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    JCO_Editorial_Nov2011 JCO_Editorial_Nov2011 Document Transcript

    • Published Ahead of Print on November 7, 2011 as 10.1200/JCO.2011.37.8604 The latest version is at http://jco.ascopubs.org/cgi/doi/10.1200/JCO.2011.37.8604 JOURNAL OF CLINICAL ONCOLOGY E D I T O R I A L SWhen a Decision Must Be Made: Role of ComputerModeling in Clinical Cancer ResearchRebecca A. Miksad, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MASee accompanying article doi: 10.1200/JCO.2010.33.8020 Every day, multidisciplinary oncology teams make dozens of treat- quantitative, individualized survival predictions on the basis of thement decisions that may have a tremendous impact on a patient’s experience of more than 1,100 patients with resected gallbladder can-survival and quality of life. Made with the best of intentions, these cer in the Surveillance, Epidemiology, and End Results–Medicaredecisions are informed by basic science and clinical research findings, linked databases. Although one must acknowledge that models thatclinical experience, and health policy. All too often, results from the are based on health claims data of the type found in the Surveil-gold standard of clinical trial research, a randomized controlled trial lance, Epidemiology, and End Results–Medicare database may lack(RCT), that fit the specific details of the patient’s situation are not important clinical variables, and that models that are based onavailable to guide these decisions. observational data may reflect selection biases, imperfect informa- Although this data gap occurs at times for all cancers, it is a tion is sometimes better than no information at all. In addition toconstant limitation for less common and biologically heterogeneous addressing critiques of a previous model of adjuvant radiation fordiseases. For these cancers, such as those of the biliary tract, practical gallbladder cancer, the current chemoradiotherapy prediction modeltime and expense limitations restrict the number and combinations of provides concrete adjuvant chemoradiotherapy survival benefit esti-therapeutic strategies evaluated, the follow-up duration, and the pop- mates on the basis of patient characteristics.26-29 The Internet-basedulations studied in RCTs.1 And even in the most common cancers, the nomogram that is built on these results provides an interactive toolcostly failure of multiple trials that involve thousands of patients to that may help patients, clinicians, and policy makers to make moremove cancer care forward has raised the need for alternate re- informed, real-time decisions.30search paradigms.2-9 Although additional research would be needed to validate the Enter computer modeling as a method to bridge current predictions of the gallbladder cancer adjuvant chemoradiotherapyknowledge gaps and to advance cancer clinical care and research. model described by Wang et al,21 examples in the literature demon-When performed correctly—rigorously developed, calibrated, and strate the potential power of computer modeling, especially compre-validated— computer modeling can maximize the information hensive microsimulation models such as the Lung Cancer Policythat is gained from current clinical, basic science, and epidemio- Model (LCPM).17 The LCPM was initiated a decade before the recentlogic research efforts to facilitate informed clinical and health publication of the National Lung Screening Trial (NLST) results.policy decisions.10 This power stems from the ability of computer Nonetheless, in contrast to two large previous clinical studies withmodels to produce novel comparative effectiveness findings, extendtrial results to longer time horizons, expand study findings to new widely divergent findings for computed tomography (CT) screeningpopulations, and refine expected outcomes. Last, but not least, com- of individuals at high risk for lung cancer, the previously publishedputer models may also help differentiate between those scientific and LCPM results are remarkably consistent with the current NLST find-clinical questions for which an RCT would be preferred but is not vital ings: a 6.7% reduction in all-cause mortality in the clinical trial of threefor decision making, and those questions for which the expense, time, annual CT screenings and a 4% reduction in all-cause mortality at 6and patient effort of an RCT is absolutely required to improve out- years in the LCPM analysis of five annual CT screenings.17,20,31,33-37comes and to guide treatment and policy decisions.11-20 This consistency in the magnitude of benefit is not a coincidence but In the article that accompanies