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Journal articles on the topic 'Time-dependent covariates'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 March 2023

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1

Yu, Qiqing, George Y. C. Wong, Michael P. Osborne, Yuting Hsu, and Xiaosong Ai. "The Lehmann Model with Time-dependent Covariates." Communications in Statistics - Theory and Methods 44, no. 20 (August 29, 2013): 4380–95. http://dx.doi.org/10.1080/03610926.2013.784991.

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2

Barnett, Adrian, and Nick Graves. "Competing risks models and time-dependent covariates." Critical Care 12, no. 2 (2008): 134. http://dx.doi.org/10.1186/cc6840.

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3

Marks, Gary F., Kenneth R. Hess, and James B. Young'. "80P Survival estimates for time-dependent covariates." Controlled Clinical Trials 15, no. 3 (June 1994): 122. http://dx.doi.org/10.1016/0197-2456(94)90208-9.

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4

Lu, Bo. "Propensity Score Matching with Time-Dependent Covariates." Biometrics 61, no. 3 (May 12, 2005): 721–28. http://dx.doi.org/10.1111/j.1541-0420.2005.00356.x.

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5

Gupta, Sunil. "Stochastic Models of Interpurchase Time with Time-Dependent Covariates." Journal of Marketing Research 28, no. 1 (February 1991): 1. http://dx.doi.org/10.2307/3172722.

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6

Gupta, Sunil. "Stochastic Models of Interpurchase Time with Time-Dependent Covariates." Journal of Marketing Research 28, no. 1 (February 1991): 1–15. http://dx.doi.org/10.1177/002224379102800101.

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7

Barabadi, Abbas, Javad Barabady, and Tore Markeset. "Maintainability analysis considering time-dependent and time-independent covariates." Reliability Engineering & System Safety 96, no. 1 (January 2011): 210–17. http://dx.doi.org/10.1016/j.ress.2010.08.007.

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8

Aydemir, Ülker, Sibel Aydemir, and Peter Dirschedl. "Analysis of time-dependent covariates in failure time data." Statistics in Medicine 18, no. 16 (August 30, 1999): 2123–34. http://dx.doi.org/10.1002/(sici)1097-0258(19990830)18:16<2123::aid-sim176>3.0.co;2-4.

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9

Vandormael, Alain, Frank Tanser, Diego Cuadros, and Adrian Dobra. "Estimating trends in the incidence rate with interval censored data and time-dependent covariates." Statistical Methods in Medical Research 29, no. 1 (February 19, 2019): 272–81. http://dx.doi.org/10.1177/0962280219829892.

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We propose a multiple imputation method for estimating the incidence rate with interval censored data and time-dependent (and/or time-independent) covariates. The method has two stages. First, we use a semi-parametric G-transformation model to estimate the cumulative baseline hazard function and the effects of the time-dependent (and/or time-independent covariates) on the interval censored infection times. Second, we derive the participant's unique cumulative distribution function and impute infection times conditional on the covariate values. To assess performance, we simulated infection times from a Cox proportional hazards model and induced interval censoring by varying the testing rate, e.g., participants test 100%, 75%, 50% of the time, etc. We then compared the incidence rate estimates from our G-imputation approach with single random-point and mid-point imputation. By comparison, our G-imputation approach gave more accurate incidence rate estimates and appropriate standard errors for models with time-independent covariates only, time-dependent covariates only, and a mixture of time-dependent and time-independent covariates across various testing rates. We demonstrate, for the first time, a multiple imputation approach for incidence rate estimation with interval censored data and time-dependent (and/or time-independent) covariates.

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10

Murad, Havi, Rachel Dankner, Alla Berlin, Liraz Olmer, and Laurence S. Freedman. "Imputing missing time-dependent covariate values for the discrete time Cox model." Statistical Methods in Medical Research 29, no. 8 (November 3, 2019): 2074–86. http://dx.doi.org/10.1177/0962280219881168.

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We describe a procedure for imputing missing values of time-dependent covariates in a discrete time Cox model using the chained equations method. The procedure multiply imputes the missing values for each time-period in a time-sequential manner, using covariates from the current and previous time-periods as well as the survival outcome. The form of the outcome variable used in the imputation model depends on the functional form of the time-dependent covariate(s) and differs from the case of Cox regression with only baseline covariates. This time-sequential approach provides an approximation to a fully conditional approach. We illustrate the procedure with data on diabetics, evaluating the association of their glucose control with the risk of selected cancers. Using simulations we show that the suggested estimator performed well (in terms of bias and coverage) for completely missing at random, missing at random and moderate non-missing-at-random patterns. However, for very strong non-missing-at-random patterns, the estimator was seriously biased and the coverage was too low. The procedure can be implemented using multiple imputation with the Fully conditional Specification (FCS) method (MI procedure in SAS with FCS statement or similar packages in other software, e.g. MICE in R). For use with event times on a continuous scale, the events would need to be grouped into time-intervals.

