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1.  Overdiagnosis in Low-Dose Computed Tomography Screening for Lung Cancer 
JAMA internal medicine  2014;174(2):269-274.
IMPORTANCE
Screening for lung cancer has the potential to reduce mortality, but in addition to detecting aggressive tumors, screening will also detect indolent tumors that otherwise may not cause clinical symptoms. These overdiagnosis cases represent an important potential harm of screening because they incur additional cost, anxiety, and morbidity associated with cancer treatment.
OBJECTIVE
To estimate overdiagnosis in the National Lung Screening Trial (NLST).
DESIGN, SETTING, AND PARTICIPANTS
We used data from the NLST, a randomized trial comparing screening using low-dose computed tomography (LDCT) vs chest radiography (CXR) among 53 452 persons at high risk for lung cancer observed for 6.4 years, to estimate the excess number of lung cancers in the LDCT arm of the NLST compared with the CXR arm.
MAIN OUTCOMES AND MEASURES
We calculated 2 measures of overdiagnosis: the probability that a lung cancer detected by screening with LDCT is an overdiagnosis (PS), defined as the excess lung cancers detected by LDCT divided by all lung cancers detected by screening in the LDCT arm; and the number of cases that were considered overdiagnosis relative to the number of persons needed to screen to prevent 1 death from lung cancer.
RESULTS
During follow-up, 1089 lung cancers were reported in the LDCT arm and 969 in the CXR arm of the NLST. The probability is 18.5% (95% CI, 5.4%–30.6%) that any lung cancer detected by screening with LDCT was an overdiagnosis, 22.5% (95% CI, 9.7%–34.3%) that a non-small cell lung cancer detected by LDCT was an overdiagnosis, and 78.9% (95% CI, 62.2%–93.5%) that a bronchioalveolar lung cancer detected by LDCT was an overdiagnosis. The number of cases of overdiagnosis found among the 320 participants who would need to be screened in the NLST to prevent 1 death from lung cancer was 1.38.
CONCLUSIONS AND RELEVANCE
More than 18% of all lung cancers detected by LDCT in the NLST seem to be indolent, and overdiagnosis should be considered when describing the risks of LDCT screening for lung cancer.
doi:10.1001/jamainternmed.2013.12738
PMCID: PMC4040004  PMID: 24322569
2.  Surrogate Endpoint Analysis: An Exercise in Extrapolation 
Surrogate endpoints offer the hope of smaller or shorter cancer trials. It is, however, important to realize they come at the cost of an unverifiable extrapolation that could lead to misleading conclusions. With cancer prevention, the focus is on hypothesis testing in small surrogate endpoint trials before deciding whether to proceed to a large prevention trial. However, it is not generally appreciated that a small surrogate endpoint trial is highly sensitive to a deviation from the key Prentice criterion needed for the hypothesis-testing extrapolation. With cancer treatment, the focus is on estimation using historical trials with both surrogate and true endpoints to predict treatment effect based on the surrogate endpoint in a new trial. Successively leaving out one historical trial and computing the predicted treatment effect in the left-out trial yields a standard error multiplier that summarizes the increased uncertainty in estimation extrapolation. If this increased uncertainty is acceptable, three additional extrapolation issues (biological mechanism, treatment following observation of the surrogate endpoint, and side effects following observation of the surrogate endpoint) need to be considered. In summary, when using surrogate endpoint analyses, an appreciation of the problems of extrapolation is crucial.
doi:10.1093/jnci/djs527
PMCID: PMC3611854  PMID: 23264679
3.  The risky reliance on small surrogate endpoint studies when planning a large prevention trial 
Summary
The definitive evaluation of treatment to prevent a chronic disease with low incidence in middle age, such as cancer or cardiovascular disease, requires a trial with a large sample size of perhaps 20,000 or more. To help decide whether to implement a large true endpoint trial, investigators first typically estimate the effect of treatment on a surrogate endpoint in a trial with a greatly reduced sample size of perhaps 200 subjects. If investigators reject the null hypothesis of no treatment effect in the surrogate endpoint trial they implicitly assume they would likely correctly reject the null hypothesis of no treatment effect for the true endpoint. Surrogate endpoint trials are generally designed with adequate power to detect an effect of treatment on surrogate endpoint. However, we show that a small surrogate endpoint trial is more likely than a large surrogate endpoint trial to give a misleading conclusion about the beneficial effect of treatment on true endpoint, which can lead to a faulty (and costly) decision about implementing a large true endpoint prevention trial. If a small surrogate endpoint trial rejects the null hypothesis of no treatment effect, an intermediate-sized surrogate endpoint trial could be a useful next step in the decision-making process for launching a large true endpoint prevention trial.
