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Journal articles on the topic 'Discrete time survival analysis'

Author: Grafiati

Published: 4 June 2021

Last updated: 15 February 2022

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1

Muthén, Bengt, and Katherine Masyn. "Discrete-Time Survival Mixture Analysis." Journal of Educational and Behavioral Statistics 30, no. 1 (2005): 27–58. http://dx.doi.org/10.3102/10769986030001027.

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This article proposes a general latent variable approach to discrete-time survival analysis of nonrepeatable events such as onset of drug use. It is shown how the survival analysis can be formulated as a generalized latent class analysis of event history indicators. The latent class analysis can use covariates and can be combined with the joint modeling of other outcomes such as repeated measures for a related process. It is shown that conventional discrete-time survival analysis corresponds to a single-class latent class analysis. Multiple-class extensions are proposed, including the special cases of a class of long-term survivors and classes defined by outcomes related to survival. The estimation uses a general latent variable framework, including both categorical and continuous latent variables and incorporated in the Mplus program. Estimation is carried out using maximum likelihood via the EM algorithm. Two examples serve as illustrations. The first example concerns recidivism after incarceration in a randomized field experiment. The second example concerns school removal related to the development of aggressive behavior in the classroom.

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2

Kretowska, Malgorzata. "Oblique Survival Trees in Discrete Event Time Analysis." IEEE Journal of Biomedical and Health Informatics 24, no. 1 (2020): 247–58. http://dx.doi.org/10.1109/jbhi.2019.2908773.

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3

Jóźwiak, Katarzyna, and Mirjam Moerbeek. "Power Analysis for Trials With Discrete-Time Survival Endpoints." Journal of Educational and Behavioral Statistics 37, no. 5 (2012): 630–54. http://dx.doi.org/10.3102/1076998611424876.

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4

Xue, Xiaonan, and Ron Brookmeyer. "Regression analysis of discrete time survival data under heterogeneity." Statistics in Medicine 16, no. 17 (1997): 1983–93. http://dx.doi.org/10.1002/(sici)1097-0258(19970915)16:17<1983::aid-sim628>3.0.co;2-3.

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5

Zwick, Rebecca, and Jeffrey C. Sklar. "A Note on Standard Errors for Survival Curves in Discrete-Time Survival Analysis." Journal of Educational and Behavioral Statistics 30, no. 1 (2005): 75–92. http://dx.doi.org/10.3102/10769986030001075.

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Cox (1972) proposed a discrete-time survival model that is somewhat analogous to the proportional hazards model for continuous time. Efron (1988) showed that this model can be estimated using ordinary logistic regression software, and Singer and Willett (1993) provided a detailed illustration of a particularly flexible form of the model that includes one parameter per time period. This work has been expanded to show how logistic regression output can also be used to estimate the standard errors of the survival functions. This is particularly simple under the model described by Singer and Willett, when there are no predictors other than time.

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6

Barbu, Vlad, Michel Boussemart, and Nikolaos Limnios. "Discrete-Time Semi-Markov Model for Reliability and Survival Analysis." Communications in Statistics - Theory and Methods 33, no. 11 (2004): 2833–68. http://dx.doi.org/10.1081/sta-200037923.

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7

Moerbeek, Mirjam. "Sufficient Sample Sizes for Discrete-Time Survival Analysis Mixture Models." Structural Equation Modeling: A Multidisciplinary Journal 21, no. 1 (2014): 63–67. http://dx.doi.org/10.1080/10705511.2014.856697.

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8

Peugh, James, and Xitao Fan. "Identifying Unobserved Hazard Functions in Discrete-Time Survival Mixture Analysis." Structural Equation Modeling: A Multidisciplinary Journal 24, no. 1 (2016): 1–16. http://dx.doi.org/10.1080/10705511.2016.1242372.

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9

Singer, Judith D., and John B. Willett. "It’s About Time: Using Discrete-Time Survival Analysis to Study Duration and the Timing of Events." Journal of Educational Statistics 18, no. 2 (1993): 155–95. http://dx.doi.org/10.3102/10769986018002155.

