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Lin Y.,Takeda Development Center Americas Inc.
Contemporary Clinical Trials Communications | Year: 2016

Responder analysis is in common use in clinical trials, and has been described and endorsed in regulatory guidance documents, especially in trials where "soft" clinical endpoints such as rating scales are used. The procedure is useful, because responder rates can be understood more intuitively than a difference in means of rating scales. However, two major issues arise: 1) such dichotomized outcomes are inefficient in terms of using the information available and can seriously reduce the power of the study; and 2) the results of clinical trials depend considerably on the response cutoff chosen, yet in many disease areas there is no consensus as to what is the most appropriate cutoff. This article addresses these two issues, offering a novel approach for responder analysis that could both improve the power of responder analysis and explore different responder cutoffs if an agreed-upon common cutoff is not present. Specifically, we propose a statistically rigorous clinical trial design that pre-specifies multiple tests of responder rates between treatment groups based on a range of pre-specified responder cutoffs, and uses the minimum of the p-values for formal inference. The critical value for hypothesis testing comes from permutation distributions. Simulation studies are carried out to examine the finite sample performance of the proposed method. We demonstrate that the new method substantially improves the power of responder analysis, and in certain cases, yields power that is approaching the analysis using the original continuous (or ordinal) measure. © 2016 Published by Elsevier Inc. Source


Dong X.,Takeda Development Center Americas Inc. | Kong L.,Penn State College of Medicine | Wahed A.S.,University of Pittsburgh
Statistics in Medicine | Year: 2016

Biomarkers are often measured over time in epidemiological studies and clinical trials for better understanding of the mechanism of diseases. In large cohort studies, case-cohort sampling provides a cost effective method to collect expensive biomarker data for revealing the relationship between biomarker trajectories and time to event. However, biomarker measurements are often limited by the sensitivity and precision of a given assay, resulting in data that are censored at detection limits and prone to measurement errors. Additionally, the occurrence of an event of interest may preclude biomarkers from being further evaluated. Inappropriate handling of these types of data can lead to biased conclusions. Under a classical case cohort design, we propose a modified likelihood-based approach to accommodate these special features of longitudinal biomarker measurements in the accelerated failure time models. The maximum likelihood estimators based on the full likelihood function are obtained by Gaussian quadrature method. We evaluate the performance of our case-cohort estimator and compare its relative efficiency to the full cohort estimator through simulation studies. The proposed method is further illustrated using the data from a biomarker study of sepsis among patients with community acquired pneumonia. © 2016 John Wiley & Sons, Ltd. Source


Lin J.H.,Takeda Development Center Americas Inc. | Giovannucci E.,Harvard University
Current Colorectal Cancer Reports | Year: 2014

Colorectal cancers are a group of heterogeneous disorders that involve interactions among environmental influences, germ-line susceptibility factors, and accumulated genetic and epigenetic changes in the colorectal epithelium. This review provides background on environmental exposure and molecular pathways during colorectal cancer pathogenesis. Additionally, the review discusses the interplay between these risk factors in the context of colorectal cancer development and progression. Smoking, obesity, and regular aspirin use appear to have profound effects on colorectal cancer initiation and prognosis through the activation of pathways targeting gene methylation, inflammation, insulin mitogenesis, and cell proliferation. New data on other types of exposure and molecular changes will continue to improve our understanding of the cause of colorectal cancer, leading to novel therapeutic and preventive strategies and ultimate reduction in disease burden. © 2014 Springer Science+Business Media. Source


Sheean P.,Loyola University Chicago | Liang H.,Takeda Development Center Americas Inc. | Schiffer L.,University of Illinois at Chicago | Arroyo C.,University of Illinois at Chicago | And 2 more authors.
Journal of Cancer Survivorship | Year: 2016

Purpose: Osteoporosis increases the risk of fracture and is often considered a late effect of breast cancer treatment. We examined the prevalence of compromised bone health in a sample of exclusively African-American (AA) breast cancer survivors since bone mineral density (BMD) varies by race/ethnicity in healthy populations. Methods: Using a case–control design, AA women in a weight loss intervention previously diagnosed and treated for stages I–IIIa breast cancer were matched 1:1 on age, race, sex, and BMI with non-cancer population controls (n = 101 pairs) from National Health and Nutrition Examination Survey (NHANES). Questionnaires and dual-energy x-ray absorptiometry (DXA) scanning were completed, and participants were categorized as having normal bone density, low bone mass, or osteoporosis using the World Health Organization (WHO) definition for femoral neck T-scores. Results: The majority of these overweight/obese survivors were 6.6 (±4.7) years post-diagnosis, had stage II (n = 46) or stage III (n = 16) disease, and treated with chemotherapy (76 %), radiation (72 %), and/or adjuvant hormone therapies (45 %). Mean femoral neck BMD was significantly lower in cases vs. matched non-cancer population controls (0.85 ± 0.15 vs. 0.91 ± 0.14 g/cm2, respectively; p = 0.007). However, the prevalence of low bone mass and osteoporosis was low and did not significantly differ between groups (n = 101 pairs; p = 0.26), even when restricted to those on adjuvant hormone therapies (n = 45 pairs; p = 0.75). Using conditional logistic regression, controlling for dietary factors and education, the odds of developing compromised bone health in AA breast cancer survivors was insignificant (OR 1.5, 95 % CI 0.52, 5.56). Conclusions: These null case–control findings challenge the clinical assumption that osteoporosis is highly prevalent among all breast cancer survivors, providing foundational evidence to support differences by race/ethnicity and body weight. Implications for Cancer Survivors: Routine bone density testing and regular patient–provider dialogue is critical in overweight/obese AA breast cancer survivors to ensure that healthy lifestyle factors (e.g., ideal weight, regular weight-bearing exercises, dietary adequacy of calcium and vitamin D) support optimal skeletal health. © 2015, Springer Science+Business Media New York. Source


Lin Y.,Takeda Development Center Americas Inc. | Zhu M.,Abbvie Inc. | Su Z.,Deerfield Institute
Contemporary Clinical Trials | Year: 2015

Randomization is fundamental to the design and conduct of clinical trials. Simple randomization ensures independence among subject treatment assignments and prevents potential selection biases, yet it does not guarantee balance in covariate distributions across treatment groups. Ensuring balance in important prognostic covariates across treatment groups is desirable for many reasons. A broad class of randomization methods for achieving balance are reviewed in this paper; these include block randomization, stratified randomization, minimization, and dynamic hierarchical randomization. Practical considerations arising from experience with using the techniques are described. A review of randomization methods used in practice in recent randomized clinical trials is also provided. © 2015 Elsevier Inc. All rights reserved. Source

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