doi:10.1111/j.1467-985X.2012.01052.x
PMCID: PMC3616635  PMID: 23565041
Cancer prevention; Cardiovascular disease; Prentice criterion; Principal stratification; Sample size calculation; Surrogate endpoint
4.  A Population Perspective on How Personalized Medicine Can Improve Health 
The term P4 medicine is used to denote an evolving field of medicine that uses systems biology approaches and information technologies to enhance wellness rather than just treat disease. Its four components include predictive, preventive, personalized, and participatory medicine. In the current paper, it is argued that in order to fulfill the promise of P4 medicine, a “fifth P” must be integrated--the population perspective--into each of the other four components. A population perspective integrates predictive medicine into the ecologic model of health; applies principles of population screening to preventive medicine; uses evidence-based practice to personalize medicine; and grounds participatory medicine on the three core functions of public health: assessment, policy development, and assurance. Population sciences--including epidemiology; behavioral, social, and communication sciences; and health economics, implementation science, and outcomes research--are needed to show the value of P4 medicine. Balanced strategies that implement both population- and individual-level interventions can best maximize health benefits, minimize harms, and avoid unnecessary healthcare costs.
doi:10.1016/j.amepre.2012.02.012
PMCID: PMC3629731  PMID: 22608383
5.  Prostate Cancer Screening in the Randomized Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial: Mortality Results after 13 Years of Follow-up 
Background
The prostate component of the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial was undertaken to determine whether there is a reduction in prostate cancer mortality from screening using serum prostate-specific antigen (PSA) testing and digital rectal examination (DRE). Mortality after 7–10 years of follow-up has been reported previously. We report extended follow-up to 13 years after the trial.
Methods
A total of 76 685 men, aged 55–74 years, were enrolled at 10 screening centers between November 1993 and July 2001 and randomly assigned to the intervention (organized screening of annual PSA testing for 6 years and annual DRE for 4 years; 38 340 men) and control (usual care, which sometimes included opportunistic screening; 38 345 men) arms. Screening was completed in October 2006. All incident prostate cancers and deaths from prostate cancer through 13 years of follow-up or through December 31, 2009, were ascertained. Relative risks (RRs) were estimated as the ratio of observed rates in the intervention and control arms, and 95% confidence intervals (CIs) were calculated assuming a Poisson distribution for the number of events. Poisson regression modeling was used to examine the interactions with respect to prostate cancer mortality between trial arm and age, comorbidity status, and pretrial PSA testing. All statistical tests were two-sided.
Results
Approximately 92% of the study participants were followed to 10 years and 57% to 13 years. At 13 years, 4250 participants had been diagnosed with prostate cancer in the intervention arm compared with 3815 in the control arm. Cumulative incidence rates for prostate cancer in the intervention and control arms were 108.4 and 97.1 per 10 000 person-years, respectively, resulting in a relative increase of 12% in the intervention arm (RR = 1.12, 95% CI = 1.07 to 1.17). After 13 years of follow-up, the cumulative mortality rates from prostate cancer in the intervention and control arms were 3.7 and 3.4 deaths per 10 000 person-years, respectively, resulting in a non-statistically significant difference between the two arms (RR = 1.09, 95% CI = 0.87 to 1.36). No statistically significant interactions with respect to prostate cancer mortality were observed between trial arm and age (Pinteraction = .81), pretrial PSA testing (Pinteraction = .52), and comorbidity (Pinteraction = .68).
Conclusions
After 13 years of follow-up, there was no evidence of a mortality benefit for organized annual screening in the PLCO trial compared with opportunistic screening, which forms part of usual care, and there was no apparent interaction with age, baseline comorbidity, or pretrial PSA testing.
doi:10.1093/jnci/djr500
PMCID: PMC3260132  PMID: 22228146
6.  Systems Biology and Cancer: Promises and Perils 
Systems biology uses systems of mathematical rules and formulas to study complex biological phenomena. In cancer research there are three distinct threads in systems biology research: modeling biology or biophysics with the goal of establishing plausibility or obtaining insights, modeling based on statistics, bioinformatics, and reverse engineering with the goal of better characterizing the system, and modeling with the goal of clinical predictions. Using illustrative examples we discuss these threads in the context of cancer research.
