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909 results on '"Feuer, Eric J"'

202. Exploring the Recent Trend in Esophageal Adenocarcinoma Incidence and Mortality Using Comparative Simulation Modeling

Author

Kong, Chung Yin, primary, Kroep, Sonja, additional, Curtius, Kit, additional, Hazelton, William D., additional, Jeon, Jihyoun, additional, Meza, Rafael, additional, Heberle, Curtis R., additional, Miller, Melecia C., additional, Choi, Sung Eun, additional, Lansdorp-Vogelaar, Iris, additional, van Ballegooijen, Marjolein, additional, Feuer, Eric J., additional, Inadomi, John M., additional, Hur, Chin, additional, and Luebeck, E. Georg, additional

Published
2014
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207. Complete and limited duration cancer prevalence estimates

Author

Mariotto Angela, Gigli Anna, Capocaccia Riccardo, Tavilla Andrea, Clegg Limin X, Depry Michael, Scoppa Steve, Ries Lynn A.G., Rowland Julia H, Tesauro Gina, and Feuer Eric J.

Abstract

Prevalence is an indicator of primary interest in public health because it measures the burden of cancer in a population and on the health care system. Prevalence is defined as thenumber or percent of people alive on a certain date in a population who previously had a diagnosis of the disease. It includes new (incidence) and pre-existing cases and is a function of both past incidence and survival. Information on prevalence can be used for health planning,resource allocation and an estimate of cancer survivorship. In past reports of the Cancer Statistics Review, US cancer prevalence was estimated by multiplying the Connecticut cancer prevalence proportions to the US population. This year, US cancer prevalence is estimated by applying SEER-9 and SEER-11 prevalence proportions to the US population. SEER proportion rates are more representative of the US and permit estimation of prevalence by racial/ethnic groups. Other changes with respect to previous reports are in the methods for tumor inclusion and complete prevalence calculation.

Published
2002

210. Using Gini coefficient to determining optimal cluster reporting sizes for spatial scan statistics.

Author

Junhee Han, Li Zhu, Kulldorff, Martin, Hostovich, Scott, Stinchcomb, David G., Tatalovich, Zaria, Lewis, Denise Riedel, and Feuer, Eric J.

Subjects
GINI coefficient, SCAN statistic, SPATIAL analysis (Statistics), PUBLIC health surveillance, PANDEMICS, CANCER-related mortality, CANCER clusters, PREVENTION
Abstract

Background: Spatial and space-time scan statistics are widely used in disease surveillance to identify geographical areas of elevated disease risk and for the early detection of disease outbreaks. With a scan statistic, a scanning window of variable location and size moves across the map to evaluate thousands of overlapping windows as potential clusters, adjusting for the multiple testing. Almost always, the method will find many very similar overlapping clusters, and it is not useful to report all of them. This paper proposes to use the Gini coefficient to help select which of the many overlapping clusters to report. Methods: The Gini coefficient provides a quick and intuitive way to evaluate the degree of the heterogeneity of the collection of clusters, which is useful to explain how well the cluster collection reveal the underlying true cluster patterns. Using simulation studies and real cancer mortality data, it is compared with the traditional approach for reporting non-overlapping clusters. Results: The Gini coefficient can identify a more refined collection of non-overlapping clusters to report. For example, it is able to determine when it makes more sense to report a collection of smaller non-overlapping clusters versus a single large cluster containing all of them. It also fulfils a set of desirable theoretical properties, such as being invariant under a uniform multiplication of the population numbers by the same constant. Conclusions: The Gini coefficient can be used to determine which set of non-overlapping clusters to report. It has been implemented in the free SaTScan™ software version 9.3 (www.satscan.org). [ABSTRACT FROM AUTHOR]

Published
2016
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211. Preliminary estimates of SEER cancer incidence for 2013.

Author

Lewis, Denise Riedel, Chen, Huann‐Sheng, Cockburn, Myles, Wu, Xiao‐Cheng, Stroup, Antoinette M., Midthune, Douglas N., Krapcho, Martin F., Miller, Daniel G., Penberthy, Lynne, and Feuer, Eric J.

Subjects
DISEASE incidence, CERVICAL cancer, THYROID cancer, LIVER cancer, MELANOMA
Abstract

Background: This article presents a first look at rates and trends for cases in the Surveillance, Epidemiology, and End Results (SEER) program diagnosed through 2013 using the February 2015 submission, and a validation of rates and trends from the February 2014 submission using the subsequent November 2014 submission. To the authors' knowledge, this is the second time SEER has published trends based on the early February submission. Three new cancer sites were added: cervix, thyroid, and liver/ intrahepatic bile duct.Methods: A reporting delay model adjusted for the undercount of cases, which is substantially larger for the February than the subsequent November submission, was used. Joinpoint regression methodology was used to assess trends. Delay-adjusted rates and trends were checked to assess validity between the February and November 2014 submissions.Results: The validation of rates and trends from the February and November 2014 submissions demonstrated even better agreement than the previously reported comparison between the February and November 2013 submissions, thereby affording additional confidence that the delay-adjusted February submission data can be used to produce valid estimates of incidence trends. Trends for cases diagnosed through 2013 revealed more rapid declines in female colon and rectal cancer and prostate cancer. A plateau in female melanoma trends and a slowing of the increases in thyroid cancer and male liver/intrahepatic bile duct cancer trends were observed.Conclusions: Analysis of early cancer data submissions can provide a preliminary indication of differences in incidence trends with an additional year of data. Although the delay adjustment correction adjusts for underreporting of cases, caution should be exercised when interpreting the results in this early submission. Cancer 2016;122:1579-87. © 2016 American Cancer Society. [ABSTRACT FROM AUTHOR]