this editorial, Wang et al21 used rather is the result of a comprehensive microsimulation model of lungsurvival model techniques to predict the benefit of adjuvant chemo- cancer development, progression, detection, treatment, and survivaltherapy and chemoradiotherapy for patients with resected gallbladder that accounts for competing mortality risks related to smoking andcancer. Although the prognosis for these patients is usually grim and benign nodules and predicts the stage-shift effect of screening. Thethe need for an effective treatment is great, there is a paucity of pub- LCPM was extensively calibrated and validated with data from a vari-lished information to guide adjuvant therapy choices.22-25 However, ety of sources. Simulating the NLST trial design and participants willdespite this data void, clinicians and policy makers still need to make provide an additional opportunity to validate the precision and accu-the best decisions possible for current patients. racy of model predictions. A model like the LCPM does not replace As an alternative to making an educated guess about the benefit randomized controlled trials such as the NLST, but models canof adjuvant therapy, the study by Wang et al21 attempts to offer uniquely extend the time horizon and expand the population studied,Journal of Clinical Oncology, Vol 29, 2011 © 2011 by American Society of Clinical Oncology 1 Information downloaded from jco.ascopubs.org and provided by at Oregon Health & Science University on November 7, Copyright © 2011 American Society of Clinical Oncology. All rights reserved. 2011 from Copyright 2011 by American Society of Clinical Oncology
    • Rebecca A. Miksadevaluate alternative screening strategies, and assess the relative value of practice, and health policy to ensure that the best decisions are madepotential policy interventions. for patients. These unique abilities of computer models are particularly valu-able when policy decisions must be made on the basis of available data, AUTHOR’S DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST The author(s) indicated no potential conflicts of interest.including observational or single-arm studies, until more definitiveRCT results are available and in situations in which funding, time, and REFERENCESpatient populations limit answerable research questions.32 For exam- 1. Knudsen AB, McMahon PM, Gazelle GS: Use of modeling to evaluate theple, a simulated control group for the single-arm Mayo lung cancer cost-effectiveness of cancer screening programs. J Clin Oncol 25:203-208, 2007screening study with the LCPM allowed exploration of the contradic- 2. Weiser MR: Rectal cancer trials: No movement. J Clin Oncol 29:2746- 2748, 2011tory results of two large clinical studies.33-37 In addition, the LCPM 3. Miksad RA: Pathologic complete response and toxicity results from thewas able to assess 15-year survival estimates for three different screen- STAR-01 Trial evaluating the addition of oxaliplatin to neodjuvant chemoradiationing strategies—a significantly longer time-line and a more complex for locally advanced rectal cancer. J Clin Oncol 29, 2011. http://jco.ascopubs.org/ content/29/20/2773/suppl/DC2analysis than is typically possible in an RCT.20 4. LoRusso PM, Schnipper LE, Stewart DJ, et al: Translating clinical trials into Simulationmodelingalongsideclinicalresearchmayalsostrengthen meaningful outcomes. Clin Cancer Res 16:5951-5955, 2010trial findings by allowing the exploration of areas of potential bias: 5. LoRusso PM, Anderson AB, Boerner SA, et al: Making the investigationalconcerns about the NSLT false-positive rate and potential for overdi- oncology pipeline more efficient and effective: Are we headed in the right direction? Clin Cancer Res 16:5956-5962, 2010agnosis that were suggested by the 16-year results of the Mayo study 6. Schnipper LE, Meropol NJ, Brock DW: Value and cancer care: Toward ancan be explicitly evaluated in a model.32 In addition, the flexibility of equitable future. Clin Cancer Res 16:6004-6008, 2010the simulation models also allows evaluation of questions raised by the 7. Miksad RA, Schnipper L, Goldstein M: Does a statistically significant survival benefit of erlotinib plus gemcitabine for advanced pancreatic cancerNSLT study that are important to clinicians and policy makers but for translate into clinical significance and value? J Clin Oncol 25:4506-4507, 2007;which repeated, long-term clinical trials are not possible: the benefit author reply 4508of screening in populations with lower or variable adherence, the effect 8. Butler D: Translational research: Crossing the valley of death. Natureof extending annual screening beyond 3 years, as well as the impact of 453:840-842, 2008 9. Booth CM: Evaluating patient-centered outcomes in the randomizedscreening on light smokers and genomic subgroups. On the societal controlled trial and beyond: Informing the future with lessons from the past. Clinlevel, an additional value of modeling is to generate novel hypotheses Cancer Res 16:5963-5971, 2010and to identify research questions that merit clinical trial resources. 