Selected Accepted Journal Articles
Stout, N., Nekhlyudov, L., Li, L., Malin, E., Ross-Degnan, D., Buist, D., Rosenberg, M., Alfisher, M., & Fletcher, S. (2013). Rapid Rise of Breast Magnetic Resonance Imaging Use: Trends from 2000-2011. JAMA Internal Medicine
Selected Published Journal Articles
Batina, N., Trentham-Dietz, A., Gangnon, R., Sprague, B., Rosenberg, M., Stout, N., Fryback, D., & Alagoz, O. (2013). Variation in tumor natural history contributes to racial disparities in breast cancer stage at diagnosis.
Breast Cancer Research and Treatment
Background: Black women tend to be diagnosed with breast cancer at a more advanced stage than whites and subsequently experience elevated breast cancer mortality. We sought to determine whether there are racial differences in tumor natural history that contribute to these disparities.
Methods: We used the University of Wisconsin Breast Cancer Simulation Model, a validated member of the National Cancer Institute’s Cancer Intervention and Surveillance Modeling Network, to evaluate the contribution of racial differences in tumor natural history to observed disparities in breast cancer incidence. We fit eight natural history parameters in race-specific models by calibrating to the observed race- and stage-specific 1975-2000 U.S. incidence rates, while accounting for known racial variation in population structure, underlying risk of breast cancer, screening mammography utilization, and mortality from other causes.
Results: The best fit models indicated that a number of natural history parameters must vary between blacks and whites to reproduce the observed stage-specific incidence patterns. The mean of the tumor growth rate parameter was 63.6% higher for blacks than whites (0.18, SE 0.04 vs. 0.11, SE 0.02). The fraction of tumors considered highly aggressive based on their tendency to metastasize at a small size was 2.2 times greater among blacks than whites (0.41, SE 0.009 vs. 0.019, SE 0.008).
Conclusion: Based on our simulation model, breast tumors in blacks grow faster and are more likely to metastasize earlier than tumors in whites. These differences suggest that targeted prevention and detection strategies that go beyond equalizing access to mammography may be needed to eliminate breast cancer disparities.
Key words: Breast Cancer Natural History, black women, white women, Simulation Model, Racial Disparities
(138), 519-528. doi: 10.1007/s10549-013-2435-z.
Wang, Y., Graubard, B., Rosenberg, M., Kuntz, K., Zauber, A., Kahle, L., Schechter, C., & Feuer, E. (2013). Derivation of Background Mortality by Smoking and Obesity in Cancer Simulation Models.
Medical Decision Making
Background. Simulation models designed to evaluate cancer prevention strategies make assumptions on background mortality—the competing risk of death from causes other than the cancer being studied. Researchers often use the U.S. life tables and assume homogeneous other-cause mortality rates. However, this can lead to bias because common risk factors such as smoking and obesity also predispose individuals for deaths from other causes such as cardiovascular disease. Methods. We obtained calendar year-, age-, and sex-specific other-cause mortality rates by removing deaths due to a specific cancer from U.S. all-cause life tables. Prevalence across 12 risk factor groups (3 smoking [never, past, and current smoker] and 4 body mass index [BMI] categories [<25, 25–30, 30–35, 35+ kg/m2]) were estimated from national surveys (National Health and Nutrition Examination Surveys [NHANES] 1971–2004). Using NHANES linked mortality data, we estimated hazard ratios for death by BMI/smoking using a Poisson regression model. Finally, we combined these results to create 12 sets of BMI and smoking-specific other-cause life tables for U.S. adults aged 40 years and older that can be used in simulation models of lung, colorectal, or breast cancer. Results. We found substantial differences in background mortality when accounting for BMI and smoking. Ignoring the heterogeneity in background mortality in cancer simulation models can lead to underestimation of competing risk of deaths for higher-risk individuals (e.g., male, 60-year old, white obese smokers) by as high as 45%. Conclusion. Not properly accounting for competing risks of death may introduce bias when using simulation modeling to evaluate population health strategies for prevention, screening, or treatment. Further research is warranted on how these biases may affect cancer-screening strategies targeted at high-risk individuals.
Rosenberg, M., Feuer, E., Yu, B., Sun, J., Henley, J., Shanks, T., Anderson, C., McMahon, P., Thun, M., & Burns, D. (2012). Cohort Life Tables By Smoking Status, Removing Lung Cancer as a Cause of Death. Risk Analysis
(32), S25-S38. doi: 10.1111/j.1539-6924.2011.01662.x.
Johnson, Jr., P., Rosenberg, M., & Frees, E. (2012). Analyses of Racial Disparities in U.S. Inpatient Mental Health Treatment. Internet Journal of Mental Health
(8), doi: 10.5580/2b50.
Wells, J., Rosenberg, M., Hoffman, G., Anstead, M., & Farrell, P. (2012). A Decision-Tree Approach to Cost Comparison of Newborn Screening Strategies for Cystic Fibrosis. Pediatrics
(129), e339-e347. doi: 10.1542/peds.2011-0096.
Frees, E., Gao, J., & Rosenberg, M. (2011). Predicting the frequency and amount of health care expenditures. North American Actuarial Journal
(15), 377-392. doi: 10.1080/10920277.2011.10597626.
Rosenberg, M., Johnson, Jr, P., & Duncan, I. (2010). Exploring Stakeholder Perspectives On What Is Affordable Health Care. Risk Management and Insurance Review
(13), 251-163. doi: 10.1111/j.1540-6296.2009.01174.x.
