Evaluating Treatment Effect in Multicenter Trials with Small Centers Using Survival Modeling

Authors

  • Usha S. Govindarajulu Department of Epidemiology and Biostatistics, SUNY Downstate School of Public Health, Brooklyn, NY 11203, USA
  • Elizabeth J. Malloy Department of Mathematics and Statistics, American University, Washington, DC 20016, USA

DOI:

https://doi.org/10.6000/1929-6029.2015.04.01.2

Keywords:

Frailty, survival, clinical trial, prognostic factor, rare disease.

Abstract

Clinical trials of rare diseases commonly enlist several centers to achieve recruitment goals. The aim of this study is to examine the estimation of treatment effects for survival outcomes in multicenter clinical trials with varying numbers of centers and few patients per center for rarer disease outcomes (i.e. rare cancers). We modeled the heterogeneity between centers using Cox frailty models to account for the variability in patients and patient care between centers and examined measures of model fit via smoothed functions of a prognostic factor. Through a simulation study, we were able to examine the consequence of having only a few centers or a few patients per center on the treatment and prognostic factor effects and model performance indices. Overall, we found it is preferable to have more patients per site and more sites in a multicenter trial as expected. However, having a few patients per site is feasible if there are many sites in a trial.

Author Biographies

Usha S. Govindarajulu, Department of Epidemiology and Biostatistics, SUNY Downstate School of Public Health, Brooklyn, NY 11203, USA

Epidemiology and Biostatistics

Elizabeth J. Malloy, Department of Mathematics and Statistics, American University, Washington, DC 20016, USA

Mathematics and Statistics

References

Matthews JN. Small clinical trials: are they all bad? Statist Med 1995; 14(2): 115-126. http://dx.doi.org/10.1002/sim.4780140204 DOI: https://doi.org/10.1002/sim.4780140204

Localio A, Berlin J, Ten Have T, Kimmel S. Adjustments for center in multicenter studies: An overview. Ann Inter Med 2007; 135(2): 112-123. http://dx.doi.org/10.7326/0003-4819-135-2-200107170-00012 DOI: https://doi.org/10.7326/0003-4819-135-2-200107170-00012

Ambler G, Seaman S, Omar RZ. An evaluation of penalised survival methods for developing prognostic models with rare events. Statist Med 2012; 31(11-12): 1150-1161. http://dx.doi.org/10.1002/sim.4371 DOI: https://doi.org/10.1002/sim.4371

Senn S. Planning and analyzing multi-centre trials. Statist Med 1998; 17: 1753-1765. http://dx.doi.org/10.1002/(SICI)1097-0258(19980815/30)17:15/16<1753::AID-SIM977>3.0.CO;2-X DOI: https://doi.org/10.1002/(SICI)1097-0258(19980815/30)17:15/16<1753::AID-SIM977>3.0.CO;2-X

Duchateau L, Janssen P, Lindsey P, Legrand C, Nguti R, Sylvester R. The shared frailty model and the power for heterogeneity tests in multicenter trials. Comput Statist Data Anal 2002; 40. DOI: https://doi.org/10.1016/S0167-9473(02)00057-9

Duchateau L, Janssen P. The frailty model. Vol New York 2008.

Jeong J-H, Costantino J. Application of smoothing methods to evaluate treatment-prognostic factor interactions in breast cancer data. Cancer Investig 2006; 24: 288-293. http://dx.doi.org/10.1080/07357900600633841 DOI: https://doi.org/10.1080/07357900600633841

Silverman BG, Siegelmann-Danieli N, Braunstein R, Kokia ES. Trends in breast cancer incidence associated with reductions in the use of hormone replacement therapy. Cancer Epidemiol 2011; 35(1): 11-16. DOI: https://doi.org/10.1016/j.canep.2010.11.006

Govindarajulu US, Malloy EJ, Ganguli B, Spiegelman D, Eisen EA. The comparison of alternative smoothing methods for fitting non-linear exposure-response relationships with Cox models in a simulation study. Int J Biostatist 2009; 5(1): Article 2. DOI: https://doi.org/10.2202/1557-4679.1104

Therneau T, Grambsch P. Modeling survival data: extending the Cox Model. New York: Springer-Verlag 2002.

Bender R, Augustin T, Blettner M. Generating survival times to simulate Cox proportional hazards models. Statist Med 2005; 24(11): 1713-1723. http://dx.doi.org/10.1002/sim.2059 DOI: https://doi.org/10.1002/sim.2059

Malloy EJ, Spiegelman D, Eisen EA. Comparing measures of model selection for penalized splines in Cox models. Comput Stat Data Anal 2009; 53(7): 2605-2616. http://dx.doi.org/10.1016/j.csda.2008.12.008 DOI: https://doi.org/10.1016/j.csda.2008.12.008

Klein J, Moeschberger M. Survival analysis: Techniques for censored and truncated data. New York: Springer 1997.

Armitage P, Doll R, (Ed) IJN. Stochastic models for carcinogenesis. Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability; Berkeley (Univ. of California) 1961.

Breslow N, Day N. Statistical methods in cancer research: The design and analysis of cohort studies. Int Agency Res Cancer 1987; 2.

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Published

2015-12-27

How to Cite

Govindarajulu, U. S., & Malloy, E. J. (2015). Evaluating Treatment Effect in Multicenter Trials with Small Centers Using Survival Modeling. International Journal of Statistics in Medical Research, 4(1), 8–25. https://doi.org/10.6000/1929-6029.2015.04.01.2

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General Articles