doi:10.1016/j.pbiomolbio.2011.03.002
PMCID: PMC3156977  PMID: 21419159
bioinformatics; microarrays; reverse engineering; receiver operating characteristic curves
7.  Clarifying the Role of Principal Stratification in the Paired Availability Design 
The paired availability design for historical controls postulated four classes corresponding to the treatment (old or new) a participant would receive if arrival occurred during either of two time periods associated with different availabilities of treatment. These classes were later extended to other settings and called principal strata. Judea Pearl asks if principal stratification is a goal or a tool and lists four interpretations of principal stratification. In the case of the paired availability design, principal stratification is a tool that falls squarely into Pearl's interpretation of principal stratification as “an approximation to research questions concerning population averages.” We describe the paired availability design and the important role played by principal stratification in estimating the effect of receipt of treatment in a population using data on changes in availability of treatment. We discuss the assumptions and their plausibility. We also introduce the extrapolated estimate to make the generalizability assumption more plausible. By showing why the assumptions are plausible we show why the paired availability design, which includes principal stratification as a key component, is useful for estimating the effect of receipt of treatment in a population. Thus, for our application, we answer Pearl's challenge to clearly demonstrate the value of principal stratification.
doi:10.2202/1557-4679.1338
PMCID: PMC3114955  PMID: 21686085
principal stratification; causal inference; paired availability design
8.  New Models for Large Prospective Studies: Is There a Better Way? 
American Journal of Epidemiology  2012;175(9):859-866.
Large prospective cohort studies are critical for identifying etiologic factors for disease, but they require substantial long-term research investment. Such studies can be conducted as multisite consortia of academic medical centers, combinations of smaller ongoing studies, or a single large site such as a dominant regional health-care provider. Still another strategy relies upon centralized conduct of most or all aspects, recruiting through multiple temporary assessment centers. This is the approach used by a large-scale national resource in the United Kingdom known as the “UK Biobank,” which completed recruitment/examination of 503,000 participants between 2007 and 2010 within budget and ahead of schedule. A key lesson from UK Biobank and similar studies is that large studies are not simply small studies made large but, rather, require fundamentally different approaches in which “process” expertise is as important as scientific rigor. Embedding recruitment in a structure that facilitates outcome determination, utilizing comprehensive and flexible information technology, automating biospecimen processing, ensuring broad consent, and establishing essentially autonomous leadership with appropriate oversight are all critical to success. Whether and how these approaches may be transportable to the United States remain to be explored, but their success in studies such as UK Biobank makes a compelling case for such explorations to begin.
doi:10.1093/aje/kwr453
PMCID: PMC3339313  PMID: 22411865
cohort studies; epidemiology; prospective studies
9.  NATIONAL CHILDREN’S STUDY: STATUS IN 2010 
The National Children’s Study (NCS) will examine the effects of the environment and genetics on the growth, development and health of children across the United States; it will follow participants from before birth until age 21 years. The goal of the Study is to improve the health and well-being of children and contribute to understanding the role various factors have on health and disease. Findings from the Study will be made available as the research progresses, making potential benefits known to the public as soon as possible.
A robust pilot or Vanguard Study is underway to generate data for designing the subsequent Main Study. The goals of the Vanguard Study are feasibility, acceptability and cost and the goals of the Main Study will be exposure-response relationships and biological, environmental and genetic interactions.
The initial Vanguard Study experience among 7 Study Centers was successful in many ways including delineating the topics to explore for the next phase of the Vanguard Study. Three different recruitment strategies are under evaluation to determine what approach to use for the Main Study. The organization of NCS operations is currentlybased on a new decentralized business model.
doi:10.1002/msj.20227
PMCID: PMC3247064  PMID: 21259268
10.  Transparency and reproducibility in data analysis: the Prostate Cancer Prevention Trial 
Biostatistics (Oxford, England)  2010;11(3):413-418.
With the analysis of complex, messy data sets, the statistics community has recently focused attention on “reproducible research,” namely research that can be readily replicated by others. One standard that has been proposed is the availability of data sets and computer code. However, in some situations, raw data cannot be disseminated for reasons of confidentiality or because the data are so messy as to make dissemination impractical. For one such situation, we propose 2 steps for reproducible research: (i) presentation of a table of data and (ii) presentation of a formula to estimate key quantities from the table of data. We illustrate this strategy in the analysis of data from the Prostate Cancer Prevention Trial, which investigated the effect of the drug finasteride versus placebo on the period prevalence of prostate cancer. With such an important result at stake, a transparent analysis was important.
doi:10.1093/biostatistics/kxq004
PMCID: PMC2883301  PMID: 20173101
Categorical data; Maximum likelihood; Missing data; Multinomial–Poisson transformation; Propensity-to-be-missing score; Randomized trials
11.  Principles of Cancer Screening: Lessons from History and Study Design Issues 
Seminars in oncology  2010;37(3):202-215.