Sun, J., Frees, E., & Rosenberg, M. (2008). Heavy-Tailed Longitudinal Data Modeling Using Copulas. Insurance: Mathematics and Economics
(42), 817-830. doi: 10.1016/j.insmatheco.2007.09.009.
Rosenberg, M., & Farrell, P. (2008). Predictive Modeling of Costs for a Chronic Disease with Acute High Cost Episodes. North American Actuarial Journal
(12), 1-18. doi: 10.1080/10920277.2008.10597497.
Rosenberg, M., Frees, E., Sun, J., Johnson, P., & Robinson, J. (2007). Predictive Modeling with Longitudinal Data: A Case Study of Wisconsin Nursing Homes. North American Actuarial Journal
(11), 54-69. doi: 10.1080/10920277.2007.10597466.
Burns, M., Rosenberg, M., & Fiore, M. (2007). Use and employer costs of a pharmacotherapy smoking cessation treatment benefit. American Journal of Preventive Medicine
(32), 139-142. doi: 10.1016/j.amepre.2006.10.003.
Fryback, ., Stout, N., Rosenberg, M., Trentham-Dietz , A., Kuruchittham, V., & Remington, P. (2006). The Wisconsin Breast Cancer Epidemiology Simulation Model. Journal of the National Cancer Institute
(36), 37-47. doi: 10.1093/jncimonographs/lgj007.
Stout, N., Rosenberg, M., Trentham-Dietz , A., Smith, M., Robinson, S., & Fryback , D. (2006). Towards Improved Policy: Lessons from a Retrospective Cost-Effectiveness Analysis of Screening Mammography. Journal of the National Cancer Institute
(98), 774-782. doi: 10.1093/jnci/djj210.
Rosenberg, M. (2006). Competing Risks to breast cancer mortality. Journal of the National Cancer Institute
(36), 15-19. doi: 10.1093/jncimonographs/lgj004.
Rosenberg, M., & Farrell, P. (2005). Assessing the Cost of Cystic Fibrosis Diagnosis and Treatment. Journal of Pediatrics
(147), S101-S105. doi: 10.1016/j.jpeds.2005.08.018.
Lee, D., Rosenberg, M., Peterson, A., Hoffman, G., Makholm, L., Laessig, R., & Farrell, P. (2003). Analysis of the Costs of Diagnosing Cystic Fibrosis with a Newborn Screening Program. Journal of Pediatrics
(142), 617-623. doi: 10.1067/mpd.2003.209.
Carnes, M., Howell, T., Rosenberg, M., Francis, J., Hildebrand , C., & Knuppel, J. (2003). Physicians vary in approaches to clinical management of delirium. Journal of American Geriatrics Society
(51), 234-9. doi: 10.1046/j.1532-5415.2003.51063.x.
Linzer, M., Rosenberg, M., McMurray, J., & Glassroth, . (2002). Respecting the lifecycle: Rational workforce planning for a Section of General Internal Medicine. American Journal of Medicine
(113), 443-448. doi: 10.1016/S0002-9343(02)01308-6.
Rosenberg, M., & Browne, M. (2001). The Impact of the Inpatient Prospective Payment System and Diagnosis Related Groups: A Survey of the Literature. North American Actuarial Journal
(5), 84-94. doi: 10.1080/10920277.2001.10596020.
Rosenberg, M. (2001). A Statistical Method for Monitoring a Change in the Rate of Non-Acceptable Inpatient Claims. North American Actuarial Journal
(5), 74-83. doi: 10.1080/10920277.2001.10596019.
Rosenberg, M. (2001). A Decision-Theoretic Method for Assessing a Change in the Rate of Non-Acceptable Inpatient Claims. Health Services and Outcomes Research Methodology
(2), 19-36. doi: 10.1023/A:1011472032604.
Foster, S., McMurray, J., Linzer, M., Leavitt, J., Rosenberg, M., & Carnes, M. (2000). Assessing the Climate: The Gender Climate Survey Report from a Midwest Academic Health Center. Academic Medicine (75), 79-86.
Selected Submitted Journal Articles
Kamiya, S., Schmit, J., & Rosenberg, M. (2012). Determinants of Insurers' Reputational Risk. Journal of Risk and Insurance
Rosenberg, M., & Guszcza, J. (2013). Overview of Linear Models.
Predictive Modeling in Actuarial Science
Linear modeling, also known as regression analysis, is a core tool in statistical practice for
data analysis, prediction and decision-support. This chapter provides a summary of the linear
model, discussing model assumptions, parameter estimation, and the process of variable selection.
Applied data analysis requires judgment, domain knowledge, and the ability to program
with and graphically explore data. This chapter is structured around a series of examples,
grounded in data, to help relate the theory to practice. All of these practical examples and
exercises are completed using the open-source R statistical computing package. Particular attention
is paid to the role of Exploratory Data Analysis in the iterative process of criticizing,
improving, and validating models. Linear models provide a foundation for many of the more
advanced statistical and machine learning techniques that are explored in the later chapters.
Collins, J., Hinshaw, J., Simcock, E., & Rosenberg, M. (2009). Radiology Faculty Compliance with Recommended Health Guidelines: Comparison with Residents. Academic Radiology
(16), 1433-1442. doi: 10.1016/j.acra.2009.06.011.