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  1. 1. 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
  2. 2. 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. 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