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Richard
Sposto
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Affiliation Professor
of Research Preventive
Medicine and Pediatrics (CHLA) Keck
Director,
Biostatistics/Bioinformatics Children’s
Center for Cancer and Blood Diseases |
Contact Information Children’s
Center for Cancer and Blood Diseases 4650
Sunset Boulevard, Mail Stop #54 Email:
rsposto@chla.usc.edu / sposto@usc.edu TEL:
323-361-8582 / FAX: 323-361-1803 |
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Research Interests · Treatment and biology of pediatric cancer · Clinical trials design · Strategies for clinical trials research in low incidence cancer · Modeling cure as an outcome of cancer treatment · Statistical modeling framework to optimize risk stratification in pediatric cancer. |
Education ·
B.A. Mathematics, ·
M.S. Biostatistics, ·
Ph.D. Biostatistics, |
Professional Experience
2004-Present Professor of Research in the Department of Preventive Medicine and the Department of Pediatrics of the Keck School of Medicine, University of Southern California; Director of Biostatistics/Bioinformatics for the Children’s Center for Cancer and Blood Diseases, Division of Hematology/Oncology, Childrens Hospital Los Angeles (CHLA).
1995-2004 Associate
Professor of Research, Department of Preventive Medicine, Keck School of
Medicine, University of Southern California: Senior Statistician, Childrens Oncology Group (
1992-1994 Co-Principal Investigator for the Data Coordinating and Analysis Center of the Aids Vaccine Evaluation Group, The Emmes Corporation, Potomac, Maryland.
1988-1992 Research Scientist, Department of
Statistics, Radiation Effects Research Foundation,
1981-1988 Assistant Professor of Research, Department of Preventive Medicine, Keck School of Medicine, University of Southern California; Senior statistician, Children's Cancer Group
Selected Publications
Sposto, R. and H.N. Sather, Determining the duration of comparative clinical trials while allowing
for cure. Journal of Chronic Diseases, 1985. 38(8): p. 683-90.
Sposto, R., D.O. Stram,
and A.A. Awa, An estimate of the
magnitude of random errors in the DS86 dosimetry from
data on chromosome aberrations and severe epilation.
Radiation Research, 1991. 128(2): p.
157-69.
Sposto, R., D.L. Preston, Y. Shimizu, and K.
Mabuchi, The effect of diagnostic
misclassification on non-cancer and cancer mortality dose response in A-bomb
survivors. Biometrics, 1992. 48(2):
p. 605-17.
Sposto, R. and D.O. Stram,
A strategic view of randomized trial
design in low-incidence paediatric cancer.
Statistics in Medicine, 1999. 18(10):
p. 1183-97.
Sposto, R., Cure
model analysis in cancer: An application to data from the Children's Cancer
Group. Statistics in Medicine, 2002. 21:
p. 293-312.
Geyer,
J.R., R. Sposto, M. Jennings, J.M. Boyett, R.A. Axtell, D. Breiger,
E. Broxson, B. Donahue, J.L. Finlay,
J.W. Goldwein, L.A. Heier,
D. Johnson, C. Mazewski, D.C. Miller, R. Packer, D. Puccetti, J. Radcliffe, M.L. Tao,
T. Shiminski-Maher, and G. Children's Cancer, Multiagent chemotherapy and deferred radiotherapy in
infants with malignant brain tumors: a report from the Children's Cancer Group.
Journal of Clinical Oncology, 2005. 23(30):
p. 7621-31.
Asgharzadeh,
S., R. Pique-Regi, R. Sposto, H. Wang, Y. Yang, H. Shimada, K. Matthay,
J. Buckley, A. Ortega, and R.C. Seeger, Prognostic
significance of gene expression profiles of metastatic
neuroblastomas lacking MYCN gene amplification.
Journal of the National Cancer Institute, 2006. 98(17): p. 1193-203.
Packer,
R.J., A. Gajjar, G. Vezina,
L. Rorke-Adams, P.C. Burger, P.L. Robertson, L.
Bayer, D. LaFond, B.R. Donahue, M.H. Marymont, K. Muraszko, J.
Langston, and R. Sposto, Phase
Cairo,
M.S., M. Gerrard, R. Sposto, A. Auperin, C.R. Pinkerton, J.
Michon, C. Weston, S.L. Perkins, M. Raphael, K.
McCarthy, and C. Patte, Results of a randomized international study of high risk central
nervous system B-non-Hodgkin's lymphoma and B-acute lymphoblastic
leukemia in children and adolescents. Blood, 2007. 109(7): p. 2736-2743.
Keshelava, N., E. Davicioni,
Z. Wan, L. Ji, R.
Sposto, T.J. Triche, and C.P. Reynolds, Histone Deacetylase 1
Gene Expression and Sensitization of Multidrug-Resistant
Neuroblastoma Cell Lines to Cytotoxic Agents by Depsipeptide. J. Natl. Cancer Inst., 2007: p. djm044.
Sposto, R., W.B. London, and T.A. Alonzo, Criteria for optimizing prognostic risk
groups in pediatric cancer: analysis of data from the Children's Oncology
Group. Journal of Clinical Oncology, 2007. 25(15): p. 2070-7.
Armenian,
S.H., C.-L. Sun, L. Francisco, J. Steinberger, S. Kurian, F.L. Wong, J. Sharp, R. Sposto, S.J. Forman, and S. Bhatia, Late Congestive Heart
Failure (CHF) Following Hematopoietic Cell
Transplantation (
Sposto,
R. and P.S. Gaynon, An
adjustment for patient heterogeneity in the design of two-stage phase II
trials. Statistics in
Medicine, 2009. [EPub ahead of print](
Ko, R.H., L. Ji, P. Barnette, B. Bostrom, R.
Hutchinson, E. Raetz, N. Seibel, C. Twist, E. Eckroth, R. Sposto,
P. Gaynon, and M.L. Loh, Outcome of Patients Treated
for Relapsed or Refractory Acute Lymphoblastic
Leukemia (
Curriculum Vitae
Software
CUREREGR - Parametric Cure Model (PCM) Regression
Software
Reference:
Sposto, R. Cure model analysis in cancer: An application to data from the
Children's Cancer Group. Statistics
in Medicine 21: 293-312, 2002.
'CUREREGR'
is Windows-compatible, PC/DOS-based command-line software that fits complex mixture
and non-mixture parametric regression models for analysis of survival data that
exhibit a 'cured' fraction. CUREREGR has a passable user interface that uses
simple GLIM-like syntax to build regression models for the cured fraction,
scale, and shape parameters of the PCM. It can also generate Microsoft Excel
macro code that produces plots comparing the parametric model to the
product-limit estimate. The software is available gratis at
the link below.
Also, Allen Buxton of the CureSearch Children's Oncology Group has created a STATA implementation of these models, which can be found at the link below.
Link to STATA-CUREREGR
sgP2 – R function to compute Phase II design that
corrects for patient heterogeneity
Reference: Sposto, R. and P.S. Gaynon, An adjustment for patient
heterogeneity in the design of two-stage phase II trials. Statistics
in Medicine, 2009.
The enclosed R function implements a method for designing two-stage Phase
II studies that accounts for patient heterogeneity and effectively stabilizes
conditional Type I and Type II error over the range of patient mixes that are
likely to arise. Use of the design requires good estimates of the expected
response rate within each population stratum as well as the stratum membership
probabilities, but its properties are similar to and often preferable to the
standard two-stage design even in situations where the underlying assumptions
do not hold absolutely.
The ZIP file also includes supplementary material to the Statistics in
Medicine publication.
Download
SpostoGaynonDesign.ZIP
Updated
2009-08-10