Early detection of cancer has held great promise and intuitive appeal in the medical community for well over a century. Its history developed in tandem with that of the periodic health examination, in which any deviations—subtle or glaring--from a clearly demarcated “normal” were to be rooted out, given the underlying hypothesis that diseases develop along progressive linear paths of increasing abnormalities. This model of disease development drove the logical deduction that early detection—by “breaking the chain” of cancer development--must be of benefit to affected individuals. In the latter half of the 20th century, researchers and guidelines organizations began to explicitly challenge the core assumptions underpinning many clinical practices. A move away from intuitive thinking began with the development of evidence-based medicine. One key method developed to explicitly quantify the overall risk-benefit profile of a given procedure was the analytic framework. The shift away from pure deductive reasoning and reliance on personal observation was driven, in part, by a rising awareness of critical biases in cancer screening that can mislead clinicians, including healthy volunteer bias, length-biased sampling, lead-time bias, and overdiagnosis. A new focus on the net balance of both benefits and harms when determining the overall worth of an intervention also arose: it was recognized that the potential downsides of early detection were frequently overlooked or discounted because screening is performed on basically healthy persons and initially involves relatively noninvasive methods. Although still inconsistently applied to early detection programs, policies, and belief systems in the United States, an evidence-based approach is essential to counteract the misleading—even potentially harmful--allure of intuition and individual observation.
doi:10.1053/j.seminoncol.2010.05.006
PMCID: PMC2921618  PMID: 20709205
13.  Mortality Results from a Randomized Prostate-Cancer Screening Trial 
The New England journal of medicine  2009;360(13):1310-1319.
BACKGROUND
The effect of screening with prostate-specific–antigen (PSA) testing and digital rectal examination on the rate of death from prostate cancer is unknown. This is the first report from the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial on prostate-cancer mortality.
METHODS
From 1993 through 2001, we randomly assigned 76,693 men at 10 U.S. study centers to receive either annual screening (38,343 subjects) or usual care as the control (38,350 subjects). Men in the screening group were offered annual PSA testing for 6 years and digital rectal examination for 4 years. The subjects and health care providers received the results and decided on the type of follow-up evaluation. Usual care sometimes included screening, as some organizations have recommended. The numbers of all cancers and deaths and causes of death were ascertained.
RESULTS
In the screening group, rates of compliance were 85% for PSA testing and 86% for digital rectal examination. Rates of screening in the control group increased from 40% in the first year to 52% in the sixth year for PSA testing and ranged from 41 to 46% for digital rectal examination. After 7 years of follow-up, the incidence of prostate cancer per 10,000 person-years was 116 (2820 cancers) in the screening group and 95 (2322 cancers) in the control group (rate ratio, 1.22; 95% confidence interval [CI], 1.16 to 1.29). The incidence of death per 10,000 person-years was 2.0 (50 deaths) in the screening group and 1.7 (44 deaths) in the control group (rate ratio, 1.13;95% CI,0.75 to 1.70). The data at 10 years were 67% complete and consistent with these overall findings.
CONCLUSIONS
After 7 to 10 years of follow-up, the rate of death from prostate cancer was very low and did not differ significantly between the two study groups.
doi:10.1056/NEJMoa0810696
PMCID: PMC2944770  PMID: 19297565
14.  American Society of Clinical Oncology Clinical Practice Guideline Update on the Use of Pharmacologic Interventions Including Tamoxifen, Raloxifene, and Aromatase Inhibition for Breast Cancer Risk Reduction 
Journal of Clinical Oncology  2009;27(19):3235-3258.
Purpose
To update the 2002 American Society of Clinical Oncology guideline on pharmacologic interventions for breast cancer (BC) risk reduction.
Methods
A literature search identified relevant randomized trials published since 2002. Primary outcome of interest was BC incidence (invasive and noninvasive). Secondary outcomes included BC mortality, adverse events, and net health benefits. An expert panel reviewed the literature and developed updated consensus guidelines.
Results
Seventeen articles met inclusion criteria. In premenopausal women, tamoxifen for 5 years reduces the risk of BC for at least 10 years, particularly estrogen receptor (ER) –positive invasive tumors. Women ≤ 50 years of age experience fewer serious side effects. Vascular and vasomotor events do not persist post-treatment across all ages. In postmenopausal women, raloxifene and tamoxifen reduce the risk of ER-positive invasive BC with equal efficacy. Raloxifene is associated with a lower risk of thromboembolic disease, benign uterine conditions, and cataracts than tamoxifen in postmenopausal women. No evidence exists establishing whether a reduction in BC risk from either agent translates into reduced BC mortality.