10. Rutter CM, Knudsen AB, Pandharipande PV: Computer disease simulation Although it is not a comprehensive microsimulation model like models: Integrating evidence for health policy. Acad Radiol 18:1077-1086, 2010 11. Zauber AG, Lansdorp-Vogelaar I, Knudsen AB, et al: Evaluating Testthe LCPM, the model described by Wang et al21 moves gallbladder Strategies for Colorectal Cancer Screening: Age to Begin, Age to Stop, andcancer clinical care forward by offering evidence in favor of adjuvant Timing of Screening Intervals—A Decision Analysis of Colorectal Cancer Screen-chemoradiotherapy for some patients and by providing guidance for ing for the U.S. Preventive Surveillance Modeling Network (CISNET). Rockville, MD, Agency for Healthcare Research and Quality, 2009gallbladder cancer research. For example, future studies can take ad- 12. Knudsen AB, Lansdorp-Vogelaar I, Rutter CM, et al: Cost-effectiveness ofvantage of model efficacy estimates to help guide clinical trial de- computed tomographic colonography screening for colorectal cancer in thesign, to identify subgroups (adjuvant chemoradiotherapy benefit medicare population. J Natl Cancer Inst 102:1238-1252, 2010may be small for patients with node-negative disease), to help 13. Pandharipande PV, Choy G, del Carmen MG, et al: MRI and PET/CT for triaging stage IB clinically operable cervical cancer to appropriate therapy:refine target populations, and to highlight areas for additional Decision analysis to assess patient outcomes. AJR Am J Roentgenol 192:802-research (the interaction between extended lymphadenectomy and 814, 2009adjuvant chemotherapy). Building from the results by Wang et al, 14. Pandharipande PV, Gervais DA, Hartman RI, et al: Renal mass biopsy to guide treatment decisions for small incidental renal tumors: A cost-effectivenessa microsimulation model calibrated to and validated with external analysis. Radiology 256:836-846, 2010data sets may increase the robustness and precision of adjuvant che- 15. Ladapo JA, Jaffer FA, Hoffmann U, et al: Clinical outcomes and cost-moradiotherapy model predictions. effectiveness of coronary computed tomography angiography in the evaluation of The statistical aspects of the survival analysis model by Wang et patients with chest pain. J Am Coll Cardiol 54:2409-2422, 2009 16. Pandharipande PV, Harisinghani MG, Ozanne EM, et al: Staging MRal,21 the majority of which was originally reported in a technical jour- lymphangiography of the axilla for early breast cancer: Cost-effectiveness anal-nal, also merit discussion.38 Although the Cox proportional hazard ysis. AJR Am J Roentgenol 191:1308-1319, 2008ratio model is commonly used in medicine, other survival analysis 17. McMahon PM, Kong CY, Johnson BE, et al: Estimating long-term effec-methods, such as the accelerated failure time log normal model used tiveness of lung cancer screening in the Mayo CT screening study. Radiology 248:278-287, 2008by Wang et al, may more appropriately reflect the biology of some 18. Gazelle GS, Hunink MG, Kuntz KM, et al: Cost-effectiveness of hepaticcancer scenarios.39 For example, accelerated failure time models allow metastasectomy in patients with metastatic colorectal carcinoma: A state-the intervention effect to change over time, as is seen when the effec- transition Monte Carlo decision analysis. Ann Surg 237:544-555, 2003 19. Huang ES, Gazelle GS, Hur C: Consensus guidelines in the management oftiveness of chemotherapy decreases over time because of resistance. branch duct intraductal papillary mucinous neoplasm: A cost-effectiveness anal-Similar to findings in other cancers,40,41 Wang et al demonstrate the ysis. Dig Dis Sci 55:852-860, 2010need for careful consideration of the best analytic method by docu- 20. McMahon PM, Kong CY, Weinstein MC, et al: Adopting helical CTmenting performance variations for five gallbladder survival model screening for lung cancer: Potential health consequences during a 15-year period. Cancer 113:3440-3449, 2008analysis approaches. The oncology community should expect rigor- 21. Wang SJ, Lemieux A, Kalpathy-Cramer J, et al: Nomogram for predictingous consideration of the appropriate survival analysis methods in all the benefit of adjuvant