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Jason P Fine - One of the best experts on this subject based on the ideXlab platform.
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A competing risks analysis should report results on all cause-specific hazards and Cumulative Incidence functions.
Journal of Clinical Epidemiology, 2013Co-Authors: Aurélien Latouche, Arthur Allignol, Jan Beyersmann, Myriam Labopin, Jason P FineAbstract:Competing risks endpoints are frequently encountered in hematopoietic stem cell transplantation where patients are exposed to relapse and treatment-related mortality. Both cause-specific hazards and direct models for the Cumulative Incidence functions have been used for analyzing such competing risks endpoints. For both approaches, the popular models are of a proportional hazards type. Such models have been used for studying prognostic factors in acute and chronic leukemias. We argue that a complete understanding of the event dynamics requires that both hazards and Cumulative Incidence be analyzed side by side, and that this is generally the most rigorous scientific approach to analyzing competing risks data. That is, understanding the effects of covariates on cause-specific hazards and Cumulative Incidence functions go hand in hand. A case study illustrates our proposal.
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Summarizing differences in Cumulative Incidence functions
Statistics in Medicine, 2008Co-Authors: Mei-jie Zhang, Jason P FineAbstract:The Cumulative Incidence function is widely reported in competing risks studies, with group differences assessed by an extension of the log-rank test. However, simple, interpretable summaries of group differences are not available. An adaptation of the proportional hazards model to the Cumulative Incidence function is often employed, but the interpretation of the hazard ratio may be somewhat awkward, unlike the usual survival set-up. We propose nonparametric inferences for general summary measures, which may be time-varying, and for time-averaged versions of the measures. Theoretical justification is provided using counting process techniques. A real data example illustrates the practical utility of the methods.
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parametric regression on Cumulative Incidence function
Biostatistics, 2007Co-Authors: Jonghyeon Jeong, Jason P FineAbstract:SUMMARY We propose parametric regression analysis of Cumulative Incidence function with competing risks data. A simple form of Gompertz distribution is used for the improper baseline subdistribution of the event of interest. Maximum likelihood inferences on regression parameters and associated Cumulative Incidence function are developed for parametric models, including a flexible generalized odds rate model. Estimation of the long-term proportion of patients with cause-specific events is straightforward in the parametric setting. Simple goodness-of-fit tests are discussed for evaluating a fixed odds rate assumption. The parametric regression methods are compared with an existing semiparametric regression analysis on a breast cancer data set where the Cumulative Incidence of recurrence is of interest. The results demonstrate that the likelihood-based parametric analyses for the Cumulative Incidence function are a practically useful alternative to the semiparametric analyses.
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direct parametric inference for the Cumulative Incidence function
Journal of The Royal Statistical Society Series C-applied Statistics, 2006Co-Authors: Jonghyeon Jeong, Jason P FineAbstract:In survival data that are collected from phase III clinical trials on breast cancer, a patient may experience more than one event, including recurrence of the original cancer, new primary cancer and death. Radiation oncologists are often interested in comparing patterns of "local" or "regional" recurrences alone as first events to identify a subgroup of patients who need to be treated by radiation therapy after surgery. The Cumulative Incidence function provides estimates of the Cumulative probability of locoregional recurrences in the presence of other competing events. A simple version of the Gompertz distribution is proposed to parameterize the Cumulative Incidence function directly. The model interpretation for the Cumulative Incidence function is more natural than it is with the usual cause-specific hazard parameterization. Maximum likelihood analysis is used to estimate simultaneously parametric models for Cumulative Incidence functions of all causes. The parametric Cumulative Incidence approach is applied to a data set from the National Surgical Adjuvant Breast and Bowel Project and compared with analyses that are based on parametric cause-specific hazard models and nonparametric Cumulative Incidence estimation. Copyright 2006 Royal Statistical Society.
Haesook T. Kim - One of the best experts on this subject based on the ideXlab platform.
