Abstract
In clinical studies with time-to-event end points we face uncertainties caused by the enrollment process that can often be viewed as a stochastic process. The observed endpoints are randomly censored and the amount of gained information is random and its actual value is not known at the design stage but becomes known only after the study completion. To take this fact into account we develop a method that maximizes the average information. We derive the average elemental Fisher information matrices for a few scenarios to illustrate the approach, assuming that enrollment can be modeled by a Poisson process.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Atkinson, A.C., Fedorov, V.V., Herzberg, A.M., Zhang, R.M.: Optimal experimental design for generalized regression models. JSPI 144, 81–91 (2012)
Balakrishnan, N., Kundu, D.: Hybrid censoring: Models, inferential results and applications. Comput. Stat. Data Anal. 53, 166–209 (2013)
Cox, D.R., Oakes, D.: Analysis of Survival Data. Chapman & Hall, London (1984)
Fedorov, V.V.: Theory of Optimal Experiment. Academic Press, New York (1972)
Fedorov, V.V., Leonov, S.: Optimal Design for Nonlinear Response Models. CRC Press, New York (2013)
Fridman, L.M., Furberg, C.D., DeMets, D.L.: Fundamentals of Clinical Trials, 4th edn. Springer, New York (2010)
Gertsbakh, I., Kagan, A.: Characterization of the Weibull distribution by properties of the Fisher information under type-I censoring. Stat. Probab. Lett. 42, 99–105 (1999)
Gupta, R.D., Kundu, D.: On the comparison of Fisher information of the Weibull and GE distributions. JSPI 9, 3130–3144 (2006)
Kingman, J.F.C.: Poisson Processes. Oxford University Press, New York (1993)
Konstantinou, M., Biedermann, S., Kimber, A.: Optimal designs for two-parameter nonlinear models for application to survival models. Statistica Sinica 24, 415–428 (2014)
Little, R.J.A., Rubin, D.B.: Statistical Analysis with Missing Data. Wiley, New York (1987)
Lopez-Fidalgo, J., Rivas-Lopez, M.J.: Optimal experimental designs for partial likelihood information. Comput. Stat. Data Anal. 71, 859–867 (2012)
Lopez-Fidalgo, J., Rivas-Lopez, M.J., Campo, R.D.: Optimal designs for Cox regression. Statistica Neerlandica 63, 135–148 (2009)
Müller, C.H.: D-optimal designs for lifetime experiments with exponential distribution and consoring. Adv. Model-Oriented Des. Anal. 161–168 (2013)
Acknowledgements
We thank the referees for their many helpful comments and insightful suggestions leading to an improved paper.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Fedorov, V.V., Xue, X. (2016). Survival Models with Censoring Driven by Random Enrollment. In: Kunert, J., Müller, C., Atkinson, A. (eds) mODa 11 - Advances in Model-Oriented Design and Analysis. Contributions to Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-31266-8_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-31266-8_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-31264-4
Online ISBN: 978-3-319-31266-8
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)