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AN ANALYSIS OF EXPECTED SURVIVAL DIFFERENTIAL

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AN ANALYSIS OF EXPECTED SURVIVAL DIFFERENTIAL AN ANALYSIS OF EXPECTED SURVIVAL DIFFERENTIAL Presentation Transcript

  • AN ANALYSIS OF EXPECTED SURVIVAL DIFFERENTIAL IN A LUNG CANCER TRIAL: AN ITERATIVE PROCEDURE WITH A CENSORED REGRESSION MODEL D. Das Purkayastha Ph.D. Biometrics Medical Affair Novartis Pharmaceuticals
    • OUTLINE
    • Introduction
    • Model
    • Definitions
    • Estimation
    • Iteration Procedure for Computation
    • Data
    • Analysis
    • Results
    • Conclusion
    • References
    • INTRODUCTION
    • An alternative look at the analysis of expected survival differential
    • A latent variable framework with a differential threshold of survival time with or without disease
    • to maximize the probability of survival differential
    • A standard censored regression model
    • Two regimes are considered in the model with a switching criterion for above and below a pre-assigned threshold level of the expected survival differential
    • EM algorithm
    • Lung cancer data (publicly available) from a randomized Phase III clinical trial
    • Treatment of locally advanced non-small cell lung cancer
    • Comparison between the stand-alone use of radiotherapy and a combination therapy
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    • ANALYSIS
    • The above iteration procedure was used for computation of parameters in the model
    • SAS IML
    •  = 0.4 ; tolerance limit from .0001 to .01 (recommended);
    • convergence issues:  = .8, .9.
    • The model was estimated for each treatment group.
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    • Results from LCSG (1988):
    • There is statistically significant difference for recurrence of disease and recurrence rate between radiotherapy and combination therapy within one year (p < .001).
    • Death rate within one year was significantly different between the therapies (p = .02).
    • Log rank test also showed statistically significant difference in time to recurrence of the disease.
    • Current findings:
    • It is interesting to note that in this paper survival difference does not have statistically significant effect of recurrence rate.
    • The results shown in Table 2 show that cell type, tumor status, recurrence, weight loss or age have no statistical impact on the survival difference of the each and overall treatment groups.
    • Only the therapy type in the overall model shows statistical significance (p = .026) on the survival difference (d i ).
    • It is well known that such models need comparatively larger observations. Also, sometimes to achieve convergence was difficult or not possible. Thus, it is imperative that the results of the overall model as depicted in Table 2 should be cautiously interpreted.
    • CONCLUSIONS
    • It facilitates the applications of such censored regression models for survival analyses.
    • Empirically, the results of the overall model show that the type of therapy (radiotherapy, or combination therapy) as used on cancer patients can have a statistically significant effect on the survival time differential. But it needs cautious interpretations of the results.
    • This model needs comparatively larger patient population to draw valid inference from the results. For small samples size, it is also computationally difficult. However, it provides an alternative look at survival analysis.
  • REFERENCES AMEMIYA, T. (1973) “Regression Analysis When the Dependent Variable is Truncated Normal” Econometrica 41; 997-1016. BLIGHT, J.N. (1970) “Estimation From a Censored Sample for the Exponential Family,” Biometrika 57(2); 389-395. COHEN, A.C. (1957) “On the Solution of Estimating Equations for Truncated and Censored Samples from Normal Populations” Biometrika 44; 225-261. DEMPSTER, A.P., LAIRD, N.M., and RUBIN, D.B. (1977) “Maximum Likelihood f rom Incomplete Data via the E.M. Algorithm” Journal of the Royal Statistical Society (series B) 39; 1-38. FAIR “A note on the Computation of the Tobit-Estimator” Econometrica 45(7); 1723-1730. LUNG CANCER STUDY GROUP(1988) “The Benefit of Adjuvant Treatment for Restricted Locally Advanced Non-Small-Cell Lung Cancer,” Journal of Clinical Oncology, Vol 6, 1 (January); 9-17. MADDALA, G.S. (1987) “Limited Dependent and Qualitative Variables in Econometrics” Cambridge University Press. PIANTADOSI, S. (1997) “Clinical Trials: A Methodologic Perspective” John Wiley & Sons, Inc. New York.