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Cumulative Incidence in competing risks data and competing risks regression analysis
Clinical Cancer Research, 2007Co-Authors: Haesook T. KimAbstract:Competing risks occur commonly in medical research. For example, both treatment-related mortality and disease recurrence are important outcomes of interest and well-known competing risks in cancer research. In the analysis of competing risks data, methods of standard survival analysis such as the Kaplan-Meier method for estimation of Cumulative Incidence, the log-rank test for comparison of Cumulative Incidence curves, and the standard Cox model for the assessment of covariates lead to incorrect and biased results. In this article, we discuss competing risks data analysis which includes methods to calculate the Cumulative Incidence of an event of interest in the presence of competing risks, to compare Cumulative Incidence curves in the presence of competing risks, and to perform competing risks regression analysis. A hypothetical numeric example and real data are used to compare those three methods in the competing risks data analysis to their respective counterparts in the standard survival analysis. The source and magnitude of bias from the Kaplan-Meier estimate is also detailed.
Danielle Greene - One of the best experts on this subject based on the ideXlab platform.
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Cumulative Incidence and diagnosis of sars cov 2 infection in new york
medRxiv, 2020Co-Authors: Eli S. Rosenberg, James M. Tesoriero, Elizabeth M. Rosenthal, Rakkoo Chung, Meredith A. Barranco, Linda M. Styer, Monica M. Parker, Shu Yin John Leung, Johanne E. Morne, Danielle GreeneAbstract:Importance: New York State (NYS) is an epicenter of the United States9 COVID-19 epidemic. Reliable estimates of Cumulative Incidence of SARS-CoV-2 infection in the population are critical to tracking the extent of transmission and informing policies, but US data are lacking, in part because societal closure complicates study conduct. Objective: To estimate the Cumulative Incidence of SARS-CoV-2 infection and percent of infections diagnosed in New York State, overall and by region, age, sex, and race and ethnicity. Design: Statewide cross-sectional seroprevalence study, conducted April 19-28, 2020. Setting: Grocery stores (n=99) located in 26 counties throughout NYS, which were essential businesses that remained open during a period of societal closure and attract a heterogenous clientele. Participants: Convenience sample of patrons >=18 years and residing in New York State, recruited consecutively upon entering stores and via an in-store flyer. Exposures: Region (New York City, Westchester/Rockland, Long Island, Rest of New York State), age, sex, race and ethnicity. Main Outcomes: Primary outcome: Cumulative Incidence of SARS-CoV-2 infection, based on dry-blood spot (DBS) SARS-CoV-2 antibody reactivity; secondary outcome: percent of infections diagnosed. Results: Among 15,101 adults with suitable DBS specimens, 1,887 (12.5%) were reactive using a validated SARS-CoV-2 IgG microsphere immunoassay (sensitivity 87.9%, specificity 99.75%). Following post-stratification weighting on region, sex, age, and race and ethnicity and adjustment for assay characteristics, estimated Cumulative Incidence through March 29 was 14.0% (95% CI: 13.3-14.7%), corresponding to 2,139,300 (95% CI: 2,035,800-2,242,800) infection-experienced adults. Cumulative Incidence was higher among Hispanic/Latino (29.2%, 95% CI: 27.2-31.2%), non-Hispanic black/African American (20.2% 95% CI, 18.1-22.3%), and non-Hispanic Asian (12.4%, 95% CI: 9.4-15.4%) adults than non-Hispanic white adults (8.1%, 95% CI: 7.4-8.7%, p<.0001). Cumulative Incidence was highest in New York City (NYC) 22.7% (95% CI: 21.5%-24.0). Dividing diagnoses reported to NYS by estimated infection-experienced adults, an estimated 8.9% (95% CI: 8.4-9.3%) of infections were diagnosed, with those ≥55 years most likely to be diagnosed (11.3%, 95% CI: 10.4-12.2%). Conclusions and Relevance: Over 2 million adults were infected through late March 2020, with substantial variations by subpopulations. As this remains below herd immunity thresholds, monitoring, testing, and contact tracing remain essential public health strategies.
