Cancer Research and Biostatistics

Seattle, WA, United States

Cancer Research and Biostatistics

Seattle, WA, United States
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Fenton J.J.,University of California at Davis | Abraham L.,Group Health Research Institute | Taplin S.H.,U.S. National Cancer Institute | Geller B.M.,University of Vermont | And 4 more authors.
Journal of the National Cancer Institute | Year: 2011

Background Computer-aided detection (CAD) is applied during screening mammography for millions of US women annually, although it is uncertain whether CAD improves breast cancer detection when used by community radiologists. Methods We investigated the association between CAD use during film-screen screening mammography and specificity, sensitivity, positive predictive value, cancer detection rates, and prognostic characteristics of breast cancers (stage, size, and node involvement). Records from 684 956 women who received more than 1.6 million filmscreen mammograms at Breast Cancer Surveillance Consortium facilities in seven states in the United States from 1998 to 2006 were analyzed. We used random-effects logistic regression to estimate associations between CAD and specificity (true-negative examinations among women without breast cancer), sensitivity (true-positive examinations among women with breast cancer diagnosed within 1 year of mammography), and positive predictive value (breast cancer diagnosed after positive mammograms) while adjusting for mammography registry, patient age, time since previous mammography, breast density, use of hormone replacement therapy, and year of examination (1998-2002 vs 2003-2006). All statistical tests were two-sided. Results Of 90 total facilities, 25 (27.8%) adopted CAD and used it for an average of 27.5 study months. In adjusted analyses, CAD use was associated with statistically significantly lower specificity (OR = 0.87, 95% confidence interval [CI] = 0.85 to 0.89, P < .001) and positive predictive value (OR = 0.89, 95% CI = 0.80 to 0.99, P = .03). A non-statistically significant increase in overall sensitivity with CAD (OR = 1.06, 95% CI = 0.84 to 1.33, P = .62) was attributed to increased sensitivity for ductal carcinoma in situ (OR = 1.55, 95% CI = 0.83 to 2.91; P = .17), although sensitivity for invasive cancer was similar with or without CAD (OR = 0.96, 95% CI = 0.75 to 1.24; P = .77). CAD was not associated with higher breast cancer detection rates or more favorable stage, size or lymph node status of invasive breast cancer. Conclusion CAD use during film-screen screening mammography in the United States is associated with decreased specificity but not with improvement in the detection rate or prognostic characteristics of invasive breast cancer. © The Author 2011. Published by Oxford University Press. All rights reserved.


Matsui S.,The Institute of Statistical Mathematics of Tokyo | Simon R.,U.S. National Cancer Institute | Qu P.,Cancer Research and Biostatistics | Shaughnessy Jr. J.D.,University of Arkansas for Medical Sciences | And 2 more authors.
Clinical Cancer Research | Year: 2012

Purpose: It is highly challenging to develop reliable diagnostic tests to predict patients' responsiveness to anticancer treatments on clinical endpoints before commencing the definitive phase III randomized trial. Development and validation of genomic signatures in the randomized trial can be a promising solution. Such signatures are required to predict quantitatively the underlying heterogeneity in the magnitude of treatment effects. Experimental Design: We propose a framework for developing and validating genomic signatures in randomized trials. Codevelopment of predictive and prognostic signatures can allow prediction of patient-level survival curves as basic diagnostic tools for treating individual patients. Results: We applied our framework to gene-expression microarray data from a large-scale randomized trial to determine whether the addition of thalidomide improves survival for patients with multiple myeloma. The results indicated that approximately half of the patients were responsive to thalidomide, and the average improvement in survival for the responsive patients was statistically significant. Cross-validated patient-level survival curves were developed to predict survival distributions of individual future patients as a function of whether or not they are treated with thalidomide and with regard to their baseline prognostic and predictive signature indices. Conclusion: The proposed framework represents an important step toward reliable predictive medicine. It provides an internally validated mechanism for using randomized clinical trials to assess treatment efficacy for a patient population in a manner that takes into consideration the heterogeneity in patients' responsiveness to treatment. It also provides cross-validated patient-level survival curves that can be used for selecting treatments for future patients. ©2012 AACR.


Ball D.,Peter MacCallum Cancer Center | Ball D.,University of Melbourne | Mitchell A.,Cancer Research and Biostatistics | Giroux D.,Cancer Research and Biostatistics | And 2 more authors.
Journal of Thoracic Oncology | Year: 2013

