1
|
Helske J, Tikka S. Estimating causal effects from panel data with dynamic multivariate panel models. ADVANCES IN LIFE COURSE RESEARCH 2024; 60:100617. [PMID: 38759570 DOI: 10.1016/j.alcr.2024.100617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 04/09/2024] [Accepted: 05/07/2024] [Indexed: 05/19/2024]
Abstract
Panel data are ubiquitous in scientific fields such as social sciences. Various modeling approaches have been presented for observational causal inference based on such data. Existing approaches typically impose restrictive assumptions on the data-generating process such as Gaussian responses or time-invariant effects, or they can only consider short-term causal effects. To surmount these restrictions, we present the dynamic multivariate panel model (DMPM) that supports time-varying, time-invariant, and individual-specific effects, multiple responses across a wide variety of distributions, and arbitrary dependency structures of lagged responses of any order. We formally demonstrate how DMPM facilitates causal inference within the structural causal modeling framework and we take a Bayesian approach for the estimation of the posterior distributions of the model parameters and causal effects of interest. We demonstrate the use of DMPM by applying the approach to both real and synthetic data.
Collapse
Affiliation(s)
- Jouni Helske
- INVEST Research Flagship Centre, University of Turku, Finland; Department of Mathematics and Statistics, University of Jyväskylä, Finland.
| | - Santtu Tikka
- Department of Mathematics and Statistics, University of Jyväskylä, Finland
| |
Collapse
|
2
|
Xu G, Amei A, Wu W, Liu Y, Shen L, Oh EC, Wang Z. RETROSPECTIVE VARYING COEFFICIENT ASSOCIATION ANALYSIS OF LONGITUDINAL BINARY TRAITS: APPLICATION TO THE IDENTIFICATION OF GENETIC LOCI ASSOCIATED WITH HYPERTENSION. Ann Appl Stat 2024; 18:487-505. [PMID: 38577266 PMCID: PMC10994004 DOI: 10.1214/23-aoas1798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/06/2024]
Abstract
Many genetic studies contain rich information on longitudinal phenotypes that require powerful analytical tools for optimal analysis. Genetic analysis of longitudinal data that incorporates temporal variation is important for understanding the genetic architecture and biological variation of complex diseases. Most of the existing methods assume that the contribution of genetic variants is constant over time and fail to capture the dynamic pattern of disease progression. However, the relative influence of genetic variants on complex traits fluctuates over time. In this study, we propose a retrospective varying coefficient mixed model association test, RVMMAT, to detect time-varying genetic effect on longitudinal binary traits. We model dynamic genetic effect using smoothing splines, estimate model parameters by maximizing a double penalized quasi-likelihood function, design a joint test using a Cauchy combination method, and evaluate statistical significance via a retrospective approach to achieve robustness to model misspecification. Through simulations we illustrated that the retrospective varying-coefficient test was robust to model misspecification under different ascertainment schemes and gained power over the association methods assuming constant genetic effect. We applied RVMMAT to a genome-wide association analysis of longitudinal measure of hypertension in the Multi-Ethnic Study of Atherosclerosis. Pathway analysis identified two important pathways related to G-protein signaling and DNA damage. Our results demonstrated that RVMMAT could detect biologically relevant loci and pathways in a genome scan and provided insight into the genetic architecture of hypertension.
