When a Decision Must Be Made: Role of ComputerModeling in Clinical Cancer ResearchRebecca A. Miksad, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA

See accompanying article doi: 10.1200/JCO.2010.33.8020

Every day, multidisciplinary oncology teams make dozens of treat-ment decisions that may have a tremendous impact on a patient’ssurvival and quality of life. Made with the best of intentions, thesedecisions are informed by basic science and clinical research findings,clinical experience, and health policy. All too often, results from thegold standard of clinical trial research, a randomized controlled trial(RCT), that fit the specific details of the patient’s situation are notavailable to guide these decisions.

Although this data gap occurs at times for all cancers, it is aconstant limitation for less common and biologically heterogeneousdiseases. For these cancers, such as those of the biliary tract, practicaltime and expense limitations restrict the number and combinations oftherapeutic strategies evaluated, the follow-up duration, and the pop-ulations studied in RCTs.1 And even in the most common cancers, thecostly failure of multiple trials that involve thousands of patients tomove cancer care forward has raised the need for alternate re-search paradigms.2-9

Enter computer modeling as a method to bridge currentknowledge gaps and to advance cancer clinical care and research.When performed correctly—rigorously developed, calibrated, andvalidated— computer modeling can maximize the informationthat is gained from current clinical, basic science, and epidemio-logic research efforts to facilitate informed clinical and healthpolicy decisions.10 This power stems from the ability of computermodels to produce novel comparative effectiveness findings, extendtrial results to longer time horizons, expand study findings to newpopulations, and refine expected outcomes. Last, but not least, com-puter models may also help differentiate between those scientific andclinical questions for which an RCT would be preferred but is not vitalfor decision making, and those questions for which the expense, time,and patient effort of an RCT is absolutely required to improve out-comes and to guide treatment and policy decisions.11-20

In the article that accompanies this editorial, Wang et al21 usedsurvival model techniques to predict the benefit of adjuvant chemo-therapy and chemoradiotherapy for patients with resected gallbladdercancer. Although the prognosis for these patients is usually grim andthe need for an effective treatment is great, there is a paucity of pub-lished information to guide adjuvant therapy choices.22-25 However,despite this data void, clinicians and policy makers still need to makethe best decisions possible for current patients.

As an alternative to making an educated guess about the benefitof adjuvant therapy, the study by Wang et al21 attempts to offer

quantitative, individualized survival predictions on the basis of theexperience of more than 1,100 patients with resected gallbladder can-cer in the Surveillance, Epidemiology, and End Results–Medicarelinked databases. Although one must acknowledge that models thatare based on health claims data of the type found in the Surveil-lance, Epidemiology, and End Results–Medicare database may lackimportant clinical variables, and that models that are based onobservational data may reflect selection biases, imperfect informa-tion is sometimes better than no information at all. In addition toaddressing critiques of a previous model of adjuvant radiation forgallbladder cancer, the current chemoradiotherapy prediction modelprovides concrete adjuvant chemoradiotherapy survival benefit esti-mates on the basis of patient characteristics.26-29 The Internet-basednomogram that is built on these results provides an interactive toolthat may help patients, clinicians, and policy makers to make moreinformed, real-time decisions.30

Although additional research would be needed to validate thepredictions of the gallbladder cancer adjuvant chemoradiotherapymodel described by Wang et al,21 examples in the literature demon-strate the potential power of computer modeling, especially compre-hensive microsimulation models such as the Lung Cancer PolicyModel (LCPM).17 The LCPM was initiated a decade before the recentpublication of the National Lung Screening Trial (NLST) results.Nonetheless, in contrast to two large previous clinical studies withwidely divergent findings for computed tomography (CT) screeningof individuals at high risk for lung cancer, the previously publishedLCPM results are remarkably consistent with the current NLST find-ings: a 6.7% reduction in all-cause mortality in the clinical trial of threeannual CT screenings and a 4% reduction in all-cause mortality at 6years in the LCPM analysis of five annual CT screenings.17,20,31,33-37