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Educational researchers frequently ask whether and, if so, when events occur. Until relatively recently, however, sound statistical methods for answering such questions have not been readily available. In this article, by empirical example and mathematical argument, we demonstrate how the methods of discrete-time survival analysis provide educational statisticians with an ideal framework for studying event occurrence. Using longitudinal data on the career paths of 3,941 special educators as a springboard, we derive maximum likelihood estimators for the parameters of a discrete-time hazard model, and we show how the model can befit using standard logistic regression software. We then distinguish among the several types of main effects and interactions that can be included as predictors in the model, offering data analytic advice for the practitioner. To aid educational statisticians interested in conducting discrete-time survival analysis, we provide illustrative computer code ( SAS, 1989 ) for fitting discrete-time hazard models and for recapturing fitted hazard and survival functions.

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10

Moerbeek, Mirjam, and Joost Schormans. "The Effect of Discretizing Survival Times in Randomized Controlled Trials." Methodology 11, no. 2 (2015): 55–64. http://dx.doi.org/10.1027/1614-2241/a000091.

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The aim of survival analysis is to study if and when some event occurs. With continuous-time analysis subjects are followed until the time they experience the event or drop out. In practice, subjects cannot always be followed continuously and event occurrence is measured in time periods. This type of survival analysis is known as discrete-time survival analysis and measuring subjects discretely rather than continuously results in a loss of information. The aim of this paper is to study the effects of discretizing survival times for randomized controlled trials by means of a simulation study. It is shown that parameter and standard error biases of both approaches are small and those of the discrete-time approach are only slightly larger than those of the continuous-time approach. The number of time periods has a negligible effect on bias. Power levels hardly differ across the two approaches, so following subjects continuously is not necessary, except when a detailed estimate of the underlying baseline hazard function is needed.

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11

Moerbeek, Mirjam, and Lieke Hesen. "The Consequences of Varying Measurement Occasions in Discrete-Time Survival Analysis." Methodology 14, no. 2 (2018): 45–55. http://dx.doi.org/10.1027/1614-2241/a000145.

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Abstract. In a discrete-time survival model the occurrence of some event is measured by the end of each time interval. In practice it is not always possible to measure all subjects at the same point in time. In this study the consequences of varying measurement occasions are investigated by means of a simulation study and the analysis of data from an empirical study. The results of the simulation study suggest that the effects of varying measurement occasions are negligible, at least for the scenarios that were covered in the simulation. The empirical example shows varying measurement occasions have minor effects on parameter estimates, standard errors, and significance levels.

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12

Scherm, H., and P. S. Ojiambo. "Applications of Survival Analysis in Botanical Epidemiology." Phytopathology® 94, no. 9 (2004): 1022–26. http://dx.doi.org/10.1094/phyto.2004.94.9.1022.

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Data on the occurrence and timing of discrete events such as spore germination, disease onset, or propagule death are recorded commonly in epidemiological studies. When analyzing such “time-to-event” data, survival analysis is superior to conventional statistical techniques because it can accommodate censored observations, i.e., cases in which the event has not occurred by the end of the study. Central to survival analysis are two mathematical functions, the survivor function, which describes the probability that an individual will “survive” (i.e., that the event will not occur) until a given point in time, and the hazard function, which gives the instantaneous risk that the event will occur at that time, given that it has not occurred previously. These functions can be compared among two or more groups using chi-square-based test statistics. The effects of discrete or continuous covariates on survival times can be quantified with two types of models, the accelerated failure time model and the proportional hazards model. When applied to longitudinal data on the timing of defoliation of individual blueberry leaves in the field, analysis with the accelerated failure time model revealed a significantly (P < 0.0001) increased defoliation risk due to Septoria leaf spot, caused by Septoria albopunctata. Defoliation occurred earlier for lower leaves than for upper leaves, but this effect was confounded in part with increased disease severity on lower leaves.

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13

Cole, John B., and Eldin A. Leighton. "Discrete time survival analysis of longevity in a colony of guide dogs." Journal of Veterinary Behavior 3, no. 4 (2008): 185. http://dx.doi.org/10.1016/j.jveb.2008.01.004.

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14

Masyn, Katherine E. "Discrete-Time Survival Factor Mixture Analysis for Low-Frequency Recurrent Event Histories." Research in Human Development 6, no. 2-3 (2009): 165–94. http://dx.doi.org/10.1080/15427600902911270.

