Browsing by Subject "Resampling"

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    Resampling phase III data to assess phase II trial designs and endpoints
    (American Association for Cancer Research, 2012) Sharma, Manish R.; Karrison, Theodore G.; Jin, Yuyan; Bies, Robert R.; Maitland, Michael L.; Stadler, Walter M.; Ratain, Mark J.; Medicine, School of Medicine
    Purpose: The best phase II design and endpoint for growth inhibitory agents is controversial. We simulated phase II trials by resampling patients from a positive (sorafenib vs. placebo; TARGET) and a negative (AE941 vs. placebo) phase III trial in metastatic renal cancer to compare the ability of various designs and endpoints to predict the known results. Experimental design: A total of 770 and 259 patients from TARGET and the AE 941 trial, respectively, were resampled (5,000 replicates) to simulate phase II trials with α = 0.10 (one-sided). Designs/endpoints: single arm, two-stage with response rate (RR) by Response Evaluation Criteria in Solid Tumors (RECIST; 37 patients); and randomized, two arm (20-35 patients per arm) with RR by RECIST, mean log ratio of tumor sizes (log ratio), progression-free survival (PFS) rate at 90 days (PFS-90), and overall PFS. Results: Single-arm trials were positive with RR by RECIST in 55% and 1% of replications for sorafenib and AE 941, respectively. Randomized trials versus placebo with 20 patients per arm were positive with RR by RECIST in 55% and 7%, log ratio in 88% and 25%, PFS-90 in 64% and 15%, and overall PFS in 69% and 9% of replications for sorafenib and AE 941, respectively. Conclusions: Compared with the single-arm design and the randomized design comparing PFS, the randomized phase II design with the log ratio endpoint has greater power to predict the positive phase III result of sorafenib in renal cancer, but a higher false positive rate for the negative phase III result of AE 941.
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    A sexually transmitted infection screening algorithm based on semiparametric regression models
    (Wiley, 2015-09-10) Li, Zhuokai; Liu, Hai; Tu, Wanzhu; Department of Biostatistics, Richard M. Fairbanks School of Public Health
    Sexually transmitted infections (STIs) with Chlamydia trachomatis, Neisseria gonorrhoeae, and Trichomonas vaginalis are among the most common infectious diseases in the United States, disproportionately affecting young women. Because a significant portion of the infections present no symptoms, infection control relies primarily on disease screening. However, universal STI screening in a large population can be expensive. In this paper, we propose a semiparametric model-based screening algorithm. The model quantifies organism-specific infection risks in individual subjects and accounts for the within-subject interdependence of the infection outcomes of different organisms and the serial correlations among the repeated assessments of the same organism. Bivariate thin-plate regression spline surfaces are incorporated to depict the concurrent influences of age and sexual partners on infection acquisition. Model parameters are estimated by using a penalized likelihood method. For inference, we develop a likelihood-based resampling procedure to compare the bivariate effect surfaces across outcomes. Simulation studies are conducted to evaluate the model fitting performance. A screening algorithm is developed using data collected from an epidemiological study of young women at increased risk of STIs. We present evidence that the three organisms have distinct age and partner effect patterns; for C. trachomatis, the partner effect is more pronounced in younger adolescents. Predictive performance of the proposed screening algorithm is assessed through a receiver operating characteristic analysis. We show that the model-based screening algorithm has excellent accuracy in identifying individuals at increased risk, and thus can be used to assist STI screening in clinical practice.