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Showing 1 to 12 of 668 entries
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Optimal Allocation of Gold Standard Testing under Constrained Availability: Application to Assessment of HIV Treatment Failure.

Journal of the American Statistical Association

Liu T, Hogan JW, Wang L, Zhang S, Kantor R.
PMID: 24672142
J Am Stat Assoc. 2013 Jan 01;108(504):1173-1188. doi: 10.1080/01621459.2013.810149.

The World Health Organization (WHO) guidelines for monitoring the effectiveness of HIV treatment in resource-limited settings (RLS) are mostly based on clinical and immunological markers (e.g., CD4 cell counts). Recent research indicates that the guidelines are inadequate and can...

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Liu T, Hogan JW, Wang L, et al. Optimal Allocation of Gold Standard Testing under Constrained Availability: Application to Assessment of HIV Treatment Failure. J Am Stat Assoc. 2013;108(504):1173-1188doi: 10.1080/01621459.2013.810149.
Liu, T., Hogan, J. W., Wang, L., Zhang, S., & Kantor, R. (2013). Optimal Allocation of Gold Standard Testing under Constrained Availability: Application to Assessment of HIV Treatment Failure. Journal of the American Statistical Association, 108(504), 1173-1188. https://doi.org/10.1080/01621459.2013.810149
Liu, Tao, et al. "Optimal Allocation of Gold Standard Testing under Constrained Availability: Application to Assessment of HIV Treatment Failure." Journal of the American Statistical Association vol. 108,504 (2013): 1173-1188. doi: https://doi.org/10.1080/01621459.2013.810149
Liu T, Hogan JW, Wang L, Zhang S, Kantor R. Optimal Allocation of Gold Standard Testing under Constrained Availability: Application to Assessment of HIV Treatment Failure. J Am Stat Assoc. 2013 Jan 01;108(504):1173-1188. doi: 10.1080/01621459.2013.810149. PMID: 24672142; PMCID: PMC3963362.
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A Semiparametric Change-Point Regression Model for Longitudinal Observations.

Journal of the American Statistical Association

Xing H, Ying Z.
PMID: 24288420
J Am Stat Assoc. 2012 Dec 01;107(500). doi: 10.1080/01621459.2012.712425.

Many longitudinal studies involve relating an outcome process to a set of possibly time-varying covariates, giving rise to the usual regression models for longitudinal data. When the purpose of the study is to investigate the covariate effects when experimental...

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Xing H, Ying Z. A Semiparametric Change-Point Regression Model for Longitudinal Observations. J Am Stat Assoc. 2012;107(500)doi: 10.1080/01621459.2012.712425.
Xing, H., & Ying, Z. (2012). A Semiparametric Change-Point Regression Model for Longitudinal Observations. Journal of the American Statistical Association, 107(500), . https://doi.org/10.1080/01621459.2012.712425
Xing, Haipeng, and Ying, Zhiliang. "A Semiparametric Change-Point Regression Model for Longitudinal Observations." Journal of the American Statistical Association vol. 107,500 (2012). doi: https://doi.org/10.1080/01621459.2012.712425
Xing H, Ying Z. A Semiparametric Change-Point Regression Model for Longitudinal Observations. J Am Stat Assoc. 2012 Dec 01;107(500). doi: 10.1080/01621459.2012.712425. PMID: 24288420; PMCID: PMC3839871.
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A Unified Approach to Semiparametric Transformation Models under General Biased Sampling Schemes.

Journal of the American Statistical Association

Kim JP, Lu W, Sit T, Ying Z.
PMID: 23667280
J Am Stat Assoc. 2013 Jan 01;108(501):217-227. doi: 10.1080/01621459.2012.746073.

We propose a unified estimation method for semiparametric linear transformation models under general biased sampling schemes. The new estimator is obtained from a set of counting process-based unbiased estimating equations, developed through introducing a general weighting scheme that offsets...