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Cumulative Incidence and diagnosis of SARS-CoV-2 infection in New York
2020Co-Authors: Eli S. Rosenberg, James M. Tesoriero, Elizabeth M. Rosenthal, Rakkoo Chung, Meredith A. Barranco, Linda M. Styer, Monica M. Parker, Shu Yin John Leung, Johanne E. Morne, Danielle GreeneAbstract:Importance: New York State (NYS) is an epicenter of the United States9 COVID-19 epidemic. Reliable estimates of Cumulative Incidence of SARS-CoV-2 infection in the population are critical to tracking the extent of transmission and informing policies, but US data are lacking, in part because societal closure complicates study conduct. Objective: To estimate the Cumulative Incidence of SARS-CoV-2 infection and percent of infections diagnosed in New York State, overall and by region, age, sex, and race and ethnicity. Design: Statewide cross-sectional seroprevalence study, conducted April 19-28, 2020. Setting: Grocery stores (n=99) located in 26 counties throughout NYS, which were essential businesses that remained open during a period of societal closure and attract a heterogenous clientele. Participants: Convenience sample of patrons >=18 years and residing in New York State, recruited consecutively upon entering stores and via an in-store flyer. Exposures: Region (New York City, Westchester/Rockland, Long Island, Rest of New York State), age, sex, race and ethnicity. Main Outcomes: Primary outcome: Cumulative Incidence of SARS-CoV-2 infection, based on dry-blood spot (DBS) SARS-CoV-2 antibody reactivity; secondary outcome: percent of infections diagnosed. Results: Among 15,101 adults with suitable DBS specimens, 1,887 (12.5%) were reactive using a validated SARS-CoV-2 IgG microsphere immunoassay (sensitivity 87.9%, specificity 99.75%). Following post-stratification weighting on region, sex, age, and race and ethnicity and adjustment for assay characteristics, estimated Cumulative Incidence through March 29 was 14.0% (95% CI: 13.3-14.7%), corresponding to 2,139,300 (95% CI: 2,035,800-2,242,800) infection-experienced adults. Cumulative Incidence was higher among Hispanic/Latino (29.2%, 95% CI: 27.2-31.2%), non-Hispanic black/African American (20.2% 95% CI, 18.1-22.3%), and non-Hispanic Asian (12.4%, 95% CI: 9.4-15.4%) adults than non-Hispanic white adults (8.1%, 95% CI: 7.4-8.7%, p
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Cumulative Incidence and diagnosis of SARS-CoV-2 infection in New York.
Annals of Epidemiology, 2020Co-Authors: Eli S. Rosenberg, James M. Tesoriero, Elizabeth M. Rosenthal, Rakkoo Chung, Meredith A. Barranco, Linda M. Styer, Monica M. Parker, Shu Yin John Leung, Johanne E. Morne, Danielle GreeneAbstract:Abstract Purpose New York State (NYS) is an epicenter of the SARS-CoV-2 pandemic in the United States. Reliable estimates of Cumulative Incidence in the population are critical to tracking the extent of transmission and informing policies. Methods We conducted a statewide seroprevalence study in a 15,101 patron convenience sample at 99 grocery stores in 26 counties throughout NYS. SARS-CoV-2 Cumulative Incidence was estimated from antibody reactivity by first poststratification weighting and then adjusting by antibody test characteristics. The percent diagnosed was estimated by dividing the number of diagnoses by the number of estimated infection-experienced adults. Results Based on 1887 of 15,101 (12.5%) reactive results, estimated Cumulative Incidence through March 29 was 14.0% (95% confidence interval [CI]: 13.3%–14.7%), corresponding to 2,139,300 (95% CI: 2,035,800–2,242,800) infection-experienced adults. Cumulative Incidence was highest in New York City 22.7% (95% CI: 21.5%–24.0%) and higher among Hispanic/Latino (29.2%), non-Hispanic black/African American (20.2%), and non-Hispanic Asian (12.4%) than non-Hispanic white adults (8.1%, P Conclusions From the largest U.S. serosurvey to date, we estimated >2 million adult New York residents were infected through late March, with substantial disparities, although Cumulative Incidence remained less than herd immunity thresholds. Monitoring, testing, and contact tracing remain essential public health strategies.