This consistency in the magnitude of benefit is not a coincidence butrather is the result of a comprehensive microsimulation model of lungcancer development, progression, detection, treatment, and survivalthat accounts for competing mortality risks related to smoking andbenign nodules and predicts the stage-shift effect of screening. TheLCPM was extensively calibrated and validated with data from a vari-ety of sources. Simulating the NLST trial design and participants willprovide an additional opportunity to validate the precision and accu-racy of model predictions. A model like the LCPM does not replacerandomized controlled trials such as the NLST, but models canuniquely extend the time horizon and expand the population studied,

JOURNAL OF CLINICAL ONCOLOGY E D I T O R I A L S

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evaluate alternative screening strategies, and assess the relative value ofpotential policy interventions.

These unique abilities of computer models are particularly valu-able when policy decisions must be made on the basis of available data,including observational or single-arm studies, until more definitiveRCT results are available and in situations in which funding, time, andpatient populations limit answerable research questions.32 For exam-ple, a simulated control group for the single-arm Mayo lung cancerscreening study with the LCPM allowed exploration of the contradic-tory results of two large clinical studies.33-37 In addition, the LCPMwas able to assess 15-year survival estimates for three different screen-ing strategies—a significantly longer time-line and a more complexanalysis than is typically possible in an RCT.20

Simulationmodelingalongsideclinicalresearchmayalsostrengthentrial findings by allowing the exploration of areas of potential bias:concerns about the NSLT false-positive rate and potential for overdi-agnosis that were suggested by the 16-year results of the Mayo studycan be explicitly evaluated in a model.32 In addition, the flexibility ofthe simulation models also allows evaluation of questions raised by theNSLT study that are important to clinicians and policy makers but forwhich repeated, long-term clinical trials are not possible: the benefitof screening in populations with lower or variable adherence, the effectof extending annual screening beyond 3 years, as well as the impact ofscreening on light smokers and genomic subgroups. On the societallevel, an additional value of modeling is to generate novel hypothesesand to identify research questions that merit clinical trial resources.

Although it is not a comprehensive microsimulation model likethe LCPM, the model described by Wang et al21 moves gallbladdercancer clinical care forward by offering evidence in favor of adjuvantchemoradiotherapy for some patients and by providing guidance forgallbladder cancer research. For example, future studies can take ad-vantage of model efficacy estimates to help guide clinical trial de-sign, to identify subgroups (adjuvant chemoradiotherapy benefitmay be small for patients with node-negative disease), to helprefine target populations, and to highlight areas for additionalresearch (the interaction between extended lymphadenectomy andadjuvant chemotherapy). Building from the results by Wang et al,a microsimulation model calibrated to and validated with externaldata sets may increase the robustness and precision of adjuvant che-moradiotherapy model predictions.

The statistical aspects of the survival analysis model by Wang etal,21 the majority of which was originally reported in a technical jour-nal, also merit discussion.38 Although the Cox proportional hazardratio model is commonly used in medicine, other survival analysismethods, such as the accelerated failure time log normal model usedby Wang et al, may more appropriately reflect the biology of somecancer scenarios.39 For example, accelerated failure time models allowthe intervention effect to change over time, as is seen when the effec-tiveness of chemotherapy decreases over time because of resistance.Similar to findings in other cancers,40,41 Wang et al demonstrate theneed for careful consideration of the best analytic method by docu-menting performance variations for five gallbladder survival modelanalysis approaches. The oncology community should expect rigor-ous consideration of the appropriate survival analysis methods in allclinical trial and modeling research. As the application of cancer mod-els expands, researchers, clinicians, and policy makers who under-stand both the clinical research and computer modeling worlds areneeded to translate between model results, clinical trial design, clinical

practice, and health policy to ensure that the best decisions are madefor patients.

AUTHOR’S DISCLOSURES OF POTENTIAL CONFLICTS OF INTERESTThe author(s) indicated no potential conflicts of interest.

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DOI: 10.1200/JCO.2011.37.8604; published online ahead of print atwww.jco.org on November 7, 2011

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Acknowledgment

R.A.M. is supported by the National Cancer Institute Grant No. 1 K23 CA139005-01A1.

Editorials

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