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11

Lalonde, Trent L., Anh Q. Nguyen, Jianqiong Yin, Kyle Irimata, and Jeffrey R. Wilson. "Modeling Correlated Binary Outcomes with Time-Dependent Covariates." Journal of Data Science 11, no. 4 (March 16, 2021): 715–38. http://dx.doi.org/10.6339/jds.201310_11(4).0005.

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12

Tsujitani, Masaaki, and Masato Sakon. "Analysis of Survival Data Having Time-Dependent Covariates." Japanese journal of applied statistics 34, no. 1 (2005): 15–29. http://dx.doi.org/10.5023/jappstat.34.15.

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13

Lalonde, Trent L., Anh Q. Nguyen, Jianqiong Yin, Kyle Irimata, and Jeffrey R. Wilson. "Modeling Correlated Binary Outcomes with Time-Dependent Covariates." Journal of Data Science 11, no. 4 (July 30, 2021): 715–38. http://dx.doi.org/10.6339/jds.2013.11(4).1195.

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14

Chiang, Yu-Kun, Robert J. Hardy, C. Morton Hawkins, and Asha S. Kapadia. "An Illness-Death Process with Time-Dependent Covariates." Biometrics 45, no. 2 (June 1989): 669. http://dx.doi.org/10.2307/2531509.

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15

Huang, Xin, Shande Chen, and Seng-jaw Soong. "Piecewise Exponential Survival Trees with Time-Dependent Covariates." Biometrics 54, no. 4 (December 1998): 1420. http://dx.doi.org/10.2307/2533668.

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16

Shi, Haolun, and Guosheng Yin. "Landmark cure rate models with time-dependent covariates." Statistical Methods in Medical Research 26, no. 5 (June 19, 2017): 2042–54. http://dx.doi.org/10.1177/0962280217708681.

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17

Tsujitani, M., and M. Sakon. "Analysis of Survival Data Having Time-Dependent Covariates." IEEE Transactions on Neural Networks 20, no. 3 (March 2009): 389–94. http://dx.doi.org/10.1109/tnn.2008.2008328.

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18

Petersen, Trond. "Fitting Parametric Survival Models with Time-Dependent Covariates." Applied Statistics 35, no. 3 (1986): 281. http://dx.doi.org/10.2307/2348028.

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19

Chalita, Liciana V. A. Silveira, Enrico A. Colosimo, and José Raimundo de Souza Passos. "Modeling Grouped Survival Data with Time-Dependent Covariates." Communications in Statistics - Simulation and Computation 35, no. 4 (November 23, 2006): 975–81. http://dx.doi.org/10.1080/03610910600880450.

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20

Schultz, Lonni R., Edward L. Peterson, and Naomi Breslau. "Graphing survival curve estimates for time-dependent covariates." International Journal of Methods in Psychiatric Research 11, no. 2 (June 2002): 68–74. http://dx.doi.org/10.1002/mpr.124.

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21

Houseman, E. Andrés, Brent A. Coull, and James P. Shine. "A Nonstationary Negative Binomial Time Series With Time-Dependent Covariates." Journal of the American Statistical Association 101, no. 476 (December 1, 2006): 1365–76. http://dx.doi.org/10.1198/016214506000000627.

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22

Gao, Feng, Amita K. Manatunga, and Shande Chen. "Non-parametric estimation for baseline hazards function and covariate effects with time-dependent covariates." Statistics in Medicine 26, no. 4 (2007): 857–68. http://dx.doi.org/10.1002/sim.2574.

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23

DEWIYANTI, LUH PUTU ARI, NI LUH PUTU SUCIPTAWATI, and I. WAYAN SUMARJAYA. "PERLUASAN REGRESI COX DENGAN PENAMBAHAN PEUBAH TERIKAT-WAKTU." E-Jurnal Matematika 3, no. 3 (August 29, 2014): 86. http://dx.doi.org/10.24843/mtk.2014.v03.i03.p069.