Nicholas J. Talley - One of the best experts on this subject based on the ideXlab platform.
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Cumulative Incidence of chronic constipation a population based study 1988 2003
Alimentary Pharmacology & Therapeutics, 2007Co-Authors: R. S. Choung, Giles R. Locke, Cathy D. Schleck, Alan R. Zinsmeister, Nicholas J. TalleyAbstract:Summary Aim To estimate the Cumulative Incidence of chronic constipation and evaluate potential risk factors. Methods In previous cross-sectional studies in 1988, random samples of Olmsted County, MN residents were mailed valid gastrointestinal symptoms surveys. A similar survey was mailed in 2003 to all the remaining eligible subjects who had been mailed to previously. An incident case of chronic constipation was defined as no reported constipation or irritable bowel syndrome on their initial survey but reported constipation on the second survey. Results In all, 5507 (79%) subjects responded to the initial survey and 2298 (55%) subjects responded to the second survey in which chronic constipation could be defined. Over 12 years, the Cumulative Incidence of chronic constipation was 17.4% (14.5, 20.5). Among those less than age 50 years at baseline, the Incidence of chronic constipation differed by gender (9.2% in men vs. 18.3% in women). In those over 70 years, the Incidence of chronic constipation was more similar for men and women (20.6% vs. 25.0%). The other risk factor associated with new onset chronic constipation was the presence of abdominal pain at baseline [OR = 2.0 (1.3, 3.0)]. Conclusion The Cumulative Incidence of chronic constipation over more than a decade was almost one in six, and more pronounced in women and the elderly.
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Cumulative Incidence of chronic constipation: a population-based study 1988–2003
Alimentary Pharmacology & Therapeutics, 2007Co-Authors: R. S. Choung, Giles R. Locke, Cathy D. Schleck, Alan R. Zinsmeister, Nicholas J. TalleyAbstract:Summary Aim To estimate the Cumulative Incidence of chronic constipation and evaluate potential risk factors. Methods In previous cross-sectional studies in 1988, random samples of Olmsted County, MN residents were mailed valid gastrointestinal symptoms surveys. A similar survey was mailed in 2003 to all the remaining eligible subjects who had been mailed to previously. An incident case of chronic constipation was defined as no reported constipation or irritable bowel syndrome on their initial survey but reported constipation on the second survey. Results In all, 5507 (79%) subjects responded to the initial survey and 2298 (55%) subjects responded to the second survey in which chronic constipation could be defined. Over 12 years, the Cumulative Incidence of chronic constipation was 17.4% (14.5, 20.5). Among those less than age 50 years at baseline, the Incidence of chronic constipation differed by gender (9.2% in men vs. 18.3% in women). In those over 70 years, the Incidence of chronic constipation was more similar for men and women (20.6% vs. 25.0%). The other risk factor associated with new onset chronic constipation was the presence of abdominal pain at baseline [OR = 2.0 (1.3, 3.0)]. Conclusion The Cumulative Incidence of chronic constipation over more than a decade was almost one in six, and more pronounced in women and the elderly.
Mei-jie Zhang - One of the best experts on this subject based on the ideXlab platform.
