Publications

2014
J Chhatwal, S Jayasuriya, and E. H. Elbasha. 2014. “Changing Cycle Lengths in State-Transition Models: Doing it the Right Way.” ISPOR Connections, 20, Pp. 12-14.
M. U. Ayvaci, O. Alagoz, J. Chhatwal, A. Munoz del Rio, E. A. Sickles, H. Nassif, K. Kerlikowske, and E.S. Burnside. 2014. “Predicting invasive breast cancer versus DCIS in different age groups.” BMC Cancer, 14, Pp. 584.Abstract
BACKGROUND: Increasing focus on potentially unnecessary diagnosis and treatment of certain breast cancers prompted our investigation of whether clinical and mammographic features predictive of invasive breast cancer versus ductal carcinoma in situ (DCIS) differ by age. METHODS: We analyzed 1,475 malignant breast biopsies, 1,063 invasive and 412 DCIS, from 35,871 prospectively collected consecutive diagnostic mammograms interpreted at University of California, San Francisco between 1/6/1997 and 6/29/2007. We constructed three logistic regression models to predict the probability of invasive cancer versus DCIS for the following groups: women >/= 65 (older group), women 50-64 (middle age group), and women < 50 (younger group). We identified significant predictors and measured the performance in all models using area under the receiver operating characteristic curve (AUC). RESULTS: The models for older and the middle age groups performed significantly better than the model for younger group (AUC = 0.848 vs, 0.778; p = 0.049 and AUC = 0.851 vs, 0.778; p = 0.022, respectively). Palpability and principal mammographic finding were significant predictors in distinguishing invasive from DCIS in all age groups. Family history of breast cancer, mass shape and mass margins were significant positive predictors of invasive cancer in the older group whereas calcification distribution was a negative predictor of invasive cancer (i.e. predicted DCIS). In the middle age group–mass margins, and in the younger group–mass size were positive predictors of invasive cancer. CONCLUSIONS: Clinical and mammographic finding features predict invasive breast cancer versus DCIS better in older women than younger women. Specific predictive variables differ based on age.
2013
S. A. Ferrante, J. Chhatwal, C. A. Brass, A. C. El Khoury, F. Poordad, J. P. Bronowicki, and E. H. Elbasha. 2013. “Boceprevir for previously untreated patients with chronic hepatitis C Genotype 1 infection: a US-based cost-effectiveness modeling study.” BMC Infect Dis, 13, Pp. 190.Abstract
BACKGROUND: SPRINT-2 demonstrated that boceprevir (BOC), an oral hepatitis C virus (HCV) nonstructural 3 (NS3) protease inhibitor, added to peginterferon alfa-2b (P) and ribavirin (R) significantly increased sustained virologic response rates over PR alone in previously untreated adult patients with chronic HCV genotype 1. We estimated the long-term impact of triple therapy vs. dual therapy on the clinical burden of HCV and performed a cost-effectiveness evaluation. METHODS: A Markov model was used to estimate the incidence of liver complications, discounted costs (2010 US$), quality-adjusted life years (QALYs), and incremental cost-effectiveness ratios (ICERs) of three treatment strategies for treatment-naive patients with chronic HCV genotype 1. The model simulates the treatment regimens studied in SPRINT-2 in which PR was administered for 4 weeks followed by: 1) placebo plus PR for 44 weeks (PR48); 2) BOC plus PR using response guided therapy (BOC/RGT); and 3) BOC plus PR for 44 weeks (BOC/PR48) and makes projections within and beyond the trial. HCV-related state-transition probabilities, costs, and utilities were obtained from previously published studies. All costs and QALYs were discounted at 3%. RESULTS: The model projected approximately 38% and 43% relative reductions in the lifetime incidence of liver complications in the BOC/RGT and BOC/PR48 regimens compared with PR48, respectively. Treatment with BOC/RGT is associated with an incremental cost of $10,348 and an increase of 0.62 QALYs compared to treatment with PR48. Treatment with BOC/PR48 is associated with an incremental cost of $35,727 and an increase of 0.65 QALYs compared to treatment with PR48. The ICERs were $16,792/QALY and $55,162/QALY for the boceprevir-based treatment groups compared with PR48, respectively. The ICER for BOC/PR48 compared with BOC/RGT was $807,804. CONCLUSION: The boceprevir-based regimens used in the SPRINT-2 trial were projected to substantially reduce the lifetime incidence of liver complications and increase the QALYs in treatment-naive patients with hepatitis C genotype 1. It was also demonstrated that boceprevir-based regimens offer patients the possibility of experiencing great clinical benefit with a shorter duration of therapy. Both boceprevir-based treatment strategies were projected to be cost-effective at a reasonable threshold in the US when compared to treatment with PR48.