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15

Nonnemaker, James M., Olivia Silber-Ashley, Matthew C. Farrelly, and Daniel Dench. "Parent–child communication and marijuana initiation: Evidence using discrete-time survival analysis." Addictive Behaviors 37, no. 12 (2012): 1342–48. http://dx.doi.org/10.1016/j.addbeh.2012.07.006.

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16

Willett, John B., and Judith D. Singer. "It’s Déjà Vu All Over Again: Using Multiple-Spell Discrete-Time Survival Analysis." Journal of Educational and Behavioral Statistics 20, no. 1 (1995): 41–67. http://dx.doi.org/10.3102/10769986020001041.

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Multiple-spell discrete-time survival analysis can be used to investigate the repeated occurrence of a single event, or the sequential occurrence of disparate events, including: students’ and teachers’ entries into, and exits from, school; childrens’ progress through stages of cognitive reasoning; disturbed adolescents’ repeated suicide attempts; and so forth. In this article, we introduce and illustrate the method using longitudinal data on exit from, and reentry into, the teaching profession. The advantages of the approach include: (a) applicability to many educational problems; (b) easy inclusion of time-invariant and time-varying predictors; (c) minimal assumptions—no proportional-hazards assumption is invoked and so the effects of predictors can vary over time within, and across, spells; and (d) all statistical models can be fit with a standard logistic regression package.

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17

Southey, B. R., S. L. Rodriguez-Zas, and K. A. Leymaster. "Discrete time survival analysis of lamb mortality in a terminal sire composite population1." Journal of Animal Science 81, no. 6 (2003): 1399–405. http://dx.doi.org/10.2527/2003.8161399x.

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18

Willett, John B., and Judith D. Singer. "It's Déjà Vu All over Again: Using Multiple-Spell Discrete-Time Survival Analysis." Journal of Educational and Behavioral Statistics 20, no. 1 (1995): 41. http://dx.doi.org/10.2307/1165387.

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19

De Leonardis, Daniele, and Roberto Rocci. "Assessing the default risk by means of a discrete-time survival analysis approach." Applied Stochastic Models in Business and Industry 24, no. 4 (2008): 291–306. http://dx.doi.org/10.1002/asmb.705.

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20

Abrokwah, Ama A., and Stephen O. Abrokwah. "Why Do Women Delay in Seeking Prenatal Care? A Discrete-Time Survival Analysis." Research in Economics and Management 6, no. 2 (2021): p1. http://dx.doi.org/10.22158/rem.v6n2p1.

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This paper evaluates the effect of access to the Ghana national health insurance on the timing of the first prenatal care visit for pregnant women after controlling for other factors. Due to the voluntary nature of the national health insurance program, insurance status is likely endogenous, this paper therefore uses the Multilevel Multiprocess (MLMP) model and the Mixed Proportional Hazard (MPH) model estimation techniques, which controls for endogeneity in survival data analysis. Results from the estimation shows that access to insurance reduces the delays in receiving prenatal care, and increases the probability of seeking prenatal care.

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21

Moerbeek, Mirjam, and Katarzyna Jóźwiak. "Optimal Designs for Event History Analysis." Zeitschrift für Psychologie 221, no. 3 (2013): 160–73. http://dx.doi.org/10.1027/2151-2604/a000144.

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The aim of event history analysis is to study the occurrence and timing of events, such as premature psychotherapy termination or drinking onset, and to relate the probability of event occurrence to relevant covariates. In the social sciences, the timing of the event is often measured discretely by using time intervals, which implies the exact timing of event occurrence is unknown. The optimal number of subjects and time intervals need to be decided upon in the design stage of a trial with discrete-time survival endpoints. This paper shows how the optimal design depends on the underlying survival function and the costs to include a subject relative to the costs to take a measurement. Furthermore, the effects of attrition on the optimal design are studied. An example on drinking onset illustrates the proposed methodology.

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22

Singer, Judith D., and John B. Willett. "It's about Time: Using Discrete-Time Survival Analysis to Study Duration and the Timing of Events." Journal of Educational Statistics 18, no. 2 (1993): 155. http://dx.doi.org/10.2307/1165085.