Collapse
Affiliation(s)
- Gang Xu
- Department of Mathematical Sciences, University of Nevada
| | - Amei Amei
- Department of Mathematical Sciences, University of Nevada
| | - Weimiao Wu
- Department of Biostatistics, Yale School of Public Health
| | - Yunqing Liu
- Department of Biostatistics, Yale School of Public Health
| | - Linchuan Shen
- Department of Mathematical Sciences, University of Nevada
| | - Edwin C. Oh
- Department of Internal Medicine, University of Nevada School of Medicine
| | - Zuoheng Wang
- Department of Biostatistics, Yale School of Public Health
| |
Collapse
|
3
|
Yang Y, Pan Z, Kang J, Brummett C, Li Y. Simultaneous selection and inference for varying coefficients with zero regions: a soft-thresholding approach. Biometrics 2023; 79:3388-3401. [PMID: 37459178 PMCID: PMC10792111 DOI: 10.1111/biom.13900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 06/05/2023] [Indexed: 08/02/2023]
Abstract
Varying coefficient models have been used to explore dynamic effects in many scientific areas, such as in medicine, finance, and epidemiology. As most existing models ignore the existence of zero regions, we propose a new soft-thresholded varying coefficient model, where the coefficient functions are piecewise smooth with zero regions. Our new modeling approach enables us to perform variable selection, detect the zero regions of selected variables, obtain point estimates of the varying coefficients with zero regions, and construct a new type of sparse confidence intervals that accommodate zero regions. We prove the asymptotic properties of the estimator, based on which we draw statistical inference. Our simulation study reveals that the proposed sparse confidence intervals achieve the desired coverage probability. We apply the proposed method to analyze a large-scale preoperative opioid study.
Collapse
Affiliation(s)
| | - Ziyang Pan
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, U.S.A
| | - Jian Kang
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, U.S.A
| | - Chad Brummett
- Department of Anesthesiology, University of Michigan, Ann Arbor, MI, U.S.A
| | - Yi Li
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, U.S.A
| |
Collapse
|
4
|
Park Y, Han K, Simpson DG. Testing Linear Operator Constraints in Functional Response Regression with Incomplete Response Functions. Electron J Stat 2023; 17:3143-3180. [PMID: 39105139 PMCID: PMC11299897 DOI: 10.1214/23-ejs2177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/07/2024]
Abstract
Hypothesis testing procedures are developed to assess linear operator constraints in function-on-scalar regression when incomplete functional responses are observed. The approach enables statistical inferences about the shape and other aspects of the functional regression coefficients within a unified framework encompassing three incomplete sampling scenarios: (i) partially observed response functions as curve segments over random sub-intervals of the domain; (ii) discretely observed functional responses with additive measurement errors; and (iii) the composition of former two scenarios, where partially observed response segments are observed discretely with measurement error. The latter scenario has been little explored to date, although such structured data is increasingly common in applications. For statistical inference, deviations from the constraint space are measured via integratedL 2 -distance between the model estimates from the constrained and unconstrained model spaces. Large sample properties of the proposed test procedure are established, including the consistency, asymptotic distribution and local power of the test statistic. Finite sample power and level of the proposed test are investigated in a simulation study covering a variety of scenarios. The proposed methodologies are illustrated by applications to U.S. obesity prevalence data, analyzing the functional shape of its trends over time, and motion analysis in a study of automotive ergonomics.
Collapse
Affiliation(s)
- Yeonjoo Park
- Department of Management Science and Statistics, University of Texas at San Antonio, San Antonio, TX
| | - Kyunghee Han
- Department of Mathematics, Statistics, and Computer Science, University of Illinois at Chicago, Chicago, IL
| | - Douglas G Simpson
- Department of Statistics, University of Illinois at Urbana-Champaign, Urbana-Champaign, IL
| |
Collapse
|
5
|
Wang D, Mou X, Liu Y. Varying-coefficient regression analysis for pooled biomonitoring. Biometrics 2022; 78:1328-1341. [PMID: 34190334 PMCID: PMC8716640 DOI: 10.1111/biom.13516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Revised: 03/16/2021] [Indexed: 12/30/2022]
Abstract
Human biomonitoring involves measuring the accumulation of contaminants in biological specimens (such as blood or urine) to assess individuals' exposure to environmental contamination. Due to the expensive cost of a single assay, the method of pooling has become increasingly common in environmental studies. The implementation of pooling starts by physically mixing specimens into pools, and then measures pooled specimens for the concentration of contaminants. An important task is to reconstruct individual-level statistical characteristics based on pooled measurements. In this article, we propose to use the varying-coefficient regression model for individual-level biomonitoring and provide methods to estimate the varying coefficients based on different types of pooled data. Asymptotic properties of the estimators are presented. We illustrate our methodology via simulation and with application to pooled biomonitoring of a brominated flame retardant provided by the National Health and Nutrition Examination Survey (NHANES).
