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Life history data are generally incomplete. Usually, they do not cover for each individual in the study the entire life span or the life segment of interest. If data are collected retrospectively, observation ends at interview date, and no information is available on events and experiences after the date. Data collected prospectively are incomplete because events and other experiences are recorded during a limited period of time only. To deal with data limitations, models are introduced. The model that is considered in this chapter describes life histories. The model is based on the premise that life histories are realisations of a continuous-time Markov process. A Markov process is a stochastic process that describes a system with multiple states and transitions between the states. The time at which a transition occurs is random but the distribution of the time to transition is known. In the continuous-time Markov process, the transition time has an exponential distribution. The rate of transition out of the current state (exit rate) is the parameter of the exponential distribution. It depends on the current state only and is independent of the history of the stochastic process. In a system with multiple states, an individual who leaves the current state may enter one of several states. In competing risks models, states in the state space are viewed as competing destinations and transition rates are destination-specific. The Markov process is a first-order process: the destination state depends on the current state only and is independent of states occupied previously.
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Allignol, A. (2013). Package mvna. Nelson-Aalen estimator of the cumulative hazard in multistate models. Published on CRAN.
Allignol, A., Beyersmann, J., & Schumacher, M. (2008). mvna: An R package for the Nelson-Aalen estimator in multistate models. R Newsletter, 8(2), 48–50.
Allignol, A., Schumacher, M., & Beyersmann, J. (2011). Empirical transition matrix of multistate models: The etm package. Journal of Statistical Software, 38(4), 15.
Aoki, M. (1996). New approaches to macroeconomic modeling. Evolutionary stochastic dynamics, multiple equilibria, and externalities as field effects. Cambridge: Cambridge University Press. CrossRef
Beyersmann, J., & Putter, H. (2014). A brief note on computing average state occupation times. Demographic Research, Forthcoming.
Blossfeld, H. P., & Rohwer, G. (2002). Techniques of event history modeling. New approaches to causal analysis (2nd ed.). Mahwah: Lawrence Erlbaum Associates. MATH
Chiang, C. L. (1968). Introduction to stochastic processes in biostatistics. New York: Wiley. Chapter 9 reprinted in Bogue, D. J., Arriage, E. E., & Anderton, E. L. (Eds.). (1993). Readings in population research methodology (Vol. 2, pp. 7.84–7.97). Chicago/New York: Social Development Center/UNFPA.
Chiang, C. L. (1984). The life table and its applications. Malabar: R.E. Krieger Publishing.
Çinlar, E. (1975). Introduction to stochastic processes. Englewood Cliffs: Prentice-Hall. MATH
de Wreede, L. C., Fiocco, M., & Putter, H. (2011). mstate: An R package for the analysis of competing risks and multistate models. Journal of Statistical Software, 38(7), 1–30.
Helbing, D. (2010). Quantitative sociodynamics. Stochastic methods and models of social interaction processes. Berlin: Springer. MATH
Hoem, J. M., & Funck Jensen, U. (1982). Multistate life table methodology: A probabilist critique. In K. C. Land & A. Rogers (Eds.), Multidimensional mathematical demography (pp. 155–264). New York: Academic. CrossRef
Hougaard, P. (2000). Analysis of multistate survival data. New York: Springer. CrossRef
Jackson, C. (2011). Multi-state models for panel data: The msm package for R. Journal of Statistical Software, 38(8), 28.
Korn, E. I., Graubard, B. I., & Midthune, D. (1997). Time-to-event analysis of longitudinal follow-up of a survey: Choice of time-scale. American Journal of Epidemiology, 145(1), 72–80. CrossRef
Mamun, A. A. (2003). Life history of cardiovascular disease and its risk factors. Amsterdam: Rozenberg Publishers.
Namboodiri, K., & Suchindran, C. M. (1987). Life table techniques and their applications. Orlando: Academic.
Putter, H. (2011a). Special issue about competing risks and multi-state models. Journal of Statistical Software, 38(1), 1–4. MathSciNet
Putter, H. (2011b). Package dynpred. Companion package to “Dynamic prediction in clinical survival analysis”. Chapman and Hall/CRC Publishers. Published on CRAN.
Reuser, M. (2010). The effect of risk factors on compression or expansion of disability a multistate analysis of the U.S. health and retirement study. Amsterdam: Rozenberg Publishers.
Rogers, A. (1975). Introduction to multiregional mathematical demography. New York: Wiley.
Rogers, A. (1986). Parameterized multistate population dynamics and projections. Journal of the American Statistical Association, 81(393), 48–61. CrossRef
Schoen, R. (1988). Modeling multigroup populations. New York: Plenum Press. CrossRef
Tuma, N. B., & Hannan, M. T. (1984). Social dynamics. Models and methods. Orlando: Academic.
Van den Hout, A. (2013). ELECT: Estimation of life expectancies using continuous-time multi-state survival models. Available at http://www.ucl.ac.uk/~ucakadl/ELECT_Manual_13_02_2013.pdf. Accessed 4 May 2014.
Van den Hout, A., Ogurtsova, E., Gampe, J., & Matthews, F. E. (2014). Investigating healthy life expectancy using a multi-state model in the presence of missing data and misclassification. Demographic Research. In print.
Van Houwelingen, H. C., & Putter, H. (2011). Dynamic prediction in clinical survival analysis. Boca Raton: Chapman and Hall/CRC Press.
Willekens, F. J. (1987). The marital status life-table. In J. Bongaarts, T. Burch, & K. W. Wachter (Eds.), Family demography: Models and applications (pp. 125–149). Oxford: Clarendon Press.
Willekens, F. J. (2009). Continuous-time microsimulation in longitudinal analysis. In A. Zaidi, A. Harding, & P. Williamson (Eds.), New frontiers in microsimulation modelling (pp. 413–436). Surrey: Ashgate.
Willekens, F. (2013a). Biograph: Explore life histories.
Willekens, F. (2013b). Chronological objects in demographic research. Demographic Research, 28(23), 649–680. CrossRef
Wolf, D. A. (1986). Simulation methods for analyzing continuous-time event history models. Sociological Methodology, 16, 283–308. CrossRef
Zinn, S. (2011). A continuous-time microsimulation and first steps towards a multi-level approach in demography. PhD dissertation, University of Rostock, Faculty of Informatics and Electrotechnics.
Zinn, S. (2014). Package MicSim. Performing continuous-time microsimulation. Published on CRAN.
Zinn, S., Himmelspach, J., Uhrmacher, A. M., & Gampe, J. (2013). Building Mic-Core, a specialized M&S software to simulate multi-state demographic micro models, based on JAMES II, a general M&S framework. Journal of Artificial Societies and Social Simulation, 16(3), 5.
Gampe, J., Zinn, S., Willekens, F., Van der Gaag, N., de Beer, J., Himmelspach, J., & Uhrmacher, A. (2009, June). The microsimulation tool of the MicMac project. Paper presented at the 2nd general conference of the international microsimulation association, Ottawa.
Allignol, A. (2014). Package etm. Empirical transition matrix. Published on CRAN.
Jackson, C. (2014a). Package msm. Multi-state Markov and hidden Markov models in continuous time. Published on CRAN.
Putter, H., de Wreede, L., & Fiocco, M. (2011). Package mstate. Data preparation, estimation and prediction in multistate models. Published on CRAN.
Weidlich, W., & Haag, G. (Eds.). (1988). Interregional migration. Dynamic theory and comparative analysis. Berlin: Springer.
- Life Histories: Real and Synthetic
- Chapter 2
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