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23

Jeon, Hye-Jin, and Mee-Sook Yoo. "Testing the determinants on the onset of child's problem behaviors: Discrete-time survival analysis." Journal of Play Therapy 23, no. 3 (2019): 15–28. http://dx.doi.org/10.32821/jpt.23.3.2.

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24

Malone, Patrick S., Dorian A. Lamis, Katherine E. Masyn, and Thomas F. Northrup. "A Dual-Process Discrete-Time Survival Analysis Model: Application to the Gateway Drug Hypothesis." Multivariate Behavioral Research 45, no. 5 (2010): 790–805. http://dx.doi.org/10.1080/00273171.2010.519277.

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25

Ha, James C., Christy L. Kimpo, and Gene P. Sackett. "Multiple-spell, discrete-time survival analysis of developmental data: Object concept in pigtailed macaques." Developmental Psychology 33, no. 6 (1997): 1054–59. http://dx.doi.org/10.1037/0012-1649.33.6.1054.

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26

Huang, David Y. C., Debra A. Murphy, and Yih-Ing Hser. "Parental Monitoring During Early Adolescence Deters Adolescent Sexual Initiation: Discrete-Time Survival Mixture Analysis." Journal of Child and Family Studies 20, no. 4 (2010): 511–20. http://dx.doi.org/10.1007/s10826-010-9418-z.

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27

Ghasri, Milad, Taha Hossein Rashidi, and Meead Saberi. "Comparing Survival Analysis and Discrete Choice Specifications Simulating Dynamics of Vehicle Ownership." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 49 (2018): 34–45. http://dx.doi.org/10.1177/0361198118791911.

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Modeling travel-related decisions of transport system users is the core of many behavioral travel demand models. It is of great significance to use these models for planning purposes in which decisions of individuals are simulated for specific time intervals or on a continuous dimension until the target year. Discrete choice and survival analysis methods are two popular econometric structures to model and forecast time-dependent outcomes. This paper elaborates the conceptual and practical differences between these two methods in the context of vehicle ownership modeling. There are meaningful differences between these two methods including data preparation approaches, interpretations of the variable of interest in the model, and the simulation procedures. Further, this paper shows how negligent application of discrete choice methods for modeling time-to-event variables results in specification bias. This discussion paves the path for using hazard-based models in travel demand modeling, as the application of these models have been quite limited compared with their capacity.

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28

SungKyungYoo and Kim Eun-seok. "Testing the Determinants on Dual-Earner Women's Resignation from job : Applying Discrete-Time Survival Analysis." Korean Journal of Woman Psychology 20, no. 4 (2015): 551–69. http://dx.doi.org/10.18205/kpa.2015.20.4.006.

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29

Yun, Jeong-Sook, and Sung-Kyung Yoo. "Testing the Determinants on the Unilateral Termination of the Clients : Applying Discrete-Time Survival Analysis." KOREAN JOURNAL OF COUNSELING AND PSYCHOTHERAPY 28, no. 1 (2016): 1. http://dx.doi.org/10.23844/kjcp.2016.02.28.1.1.

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30

Louwers, Timothy J., Frank M. Messina, and Michael D. Richard. "The Auditor's Going-Concern Disclosure as a Self-Fulfilling Prophecy: A Discrete-Time Survival Analysis." Decision Sciences 30, no. 3 (1999): 805–24. http://dx.doi.org/10.1111/j.1540-5915.1999.tb00907.x.

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31

Ata Tutkun, Nihal, Nursel Koyuncu, and Uğur Karabey. "Discrete-time survival analysis under ranked set sampling: an application to Turkish motor insurance data." Journal of Statistical Computation and Simulation 89, no. 4 (2019): 660–67. http://dx.doi.org/10.1080/00949655.2018.1564308.

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32

Mitchell, Christina M., Nancy Rumbaugh Whitesell, Paul Spicer, Janette Beals, and Carol E. Kaufman. "Cumulative Risk for Early Sexual Initiation Among American Indian Youth: A Discrete-Time Survival Analysis." Journal of Research on Adolescence 17, no. 2 (2007): 387–412. http://dx.doi.org/10.1111/j.1532-7795.2007.00527.x.