E. H. Elbasha, J. Chhatwal, S. A. Ferrante, A. C. El Khoury, and P. A. Laires. 2013. “Cost-effectiveness analysis of boceprevir for the treatment of chronic hepatitis C virus genotype 1 infection in Portugal.” Appl Health Econ Health Policy, 11, Pp. 65-78.Abstract
BACKGROUND: The recent approval of two protease inhibitors, boceprevir and telaprevir, is likely to change the management of chronic hepatitis C virus (HCV) genotype 1 infection. OBJECTIVES: We evaluated the long-term clinical outcomes and the cost effectiveness of therapeutic strategies using boceprevir with peginterferon plus ribavirin (PR) in comparison with PR alone for treating HCV genotype 1 infection in Portugal. METHODS: A Markov model was developed to project the expected lifetime costs and quality-adjusted life-years (QALYs) associated with PR alone and the treatment strategies outlined by the European Medicines Agency in the boceprevir summary of product characteristics. The boceprevir-based therapeutic strategies differ according to whether or not the patient was previously treated and whether or not the patient had compensated cirrhosis. The model simulated the experience of a series of cohorts of chronically HCV-infected patients (each defined by age, sex, race and fibrosis score). All treatment-related inputs were obtained from boceprevir clinical trials - SPRINT-2, RESPOND-2 and PROVIDE. Estimates of the natural history parameters and health state utilities were based on published studies. Portugal-specific annual direct costs of HCV health states were estimated by convening a panel of experts to derive health state resource use and multiplying the results by national unit costs. The model was developed from a healthcare system perspective with a timeframe corresponding to the remaining duration of the patients' lifetimes. Both future costs and QALYs were discounted at 5 %. To test the robustness of the conclusions, we conducted deterministic and probabilistic sensitivity analyses. RESULTS: In comparison with the treatment with PR alone, boceprevir-based regimens were projected to reduce the lifetime incidence of advanced liver disease, liver transplantation, and liver-related death by 45-51 % and increase life expectancy by 2.3-4.3 years. Although the addition of BOC increased treatment costs by euro13,300-euro19,700, the reduction of disease burden resulted in a decrease of euro5,400-euro9,000 in discounted health state costs and an increase of 0.68-1.23 in discounted QALYs per patient. The incremental cost-effectiveness ratios of the boceprevir-based regimens compared with PR among previously untreated and previously treated patients were euro11,600/QALY and euro8,700/QALY, respectively. The results were most sensitive to variations in sustained virologic response rates, discount rates and age at treatment. CONCLUSIONS: Adding boceprevir to PR was projected to reduce the number of liver complications and liver-related deaths, and to be cost effective in treating both previously untreated and treated patients.
J. Chhatwal, S. A. Ferrante, C. Brass, A. C. El Khoury, M. Burroughs, B. Bacon, R. Esteban-Mur, and E. H. Elbasha. 2013. “Cost-effectiveness of boceprevir in patients previously treated for chronic hepatitis C genotype 1 infection in the United States.” Value Health, 16, Pp. 973-86.Abstract
OBJECTIVES: The phase 3 trial, Serine Protease Inhibitor Boceprevir and PegIntron/Rebetol-2 (RESPOND-2), demonstrated that the addition of boceprevir (BOC) to peginterferon-ribavirin (PR) resulted in significantly higher rates of sustained virologic response (SVR) in previously treated patients with chronic hepatitis C virus (HCV) genotype-1 infection as compared with PR alone. We evaluated the cost-effectiveness of treatment with BOC in previously treated patients with chronic hepatitis C in the United States using treatment-related data from RESPOND-2 and PROVIDE studies. METHODS: We developed a Markov cohort model to project the burden of HCV disease, lifetime costs, and quality-adjusted life-years associated with PR and two BOC-based therapies-response-guided therapy (BOC/RGT) and fixed-duration therapy for 48 weeks (BOC/PR48). We estimated treatment-related inputs (efficacy, adverse events, and discontinuations) from clinical trials and obtained disease progression rates, costs, and quality-of-life data from published studies. We estimated the incremental cost-effectiveness ratio (ICER) for BOC-based regimens as studied in RESPOND-2, as well as by patient's prior response to treatment and the IL-28B genotype. RESULTS: BOC-based regimens were projected to reduce the lifetime incidence of liver-related complications by 43% to 53% in comparison with treatment with PR. The ICER of BOC/RGT in comparison with that of PR was $30,200, and the ICER of BOC/PR48 in comparison with that of BOC/RGT was $91,500. At a willingness-to-pay threshold of $50,000, the probabilities of BOC/RGT and BOC/PR48 being the preferred option were 0.74 and 0.25, respectively. CONCLUSIONS: In patients previously treated for chronic HCV genotype-1 infection, BOC was projected to increase quality-adjusted life-years and reduce the lifetime incidence of liver complications. In addition, BOC-based therapies were projected to be cost-effective in comparison with PR alone at commonly used willingness-to-pay thresholds.