Collapse
Affiliation(s)
- Dewei Wang
- Department of Statistics, University of South Carolina, Columbia, SC 29208, U.S.A
| | - Xichen Mou
- Division of Epidemiology, Biostatistics, and Environmental Health, Scholl of Public Health, University of Memphis, Memphis, TN 38152, U.S.A
| | - Yan Liu
- School of Community Health Sciences, University of Nevada, Reno, NV 89557, U.S.A
| |
Collapse
|
6
|
Liu H, Song X, Zhang B. Varying-coefficient hidden Markov models with zero-effect regions. Comput Stat Data Anal 2022. [DOI: 10.1016/j.csda.2022.107482] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
|
7
|
Bayesian P-Splines Quantile Regression of Partially Linear Varying Coefficient Spatial Autoregressive Models. Symmetry (Basel) 2022. [DOI: 10.3390/sym14061175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
This paper deals with spatial data that can be modelled by partially linear varying coefficient spatial autoregressive models with Bayesian P-splines quantile regression. We evaluate the linear and nonlinear effects of covariates on the response and use quantile regression to present comprehensive information at different quantiles. We not only propose an empirical Bayesian approach of quantile regression using the asymmetric Laplace error distribution and employ P-splines to approximate nonparametric components but also develop an efficient Markov chain Monte Carlo technique to explore the joint posterior distributions of unknown parameters. Monte Carlo simulations show that our estimators not only have robustness for different spatial weight matrices but also perform better compared with quantile regression and instrumental variable quantile regression estimators in finite samples at different quantiles. Finally, a set of Sydney real estate data applications is analysed to illustrate the performance of the proposed method.
Collapse
|
8
|
Zhang J. Variational inference for varying-coefficient model. COMMUN STAT-SIMUL C 2022. [DOI: 10.1080/03610918.2019.1657451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Affiliation(s)
- Jiamin Zhang
- School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, China
| |
Collapse
|
9
|
Bhattacharjee S, Müller HG. Concurrent object regression. Electron J Stat 2022. [DOI: 10.1214/22-ejs2040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
| | - Hans-Georg Müller
- Department of Statistics, University of California, Davis Davis, CA 95616 USA
| |
Collapse
|
10
|
Zhou X, Shen H, Ni B, Xu Y. Wavelet- L1-estimation for non parametric location-scale models under a general dependence framework. COMMUN STAT-THEOR M 2021. [DOI: 10.1080/03610926.2021.1972312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Xingcai Zhou
- School of Economics and Management, Southeast University, Nanjing, China
- School of Statistics and Data Science, Nanjing Audit University, Nanjing, China
| | - Hao Shen
- School of Statistics and Data Science, Nanjing Audit University, Nanjing, China
| | - Beibei Ni
- School of Statistics and Data Science, Nanjing Audit University, Nanjing, China
| | - Yingzhi Xu
- School of Economics and Management, Southeast University, Nanjing, China
| |
Collapse
|
11
|
Sosa J, Buitrago L. Time-varying coefficient model estimation through radial basis functions. J Appl Stat 2021; 49:2510-2534. [PMID: 35757039 PMCID: PMC9225525 DOI: 10.1080/02664763.2021.1910938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Accepted: 03/25/2021] [Indexed: 10/21/2022]
Abstract
In this paper, we estimate the dynamic parameters of a time-varying coefficient model through radial kernel functions in the context of a longitudinal study. Our proposal is based on a linear combination of weighted kernel functions involving a bandwidth, centered around a given set of time points. In addition, we study different alternatives of estimation and inference including a Frequentist approach using weighted least squares along with bootstrap methods, and a Bayesian approach through both Markov chain Monte Carlo and variational methods. We compare the estimation strategies mention above with each other, and our radial kernel functions proposal with an expansion based on regression spline, by means of an extensive simulation study considering multiples scenarios in terms of sample size, number of repeated measurements, and subject-specific correlation. Our experiments show that the capabilities of our proposal based on radial kernel functions are indeed comparable with or even better than those obtained from regression splines. We illustrate our methodology by analyzing data from two AIDS clinical studies.