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33

Cvetkovski, Stefan, Anthony F. Jorm, and Andrew J. Mackinnon. "Student psychological distress and degree dropout or completion: a discrete-time, competing risks survival analysis." Higher Education Research & Development 37, no. 3 (2017): 484–98. http://dx.doi.org/10.1080/07294360.2017.1404557.

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34

Eglit, Graham M. L., Joel S. Eppig, Kelsey R. Thomas, et al. "P4-206: COGNITIVE STAGE AND BIOMARKER PROFILE PREDICTORS OF DEMENTIA: A DISCRETE-TIME SURVIVAL ANALYSIS." Alzheimer's & Dementia 15 (July 2019): P1355. http://dx.doi.org/10.1016/j.jalz.2019.06.3869.

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35

Hunsberger, Sally, Paul S. Albert, and Lori Dodd. "Analysis of progression-free survival data using a discrete time survival model that incorporates measurements with and without diagnostic error." Clinical Trials: Journal of the Society for Clinical Trials 7, no. 6 (2010): 634–42. http://dx.doi.org/10.1177/1740774510384887.

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36

Kim, Daejin, and Jae Jin Im. "Discrete Time Survival Analysis and Time Dependence of Policy Adoption: A Focus on the Dispute of “Diffusion or Confusion”." korean policy sciences review 25, no. 1 (2021): 87–116. http://dx.doi.org/10.31553/kpsr.2021.3.25.1.87.

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37

Stefanov, Valeri T. "On some waiting time problems." Journal of Applied Probability 37, no. 03 (2000): 756–64. http://dx.doi.org/10.1017/s0021900200015977.

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A unifying technology is introduced for finding explicit closed form expressions for joint moment generating functions of various random quantities associated with some waiting time problems. Sooner and later waiting times are covered for general discrete- and continuous-time models. The models are either Markov chains or semi-Markov processes with a finite number of states. Waiting times associated with generalized phase-type distributions, that are of interest in survival analysis and other areas, are also covered.

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38

Stefanov, Valeri T. "On some waiting time problems." Journal of Applied Probability 37, no. 3 (2000): 756–64. http://dx.doi.org/10.1239/jap/1014842834.

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A unifying technology is introduced for finding explicit closed form expressions for joint moment generating functions of various random quantities associated with some waiting time problems. Sooner and later waiting times are covered for general discrete- and continuous-time models. The models are either Markov chains or semi-Markov processes with a finite number of states. Waiting times associated with generalized phase-type distributions, that are of interest in survival analysis and other areas, are also covered.

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39

Henry, Kimberly L., Terence P. Thornberry, and David H. Huizinga. "A Discrete-Time Survival Analysis of the Relationship Between Truancy and the Onset of Marijuana Use." Journal of Studies on Alcohol and Drugs 70, no. 1 (2009): 5–15. http://dx.doi.org/10.15288/jsad.2009.70.5.

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40

Kamruzzaman, Md, Billie Giles-Corti, Jonas De Vos, Frank Witlox, Farjana Shatu, and Gavin Turrell. "The life and death of residential dissonants in transit-oriented development: A discrete time survival analysis." Journal of Transport Geography 90 (January 2021): 102921. http://dx.doi.org/10.1016/j.jtrangeo.2020.102921.

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41

Raykov, Tenko, Anna Zajacova, Philip B. Gorelick, and George A. Marcoulides. "Using Latent Variable Modeling for Discrete Time Survival Analysis: Examining the Links of Depression to Mortality." Structural Equation Modeling: A Multidisciplinary Journal 25, no. 2 (2017): 287–93. http://dx.doi.org/10.1080/10705511.2017.1364969.

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42

Zajacova, Anna, and Jennifer Ailshire. "Body Mass Trajectories and Mortality Among Older Adults: A Joint Growth Mixture–Discrete-Time Survival Analysis." Gerontologist 54, no. 2 (2013): 221–31. http://dx.doi.org/10.1093/geront/gns164.

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43

Herzog, Wolfgang, Dieter Schellberg, and Hans-Christian Deter. "First recovery in anorexia nervosa patients in the long-term course: A discrete-time survival analysis." Journal of Consulting and Clinical Psychology 65, no. 1 (1997): 169–77. http://dx.doi.org/10.1037/0022-006x.65.1.169.

