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      The Consequences of Varying Measurement Occasions in Discrete-Time Survival Analysis

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          Abstract

          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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          How to Use a Monte Carlo Study to Decide on Sample Size and Determine Power

          Linda Muthén, Bengt O. Muthén (2002)
          Article 0 comments Cited 431 times – based on 0 reviews     Review now
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            Generating survival times to simulate Cox proportional hazards models.

            Ralf Bender, Thomas Augustin, Maria Blettner (2005)
            Simulation studies present an important statistical tool to investigate the performance, properties and adequacy of statistical models in pre-specified situations. One of the most important statistical models in medical research is the proportional hazards model of Cox. In this paper, techniques to generate survival times for simulation studies regarding Cox proportional hazards models are presented. A general formula describing the relation between the hazard and the corresponding survival time of the Cox model is derived, which is useful in simulation studies. It is shown how the exponential, the Weibull and the Gompertz distribution can be applied to generate appropriate survival times for simulation studies. Additionally, the general relation between hazard and survival time can be used to develop own distributions for special situations and to handle flexibly parameterized proportional hazards models. The use of distributions other than the exponential distribution is indispensable to investigate the characteristics of the Cox proportional hazards model, especially in non-standard situations, where the partial likelihood depends on the baseline hazard. A simulation study investigating the effect of measurement errors in the German Uranium Miners Cohort Study is considered to illustrate the proposed simulation techniques and to emphasize the importance of a careful modelling of the baseline hazard in Cox models. Copyright 2005 John Wiley & Sons, Ltd
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              It's About Time: Using Discrete-Time Survival Analysis to Study Duration and the Timing of Events

              J. Singer, J Willett (1993)
              Article 0 comments Cited 80 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Mirjam Moerbeek
                Journal
                Journal ID (publisher): med
                Title: Methodology
                Subtitle: European Journal of Research Methods for the Behavioral and Social Sciences
                Publisher: Hogrefe Publishing
                ISSN (Print): 1614-1881
                ISSN (Electronic): 1614-2241
                Publication date (Electronic): June 21, 2018
                Volume: 14
                Issue: 2
                Pages: 45-55
                Affiliations
                [ 1 ]Department of Methods and Statistics, Utrecht University, Utrecht, The Netherlands
                Author notes
                Mirjam Moerbeek, Department of Methods and Statistics, Utrecht University, PO Box 80140, 3508 TC Utrecht, The Netherlands, m.moerbeek@ 123456uu.nl
                Article
                ApaID ID: med_14_2_45
                DOI: 10.1027/1614-2241/a000145
                SO-VID: 1953c7d4-81ed-4e4e-9a2a-5ae55f1b2e87
                Copyright statement: Copyright @ 2018
                History
                Date received : February 4, 2016
                Date revision received : September 12, 2017
                Date accepted : December 10, 2017
                Categories
                Subject: Original Article

                ScienceOpen disciplines: Psychology,Applications,General social science,Methodology,Clinical Psychology & Psychiatry
                Keywords: simulation study,longitudinal study,measurement occasion,survival analysis,experimental design
                Data availability:
                ScienceOpen disciplines: Psychology, Applications, General social science, Methodology, Clinical Psychology & Psychiatry
                Keywords: simulation study, longitudinal study, measurement occasion, survival analysis, experimental design

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