Collapse
Affiliation(s)
- Juan Sosa
- Departamento de Estadística, Universidad Nacional de Colombia, Carrera 45 # 26-85, Bogotá, Colombia
| | - Lina Buitrago
- Departamento de Estadística, Universidad Nacional de Colombia, Carrera 45 # 26-85, Bogotá, Colombia
| |
Collapse
|
12
|
Wu Q, Deng X, Wang S, Zeng L. Constrained Varying-Coefficient Model for Time-Course Experiments in Soft Tissue Fabrication. Technometrics 2021. [DOI: 10.1080/00401706.2020.1731604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Affiliation(s)
- Qian Wu
- Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX
| | - Xinwei Deng
- Department of Statistics, Virginia Tech, Blacksburg, VA
| | - Shiren Wang
- Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX
| | - Li Zeng
- Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX
| |
Collapse
|
13
|
Robust estimation and inference for general varying coefficient models with missing observations. TEST-SPAIN 2020. [DOI: 10.1007/s11749-019-00692-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
AbstractThis paper considers estimation and inference for a class of varying coefficient models in which some of the responses and some of the covariates are missing at random and outliers are present. The paper proposes two general estimators—and a computationally attractive and asymptotically equivalent one-step version of them—that combine inverse probability weighting and robust local linear estimation. The paper also considers inference for the unknown infinite-dimensional parameter and proposes two Wald statistics that are shown to have power under a sequence of local Pitman drifts and are consistent as the drifts diverge. The results of the paper are illustrated with three examples: robust local generalized estimating equations, robust local quasi-likelihood and robust local nonlinear least squares estimation. A simulation study shows that the proposed estimators and test statistics have competitive finite sample properties, whereas two empirical examples illustrate the applicability of the proposed estimation and testing methods.
Collapse
|
14
|
New efficient spline estimation for varying-coefficient models with two-step knot number selection. METRIKA 2020. [DOI: 10.1007/s00184-020-00798-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
|
15
|
Ghosal R, Maity A, Clark T, Longo SB. Variable selection in functional linear concurrent regression. J R Stat Soc Ser C Appl Stat 2020. [DOI: 10.1111/rssc.12408] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
|
16
|
Composite Quantile Regression for Varying Coefficient Models with Response Data Missing at Random. Symmetry (Basel) 2019. [DOI: 10.3390/sym11091065] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Composite quantile regression (CQR) estimation and inference are studied for varying coefficient models with response data missing at random. Three estimators including the weighted local linear CQR (WLLCQR) estimator, the nonparametric WLLCQR (NWLLCQR) estimator, and the imputed WLLCQR (IWLLCQR) estimator are proposed for unknown coefficient functions. Under some mild conditions, the proposed estimators are asymptotic normal. Simulation studies demonstrate that the unknown coefficient estimators with IWLLCQR are superior to the other two with WLLCQR and NWLLCQR. Moreover, bootstrap test procedures based on the IWLLCQR fittings is developed to test whether the coefficient functions are actually varying. Finally, a type of investigated real-life data is analyzed to illustrated the applications of the proposed method.