Recommendations
In women at increased risk for BC, tamoxifen (20 mg/d for 5 years) may be offered to reduce the risk of invasive ER-positive BC, with benefits for at least 10 years. In postmenopausal women, raloxifene (60 mg/d for 5 years) may also be considered. Use of aromatase inhibitors, fenretinide, or other selective estrogen receptor modulators to lower BC risk is not recommended outside of a clinical trial. Discussion of risks and benefits of preventive agents by health providers is critical to patient decision making.
doi:10.1200/JCO.2008.20.5179
PMCID: PMC2716943  PMID: 19470930
15.  Use of 5-α-Reductase Inhibitors for Prostate Cancer Chemoprevention: American Society of Clinical Oncology/American Urological Association 2008 Clinical Practice Guideline 
Journal of Clinical Oncology  2009;27(9):1502-1516.
Purpose
To develop an evidence-based guideline on the use of 5-α-reductase inhibitors (5-ARIs) for prostate cancer chemoprevention.
Methods
The American Society of Clinical Oncology (ASCO) Health Services Committee (HSC), ASCO Cancer Prevention Committee, and the American Urological Association Practice Guidelines Committee jointly convened a Panel of experts, who used the results from a systematic review of the literature to develop evidence-based recommendations on the use of 5-ARIs for prostate cancer chemoprevention.
Results
The systematic review completed for this guideline identified 15 randomized clinical trials that met the inclusion criteria, nine of which reported prostate cancer period prevalence.
Conclusion
Asymptomatic men with a prostate-specific antigen (PSA) ≤ 3.0 ng/mL who are regularly screened with PSA or are anticipating undergoing annual PSA screening for early detection of prostate cancer may benefit from a discussion of both the benefits of 5-ARIs for 7 years for the prevention of prostate cancer and the potential risks (including the possibility of high-grade prostate cancer). Men who are taking 5-ARIs for benign conditions such as lower urinary tract [obstructive] symptoms (LUTS) may benefit from a similar discussion, understanding that the improvement of LUTS relief should be weighed with the potential risks of high-grade prostate cancer from 5-ARIs (although the majority of the Panel members judged the latter risk to be unlikely). A reduction of approximately 50% in PSA by 12 months is expected in men taking a 5-ARI; however, because these changes in PSA may vary across men, and within individual men over time, the Panel cannot recommend a specific cut point to trigger a biopsy for men taking a 5-ARI. No specific cut point or change in PSA has been prospectively validated in men taking a 5-ARI.
doi:10.1200/JCO.2008.16.9599
PMCID: PMC2668556  PMID: 19252137
16.  American Society of Clinical Oncology Policy Statement: The Role of the Oncologist in Cancer Prevention and Risk Assessment 
Journal of Clinical Oncology  2008;27(6):986-993.
Oncologists have a critical opportunity to utilize risk assessment and cancer prevention strategies to interrupt the initiation or progression of cancer in cancer survivors and individuals at high risk of developing cancer. Expanding knowledge about the natural history and prognosis of cancers positions oncologists to advise patients regarding the risk of second malignancies and treatment-related cancers. In addition, as recognized experts in the full spectrum of cancer care, oncologists are afforded opportunities for involvement in community-based cancer prevention activities.
Although oncologists are currently providing many cancer prevention and risk assessment services to their patients, economic barriers exist, including inadequate or lack of insurance, that may compromise uniform patient access to these services. Additionally, insufficient reimbursement for existing and developing interventions may discourage patient access to these services.
The American Society of Clinical Oncology (ASCO), the medical society representing cancer specialists involved in patient care and clinical research, is committed to supporting oncologists in their wide-ranging involvement in cancer prevention. This statement on risk assessment and prevention counseling, although not intended to be a comprehensive overview of cancer prevention describes the current role of oncologists in risk assessment and prevention; provides examples of risk assessment and prevention activities that should be offered by oncologists; identifies potential opportunities for coordination between oncologists and primary care physicians in prevention education and coordination of care for cancer survivors; describes ASCO's involvement in education and training of oncologists regarding prevention; and proposes improvement in the payment environment to encourage patient access to these services.
doi:10.1200/JCO.2008.16.3691
PMCID: PMC2668639  PMID: 19075281
17.  Clinical Cancer Advances 2008: Major Research Advances in Cancer Treatment, Prevention, and Screening—A Report From the American Society of Clinical Oncology 
Journal of Clinical Oncology  2008;27(5):812-826.