chemoradiotherapy for resected gallbladder cancer. J Clinclinical trial and modeling research. As the application of cancer mod- Oncol doi: 10.1200/JCO.2010.33.8020 22. Macdonald OK, Crane CH: Palliative and postoperative radiotherapy inels expands, researchers, clinicians, and policy makers who under- biliary tract cancer. Surg Oncol Clin N Am 11:941-954, 2002stand both the clinical research and computer modeling worlds are 23. Southwest Oncology Group: S0809 –Phase II: A Phase II Trial of Adjuvantneeded to translate between model results, clinical trial design, clinical Capecitabine/Gemcitabine Chemotherapy Followed by Concurrent Capecitabine2 © 2011 by American Society of Clinical Oncology JOURNAL OF CLINICAL ONCOLOGY Information downloaded from jco.ascopubs.org and provided by at Oregon Health & Science University on November 7, Copyright © 2011 American Society of Clinical Oncology. All rights reserved. 2011 from
    • Editorialsand Radiotherapy in Extrahepatic Cholangiocarcinoma [protocol abstract]. http:// 33. Henschke CI, Naidich DP, Yankelevitz DF, et al: Early lung cancer actionswog.org/Visitors/ViewProtocolDetails.asp?ProtocolNumber S0809 project: Initial findings on repeat screenings. Cancer 92:153-159, 2001 24. Jarnagin WR, Ruo L, Little SA, et al: Patterns of initial disease recurrence 34. Swensen SJ, Jett JR, Hartman TE, et al: CT screening for lung cancer:after resection of gallbladder carcinoma and hilar cholangiocarcinoma: Implica- Five-year prospective experience. Radiology 235:259-265, 2005tions for adjuvant therapeutic strategies. Cancer 98:1689-1700, 2003 35. Swensen SJ, Jett JR, Sloan JA, et al: Screening for lung cancer with 25. National Comprehensive Cancer Network: NCCN Guidelines: Hepatobiliary low-dose spiral computed tomography. Am J Respir Crit Care Med 165:508-513,Cancers, Version 2.2011. http://www.nccn.org/professionals/physician_gls/f_ 2002guidelines.asp 36. International Early Lung Cancer Action Program Investigators, Henschke 26. Yu JB, Zelterman D, Decker RH, et al: Impact of immediate postoperative CI, Yankelevitz DF, et al: Survival of patients with stage I lung cancer detected ondeath on the estimation of a survival benefit from postoperative radiation therapy CT screening. N Engl J Med 355:1763-1771, 2006for cancer of the gallbladder. J Clin Oncol 26:4523, 2008; author reply 4524-4526 37. Bach PB, Jett JR, Pastorino U, et al: Computed tomography screening and 27. Cleary SP, Tan JC, Law CH, et al: Treatment considerations for gallbladder lung cancer outcomes. JAMA 297:953-961, 2007cancer should include extent of surgery. J Clin Oncol 26:4521-4522, 2008; author 38. Wang SJ, Kalpathy-Cramer J, Kim JS, et al: Parametric survival models forreply 4524-4526 predicting the benefit of adjuvant chemoradiotherapy in gallbladder cancer. AMIA 28. Arroyo GF, Lemoine G: Prediction model for adjuvant radiation therapy for Annu Symp Proc 2010:847-851, 2010gallbladder cancer: Not ready to be used. J Clin Oncol 26:4522-4523, 2008; 39. Ahmed FE, Vos PW, Holbert D: Modeling survival in colon cancer: Aauthor reply 4524-4526 methodological review. Mol Cancer 6:15, 2007 29. Wang SJ, Fuller CD, Kim JS, et al: Prediction model for estimating the 40. Anderson JR, Cain KC, Gelber RD, et al: Analysis and interpretation of thesurvival benefit of adjuvant radiotherapy for gallbladder cancer. J Clin Oncol comparison of survival by treatment outcome variables in cancer clinical trials.26:2112-2117, 2008 Cancer Treat Rep 69:1139-1146, 1985 30. Knight Cancer Institute of Oregon Health and Science University: Gallbladder 41. Smith LK, Lambert PC, Botha JL, et al: Providing more up-to-date esti-Cancer Adjuvant Therapy, 2011. http://skynet.ohsu.edu/nomograms/gallbladder/ mates of patient survival: A comparison of standard survival analysis with period 31. National Lunch Screening Trial Research Team, Aberle DR, Adams AM, et analysis using life-table methods and proportional hazards models. J Clin Epide-al: Reduced lung-cancer mortality with low-dose computed tomographic screen- miol 57:14-20, 2004ing. N Engl J Med 365:395-409, 2010 32. Sox HC: Better evidence about screening for lung cancer. N Engl J Med DOI: 10.1200/JCO.2011.37.8604; published online ahead of print atAug 365:455-457, 2011 www.jco.org on November 7, 2011 ■ ■ ■ Acknowledgment R.A.M. is supported by the National Cancer Institute Grant No. 1 K23 CA139005-01A1.www.jco.org © 2011 by American Society of Clinical Oncology 3 Information downloaded from jco.ascopubs.org and provided by at Oregon Health & Science University on November 7, Copyright © 2011 American Society of Clinical Oncology. All rights reserved. 2011 from