Collapse
|
17
|
Ye M, Lu ZH, Li Y, Song X. Finite mixture of varying coefficient model: Estimation and component selection. J MULTIVARIATE ANAL 2019. [DOI: 10.1016/j.jmva.2019.01.013] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
|
18
|
Zhao K, Lian H. Sparsistent and constansistent estimation of the varying-coefficient model with a diverging number of predictors. COMMUN STAT-THEOR M 2016. [DOI: 10.1080/03610926.2014.890224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
|
19
|
Wang S, Huang M, Wu X, Yao W. Mixture of functional linear models and its application to CO 2-GDP functional data. Comput Stat Data Anal 2016. [DOI: 10.1016/j.csda.2015.11.008] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
|
20
|
Lian H. Quantile regression for dynamic partially linear varying coefficient time series models. J MULTIVARIATE ANAL 2015. [DOI: 10.1016/j.jmva.2015.06.013] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
|
21
|
|
22
|
|
23
|
Matsui H, Misumi T. Variable selection for varying-coefficient models with the sparse regularization. Comput Stat 2014. [DOI: 10.1007/s00180-014-0520-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
|
24
|
|
25
|
Variable selection for high-dimensional varying coefficient partially linear models via nonconcave penalty. METRIKA 2012. [DOI: 10.1007/s00184-012-0422-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
|
26
|
Sánchez BN, Wu M, Raghunathan TE, Diez-Roux AV. Modeling the salivary cortisol profile in population research: the multi-ethnic study of atherosclerosis. Am J Epidemiol 2012; 176:918-28. [PMID: 23100245 DOI: 10.1093/aje/kws182] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
In many studies, it has been hypothesized that stress and its biologic consequences may contribute to disparities in rates of cardiovascular disease. However, understanding of the most appropriate statistical methods to analyze biologic markers of stress, such as salivary cortisol, remains limited. The authors explore the utility of various statistical methods in modeling daily cortisol profiles in population-based studies. They demonstrate that the proposed methods allow additional insight into the cortisol profile compared with commonly used summaries of the profiles based on raw data. For instance, one can gain insights regarding the shape of the population average curve, characterize the types of individual-level departures from the average curve, and better understand the relation between covariates and attained cortisol levels or slopes at various points of the day, in addition to drawing inferences regarding common features of the cortisol profile, such as the cortisol awakening response and the area under the curve. The authors compare the inference and interpretations drawn from these methods and use data collected as part of the Multi-Ethnic Study of Atherosclerosis to illustrate them.
Collapse
Affiliation(s)
- Brisa N Sánchez
- Department of Biostatistics, University of Michigan School of Public Health, 1415 Washington Heights, Rm 4164, Ann Arbor, MI 48109, USA.
| | | | | | | |
Collapse
|
27
|
Lu Z, Song X. Finite mixture varying coefficient models for analyzing longitudinal heterogenous data. Stat Med 2011; 31:544-60. [PMID: 22161474 DOI: 10.1002/sim.4420] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2010] [Accepted: 09/01/2011] [Indexed: 11/11/2022]
Abstract
This paper aims to develop a mixture model to study heterogeneous longitudinal data on the treatment effect of heroin use from a California Civil Addict Program. Each component of the mixture is characterized by a varying coefficient mixed effect model. We use the Bayesian P-splines approach to approximate the varying coefficient functions. We develop Markov chain Monte Carlo algorithms to estimate the smooth functions, unknown parameters, and latent variables in the model. We use modified deviance information criterion to determine the number of components in the mixture. A simulation study demonstrates that the modified deviance information criterion selects the correct number of components and the estimation of unknown quantities is accurate. We apply the proposed model to the heroin treatment study. Furthermore, we identify heterogeneous longitudinal patterns.
