Statistical Code for Clinical Research Papers in a High-Impact Specialist Medical Journal

When Tissue Is No Longer the Issue: Tissue-Agnostic Cancer Therapy Comes of Age

Matching unique features of cancer types with effective therapies is a cornerstone of precision medicine. Clinical success has been seen in inhibiting specific molecular alterations that drive the growth of cancer cells and targeting molecules whose elevated expression is confined to cancer cells. In addition, cancer cells can have vulnerabilities induced by somatic mutations they carry; attacks on these vulnerabilities range from specific molecular alterations pointing to direct drug strategies to harnessing immune recognition of genetically altered epitopes produced by the cancer cells. Recent advances have found that the success of biomarker-driven cancer therapy may be relevant across sites of origin. For example, cancer types that show DNA mismatch repair deficiency, such as colon, biliary, and endometrial cancer, are more sensitive to immune checkpoint inhibition. Several large, ongoing clinical trials with a “basket” design are combining tumor tissue genomics with potential off-the-shelf therapies in drug development, and more tissue-agnostic biomarker therapies are reaching the bedside.

We Should Measure What Matters in Bundled Payment Programs

Implications of Nine Risk Prediction Models for Selecting Ever-Smokers for Computed Tomography Lung Cancer Screening

Background: Lung cancer screening guidelines recommend using individualized risk models to refer ever-smokers for screening. However, different models select different screening populations. The performance of each model in selecting ever-smokers for screening is unknown. Objective: To compare the U.S. screening populations selected by 9 lung cancer risk models (the Bach model; the Spitz model; the Liverpool Lung Project [LLP] model; the LLP Incidence Risk Model [LLPi]; the Hoggart model; the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial Model 2012 [PLCOM2012]; the Pittsburgh Predictor; the Lung Cancer Risk Assessment Tool [LCRAT]; and the Lung Cancer Death Risk Assessment Tool [LCDRAT]) and to examine their predictive performance in 2 cohorts. Design: Population-based prospective studies. Setting: United States. Participants: Models selected U.S. screening populations by using data from the National Health Interview Survey from 2010 to 2012. Model performance was evaluated using data from 337 388 ever-smokers in the National Institutes of Health–AARP Diet and Health Study and 72 338 ever-smokers in the CPS-II (Cancer Prevention Study II) Nutrition Survey cohort. Measurements: Model calibration (ratio of model-predicted to observed cases [expected–observed ratio]) and discrimination (area under the curve [AUC]). Results: At a 5-year risk threshold of 2.0%, the models chose U.S. screening populations ranging from 7.6 million to 26 million ever-smokers. These disagreements occurred because, in both validation cohorts, 4 models (the Bach model, PLCOM2012, LCRAT, and LCDRAT) were well-calibrated (expected–observed ratio range, 0.92 to 1.12) and had higher AUCs (range, 0.75 to 0.79) than 5 models that generally overestimated risk (expected–observed ratio range, 0.83 to 3.69) and had lower AUCs (range, 0.62 to 0.75). The 4 best-performing models also had the highest sensitivity at a fixed specificity (and vice versa) and similar discrimination at a fixed risk threshold. These models showed better agreement on size of the screening population (7.6 million to 10.9 million) and achieved consensus on 73% of persons chosen. Limitation: No consensus on risk thresholds for screening. Conclusion: The 9 lung cancer risk models chose widely differing U.S. screening populations. However, 4 models (the Bach model, PLCOM2012, LCRAT, and LCDRAT) most accurately predicted risk and performed best in selecting ever-smokers for screening. Primary Funding Source: Intramural Research Program of the National Institutes of Health/National Cancer Institute.

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When Clinical Practice Guidelines Collide: Finding a Way Forward

A Cautionary Tale of Warning Messages on Food Products: The Case of Sugar-Sweetened Beverages

Engaging Survivors of Human Trafficking: Complex Health Care Needs and Scarce Resources

Human trafficking, also known as modern-day slavery, is an egregious human rights violation associated with wide-ranging medical and mental health consequences. Because of the extensive health problems related to trafficking, health care providers play a critical role in identifying survivors and engaging them in ongoing care. Although guidelines for recognizing affected patients and a framework for developing response protocols in health care settings have been described, survivors' ongoing engagement in health care services is very challenging. High rates of disengagement, lost contact, premature termination, and attrition are common outcomes. For interventions to be effective in this marginalized population, challenges in engaging survivors in long-term therapeutic primary and mental health care must be better understood and overcome. This article uses the socioecological model of public health to identify barriers to engagement; offers evidence- and practice-based recommendations for overcoming these barriers; and proposes an interdisciplinary call to action for developing more flexible, adaptable models of care.

Identifying Patients for Whom Lung Cancer Screening Is Preference-Sensitive: A Microsimulation Study: Annals of Internal Medicine: Vol 169, No 1

Background: Many health systems are exploring how to implement low-dose computed tomography (LDCT) screening programs that are effective and patient-centered. Objective: To examine factors that influence when LDCT screening is preference-sensitive. Design: State-transition microsimulation model. Data Sources: Two large randomized trials, published decision analyses, and the SEER (Surveillance, Epidemiology, and End Results) cancer registry. Target Population: U.S.-representative sample of simulated patients meeting current U.S. Preventive Services Task Force criteria for screening eligibility. Time Horizon: Lifetime. Perspective: Individual. Intervention: LDCT screening annually for 3 years. Outcome Measures: Lifetime quality-adjusted life-year gains and reduction in lung cancer mortality. To examine the effect of preferences on net benefit, disutilities (the “degree of dislike”) quantifying the burden of screening and follow-up were varied across a likely range. The effect of varying the rate of false-positive screening results and overdiagnosis associated with screening was also examined. Results of Base-Case Analysis: Moderate differences in preferences about the downsides of LDCT screening influenced whether screening was appropriate for eligible persons with annual lung cancer risk less than 0.3% or life expectancy less than 10.5 years. For higher-risk eligible persons with longer life expectancy (roughly 50% of the study population), the benefits of LDCT screening overcame even highly negative views about screening and its downsides. Results of Sensitivity Analysis: Rates of false-positive findings and overdiagnosed lung cancer were not highly influential. Limitation: The quantitative thresholds that were identified may vary depending on the structure of the microsimulation model. Conclusion: Identifying circumstances in which LDCT screening is more versus less preference-sensitive may help clinicians personalize their screening discussions, tailoring to both preferences and clinical benefit. Primary Funding Source: None.

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