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Kim JP, Lu W, Sit T, et al. A Unified Approach to Semiparametric Transformation Models under General Biased Sampling Schemes. J Am Stat Assoc. 2013;108(501):217-227doi: 10.1080/01621459.2012.746073.
Kim, J. P., Lu, W., Sit, T., & Ying, Z. (2013). A Unified Approach to Semiparametric Transformation Models under General Biased Sampling Schemes. Journal of the American Statistical Association, 108(501), 217-227. https://doi.org/10.1080/01621459.2012.746073
Kim, Jane Paik, et al. "A Unified Approach to Semiparametric Transformation Models under General Biased Sampling Schemes." Journal of the American Statistical Association vol. 108,501 (2013): 217-227. doi: https://doi.org/10.1080/01621459.2012.746073
Kim JP, Lu W, Sit T, Ying Z. A Unified Approach to Semiparametric Transformation Models under General Biased Sampling Schemes. J Am Stat Assoc. 2013 Jan 01;108(501):217-227. doi: 10.1080/01621459.2012.746073. PMID: 23667280; PMCID: PMC3649773.
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Asymptotically Normal and Efficient Estimation of Covariate-Adjusted Gaussian Graphical Model.

Journal of the American Statistical Association

Chen M, Ren Z, Zhao H, Zhou H.
PMID: 27499564
J Am Stat Assoc. 2016 Mar;111(513):394-406. doi: 10.1080/01621459.2015.1010039. Epub 2016 May 05.

A tuning-free procedure is proposed to estimate the covariate-adjusted Gaussian graphical model. For each finite subgraph, this estimator is asymptotically normal and efficient. As a consequence, a confidence interval can be obtained for each edge. The procedure enjoys easy...

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Chen M, Ren Z, Zhao H, et al. Asymptotically Normal and Efficient Estimation of Covariate-Adjusted Gaussian Graphical Model. J Am Stat Assoc. 2016;111(513):394-406doi: 10.1080/01621459.2015.1010039.
Chen, M., Ren, Z., Zhao, H., & Zhou, H. (2016). Asymptotically Normal and Efficient Estimation of Covariate-Adjusted Gaussian Graphical Model. Journal of the American Statistical Association, 111(513), 394-406. https://doi.org/10.1080/01621459.2015.1010039
Chen, Mengjie, et al. "Asymptotically Normal and Efficient Estimation of Covariate-Adjusted Gaussian Graphical Model." Journal of the American Statistical Association vol. 111,513 (2016): 394-406. doi: https://doi.org/10.1080/01621459.2015.1010039
Chen M, Ren Z, Zhao H, Zhou H. Asymptotically Normal and Efficient Estimation of Covariate-Adjusted Gaussian Graphical Model. J Am Stat Assoc. 2016 Mar;111(513):394-406. doi: 10.1080/01621459.2015.1010039. Epub 2016 May 05. PMID: 27499564; PMCID: PMC4974017.
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Robust treatment comparison based on utilities of semi-competing risks in non-small-cell lung cancer.

Journal of the American Statistical Association

Murray TA, Thall PF, Yuan Y, McAvoy S, Gomez DR.
PMID: 28943681
J Am Stat Assoc. 2017;112:11-23. doi: 10.1080/01621459.2016.1176926. Epub 2017 May 03.

A design is presented for a randomized clinical trial comparing two second-line treatments, chemotherapy versus chemotherapy plus reirradiation, for treatment of recurrent non-small-cell lung cancer. The central research question is whether the potential efficacy benefit that adding reirradiation to...