Collapse
Affiliation(s)
- Zhaohua Lu
- Department of Statistics, Chinese University of Hong Kong, Shatin, NT, Hong Kong
| | | |
Collapse
|
28
|
Componentwise B-spline estimation for varying coefficient models with longitudinal data. Stat Pap (Berl) 2011. [DOI: 10.1007/s00362-011-0369-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
|
29
|
|
30
|
Functional Concurrent Linear Regression Model for Spatial Images. JOURNAL OF AGRICULTURAL, BIOLOGICAL, AND ENVIRONMENTAL STATISTICS 2010. [DOI: 10.1007/s13253-010-0047-1] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
|
31
|
Eggermont P, Eubank R, LaRiccia V. Convergence rates for smoothing spline estimators in varying coefficient models. J Stat Plan Inference 2010. [DOI: 10.1016/j.jspi.2009.06.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
|
32
|
Wong H, Guo S, Chen M, IP WC. On locally weighted estimation and hypothesis testing of varying-coefficient models with missing covariates. J Stat Plan Inference 2009. [DOI: 10.1016/j.jspi.2009.01.016] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
|
33
|
Qingguo T, Longsheng C. Asymptotic Normality of M-Estimators for Varying Coefficient Models with Longitudinal Data. COMMUN STAT-THEOR M 2009. [DOI: 10.1080/03610920802452586] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
|
34
|
Qingguo T, Jinde W. Reducing component estimation for varying coefficient models. STATISTICS-ABINGDON 2009. [DOI: 10.1080/02331880701580079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
|
35
|
Qingguo T, Longsheng C. M-estimation and B-spline approximation for varying coefficient models with longitudinal data. J Nonparametr Stat 2008. [DOI: 10.1080/10485250802375950] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
|
36
|
Holtz DO, Krafty RT, Mohamed-Hadley A, Zhang L, Alagkiozidis I, Leiby B, Guo W, Gimotty PA, Coukos G. Should tumor VEGF expression influence decisions on combining low-dose chemotherapy with antiangiogenic therapy? Preclinical modeling in ovarian cancer. J Transl Med 2008; 6:2. [PMID: 18182107 PMCID: PMC2235830 DOI: 10.1186/1479-5876-6-2] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2007] [Accepted: 01/08/2008] [Indexed: 11/10/2022] Open
Abstract
Because of its low toxicity, low-dose (LD) chemotherapy is ideally suited for combination with antiangiogenic drugs. We investigated the impact of tumor vascular endothelial growth factor A (VEGF-A) expression on the efficacy of LD paclitaxel chemotherapy and its interactions with the tyrosine kinase inhibitor SU5416 in the ID8 and ID8-Vegf models of ovarian cancer. Functional linear models using weighted penalized least squares were utilized to identify interactions between Vegf, LD paclitaxel and antiangiogenic therapy. LD paclitaxel yielded additive effects with antiangiogenic therapy against tumors with low Vegf expression, while it exhibited antagonism to antiangiogenic therapy in tumors with high Vegf expression. This is the first preclinical study that models interactions of LD paclitaxel chemotherapy with antiangiogenic therapy and tumor VEGF expression and offers important lessons for the rational design of clinical trials.
Collapse
Affiliation(s)
- David O Holtz
- Center for Research on Early Detection and Cure of Ovarian Cancer, University of Pennsylvania, Philadelphia, USA
- Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, University of Pennsylvania, Philadelphia, USA
| | - Robert T Krafty
- Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, USA
| | - Alisha Mohamed-Hadley
- Center for Research on Early Detection and Cure of Ovarian Cancer, University of Pennsylvania, Philadelphia, USA
| | - Lin Zhang
- Center for Research on Early Detection and Cure of Ovarian Cancer, University of Pennsylvania, Philadelphia, USA
| | - Ioannis Alagkiozidis
- Center for Research on Early Detection and Cure of Ovarian Cancer, University of Pennsylvania, Philadelphia, USA
| | - Benjamin Leiby
- Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, USA
| | - Wensheng Guo
- Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, USA
- Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, USA
| | - Phyllis A Gimotty
- Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, USA
- Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, USA
| | - George Coukos
- Center for Research on Early Detection and Cure of Ovarian Cancer, University of Pennsylvania, Philadelphia, USA
- Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, University of Pennsylvania, Philadelphia, USA
- Abramson Family Cancer Research Institute, University of Pennsylvania, Philadelphia, USA
| |
Collapse
|
37
|
|
38
|
|