A MESSAGE FROM ASCO'S PRESIDENT
Nearly 40 years ago, President Richard Nixon signed the National Cancer Act, mobilizing the country's resources to make the “conquest of cancer a national crusade.” That declaration led to a major investment in cancer research that has significantly improved cancer prevention, treatment, and survival. As a result, two thirds of people diagnosed with cancer today will live at least 5 years after diagnosis, compared with just half in the 1970s. In addition, there are now more than 12 million cancer survivors in the United States—up from 3 million in 1971.
Scientifically, we have never been in a better position to advance cancer treatment. Basic scientific research, fueled in recent years by the tools of molecular biology, has generated unprecedented knowledge of cancer development. We now understand many of the cellular pathways that can lead to cancer. We have learned how to develop drugs that block those pathways; increasingly, we know how to personalize therapy to the unique genetics of the tumor and the patient.
Yet in 2008, 1.4 million people in the United States will still be diagnosed with cancer, and more than half a million will die as a result of the disease. Some cancers remain stubbornly resistant to treatment, whereas others cannot be detected until they are in their advanced, less curable stages. Biologically, the cancer cell is notoriously wily; each time we throw an obstacle in its path, it finds an alternate route that must then be blocked.
To translate our growing basic science knowledge into better treatments for patients, a new national commitment to cancer research is urgently needed. However, funding for cancer research has stagnated. The budgets of the National Institutes of Health and the National Cancer Institute have failed to keep pace with inflation, declining up to 13% in real terms since 2004. Tighter budgets reduce incentives to support high-risk research that could have the largest payoffs. The most significant clinical research is conducted increasingly overseas. In addition, talented young physicians in the United States, seeing less opportunity in the field of oncology, are choosing other specialties instead.
Although greater investment in research is critical, the need for new therapies is only part of the challenge. Far too many people in the United States lack access to the treatments that already exist, leading to unnecessary suffering and death. Uninsured cancer patients are significantly more likely to die than those with insurance, racial disparities in cancer incidence and mortality remain stark, and even insured patients struggle to keep up with the rapidly rising cost of cancer therapies.
As this annual American Society of Clinical Oncology report of the major cancer research advances during the last year demonstrates, we are making important progress against cancer. But sound public policies are essential to accelerate that progress. In 2009, we have an opportunity to reinvest in cancer research, and to support policies that will help ensure that every individual in the United States receives potentially life-saving cancer prevention, early detection, and treatment.
Sincerely, Richard L. Schilsky, MD President American Society of Clinical Oncology
doi:10.1200/JCO.2008.21.2134
PMCID: PMC2645086  PMID: 19103723
18.  Using relative utility curves to evaluate risk prediction 
Summary
Because many medical decisions are based on risk prediction models constructed from medical history and results of tests, the evaluation of these prediction models is important. This paper makes five contributions to this evaluation: (1) the relative utility curve which gauges the potential for better prediction in terms of utilities, without the need for a reference level for one utility, while providing a sensitivity analysis for missipecification of utilities, (2) the relevant region, which is the set of values of prediction performance consistent with the recommended treatment status in the absence of prediction (3) the test threshold, which is the minimum number of tests that would be traded for a true positive in order for the expected utility to be non-negative, (4) the evaluation of two-stage predictions that reduce test costs, and (5) connections among various measures of prediction performance. An application involving the risk of cardiovascular disease is discussed.
doi:10.1111/j.1467-985X.2009.00592.x
PMCID: PMC2804257  PMID: 20069131
decision analysis; decision curve; receiver operating characteristic curve; utility
19.  Using microarrays to study the microenvironment in tumor biology: The crucial role of statistics 
Seminars in cancer biology  2008;18(5):305-310.
Microarrays represent a potentially powerful tool for better understanding the role of the microenvironment on tumor biology. To make the best use of microarray data and avoid incorrect or unsubstantiated conclusions, care must be taken in the statistical analysis. To illustrate the statistical issues involved we discuss three microarray studies related to the microenvironment and tumor biology involving: (i) prostatic stroma cells in cancer and non-cancer tissues; (ii) breast stroma and epithelial cells in breast cancer patients and non-cancer patients; and (iii) serum associated with wound response and stroma in cancer patients. Using these examples we critically discuss three types of analyses: differential gene expression, cluster analysis, and class prediction. We also discuss design issues.
doi:10.1016/j.semcancer.2008.03.001
PMCID: PMC2584335  PMID: 18455427
Bonferroni; Class prediction; Cluster analysis; Differential expression; False discovery rate; Sample size
20.  Plausibility of stromal initiation of epithelial cancers without a mutation in the epithelium: a computer simulation of morphostats 
BMC Cancer  2009;9:89.
