivert, M. F., & Dupuis, J. (2021). Detecting differentially methylated regions with multiple distinct associations. Epigenomics, 13(6), 451-464. https://doi.org/10.2217/epi-2020-0344
Lent, Samantha, et al. "Detecting differentially methylated regions with multiple distinct associations." Epigenomics vol. 13,6 (2021): 451-464. doi: https://doi.org/10.2217/epi-2020-0344
Lent S, Cardenas A, Rifas-Shiman SL, Perron P, Bouchard L, Liu CT, Hivert MF, Dupuis J. Detecting differentially methylated regions with multiple distinct associations. Epigenomics. 2021 Mar;13(6):451-464. doi: 10.2217/epi-2020-0344. Epub 2021 Mar 01. PMID: 33641349; PMCID: PMC8023344.
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APOE E2/E2 Is Associated with Slower Rate of Cognitive Decline with Age.

Journal of Alzheimer's disease : JAD

Sweigart B, Andersen SL, Gurinovich A, Cosentino S, Schupf N, Perls TT, Sebastiani P.
PMID: 34366332
J Alzheimers Dis. 2021;83(2):853-860. doi: 10.3233/JAD-201205.

BACKGROUND: The E4 allele of the APOE gene is known to be associated with cognitive impairment. However, a limited number of studies have examined the association between the E2 allele and longitudinal changes of cognitive function.OBJECTIVE: To determine whether...

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Sweigart B, Andersen SL, Gurinovich A, et al. APOE E2/E2 Is Associated with Slower Rate of Cognitive Decline with Age. J Alzheimers Dis. 2021;83(2):853-860doi: 10.3233/JAD-201205.
Sweigart, B., Andersen, S. L., Gurinovich, A., Cosentino, S., Schupf, N., Perls, T. T., & Sebastiani, P. (2021). APOE E2/E2 Is Associated with Slower Rate of Cognitive Decline with Age. Journal of Alzheimer's disease : JAD, 83(2), 853-860. https://doi.org/10.3233/JAD-201205
Sweigart, Benjamin, et al. "APOE E2/E2 Is Associated with Slower Rate of Cognitive Decline with Age." Journal of Alzheimer's disease : JAD vol. 83,2 (2021): 853-860. doi: https://doi.org/10.3233/JAD-201205
Sweigart B, Andersen SL, Gurinovich A, Cosentino S, Schupf N, Perls TT, Sebastiani P. APOE E2/E2 Is Associated with Slower Rate of Cognitive Decline with Age. J Alzheimers Dis. 2021;83(2):853-860. doi: 10.3233/JAD-201205. PMID: 34366332.
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An efficient technique for Bayesian modeling of family data using the BUGS software.

Frontiers in genetics

Bae HT, Perls TT, Sebastiani P.
PMID: 25477899
Front Genet. 2014 Nov 18;5:390. doi: 10.3389/fgene.2014.00390. eCollection 2014.

Linear mixed models have become a popular tool to analyze continuous data from family-based designs by using random effects that model the correlation of subjects from the same family. However, mixed models for family data are challenging to implement...

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Bae HT, Perls TT, Sebastiani P. An efficient technique for Bayesian modeling of family data using the BUGS software. Front Genet. 2014;5:390doi: 10.3389/fgene.2014.00390.
Bae, H. T., Perls, T. T., & Sebastiani, P. (2014). An efficient technique for Bayesian modeling of family data using the BUGS software. Frontiers in genetics, 5390. https://doi.org/10.3389/fgene.2014.00390
Bae, Harold T, et al. "An efficient technique for Bayesian modeling of family data using the BUGS software." Frontiers in genetics vol. 5 (2014): 390. doi: https://doi.org/10.3389/fgene.2014.00390
Bae HT, Perls TT, Sebastiani P. An efficient technique for Bayesian modeling of family data using the BUGS software. Front Genet. 2014 Nov 18;5:390. doi: 10.3389/fgene.2014.00390. eCollection 2014. PMID: 25477899; PMCID: PMC4235415.
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Differences in Regional Patterns of Influenza Activity Across Surveillance Systems in the United States: Comparative Evaluation.

JMIR public health and surveillance

Baltrusaitis K, Vespignani A, Rosenfeld R, Gray J, Raymond D, Santillana M.
PMID: 31579019
JMIR Public Health Surveill. 2019 Sep 14;5(4):e13403. doi: 10.2196/13403.

BACKGROUND: The Centers for Disease Control and Prevention (CDC) tracks influenza-like illness (ILI) using information on patient visits to health care providers through the Outpatient Influenza-like Illness Surveillance Network (ILINet). As participation in this system is voluntary, the composition,...

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Baltrusaitis K, Vespignani A, Rosenfeld R, et al. Differences in Regional Patterns of Influenza Activity Across Surveillance Systems in the United States: Comparative Evaluation. JMIR Public Health Surveill. 2019;5(4):e13403doi: 10.2196/13403.
Baltrusaitis, K., Vespignani, A., Rosenfeld, R., Gray, J., Raymond, D., & Santillana, M. (2019). Differences in Regional Patterns of Influenza Activity Across Surveillance Systems in the United States: Comparative Evaluation. JMIR public health and surveillance, 5(4), e13403. https://doi.org/10.2196/13403
Baltrusaitis, Kristin, et al. "Differences in Regional Patterns of Influenza Activity Across Surveillance Systems in the United States: Comparative Evaluation." JMIR public health and surveillance vol. 5,4 (2019): e13403. doi: https://doi.org/10.2196/13403
Baltrusaitis K, Vespignani A, Rosenfeld R, Gray J, Raymond D, Santillana M. Differences in Regional Patterns of Influenza Activity Across Surveillance Systems in the United States: Comparative Evaluation. JMIR Public Health Surveill. 2019 Sep 14;5(4):e13403. doi: 10.2196/13403. PMID: 31579019; PMCID: PMC6777281.
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Showing 1 to 12 of 19 entries