Odhiambo R, Chhatwal J, Ferrante SA, El Khoury AC, and Elbasha EH. 2013. “Economic evaluation of boceprevir for the treatment of patients with genotype 1 chronic hepatitis C virus infection in Hungary.” Journal of Health Economics and Outcomes Research.
O. Alagoz, J. Chhatwal, and E.S. Burnside. 2013. “Optimal Policies for Reducing Unnecessary Follow-up Mammography Exams in Breast Cancer Diagnosis.” Decis Anal, 10, Pp. 200-224.Abstract
Mammography is the most effective screening tool for early diagnosis of breast cancer. Based on the mammography findings, radiologists need to choose from one of the following three alternatives: 1) take immediate diagnostic actions including prompt biopsy to confirm breast cancer; 2) recommend a follow-up mammogram; 3) recommend routine annual mammography. There are no validated structured guidelines based on a decision-analytical framework to aid radiologists in making such patient management decisions. Surprisingly, only 15-45% of the breast biopsies and less than 1% of short-interval follow-up recommendations are found to be malignant, resulting in unnecessary tests and patient-anxiety. We develop a finite-horizon discrete-time Markov decision process (MDP) model that may help radiologists make patient-management decisions to maximize a patient's total expected quality-adjusted life years. We use clinical data to find the policies recommended by the MDP model and also compare them to decisions made by radiologists at a large mammography practice. We also derive the structural properties of the MDP model, including sufficiency conditions that ensure the existence of a double control-limit type policy.
K. L. Luangkesorn, F. Ghiasabadi, and J. Chhatwal. 2013. “A sequential experimental design method to evaluate a combination of school closure and vaccination policies to control an H1N1-like pandemic.” J Public Health Manag Pract, 19 Suppl 2, Pp. S37-41.Abstract
CONTEXT: During the 2009 H1N1 pandemic, computational agent-based models (ABMs) were extensively used to evaluate interventions to control the spread of emerging pathogens. However, evaluating different possible combinations of interventions using ABMs can be computationally very expensive and time-consuming. Therefore, most policy studies have examined the impact of a single policy decision. OBJECTIVE: To apply a sequential experimental design method with an ABM to analyze policy alternatives composed of a combination of school closure and vaccination policies to provide a set of promising "optimal" combinations of policies to control an H1N1-type epidemic to policy makers. METHODS: We used an open-source agent-based modeling system, FRED (A Framework for Reconstructing Epidemiological Dynamic), to simulate the spread of an H1N1 epidemic in Alleghany County, Pennsylvania, with a census-based synthetic population. We used an approach called best subset selection method to evaluate 72 alternative policies consisting of a combination of options for school closure threshold, closure duration, Advisory Committee on Immunization Practices prioritization, and second-dose vaccination prioritization policies. Using the attack rate as a performance measure, best subset selection enabled us to eliminate inferior alternatives and identify a small group of alternative policies that could be further evaluated on the basis of other criteria. RESULTS: Our sequential design approach to evaluate a combination of alternative mitigation policies leads to a savings in computational effort by a factor of 2 when examining combinations of school closure and vaccination policies. CONCLUSIONS: Best subset selection demonstrates a substantial reduction in the computational burden of a large-scale ABM in evaluating several alternative policies. Our method also provides policy makers with a set of promising policy combinations for further evaluation based on implementation considerations or other criteria.