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44

Ye, H., D. J. Krauss, J. T. Dilworth, and C. W. Stevens. "Using Discrete-Time Survival Analysis to Examine Hazard Estimates of Biochemical Failure Among Prostate Cancer Patients." International Journal of Radiation Oncology*Biology*Physics 99, no. 2 (2017): E420—E421. http://dx.doi.org/10.1016/j.ijrobp.2017.06.1609.

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45

Gaines, T. L., K. D. Wagner, M. L. Mittal, et al. "Transitioning from pharmaceutical opioids: A discrete-time survival analysis of heroin initiation in suburban/exurban communities." Drug and Alcohol Dependence 213 (August 2020): 108084. http://dx.doi.org/10.1016/j.drugalcdep.2020.108084.

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46

Trim, Ryan S., Marc A. Schuckit, and Tom L. Smith. "Predicting drinking onset with discrete-time survival analysis in offspring from the San Diego prospective study." Drug and Alcohol Dependence 107, no. 2-3 (2010): 215–20. http://dx.doi.org/10.1016/j.drugalcdep.2009.10.015.

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47

Sączewska-Piotrowska, Anna. "Poverty dynamics in urban and rural households." Wiadomości Statystyczne. The Polish Statistician 61, no. 7 (2016): 39–59. http://dx.doi.org/10.5604/01.3001.0014.1038.

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The author used discrete-time event history methods to study poverty and non-poverty survival time of urban and rural households. To analyse there were used nonparametric estimators of hazard function and logit models, which are discrete-time survival models. On the basis on conducted analysis it can be concluded that rural households survive shorter in non-poverty and simultaneously longer in poverty than urban households. Besides, urban households have more chance of poverty exit and less chance of poverty entry than rural households.

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48

Yoon, Heeyeun, and Elizabeth Currid-Halkett. "Industrial gentrification in West Chelsea, New York: Who survived and who did not? Empirical evidence from discrete-time survival analysis." Urban Studies 52, no. 1 (2014): 20–49. http://dx.doi.org/10.1177/0042098014536785.

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49

Neverova, G. P., O. L. Zhdanova, and A. I. Abakumov. "Discrete-Time Model of Seasonal Plankton Bloom." Mathematical Biology and Bioinformatics 15, no. 2 (2020): 235–50. http://dx.doi.org/10.17537/2020.15.235.

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The most interesting results in modeling phytoplankton bloom were obtained based on a modification of the classical system of phytoplankton and zooplankton interaction. The modifications using delayed equations, as well as piecewise continuous functions with a delayed response to intoxication processes, made it possible to obtain adequate phytoplankton dynamics like in nature. This work develops a dynamic model of phytoplankton-zooplankton community consisting of two equations with discrete time. We use recurrent equations, which allows to describe delay in response naturally. The proposed model takes into account the phytoplankton toxicity and zooplankton response associated with phytoplankton toxicity. We use a discrete analogue of the Verhulst model to describe the dynamics of each of the species in the community under autoregulation processes. We use Holling-II type response function taking into account predator saturation to describe decrease in phytoplankton density due to its consumption by zooplankton. Growth and survival rates of zooplankton also depend on its feeding. Zooplankton mortality, caused by an increase in the toxic substances concentration with high density of zooplankton, is included in the limiting processes. An analytical and numerical study of the model proposed is made. The analysis shows that the stability loss of nontrivial fixed point corresponding to the coexistence of phytoplankton and zooplankton can occur through a cascade of period doubling bifurcations and according to the Neimark-Saker scenario leading to the appearance of quasiperiodic fluctuations as well. The proposed dynamic model of the phytoplankton and zooplankton community allows observing long-period oscillations, which is consistent with the results of field experiments. As well, the model have multistability areas, where a variation in initial conditions with the unchanged values of all model parameters can result in a shift of the current dynamic mode.

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50

Hudson, Christopher D., Andrew J. Bradley, James E. Breen, and Martin J. Green. "Dairy herd mastitis and reproduction: Using simulation to aid interpretation of results from discrete time survival analysis." Veterinary Journal 204, no. 1 (2015): 47–53. http://dx.doi.org/10.1016/j.tvjl.2015.01.024.

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