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The aim of this study is to model job hunting period in Bali in 2012 using Extended Cox model. Previous study concluded that household status and age variables were not significantly influenced the job hunting period. However, previous study on factors that influence job waiting suggests that both variables should play important role in determining the waiting time for job hunters. Thus incorporating time-dependent covariates into model is necessary. After incorporating time-dependent covariates we found that age with time-dependent covariate is significant.

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24

Keshavarzi, Sareh, Seyyed Mohammad Taghi Ayatollahi, Najaf Zare, and Maryam Pakfetrat. "Application of Seemingly Unrelated Regression in Medical Data with Intermittently Observed Time-Dependent Covariates." Computational and Mathematical Methods in Medicine 2012 (2012): 1–11. http://dx.doi.org/10.1155/2012/821643.

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Background. In many studies with longitudinal data, time-dependent covariates can only be measured intermittently (not at all observation times), and this presents difficulties for standard statistical analyses. This situation is common in medical studies, and methods that deal with this challenge would be useful.Methods. In this study, we performed the seemingly unrelated regression (SUR) based models, with respect to each observation time in longitudinal data with intermittently observed time-dependent covariates and further compared these models with mixed-effect regression models (MRMs) under three classic imputation procedures. Simulation studies were performed to compare the sample size properties of the estimated coefficients for different modeling choices.Results. In general, the proposed models in the presence of intermittently observed time-dependent covariates showed a good performance. However, when we considered only the observed values of the covariate without any imputations, the resulted biases were greater. The performances of the proposed SUR-based models in comparison with MRM using classic imputation methods were nearly similar with approximately equal amounts of bias and MSE.Conclusion. The simulation study suggests that the SUR-based models work as efficiently as MRM in the case of intermittently observed time-dependent covariates. Thus, it can be used as an alternative to MRM.

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25

MENDES, ALEXANDRE C., and NASSER FARD. "BINARY LOGISTIC REGRESSION AND PHM ANALYSIS FOR RELIABILITY DATA." International Journal of Reliability, Quality and Safety Engineering 21, no. 05 (September 18, 2014): 1450023. http://dx.doi.org/10.1142/s0218539314500235.

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This study proposes a modification for the binary logistic regression to treat time-dependent covariates for reliability studies. The proportional hazard model (PHM) properties are well suited for modeling survival data when there are categorical predictors; as it compares hazards to a reference category. However, time-dependent covariates present a challenge for the analysis as stratification does not produce hazards for the covariate stratified or creation of dummy time-dependent covariates faces difficulty on selecting the time interval for the interaction and the coefficient results may be difficult to interpret. The findings show that the logistic regression can provide equal or better results than the PHM applied for reliability analysis when time-dependent covariate is evaluated. The PHM is potentially preferred to address data set without time-dependent variables as it does not require any data manipulation. The logistic regression ignores the information on timing of the events; which is corrected by breaking each subject survival history into a set of discrete time intervals that are treated as distinct observations evaluated as a binary distribution. Recurrent events can be addressed by both methods with proper correction for lack of heterogeneity. The application of the modified logistic regression model for the study of reliability is innovative and with readily potential application for step-stress time-dependent accelerated life testing.

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26

MENDES, ALEXANDRE C., and NASSER FARD. "ACCELERATED FAILURE TIME MODELS COMPARISON TO THE PROPORTIONAL HAZARD MODEL FOR TIME-DEPENDENT COVARIATES WITH RECURRING EVENTS." International Journal of Reliability, Quality and Safety Engineering 21, no. 02 (April 2014): 1450010. http://dx.doi.org/10.1142/s0218539314500107.

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This paper presents an analysis of parametric survival models and compares their applications to time to event data used to validate the approximation for repeated events applying the Proportional Hazard Model (PHM) proposed in Mendes and Fard [Int. J. Reliab., Qual. Saf. Eng.19(6) (2012) 1240004.1–1240004.18]. The subjects studied do not show degrading failures, allowing the comparison between accelerated failure time models with the PHM. Results showed the applicability of the Weibull model and the versatility of the PHM not only to match the results of the parametric model, but also to allow the implementation of time-dependent covariates, resulting in superior model fit and more insightful interpretation for the covariate hazards. The paper contribution is to present the PHM as a simpler, more robust model to determine the acceleration factor for reliability testing when compared to the formidable task of fitting a parametric model for the distribution of failure. The Kaplan–Meier method may provide misleading guidance for covariate significance when time-dependent covariates are applied; however, relevant graphical screening is supplied. Notwithstanding, the PHM provides additional options to treat the repeated observations applying robust covariance correction for lack of heterogeneity in the fixed effects model or adopting the stratified model that absorbs the error using the stratification concept.