2012
E.S. Burnside, J. Chhatwal, and O. Alagoz. 2012. “What is the optimal threshold at which to recommend breast biopsy?” PLoS One, 7, Pp. e48820.Abstract
BACKGROUND: A 2% threshold, traditionally used as a level above which breast biopsy recommended, has been generalized to all patients from several specific situations analyzed in the literature. We use a sequential decision analytic model considering clinical and mammography features to determine the optimal general threshold for image guided breast biopsy and the sensitivity of this threshold to variation of these features. METHODOLOGY/PRINCIPAL FINDINGS: We built a decision analytical model called a Markov Decision Process (MDP) model, which determines the optimal threshold of breast cancer risk to perform breast biopsy in order to maximize a patient's total quality-adjusted life years (QALYs). The optimal biopsy threshold is determined based on a patient's probability of breast cancer estimated by a logistic regression model (LRM) which uses demographic risk factors (age, family history, and hormone use) and mammographic findings (described using the established lexicon-BI-RADS). We estimate the MDP model's parameters using SEER data (prevalence of invasive vs. in situ disease, stage at diagnosis, and survival), US life tables (all cause mortality), and the medical literature (biopsy disutility and treatment efficacy) to determine the optimal "base case" risk threshold for breast biopsy and perform sensitivity analysis. The base case MDP model reveals that 2% is the optimal threshold for breast biopsy for patients between 42 and 75 however the thresholds below age 42 is lower (1%) and above age 75 is higher (range of 3-5%). Our sensitivity analysis reveals that the optimal biopsy threshold varies most notably with changes in age and disutility of biopsy. CONCLUSIONS/SIGNIFICANCE: Our MDP model validates the 2% threshold currently used for biopsy but shows this optimal threshold varies substantially with patient age and biopsy disutility.
2010
T. Ayer, O. Alagoz, J. Chhatwal, J. W. Shavlik, Jr. Kahn, C. E., and E.S. Burnside. 2010. “Breast cancer risk estimation with artificial neural networks revisited: discrimination and calibration.” Cancer, 116, Pp. 3310-21.Abstract
BACKGROUND: Discriminating malignant breast lesions from benign ones and accurately predicting the risk of breast cancer for individual patients are crucial to successful clinical decisions. In the past, several artificial neural network (ANN) models have been developed for breast cancer-risk prediction. All studies have reported discrimination performance, but not one has assessed calibration, which is an equivalently important measure for accurate risk prediction. In this study, the authors have evaluated whether an artificial neural network (ANN) trained on a large prospectively collected dataset of consecutive mammography findings can discriminate between benign and malignant disease and accurately predict the probability of breast cancer for individual patients. METHODS: Our dataset consisted of 62,219 consecutively collected mammography findings matched with the Wisconsin State Cancer Reporting System. The authors built a 3-layer feedforward ANN with 1000 hidden-layer nodes. The authors trained and tested their ANN by using 10-fold cross-validation to predict the risk of breast cancer. The authors used area the under the receiver-operating characteristic curve (AUC), sensitivity, and specificity to evaluate discriminative performance of the radiologists and their ANN. The authors assessed the accuracy of risk prediction (ie, calibration) of their ANN by using the Hosmer-Lemeshow (H-L) goodness-of-fit test. RESULTS: Their ANN demonstrated superior discrimination (AUC, 0.965) compared with the radiologists (AUC, 0.939; P<.001). The authors' ANN was also well calibrated as shown by an H-L goodness of fit P-value of .13. CONCLUSIONS: The authors' ANN can effectively discriminate malignant abnormalities from benign ones and accurately predict the risk of breast cancer for individual abnormalities.
T. Ayer, J. Chhatwal, O. Alagoz, Jr. Kahn, C. E., R.W. Woods, and E.S. Burnside. 2010. “Informatics in radiology: comparison of logistic regression and artificial neural network models in breast cancer risk estimation.” Radiographics, 30, Pp. 13-22.Abstract
Computer models in medical diagnosis are being developed to help physicians differentiate between healthy patients and patients with disease. These models can aid in successful decision making by allowing calculation of disease likelihood on the basis of known patient characteristics and clinical test results. Two of the most frequently used computer models in clinical risk estimation are logistic regression and an artificial neural network. A study was conducted to review and compare these two models, elucidate the advantages and disadvantages of each, and provide criteria for model selection. The two models were used for estimation of breast cancer risk on the basis of mammographic descriptors and demographic risk factors. Although they demonstrated similar performance, the two models have unique characteristics-strengths as well as limitations-that must be considered and may prove complementary in contributing to improved clinical decision making.