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Murray TA, Thall PF, Yuan Y, et al. Robust treatment comparison based on utilities of semi-competing risks in non-small-cell lung cancer. J Am Stat Assoc. 2017;112:11-23doi: 10.1080/01621459.2016.1176926.
Murray, T. A., Thall, P. F., Yuan, Y., McAvoy, S., & Gomez, D. R. (2017). Robust treatment comparison based on utilities of semi-competing risks in non-small-cell lung cancer. Journal of the American Statistical Association, 11211-23. https://doi.org/10.1080/01621459.2016.1176926
Murray, Thomas A, et al. "Robust treatment comparison based on utilities of semi-competing risks in non-small-cell lung cancer." Journal of the American Statistical Association vol. 112 (2017): 11-23. doi: https://doi.org/10.1080/01621459.2016.1176926
Murray TA, Thall PF, Yuan Y, McAvoy S, Gomez DR. Robust treatment comparison based on utilities of semi-competing risks in non-small-cell lung cancer. J Am Stat Assoc. 2017;112:11-23. doi: 10.1080/01621459.2016.1176926. Epub 2017 May 03. PMID: 28943681; PMCID: PMC5607962.
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Analysis of longitudinal multivariate outcome data from couples cohort studies: application to HPV transmission dynamics.

Journal of the American Statistical Association

Kong X, Wang MC, Gray R.
PMID: 26195849
J Am Stat Assoc. 2015 Jun 01;110(510):472-485. doi: 10.1080/01621459.2014.991394.

We consider a specific situation of correlated data where multiple outcomes are repeatedly measured on each member of a couple. Such multivariate longitudinal data from couples may exhibit multi-faceted correlations which can be further complicated if there are polygamous...

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Kong X, Wang MC, Gray R. Analysis of longitudinal multivariate outcome data from couples cohort studies: application to HPV transmission dynamics. J Am Stat Assoc. 2015;110(510):472-485doi: 10.1080/01621459.2014.991394.
Kong, X., Wang, M. C., & Gray, R. (2015). Analysis of longitudinal multivariate outcome data from couples cohort studies: application to HPV transmission dynamics. Journal of the American Statistical Association, 110(510), 472-485. https://doi.org/10.1080/01621459.2014.991394
Kong, Xiangrong, et al. "Analysis of longitudinal multivariate outcome data from couples cohort studies: application to HPV transmission dynamics." Journal of the American Statistical Association vol. 110,510 (2015): 472-485. doi: https://doi.org/10.1080/01621459.2014.991394
Kong X, Wang MC, Gray R. Analysis of longitudinal multivariate outcome data from couples cohort studies: application to HPV transmission dynamics. J Am Stat Assoc. 2015 Jun 01;110(510):472-485. doi: 10.1080/01621459.2014.991394. PMID: 26195849; PMCID: PMC4505367.
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The E-MS Algorithm: Model Selection with Incomplete Data.

Journal of the American Statistical Association

Jiang J, Nguyen T, Rao JS.
PMID: 26783375
J Am Stat Assoc. 2015 Apr 04;110(511):1136-1147. doi: 10.1080/01621459.2014.948545. Epub 2014 Aug 15.

We propose a procedure associated with the idea of the E-M algorithm for model selection in the presence of missing data. The idea extends the concept of parameters to include both the model and the parameters under the model,...

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Jiang J, Nguyen T, Rao JS. The E-MS Algorithm: Model Selection with Incomplete Data. J Am Stat Assoc. 2014;110(511):1136-1147doi: 10.1080/01621459.2014.948545.
Jiang, J., Nguyen, T., & Rao, J. S. (2015). The E-MS Algorithm: Model Selection with Incomplete Data. Journal of the American Statistical Association, 110(511), 1136-1147. https://doi.org/10.1080/01621459.2014.948545
Jiang, Jiming, et al. "The E-MS Algorithm: Model Selection with Incomplete Data." Journal of the American Statistical Association vol. 110,511 (2015): 1136-1147. doi: https://doi.org/10.1080/01621459.2014.948545
Jiang J, Nguyen T, Rao JS. The E-MS Algorithm: Model Selection with Incomplete Data. J Am Stat Assoc. 2015 Apr 04;110(511):1136-1147. doi: 10.1080/01621459.2014.948545. Epub 2014 Aug 15. PMID: 26783375; PMCID: PMC4714800.
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Estimating the lifetime risk of dementia in the Canadian elderly population using cross-sectional cohort survival data.

Journal of the American Statistical Association

Carone M, Asgharian M, Jewell NP.
PMID: 26139951
J Am Stat Assoc. 2014;109(505):24-35. doi: 10.1080/01621459.2013.859076.