Published
2016
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214. A statistical model validating triage for the peer review process: keeping the competitive applications in the review pipeline

Author

Vener, Kirt J., Feuer, Eric J., and Gorelic, Lester

Subjects
Peer review -- Evaluation, Peer review of research grant proposals -- Evaluation, Business, Evaluation
Abstract

Triage of grant applications at the National Institutes of Health (NIH) is a process whereby an initial screening of applications by a scientific peer review group eliminates applications that are not competitive for awards. The process of application triage has been limited to those applications submitted to the NIH in response to an RFA (Request for Applications). A hypergeometric model was developed to determine the extent to which five, six, seven, or eight member triage teams or subsets of 12- to 20-member full committees could provide a statistically defensible triage decision. Although the intent of triage is to remove from review those applications which are noncompetitive, the model was weighted in favor of the applicant to minimize the likelihood that highly competitive applications would be eliminated. Within the assumptions and rules developed, it was determined that there was little likelihood that the latter would occur. For example, in the worst case scenario, the greatest probability that a highly competitive application would be knocked out of competition is P [is less than or equal to] 0.014 in the case of a five-member triage subset of a 20-member committee. Using the latter case, the model was tested on a set of 73 applications that were submitted to the National Cancer Institute for action at the February 1993 National Cancer Advisory Board. The model selected for triage required that each application be assigned to five reviewers, that each reviewer be blinded to the review assignments of the other reviewers, and that four noncompetitive votes be registered to triage out an application. Each of 19 applications received four to five noncompetitive votes, and were triaged out of the review process. The remaining 54 applications were then reviewed according to the usual NIH review process. Four of the applications received three noncompetitive triage votes each and were either rated as not recommended for further consideration (NRF, n = 2) or received priority scores [is greater than or equal to] 250 (n = 2). (The smaller the priority score the better the technical merit.) Thirteen of the 53 applications received two noncompetitive votes. Of the latter, two were not recommended for further consideration and the remaining 11 received priority scores in excess of 200. The distribution of competitive applications was such that funding was limited to those applications with priority scores of less than 190. Thus, the data suggest that the conservative model is valid such that the likelihood of eliminating a highly competitive application from consideration for funding is remotely small. With this model, the process triage is fair to applicants on the one hand and is also effective in reducing consultant workloads on the other. The model could be applied to many different types of review situations (private sponsors as well as federal), especially when few awards are to be issued relative to the number of competing applications., Kirt J. Vener is chief of the Prevention, Epidemiology and Control Review Section, National Cancer Institute; Eric J. Feuer is mathematical statistician in the Division of Cancer Prevention and Control, [...]

Published
1994

215. Joinpoint Regression Methods of Aggregate Outcomes for Complex Survey Data.

Author

Liu, Benmei, Kim, Hyune-Ju, Feuer, Eric J, and Graubard, Barry I

Subjects
*REGRESSION analysis, *UNITS of time, *TREND analysis, *MEDICAL registries
Abstract

Joinpoint regression can model trends in time-specific estimates from aggregated data. These methods have been developed mainly for nonsurvey data such as cancer registry data assuming that the time-specific estimates are uncorrelated from time point to time point. This independence assumption can be violated for trends in time-specific estimates from complex survey samples due to using the same primary sampling units across time and, therefore, the full variance–covariance matrix of the time-specific estimates should be incorporated into the regression model fitting. This article extends these joinpoint methods for analyzing complex survey data within the National Cancer Institute's Joinpoint software and empirically compares the extended method to existing methods for analyses of time trends in three surveys. [ABSTRACT FROM AUTHOR]

Published
2023
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219. The prostate cancer conundrum revisited

Author

Etzioni, Ruth, primary, Gulati, Roman, additional, Tsodikov, Alex, additional, Wever, Elisabeth M., additional, Penson, David F., additional, Heijnsdijk, Eveline A.M., additional, Katcher, Jeffrey, additional, Draisma, Gerrit, additional, Feuer, Eric J., additional, de Koning, Harry J., additional, and Mariotto, Angela B., additional

Published
2012
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220. The Cancer Survival Query System: Making survival estimates from the Surveillance, Epidemiology, and End Results program more timely and relevant for recently diagnosed patients

Author

Feuer, Eric J., primary, Lee, Minjung, additional, Mariotto, Angela B., additional, Cronin, Kathy A., additional, Scoppa, Steve, additional, Penson, David F., additional, Hachey, Mark, additional, Cynkin, Laurie, additional, Carter, Ginger A., additional, Campbell, David, additional, Percy-Laurry, Antoinette, additional, Zou, Zhaohui, additional, Schrag, Deborah, additional, and Hankey, Benjamin F., additional