BACKGROUND: Analysis of the International Association for the Study of Lung Cancer database revealed that for patients with completely resected, node-negative, non-small-cell lung cancer (NSCLC), increasing tumor size was associated with worsening survival. This analysis was performed to determine the effect of size on prognosis in patients in the same database but who were treated with radiotherapy or chemoradiotherapy. METHODS: Patients were eligible if they had pathologically confirmed NSCLC, no evidence of distant metastases, intended treatment was radical radiotherapy (minimum 50 Gy) or combined chemotherapy and radiotherapy, no surgery, and tumor diameter was available. RESULTS: Eight hundred and sixty-eight patients were available for analysis. Patient characteristics were: sex (men) 65.3%; median age 64 years (range, 32-88); Eastern Cooperative Oncology Group performance status 0: 55%, 1: 33%, 2 or more: 5%; chemotherapy 74%; no chemotherapy 18%; weight loss less than 5 %: 70%, and more than 5%: 25%. Primary tumor size was categorized according to tumor, node, metastasis 7th edition. On univariate analysis, the following factors were prognostic for survival: age (continuous) (p = 0.0035); performance status of 1 or more (p = 0.0021); weight loss less than 5% (p < 0.0001); chemotherapy (p = 0.0189); and primary tumor size (continuous) (p = 0.0002). Sex and clinical nodal stage were not significant. On multivariate analysis, age and weight loss remained significant factors for survival, as was tumor size less than 3 cm. CONCLUSIONS: In patients treated with radiotherapy with or without chemotherapy, tumor size less than 3 cm was associated with longer survival than larger tumors. Evidence of the effect of size on prognosis above this was weak. Five-year survival of more than 10% was observed in all four size categories. Copyright © 2013 by the International Association for the Study of Lung Cancer.


Othus M.,Fred Hutchinson Cancer Research Center | Barlogie B.,University of Arkansas for Medical Sciences | LeBlanc M.L.,Fred Hutchinson Cancer Research Center | Crowley J.J.,Cancer Research and Biostatistics
Clinical Cancer Research | Year: 2012

Cure models are a popular topic within statistical literature but are not as widely known in the clinical literature. Many patients with cancer can be long-term survivors of their disease, and cure models can be a useful tool to analyze and describe cancer survival data. The goal of this article is to review what a cure model is, explain when cure models can be used, and use cure models to describe multiple myeloma survival trends. Multiple myeloma is generally considered an incurable disease, and this article shows that by using cure models, rather than the standard Cox proportional hazards model, we can evaluate whether there is evidence that therapies at the University of Arkansas for Medical Sciences induce a proportion of patients to be long-term survivors. ©2012 ACCR.


Janes H.,Fred Hutchinson Cancer Research Center | Pepe M.S.,Fred Hutchinson Cancer Research Center | Bossuyt P.M.,University of Amsterdam | Barlow W.E.,Cancer Research and Biostatistics
Annals of Internal Medicine | Year: 2011

Treatment selection markers, sometimes called predictive markers, are factors that help clinicians select therapies that maximize good outcomes and minimize adverse outcomes for patients. Existing statistical methods for evaluating a treatment selection marker include assessing its prognostic value, evaluating treatment effects in patients with a restricted range of marker values, and testing for a statistical interaction between marker value and treatment. These methods are inadequate, because they give misleading measures of performance that do not answer key clinical questions about how the marker might help patients choose treatment, how treatment decisions should be made on the basis of a continuous marker measurement, what effect using the marker to select treatment would have on the population, or what proportion of patients would have treatment changes on the basis of marker measurement. Marker-by-treatment predictiveness curves are proposed as a more useful aid to answering these clinically relevant questions, because they illustrate treatment effects as a function of marker value, outcomes when using or not using the marker to select treatment, and the proportion of patients for whom treatment recommendations change after marker measurement. Randomized therapeutic clinical trials, in which entry criteria and treatment regimens are not restricted by the marker, are also proposed as the basis for constructing the curves and evaluating and comparing markers, © 2011 American College of Physicians.


Rami-Porta R.,Hospital Universitari Mutua Terrassa | Bolejack V.,Cancer Research and Biostatistics | Goldstraw P.,Imperial College London
Seminars in Respiratory and Critical Care Medicine | Year: 2011

The seventh edition of the tumor, node, and metastasis (TNM) classification is based on the proposals of the Staging Project of the International Association for the Study of Lung Cancer (IASLC). The analyses of the IASLC international database of 81,015 patients diagnosed with lung cancer between 1990 and 2000 were used to validate the TNM descriptors. The changes include: the subclassification of T1 and T2 tumors into T1a (≤2 cm) and T1b (>2 and ≤3 cm), and T2a (>3 and ≤5 cm) and T2b (>5 and ≤7 cm), respectively; the reclassification of T2 tumors >7 cm as T3; the reclassification of T4 tumors by additional nodules in the same lobe of the primary tumor as T3; the reclassification of M1 tumors by additional nodules in another ipsilateral lobe as T4; the reclassification of pleural and pericardial dissemination, and contralateral M1 nodules as M1a; and the separation of intrathoracic (M1a) and extrathoracic (M1b) metastases. Other innovations include the emphasis on the use of the TNM classification for small cell carcinoma, the inclusion of bronchopulmonary carcinoids into this staging system, the proposal of a new lymph node map, and the adoption of a new, internationally agreed definition of visceral pleura invasion. All these changes improve the separation of tumors with significantly different prognosis. © Georg Thieme Verlag KG Stuttgart. New York.