Background
There is experimental evidence from animal models favoring the notion that the disruption of interactions between stroma and epithelium plays an important role in the initiation of carcinogenesis. These disrupted interactions are hypothesized to be mediated by molecules, termed morphostats, which diffuse through the tissue to determine cell phenotype and maintain tissue architecture.
Methods
We developed a computer simulation based on simple properties of cell renewal and morphostats.
Results
Under the computer simulation, the disruption of the morphostat gradient in the stroma generated epithelial precursors of cancer without any mutation in the epithelium.
Conclusion
The model is consistent with the possibility that the accumulation of genetic and epigenetic changes found in tumors could arise after the formation of a founder population of aberrant cells, defined as cells that are created by low or insufficient morphostat levels and that no longer respond to morphostat concentrations. Because the model is biologically plausible, we hope that these results will stimulate further experiments.
doi:10.1186/1471-2407-9-89
PMCID: PMC2663766  PMID: 19309499
21.  Early reporting for cancer screening trials 
Journal of medical screening  2008;15(3):122-129.
Objective
Many cancer screening trials involve a screening programme of one or more screenings with follow-up after the last screening. Usually a maximum follow-up time is selected in advance. However, during the follow-up period there is an opportunity to report the results of the trial sooner than planned. Early reporting of results from a randomized screening trial is important because obtaining a valid result sooner translates into health benefits reaching the general population sooner. The health benefits are reduction in cancer deaths if screening is found to be beneficial and more screening is recommended, or avoidance of unnecessary biopsies, work-ups and morbidity if screening is not found to be beneficial and the rate of screening drops.
Methods
Our proposed method for deciding if results from a cancer screening trial should be reported earlier in the follow-up period is based on considerations involving postscreening noise. Postscreening noise (sometimes called dilution) refers to cancer deaths in the follow-up period that could not have been prevented by screening: (1) cancer deaths in the screened group that occurred after the last screening in subjects whose cancers were not detected during the screening program and (2) cancer deaths in the control group that occurred after the time of the last screening and whose cancers would not have been detected during the screening programme had they been randomized to screening (the number of which is unobserved). Because postscreening noise increases with follow-up after the last screening, we propose early reporting at the time during the follow-up period when postscreening noise first starts to overwhelm the estimated effect of screening as measured by a z-statistic. This leads to a confidence interval, adjusted for postscreening noise, that would not change substantially with additional follow-up. Details of the early reporting rule were refined by simulation, which also accounts for multiple looks.
Results
For the re-analysis of the Health Insurance Plan trial for breast cancer screening and the Mayo Lung Project for lung cancer screening, estimates and confidence intervals for the effect of screening on cancer mortality were similar on early reporting and later.
Conclusion
The proposed early reporting rule for a cancer screening trial with post-screening follow-up is a promising method for making results from the trial available sooner, which translates into health benefits (reduction in cancer deaths or avoidance of unnecessary morbidity) reaching the population sooner.
doi:10.1258/jms.2008.007058
PMCID: PMC2586667  PMID: 18927094
22.  Paradoxes in carcinogenesis: New opportunities for research directions 
BMC Cancer  2007;7:151.
Background
The prevailing paradigm in cancer research is the somatic mutation theory that posits that cancer begins with a single mutation in a somatic cell followed by successive mutations. Much cancer research involves refining the somatic mutation theory with an ever increasing catalog of genetic changes. The problem is that such research may miss paradoxical aspects of carcinogenesis for which there is no likely explanation under the somatic mutation theory. These paradoxical aspects offer opportunities for new research directions that should not be ignored.
Discussion
Various paradoxes related to the somatic mutation theory of carcinogenesis are discussed: (1) the presence of large numbers of spatially distinct precancerous lesions at the onset of promotion, (2) the large number of genetic instabilities found in hyperplastic polyps not considered cancer, (3) spontaneous regression, (4) higher incidence of cancer in patients with xeroderma pigmentosa but not in patients with other comparable defects in DNA repair, (5) lower incidence of many cancers except leukemia and testicular cancer in patients with Down's syndrome, (6) cancer developing after normal tissue is transplanted to other parts of the body or next to stroma previously exposed to carcinogens, (7) the lack of tumors when epithelial cells exposed to a carcinogen were transplanted next to normal stroma, (8) the development of cancers when Millipore filters of various pore sizes were was inserted under the skin of rats, but only if the holes were sufficiently small. For the latter paradox, a microarray experiment is proposed to try to better understand the phenomena.