Published
2012
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222. Impact of Reduced Tobacco Smoking on Lung Cancer Mortality in the United States During 1975–2000

Author

Moolgavkar, Suresh H., primary, Holford, Theodore R., additional, Levy, David T., additional, Kong, Chung Yin, additional, Foy, Millenia, additional, Clarke, Lauren, additional, Jeon, Jihyoun, additional, Hazelton, William D., additional, Meza, Rafael, additional, Schultz, Frank, additional, McCarthy, William, additional, Boer, Robert, additional, Gorlova, Olga, additional, Gazelle, G. Scott, additional, Kimmel, Marek, additional, McMahon, Pamela M., additional, de Koning, Harry J., additional, and Feuer, Eric J., additional

Published
2012
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224. Predicting US- and state-level cancer counts for the current calendar year

Author

Zhu, Li, primary, Pickle, Linda W., additional, Ghosh, Kaushik, additional, Naishadham, Deepa, additional, Portier, Kenneth, additional, Chen, Huann-Sheng, additional, Kim, Hyune-Ju, additional, Zou, Zhaohui, additional, Cucinelli, James, additional, Kohler, Betsy, additional, Edwards, Brenda K., additional, King, Jessica, additional, Feuer, Eric J., additional, and Jemal, Ahmedin, additional

Published
2012
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226. Re: Think before you leap.

Author

Han, Summer S., ten Haaf, Kevin, Hazelton, William D., Jeon, Jihyoun, Meza, Rafael, Kong, Chung Yin, Feuer, Eric J., de Koning, Harry J., and Plevritis, Sylvia K.

Published
2018
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228. Next generation analytic tools for large scale genetic epidemiology studies of complex diseases

Author

Mechanic, Leah E., primary, Chen, Huann‐Sheng, additional, Amos, Christopher I., additional, Chatterjee, Nilanjan, additional, Cox, Nancy J., additional, Divi, Rao L., additional, Fan, Ruzong, additional, Harris, Emily L., additional, Jacobs, Kevin, additional, Kraft, Peter, additional, Leal, Suzanne M., additional, McAllister, Kimberly, additional, Moore, Jason H., additional, Paltoo, Dina N., additional, Province, Michael A., additional, Ramos, Erin M., additional, Ritchie, Marylyn D., additional, Roeder, Kathryn, additional, Schaid, Daniel J., additional, Stephens, Matthew, additional, Thomas, Duncan C., additional, Weinberg, Clarice R., additional, Witte, John S., additional, Zhang, Shunpu, additional, Zöllner, Sebastian, additional, Feuer, Eric J., additional, and Gillanders, Elizabeth M., additional

Published
2011
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229. Comparative Economic Evaluation of Data from the ACRIN National CT Colonography Trial with Three Cancer Intervention and Surveillance Modeling Network Microsimulations

Author

Vanness, David J., primary, Knudsen, Amy B., additional, Lansdorp-Vogelaar, Iris, additional, Rutter, Carolyn M., additional, Gareen, Ilana F., additional, Herman, Benjamin A., additional, Kuntz, Karen M., additional, Zauber, Ann G., additional, van Ballegooijen, Marjolein, additional, Feuer, Eric J., additional, Chen, Mei-Hsiu, additional, and Johnson, C. Daniel, additional

Published
2011
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231. Modeling the impact of population screening on breast cancer mortality in the United States

Author

Mandelblatt, Jeanne S., primary, Cronin, Kathleen A., additional, Berry, Donald A., additional, Chang, Yaojen, additional, de Koning, Harry J., additional, Lee, Sandra J., additional, Plevritis, Sylvia K., additional, Schechter, Clyde B., additional, Stout, Natasha K., additional, van Ravesteyn, Nicolien T., additional, Zelen, Marvin, additional, and Feuer, Eric J., additional

Published
2011
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233. Clarifying Differences in Natural History between Models of Screening

Author

van Ballegooijen, Marjolein, primary, Rutter, Carolyn M., additional, Knudsen, Amy B., additional, Zauber, Ann G., additional, Savarino, James E., additional, Lansdorp-Vogelaar, Iris, additional, Boer, Rob, additional, Feuer, Eric J., additional, Habbema, J. Dik F., additional, and Kuntz, Karen M., additional

Published
2011
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235. Authors' Reply

Author

Clegg, Limin X., primary, Hankey, Benjamin F., additional, Tiwari, Ram, additional, Feuer, Eric J., additional, and Edwards, Brenda K., additional

Published
2010
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241. Bias Associated With Self-Report of Prior Screening Mammography

Author

Cronin, Kathleen A., primary, Miglioretti, Diana L., additional, Krapcho, Martin, additional, Yu, Binbing, additional, Geller, Berta M., additional, Carney, Patricia A., additional, Onega, Tracy, additional, Feuer, Eric J., additional, Breen, Nancy, additional, and Ballard-Barbash, Rachel, additional

Published
2009
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