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27

Verweij, Pierre J. M., and Hans C. van Houwelingen. "Time-Dependent Effects of Fixed Covariates in Cox Regression." Biometrics 51, no. 4 (December 1995): 1550. http://dx.doi.org/10.2307/2533286.

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28

Ray, Bonnie K., Zhaohui Liu, and Nalini Ravishanker. "Dynamic Reliability Models for Software Using Time-Dependent Covariates." Technometrics 48, no. 1 (February 2006): 1–10. http://dx.doi.org/10.1198/004017005000000292.

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29

Donmez, Birsen, Linda Ng Boyle, and John D. Lee. "Accounting for time-dependent covariates in driving simulator studies." Theoretical Issues in Ergonomics Science 9, no. 3 (May 2008): 189–99. http://dx.doi.org/10.1080/14639220701281135.

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30

Cai, B., A. B. Lawson, M. M. Hossain, and J. Choi. "Bayesian latent structure models with space-time dependent covariates." Statistical Modelling 12, no. 2 (April 1, 2012): 145–64. http://dx.doi.org/10.1177/1471082x1001200202.

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31

Robins, James, and Anastosios A. Tsiatis. "Semiparametric Estimation of an Accelerated Failure Time Model with Time- Dependent Covariates." Biometrika 79, no. 2 (June 1992): 311. http://dx.doi.org/10.2307/2336842.

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32

Yi, Fengting, Niansheng Tang, and Jianguo Sun. "Regression analysis of interval-censored failure time data with time-dependent covariates." Computational Statistics & Data Analysis 144 (April 2020): 106848. http://dx.doi.org/10.1016/j.csda.2019.106848.

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33

ROBINS, JAMES, and ANASTASIOS A. TSIATIS. "Semiparametric estimation of an accelerated failure time model with time-dependent covariates." Biometrika 79, no. 2 (1992): 311–19. http://dx.doi.org/10.1093/biomet/79.2.311.

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34

Gorfine, Malka, Yair Goldberg, and Ya’acov Ritov. "A quantile regression model for failure-time data with time-dependent covariates." Biostatistics 18, no. 1 (August 2, 2016): 132–46. http://dx.doi.org/10.1093/biostatistics/kxw036.

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35

Blanche, Paul, Bochra Zareini, and Peter V. Rasmussen. "A hazard ratio above one does not necessarily mean higher risk, when using a time-dependent cox model." Research Methods in Medicine & Health Sciences 3, no. 2 (February 21, 2022): 42–48. http://dx.doi.org/10.1177/26320843211061288.

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The Cox model is one of the most used statistical models in medical research. It models the hazard rate of an event and its association with covariates through hazard ratios. In the simple setting without competing risks nor time-dependent covariates, there exists a one-to-one mathematical connection between the hazard rate and the risk of experiencing the event within any given time period (e.g., 5 years). This makes it possible to conclude that a covariate associated with a hazard ratio above one is associated with a higher risk of event. Although it is becoming widely known that this connection is lost in the presence of competing risks, it seems that fewer users of the Cox model are aware that this connection is also lost when using time-dependent covariates. In other words, it seems still widely unknown that, when using a time-dependent Cox model, a hazard ratio estimated above one does not necessarily mean that there is a higher risk. Hence, this note aims to clarify why this is not the case with a detailed pedagogical example.

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36

Miloslavsky, Maja, Sunduz Keles, Mark J. Laan, and Steve Butler. "Recurrent events analysis in the presence of time-dependent covariates and dependent censoring." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 66, no. 1 (February 2004): 239–57. http://dx.doi.org/10.1111/j.1467-9868.2004.00442.x.

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37

Yamaguchi, Kazuo. "8. Mover-Stayer Models for Analyzing Event Nonoccurrence and Event Timing with Time-Dependent Covariates: An Application to an Analysis of Remarriage." Sociological Methodology 28, no. 1 (August 1998): 327–61. http://dx.doi.org/10.1111/0081-1750.00051.

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This paper introduces a novel extension of mover-stayer models for duration data that allows time-dependent covariates to be used for both a pair of regression equations, one that identifies the determinants of event timing and one that identifies the determinants of the probability of ultimate event nonoccurrence. Existing models intended to distinguish covariate effects on event timing from those on event nonoccurrence cannot use time-dependent covariates in the equation for the probability of ultimate event nonoccurrence. This paper applies the new model to an analysis of remarriage among American women. The analysis generally demonstrates that some covariates effect remarriage timing while others affect the probability of ultimate remarriage nonoccurrence. Some differences in patterns of remarriage between black women and white women are also reported. Theoretical implications of these findings are discussed.