Summary
The famous physicist Niels Bohr said "How wonderful that we have met with a paradox. Now we have some hope of making progress." The same viewpoint should apply to cancer research. It is easy to ignore this piece of wisdom about the means to advance knowledge, but we do so at our peril.
doi:10.1186/1471-2407-7-151
PMCID: PMC1993836  PMID: 17683619
23.  Identifying genes that contribute most to good classification in microarrays 
BMC Bioinformatics  2006;7:407.
Background
The goal of most microarray studies is either the identification of genes that are most differentially expressed or the creation of a good classification rule. The disadvantage of the former is that it ignores the importance of gene interactions; the disadvantage of the latter is that it often does not provide a sufficient focus for further investigation because many genes may be included by chance. Our strategy is to search for classification rules that perform well with few genes and, if they are found, identify genes that occur relatively frequently under multiple random validation (random splits into training and test samples).
Results
We analyzed data from four published studies related to cancer. For classification we used a filter with a nearest centroid rule that is easy to implement and has been previously shown to perform well. To comprehensively measure classification performance we used receiver operating characteristic curves. In the three data sets with good classification performance, the classification rules for 5 genes were only slightly worse than for 20 or 50 genes and somewhat better than for 1 gene. In two of these data sets, one or two genes had relatively high frequencies not noticeable with rules involving 20 or 50 genes: desmin for classifying colon cancer versus normal tissue; and zyxin and secretory granule proteoglycan genes for classifying two types of leukemia.
Conclusion
Using multiple random validation, investigators should look for classification rules that perform well with few genes and select, for further study, genes with relatively high frequencies of occurrence in these classification rules.
doi:10.1186/1471-2105-7-407
PMCID: PMC1574352  PMID: 16959042
24.  Getting It Right: Being Smarter about Clinical Trials 
PLoS Medicine  2006;3(6):e144.
A major NIH meeting led to recommendations for conducting better clinical trials.
doi:10.1371/journal.pmed.0030144
PMCID: PMC1435786  PMID: 16608383
25.  The fallacy of enrolling only high-risk subjects in cancer prevention trials: Is there a "free lunch"? 
Background
There is a common belief that most cancer prevention trials should be restricted to high-risk subjects in order to increase statistical power. This strategy is appropriate if the ultimate target population is subjects at the same high-risk. However if the target population is the general population, three assumptions may underlie the decision to enroll high-risk subject instead of average-risk subjects from the general population: higher statistical power for the same sample size, lower costs for the same power and type I error, and a correct ratio of benefits to harms. We critically investigate the plausibility of these assumptions.
Methods
We considered each assumption in the context of a simple example. We investigated statistical power for fixed sample size when the investigators assume that relative risk is invariant over risk group, but when, in reality, risk difference is invariant over risk groups. We investigated possible costs when a trial of high-risk subjects has the same power and type I error as a larger trial of average-risk subjects from the general population. We investigated the ratios of benefit to harms when extrapolating from high-risk to average-risk subjects.
Results
Appearances here are misleading. First, the increase in statistical power with a trial of high-risk subjects rather than the same number of average-risk subjects from the general population assumes that the relative risk is the same for high-risk and average-risk subjects. However, if the absolute risk difference rather than the relative risk were the same, the power can be less with the high-risk subjects. In the analysis of data from a cancer prevention trial, we found that invariance of absolute risk difference over risk groups was nearly as plausible as invariance of relative risk over risk groups. Therefore a priori assumptions of constant relative risk across risk groups are not robust, limiting extrapolation of estimates of benefit to the general population. Second, a trial of high-risk subjects may cost more than a larger trial of average risk subjects with the same power and type I error because of additional recruitment and diagnostic testing to identify high-risk subjects. Third, the ratio of benefits to harms may be more favorable in high-risk persons than in average-risk persons in the general population, which means that extrapolating this ratio to the general population would be misleading. Thus there is no free lunch when using a trial of high-risk subjects to extrapolate results to the general population.
Conclusion
Unless the intervention is targeted to only high-risk subjects, cancer prevention trials should be implemented in the general population.
doi:10.1186/1471-2288-4-24
PMCID: PMC524373  PMID: 15461821

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