Stochastic Models in Clinical Trials

  • Stephen W. Lagakos
Part of the Lecture Notes in Medical Informatics book series (LNMED, volume 4)


In most analyses of clinical trials, the therapies being investigated are assessed on the basis of several time-dependent events. For example, in cancer clinical trials events such as drug toxicity, disease relapse, tumor remission, and death are common measures of therapeutic effect. These events are time-dependent in the sense that each can occur at various points in time after initiation of treatment. For those such as death which are certain to eventually occur, the interest is in the time until the event. For events such as tumor remission, which may or may not occur, both frequency and time until the event are of interest. Furthermore, knowledge of the relationships between events is often valuable. For example: How is remission related to survival? Does an elevated tumor marker signal impending failure? Is metastatic disease associated with early death?


Sojourn Time Small Cell Carcinoma Lymphnode Stage Tumor Remission Sojourn Time Distribution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Aalen, O. (1978). Nonparametric inference for a family of counting processes. Annals of Statistics, 6, 701–26.CrossRefGoogle Scholar
  2. Crowley, J.J. and Hu, M. (1977). Covariance analysis of heart transplant survival data. Journal of the American Statistical Association, 72, 27–36.CrossRefGoogle Scholar
  3. Gill, R.D. (1978). Nonparametric estimation based on censored observations of a Markov renewal process. Technical report SW 64/78, Stichting Mathematisch Centrum, Amsterdam, Netherlands.Google Scholar
  4. Hanley, J.A. and Parnes, M. (1978) Estimation of multivariate distribution in the presence of censoring. Submitted to a technical journalGoogle Scholar
  5. Kaplan, E.L. and Meier, P. (1958) Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53, 457–81.CrossRefGoogle Scholar
  6. Lagakos, S.W., Sommer, C.J., and Zelen, M. (1978): Semi-Markov models for partially censored data.Biometrika,65, 2, 311–17.Google Scholar
  7. Lagakos, S.W. and Zelen, M. (1978). The use of semi-Markov models in clinical trials. Submitted to a technical mournal.Google Scholar
  8. Prentice, R., et al. (1978). The analysis of failure times in the presence of competing risks. Biometrics, 34, 541–54.PubMedCrossRefGoogle Scholar
  9. Turnbull, B.W., Brown, B.W. and Hu, M. (1974). Survivorship analysis of heart transplant data. Journal of the American Statistical Association, 69, 74–80.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1979

Authors and Affiliations

  • Stephen W. Lagakos
    • 1
    • 2
  1. 1.Department of BiostatisticsHarvard School of Public HealthUSA
  2. 2.Sidney Farber Cancer InstituteBostonUSA

Personalised recommendations