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38

Huang, Lan, Ming-Hui Chen, Fang Yu, Paul R. Neal, and Gregory J. Anderson. "On modeling repeated binary responses and time-dependent missing covariates." Journal of Agricultural, Biological, and Environmental Statistics 13, no. 3 (September 2008): 270–93. http://dx.doi.org/10.1198/108571108x338023.

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39

Tseng, Yi-Kuan, Ken-Ning Hsu, and Ya-Fang Yang. "A semiparametric extended hazard regression model with time-dependent covariates." Journal of Nonparametric Statistics 26, no. 1 (September 26, 2013): 115–28. http://dx.doi.org/10.1080/10485252.2013.836521.

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40

Fisher, Lloyd D., and D. Y. Lin. "TIME-DEPENDENT COVARIATES IN THE COX PROPORTIONAL-HAZARDS REGRESSION MODEL." Annual Review of Public Health 20, no. 1 (May 1999): 145–57. http://dx.doi.org/10.1146/annurev.publhealth.20.1.145.

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41

Wu, Lang, and Hulin Wu. "Missing time-dependent covariates in human immunodeficiency virus dynamic models." Journal of the Royal Statistical Society: Series C (Applied Statistics) 51, no. 3 (July 2002): 297–318. http://dx.doi.org/10.1111/1467-9876.00270.

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42

PETERSEN, TROND. "Estimating Fully Parametric Hazard Rate Models with Time-Dependent Covariates." Sociological Methods & Research 14, no. 3 (February 1986): 219–46. http://dx.doi.org/10.1177/0049124186014003001.

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43

Wu, Hulin, and Hua Liang. "Backfitting Random Varying-Coefficient Models with Time-Dependent Smoothing Covariates." Scandinavian Journal of Statistics 31, no. 1 (March 2004): 3–19. http://dx.doi.org/10.1111/j.1467-9469.2004.00369.x.

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44

Geskus, Ronald. "Censoring Strategies When Using Competing Risks With Time-Dependent Covariates." JAIDS Journal of Acquired Immune Deficiency Syndromes 46, no. 4 (December 2007): 512. http://dx.doi.org/10.1097/qai.0b013e3181576ce9.

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45

de Bruijne, Mattheus H. J., Yvo W. J. Sijpkens, Leendert C. Paul, Rudi G. J. Westendorp, Hans C. van Houwelingen, and Aeilko H. Zwinderman. "Predicting kidney graft failure using time-dependent renal function covariates." Journal of Clinical Epidemiology 56, no. 5 (May 2003): 448–55. http://dx.doi.org/10.1016/s0895-4356(03)00004-0.

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46

Cortese, Giuliana, Thomas A. Gerds, and Per K. Andersen. "Comparing predictions among competing risks models with time-dependent covariates." Statistics in Medicine 32, no. 18 (March 13, 2013): 3089–101. http://dx.doi.org/10.1002/sim.5773.

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47

Porras, Ana Maria Lara, Julia García Leal, and Esteban Navarrete Álvarez. "A proportional hazard model with time-dependent parameters and covariates." Journal of the Italian Statistical Society 7, no. 3 (December 1998): 233–42. http://dx.doi.org/10.1007/bf03178932.

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48

Guerra, Matthew W., Justine Shults, Jay Amsterdam, and Thomas Ten-Have. "The analysis of binary longitudinal data with time-dependent covariates." Statistics in Medicine 31, no. 10 (January 13, 2012): 931–48. http://dx.doi.org/10.1002/sim.4465.

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49

Jiang, Shu, Richard J. Cook, and Leilei Zeng. "Mitigating bias from intermittent measurement of time‐dependent covariates in failure time analysis." Statistics in Medicine 39, no. 13 (March 3, 2020): 1833–45. http://dx.doi.org/10.1002/sim.8517.

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50

Fang, Ji-Qian, Zhong-Lu Shi, Yi Wang, Xia Zhang, Dong-Lu Zeng, and Jian-Na Zhang. "Parametric Inference in a Multiple Renewal Process with Time-Dependent Covariates." Biometrics 46, no. 3 (September 1990): 849. http://dx.doi.org/10.2307/2532102.

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