J. Chhatwal, O. Alagoz, and E.S. Burnside. 2010. “Optimal Breast Biopsy Decision-Making Based on Mammographic Features and Demographic Factors.” Oper Res, 58, Pp. 1577-1591.Abstract
Breast cancer is the most common non-skin cancer affecting women in the United States, where every year more than 20 million mammograms are performed. Breast biopsy is commonly performed on the suspicious findings on mammograms to confirm the presence of cancer. Currently, 700,000 biopsies are performed annually in the U.S.; 55%-85% of these biopsies ultimately are found to be benign breast lesions, resulting in unnecessary treatments, patient anxiety, and expenditures. This paper addresses the decision problem faced by radiologists: When should a woman be sent for biopsy based on her mammographic features and demographic factors? This problem is formulated as a finite-horizon discrete-time Markov decision process. The optimal policy of our model shows that the decision to biopsy should take the age of patient into account; particularly, an older patient's risk threshold for biopsy should be higher than that of a younger patient. When applied to the clinical data, our model outperforms radiologists in the biopsy decision-making problem. This study also derives structural properties of the model, including sufficiency conditions that ensure the existence of a control-limit type policy and nondecreasing control-limits with age.
2009
J. Chhatwal, O. Alagoz, M. J. Lindstrom, Jr. Kahn, C. E., K. A. Shaffer, and E.S. Burnside. 2009. “A logistic regression model based on the national mammography database format to aid breast cancer diagnosis.” AJR Am J Roentgenol, 192, Pp. 1117-27.Abstract
OBJECTIVE: The purpose of our study was to create a breast cancer risk estimation model based on the descriptors of the National Mammography Database using logistic regression that can aid in decision making for the early detection of breast cancer. MATERIALS AND METHODS: We created two logistic regression models based on the mammography features and demographic data for 62,219 consecutive mammography records from 48,744 studies in 18,269 [corrected] patients reported using the Breast Imaging Reporting and Data System (BI-RADS) lexicon and the National Mammography Database format between April 5, 1999 and February 9, 2004. State cancer registry outcomes matched with our data served as the reference standard. The probability of cancer was the outcome in both models. Model 2 was built using all variables in Model 1 plus radiologists' BI-RADS assessment categories. We used 10-fold cross-validation to train and test the model and to calculate the area under the receiver operating characteristic curves (A(z)) to measure the performance. Both models were compared with the radiologists' BI-RADS assessments. RESULTS: Radiologists achieved an A(z) value of 0.939 +/- 0.011. The A(z) was 0.927 +/- 0.015 for Model 1 and 0.963 +/- 0.009 for Model 2. At 90% specificity, the sensitivity of Model 2 (90%) was significantly better (p < 0.001) than that of radiologists (82%) and Model 1 (83%). At 85% sensitivity, the specificity of Model 2 (96%) was significantly better (p < 0.001) than that of radiologists (88%) and Model 1 (87%). CONCLUSION: Our logistic regression model can effectively discriminate between benign and malignant breast disease and can identify the most important features associated with breast cancer.
E.S. Burnside, J. Davis, J. Chhatwal, O. Alagoz, M. J. Lindstrom, B. M. Geller, B. Littenberg, K. A. Shaffer, Jr. Kahn, C. E., and C. D. Page. 2009. “Probabilistic computer model developed from clinical data in national mammography database format to classify mammographic findings.” Radiology, 251, Pp. 663-72.Abstract
{PURPOSE: To determine whether a Bayesian network trained on a large database of patient demographic risk factors and radiologist-observed findings from consecutive clinical mammography examinations can exceed radiologist performance in the classification of mammographic findings as benign or malignant. MATERIALS AND METHODS: The institutional review board exempted this HIPAA-compliant retrospective study from requiring informed consent. Structured reports from 48 744 consecutive pooled screening and diagnostic mammography examinations in 18 269 patients from April 5, 1999 to February 9, 2004 were collected. Mammographic findings were matched with a state cancer registry, which served as the reference standard. By using 10-fold cross validation, the Bayesian network was tested and trained to estimate breast cancer risk by using demographic risk factors (age, family and personal history of breast cancer, and use of hormone replacement therapy) and mammographic findings recorded in the Breast Imaging Reporting and Data System lexicon. The performance of radiologists compared with the Bayesian network was evaluated by using area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. RESULTS: The Bayesian network significantly exceeded the performance of interpreting radiologists in terms of AUC (0.960 vs 0.939

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