Dementia is one of the world's major public health challenges. The lifetime risk of dementia is the proportion of individuals who ever develop dementia during their lifetime. Despite its importance to epidemiologists and policy-makers, this measure does not seem...

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Carone M, Asgharian M, Jewell NP. Estimating the lifetime risk of dementia in the Canadian elderly population using cross-sectional cohort survival data. J Am Stat Assoc. 2014;109(505):24-35doi: 10.1080/01621459.2013.859076.
Carone, M., Asgharian, M., & Jewell, N. P. (2014). Estimating the lifetime risk of dementia in the Canadian elderly population using cross-sectional cohort survival data. Journal of the American Statistical Association, 109(505), 24-35. https://doi.org/10.1080/01621459.2013.859076
Carone, Marco, et al. "Estimating the lifetime risk of dementia in the Canadian elderly population using cross-sectional cohort survival data." Journal of the American Statistical Association vol. 109,505 (2014): 24-35. doi: https://doi.org/10.1080/01621459.2013.859076
Carone M, Asgharian M, Jewell NP. Estimating the lifetime risk of dementia in the Canadian elderly population using cross-sectional cohort survival data. J Am Stat Assoc. 2014;109(505):24-35. doi: 10.1080/01621459.2013.859076. PMID: 26139951; PMCID: PMC4485460.
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Sparse Additive Ordinary Differential Equations for Dynamic Gene Regulatory Network Modeling.

Journal of the American Statistical Association

Wu H, Lu T, Xue H, Liang H.
PMID: 25061254
J Am Stat Assoc. 2014 Apr 02;109(506):700-716. doi: 10.1080/01621459.2013.859617.

The gene regulation network (GRN) is a high-dimensional complex system, which can be represented by various mathematical or statistical models. The ordinary differential equation (ODE) model is one of the popular dynamic GRN models. High-dimensional linear ODE models have...

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Wu H, Lu T, Xue H, et al. Sparse Additive Ordinary Differential Equations for Dynamic Gene Regulatory Network Modeling. J Am Stat Assoc. 2014;109(506):700-716doi: 10.1080/01621459.2013.859617.
Wu, H., Lu, T., Xue, H., & Liang, H. (2014). Sparse Additive Ordinary Differential Equations for Dynamic Gene Regulatory Network Modeling. Journal of the American Statistical Association, 109(506), 700-716. https://doi.org/10.1080/01621459.2013.859617
Wu, Hulin, et al. "Sparse Additive Ordinary Differential Equations for Dynamic Gene Regulatory Network Modeling." Journal of the American Statistical Association vol. 109,506 (2014): 700-716. doi: https://doi.org/10.1080/01621459.2013.859617
Wu H, Lu T, Xue H, Liang H. Sparse Additive Ordinary Differential Equations for Dynamic Gene Regulatory Network Modeling. J Am Stat Assoc. 2014 Apr 02;109(506):700-716. doi: 10.1080/01621459.2013.859617. PMID: 25061254; PMCID: PMC4104722.
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Analysis of the Proportional Hazards Model with Sparse Longitudinal Covariates.

Journal of the American Statistical Association

Cao H, Churpek MM, Zeng D, Fine JP.
PMID: 26576066
J Am Stat Assoc. 2015;110(511):1187-1196. doi: 10.1080/01621459.2014.957289. Epub 2015 Nov 07.

Regression analysis of censored failure observations via the proportional hazards model permits time-varying covariates which are observed at death times. In practice, such longitudinal covariates are typically sparse and only measured at infrequent and irregularly spaced follow-up times. Full...