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Weighted comparison of two Cumulative Incidence functions with R-CIFsmry package
Computer Methods and Programs in Biomedicine, 2014Co-Authors: Jennifer Le-rademacher, Mei-jie ZhangAbstract:In this paper we propose a class of flexible weight functions for use in comparison of two Cumulative Incidence functions. The proposed weights allow the users to focus their comparison on an early or a late time period post treatment or to treat all time points with equal emphasis. These weight functions can be used to compare two Cumulative Incidence functions via their risk difference, their relative risk, or their odds ratio. The proposed method has been implemented in the R-CIFsmry package which is readily available for download and is easy to use as illustrated in the example.
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SAS macros for estimation of direct adjusted Cumulative Incidence curves under proportional subdistribution hazards models
Computer Methods and Programs in Biomedicine, 2011Co-Authors: Xu Zhang, Mei-jie ZhangAbstract:The Cumulative Incidence function is commonly reported in studies with competing risks. The aim of this paper is to compute the treatment-specific Cumulative Incidence functions, adjusting for potentially imbalanced prognostic factors among treatment groups. The underlying regression model considered in this study is the proportional hazards model for a subdistribution function [1]. We propose estimating the direct adjusted Cumulative Incidences for each treatment using the pooled samples as the reference population. We develop two SAS macros for estimating the direct adjusted Cumulative Incidence function for each treatment based on two regression models. One model assumes the constant subdistribution hazard ratios between the treatments and the alternative model allows each treatment to have its own baseline subdistribution hazard function. The macros compute the standard errors for the direct adjusted Cumulative Incidence estimates, as well as the standard errors for the differences of adjusted Cumulative Incidence functions between any two treatments. Based on the macros' output, one can assess treatment effects at predetermined time points. A real bone marrow transplant data example illustrates the practical utility of the SAS macros.
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Summarizing differences in Cumulative Incidence functions
Statistics in Medicine, 2008Co-Authors: Mei-jie Zhang, Jason P FineAbstract:The Cumulative Incidence function is widely reported in competing risks studies, with group differences assessed by an extension of the log-rank test. However, simple, interpretable summaries of group differences are not available. An adaptation of the proportional hazards model to the Cumulative Incidence function is often employed, but the interpretation of the hazard ratio may be somewhat awkward, unlike the usual survival set-up. We propose nonparametric inferences for general summary measures, which may be time-varying, and for time-averaged versions of the measures. Theoretical justification is provided using counting process techniques. A real data example illustrates the practical utility of the methods.
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Predicting Cumulative Incidence probability by direct binomial regression
Biometrika, 2008Co-Authors: Thomas H. Scheike, Mei-jie Zhang, Thomas A. GerdsAbstract:We suggest a new simple approach for estimation and assessment of covariate effects for the Cumulative Incidence curve in the competing risks model. We consider a semiparametric regression model where some effects may be time-varying and some may be constant over time. Our estimator can be implemented by standard software. Our simulation study shows that the estimator works well and has finite-sample properties comparable with the subdistribution approach. We apply the method to bone marrow transplant data and estimate the Cumulative Incidence of death in complete remission following a bone marrow transplantation. Here death in complete remission and relapse are two competing events. Copyright 2008, Oxford University Press.
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Modeling Cumulative Incidence function for competing risks data.
Expert Review of Clinical Pharmacology, 2008Co-Authors: Mei-jie Zhang, Xu Zhang, Thomas H. ScheikeAbstract:A frequent occurrence in medical research is that a patient is subject to different causes of failure, where each cause is known as a competing risk. The Cumulative Incidence curve is a proper summary curve, showing the Cumulative failure rates over time due to a particular cause. A common question in medical research is to assess the covariate effects on a Cumulative Incidence function. The standard approach is to construct regression models for all cause-specific hazard rate functions and then model a covariate-adjusted Cumulative Incidence curve as a function of all cause-specific hazards for a given set of covariates. New methods have been proposed in recent years, emphasizing direct assessment of covariate effects on Cumulative Incidence function. Fine and Gray proposed modeling the effects of covariates on a subdistribution hazard function. A different approach is to directly model a covariate-adjusted Cumulative Incidence function, including a pseudovalue approach by Andersen and Klein and a direct...