Rami-Porta R.,University of Barcelona | Rami-Porta R.,CIBERES Lung Cancer Group | Bolejack V.,Cancer Research and Biostatistics | Giroux D.J.,Cancer Research and Biostatistics | And 4 more authors.
Journal of Thoracic Oncology | Year: 2014

The analyses of the retrospective database of the International Association for the Study of Lung Cancer (IASLC), consisting of more than 81,000 evaluable patients diagnosed with lung cancer between 1990 and 2000, formed the basis of recommendations to the Union for International Cancer Control and the American Joint Committee on Cancer for the revision of the sixth edition of the tumor, node, and metastasis (TNM) classification of lung cancer. However, despite the large number of patients, not all descriptors could be validated. This prompted a new collection of retrospective and prospective data to overcome the limitations of the original retrospective database. The new IASLC database has information on 94,708 new patients diagnosed of lung cancer between 1999 and 2010. They originated from 35 sources in 16 countries, and 4,667 were submitted via the online electronic data capture system. Europe contributed 46,560 patients, Asia: 41,705, North America: 4,660, Australia: 1,593, and South America: 190. After exclusions, 77,156 (70,967 with nonsmall cell lung cancer and 6,189 with small cell lung cancer) remained for analysis. This database will be analyzed according to established objectives for the T, the N, and the M components to inform the eighth edition of the TNM classification of lung cancer due to be published in 2016. The IASLC hopes for the continuing contribution of our partners around the world to improve the classification of anatomical extent of disease, but also to create prognostic groups in a parallel project of the IASLC Staging and Prognostic Factors Committee. Copyright © 2014 by the International Association for the Study of Lung Cancer.


Korn R.L.,Imaging Endpoints Core Laboratory | Crowley J.J.,Cancer Research And Biostatistics
Clinical Cancer Research | Year: 2013

Progression-free survival (PFS) is increasingly used as an important and even a primary endpoint in randomized cancer clinical trials in the evaluation of patients with solid tumors for both practical and clinical considerations. Although in its simplest form, PFS is the time from randomization to a predefined endpoint, there are many factors that can influence the exact moment of when disease progression is recorded. In this overview, we review the circumstances that can devalue the use of PFS as a primary endpoint and attempt to provide a pathway for a future desired state when PFS will become not just a secondary alternative to overall survival but rather an endpoint of choice. © 2013 AACR.


Usmani S.Z.,University of Arkansas for Medical Sciences | Crowley J.,Cancer Research and Biostatistics | Hoering A.,Cancer Research and Biostatistics | Mitchell A.,Cancer Research and Biostatistics | And 5 more authors.
Leukemia | Year: 2013

The concept of applying all active therapeutic agents in Total Therapy (TT) clinical trials for newly diagnosed multiple myeloma was pursued with the intent of developing curative treatment. The results of TT1 (n=231), TT2 (n=668) without or with thalidomide and TT3 with added bortezomib (n=303) have been reported. An update with median follow-up times of 17.1, 8.7 and 5.5 years, respectively, is provided. Conditional overall survival (OS) analysis from a 4-year landmark was applied to account for earlier protocol failure owing to disease aggressiveness and toxicities. Cumulative relative survival was computed in the context of age-and gender-matched US population, and interval-specific relative survival ratios were estimated to determine times to normal survival expectation. Based on Cox model-adjusted statistics, OS, progression-free survival and complete-response duration all improved with the transitions from TT1 to TT2 to TT3; improvement was also evident from time-to-progression estimates, 4-year conditional survival data and cumulative relative survival. Interval-specific relative survival normalized progressively sooner, reaching near-normal levels with TT3 in patients who attained complete response. Thus, a strategy using all myeloma-effective agents up-front seems effective at preventing, in progressively larger patient cohorts over time, the outgrowth of resistant tumor cells that account for ongoing relapses. © 2013 Macmillan Publishers Limited All rights reserved.


Barlogie B.,University of Arkansas for Medical Sciences | Mitchell A.,Cancer Research and Biostatistics | Van Rhee F.,University of Arkansas for Medical Sciences | Epstein J.,University of Arkansas for Medical Sciences | And 2 more authors.
Blood | Year: 2014

Does the dogma that multiple myeloma is incurable still hold?. The genomic chaos and resulting resistance to apoptosis of myeloma, long considered an obstacle to cure, formed the basis of Total Therapy (TT) program. The TT approach uses all myeloma-active drugs upfront to target drug-resistant subclones during initial treatment to prevent later relapse. Long-term follow-up of 1202 patients (TT1:n=231, median follow-up: 21 years; TT2: 668, median follow-up: 12 years; TT3a:n=303,medianfollow-up: 9 years) permitted investigationofwhether progression-free survival (PFS) and complete response (CR) duration were consistent with curability, ie observation of plateausinKaplan-Meier plots for PFS and CR duration. In the subset of 627 patients with plasma cell gene expression profiling data, cure plateaus were apparent at 5 years in the 14% with high-risk myeloma compared with10yearsintheremainderwithlow-risk disease. Aparametric model basedonPFS and CR duration supported an increase incurability: 10-year PFSandCRestimates increasedfrom8.8%/17.9%inTT1to15.5%/ 28.2% in TT2's control arm to 25.1%/35.6% in TT2's thalidomide arm and to 32.9%/48.8% in TT3a. Toward developing novel therapies, we recommend a concerted focus on patients with high-risk myeloma whose outcome has not been advanced. © 2014 by The American Society of Hematology.

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