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Cao H, Churpek MM, Zeng D, et al. Analysis of the Proportional Hazards Model with Sparse Longitudinal Covariates. J Am Stat Assoc. 2015;110(511):1187-1196doi: 10.1080/01621459.2014.957289.
Cao, H., Churpek, M. M., Zeng, D., & Fine, J. P. (2015). Analysis of the Proportional Hazards Model with Sparse Longitudinal Covariates. Journal of the American Statistical Association, 110(511), 1187-1196. https://doi.org/10.1080/01621459.2014.957289
Cao, Hongyuan, et al. "Analysis of the Proportional Hazards Model with Sparse Longitudinal Covariates." Journal of the American Statistical Association vol. 110,511 (2015): 1187-1196. doi: https://doi.org/10.1080/01621459.2014.957289
Cao H, Churpek MM, Zeng D, Fine JP. Analysis of the Proportional Hazards Model with Sparse Longitudinal Covariates. J Am Stat Assoc. 2015;110(511):1187-1196. doi: 10.1080/01621459.2014.957289. Epub 2015 Nov 07. PMID: 26576066; PMCID: PMC4643320.
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Mechanistic Hierarchical Gaussian Processes.

Journal of the American Statistical Association

Wheeler MW, Dunson DB, Pandalai SP, Baker BA, Herring AH.
PMID: 25541568
J Am Stat Assoc. 2014 Jul;109(507):894-904. doi: 10.1080/01621459.2014.899234.

The statistics literature on functional data analysis focuses primarily on flexible black-box approaches, which are designed to allow individual curves to have essentially any shape while characterizing variability. Such methods typically cannot incorporate mechanistic information, which is commonly expressed...

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Wheeler MW, Dunson DB, Pandalai SP, et al. Mechanistic Hierarchical Gaussian Processes. J Am Stat Assoc. 2014;109(507):894-904doi: 10.1080/01621459.2014.899234.
Wheeler, M. W., Dunson, D. B., Pandalai, S. P., Baker, B. A., & Herring, A. H. (2014). Mechanistic Hierarchical Gaussian Processes. Journal of the American Statistical Association, 109(507), 894-904. https://doi.org/10.1080/01621459.2014.899234
Wheeler, Matthew W, et al. "Mechanistic Hierarchical Gaussian Processes." Journal of the American Statistical Association vol. 109,507 (2014): 894-904. doi: https://doi.org/10.1080/01621459.2014.899234
Wheeler MW, Dunson DB, Pandalai SP, Baker BA, Herring AH. Mechanistic Hierarchical Gaussian Processes. J Am Stat Assoc. 2014 Jul;109(507):894-904. doi: 10.1080/01621459.2014.899234. PMID: 25541568; PMCID: PMC4273873.
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Intrinsic Regression Models for Medial Representation of Subcortical Structures.

Journal of the American Statistical Association

Shi X, Zhu H, Ibrahim JG, Liang F, Lieberman J, Styner M.
PMID: 23794769
J Am Stat Assoc. 2012 Mar 01;107(497):12-23. doi: 10.1080/01621459.2011.643710.

The aim of this paper is to develop a semiparametric model for describing the variability of the medial representation of subcortical structures, which belongs to a Riemannian manifold, and establishing its association with covariates of interest, such as diagnostic...

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Shi X, Zhu H, Ibrahim JG, et al. Intrinsic Regression Models for Medial Representation of Subcortical Structures. J Am Stat Assoc. 2012;107(497):12-23doi: 10.1080/01621459.2011.643710.
Shi, X., Zhu, H., Ibrahim, J. G., Liang, F., Lieberman, J., & Styner, M. (2012). Intrinsic Regression Models for Medial Representation of Subcortical Structures. Journal of the American Statistical Association, 107(497), 12-23. https://doi.org/10.1080/01621459.2011.643710
Shi, Xiaoyan, et al. "Intrinsic Regression Models for Medial Representation of Subcortical Structures." Journal of the American Statistical Association vol. 107,497 (2012): 12-23. doi: https://doi.org/10.1080/01621459.2011.643710
Shi X, Zhu H, Ibrahim JG, Liang F, Lieberman J, Styner M. Intrinsic Regression Models for Medial Representation of Subcortical Structures. J Am Stat Assoc. 2012 Mar 01;107(497):12-23. doi: 10.1080/01621459.2011.643710. PMID: 23794769; PMCID: PMC3685886.
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Showing 1 to 12 of 668 entries