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In cohort studies, it can be infeasible to collect specimens on an entire cohort. For example, to estimate sensitivity of multiple multi-cancer detection (MCD) assays, we desire an extra 80 mL of cell-free DNA (cfDNA) blood, but this much extra blood is too expensive for us to collect on everyone. We propose a novel epidemiologic study design that efficiently oversamples those at highest baseline disease risk from whom to collect specimens, to increase the number of future cases with cfDNA blood collection. The variance reduction ratio from our risk-based subsample versus a simple random (sub)sample (SRS) depends primarily on the ratio of risk model sensitivity to the fraction of the cohort selected for specimen collection subject to constraining the risk model specificity. In a simulation where we chose 34% of the Prostate, Lung, Colorectal, and Ovarian Screening Trial cohort at highest risk of lung cancer for cfDNA blood collection, we could enrich the number of lung cancers 2.42-fold. The standard deviation of lung-cancer MCD sensitivity was 31%-33% reduced versus SRS. Risk-based collection of specimens on a subsample of the cohort could be a feasible and efficient approach to collecting extra specimens for molecular epidemiology. [ABSTRACT FROM AUTHOR]

Accurate cancer risk estimation is crucial to clinical decision-making, such as identifying high-risk people for screening. However, most existing cancer risk models incorporate data from epidemiologic studies, which usually cannot represent the target population. While population-based health surveys are ideal for making inference to the target population, they typically do not collect time-to-cancer incidence data. Instead, time-to-cancer specific mortality is often readily available on surveys via linkage to vital statistics. We develop calibrated pseudoweighting methods that integrate individual-level data from a cohort and a survey, and summary statistics of cancer incidence from national cancer registries. By leveraging individual-level cancer mortality data in the survey, the proposed methods impute time-to-cancer incidence for survey sample individuals and use survey calibration with auxiliary variables of influence functions generated from Cox regression to improve robustness and efficiency of the inverse-propensity pseudoweighting method in estimating pure risks. We develop a lung cancer incidence pure risk model from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial using our proposed methods by integrating data from the National Health Interview Survey and cancer registries. [ABSTRACT FROM AUTHOR]

Background The US Preventive Services Task Force (USPSTF) recommend lung cancer screening for individuals aged 50-80 years with at least 20 pack-years and no more than 15 quit-years, but uptake is low. The risk and benefit profiles of screening attendees are unknown; consequently, the impact and lost opportunity of ongoing lung cancer screening in the United States remains unclear. Methods We estimated lung cancer death risk (using the Lung Cancer Death Risk Assessment Tool) and life gained from screening (using the Life Years Gained From Screening-Computed Tomography model) for individuals aged 50-79 years who ever-smoked in the US representative 2022 Behavioral Risk Factor Surveillance System. We compared lung cancer death risk and life gained among USPSTF-eligible individuals by screening status (self-reported screened vs not screened in past year) and estimated the number of lung cancer deaths averted and life-years gained under current screening levels and if everyone eligible was screened. Results USPSTF eligibility was 33.7% (95% confidence interval [CI] = 33.1% to 34.4%), of whom 17.9% (95% CI = 17.0% to 18.8%) self-reported screening. Screening uptake increased with increasing lung cancer death risk quintile (Q1 = 5.2%, 95% CI = 3.0% to 8.8%; Q5 = 21.8%, 95% CI = 20.3% to 23.3%) and life-gain from screening quintile (Q1 = 6.2%, 95% CI = 3.8% to 9.9%; Q5 = 20.8%, 95% CI = 19.5% to 22.2%). Screened individuals had higher lung cancer death risk (risk ratio [RR] = 1.35, 95% CI = 1.26 to 1.46) and life-years gained (RR = 1.19, 95% CI = 1.12 to 1.25) than unscreened individuals. Currently, screening averts 19 306 lung cancer deaths and gains 237 564 life-years; screening everyone eligible would additionally avert 56 956 lung cancer deaths and gain 751 850 life-years. Two-thirds of USPSTF lung-eligible women were up to date with breast cancer screening, but only 17.3% attended lung screening in the past year. Conclusions Eligible screening attendees had higher lung cancer death risk and benefit from screening. Higher rates of screening could substantially increase the number of lung cancer deaths prevented. [ABSTRACT FROM AUTHOR]

Shared decision making (SDM) between health care professionals and patients is essential to help patients make well informed choices about lung cancer screening (LCS). Patients who participate in SDM have greater LCS knowledge, reduced decisional conflict, and improved adherence to annual screening compared with patients who do not participate in SDM. SDM tools are acceptable to patients and clinicians. The importance of SDM in LCS is emphasized in recommendations from professional organizations and highlighted as a priority in the 2022 President's Cancer Panel Report. The updated 2022 national coverage determination from the Centers for Medicare & Medicaid Services reaffirms the value of SDM in offering LCS to eligible beneficiaries. The Shared Decision‐Making Task Group of the American Cancer Society National Lung Cancer Roundtable undertook a group consensus process to identify priorities for research and implementation related to SDM for LCS and then evaluated current knowledge in these areas. Priority areas included: (1) developing feasible, adaptable SDM training programs for health care professionals; (2) understanding the impact of alternative health system LCS models on SDM practice and outcomes; (3) developing and evaluating new patient decision aids for use with diverse populations and in varied settings; (4) offering conceptual clarity about what constitutes a high‐quality decision and developing appropriate quality measures; and (5) studying the use of prediction‐augmented screening to support SDM in practice. Gaps in current research in all areas were observed. The authors conclude with a research and implementation agenda to advance the quality and implementation of SDM for persons who might benefit from LCS. Patients who participate in shared decision making about lung cancer screening have greater knowledge, lower decisional conflict, and better adherence to screening compared with patients who do not participate. The American Cancer Society Shared Decision‐Making Task Group identified five priority areas for research and to improve the implementation of lung cancer screening in the United States. [ABSTRACT FROM AUTHOR]

Mismeasured time to event data used as a predictor in risk prediction models will lead to inaccurate predictions. This arises in the context of self-reported family history, a time to event predictor often measured with error, used in Mendelian risk prediction models. Using validation data, we propose a method to adjust for this type of error. We estimate the measurement error process using a nonparametric smoothed Kaplan-Meier estimator, and use Monte Carlo integration to implement the adjustment. We apply our method to simulated data in the context of both Mendelian and multivariate survival prediction models. Simulations are evaluated using measures of mean squared error of prediction (MSEP), area under the response operating characteristics curve (ROC-AUC), and the ratio of observed to expected number of events. These results show that our method mitigates the effects of measurement error mainly by improving calibration and total accuracy. We illustrate our method in the context of Mendelian risk prediction models focusing on misreporting of breast cancer, fitting the measurement error model on data from the University of California at Irvine, and applying our method to counselees from the Cancer Genetics Network. We show that our method improves overall calibration, especially in low risk deciles.

Background Cancer screening trials have required large sample sizes and long time-horizons to demonstrate cancer mortality reductions, the primary goal of cancer screening. We examine assumptions and potential power gains from exploiting information from testing control-arm specimens, which we call the "intended effect" (IE) analysis that we explain in detail herein. The IE analysis is particularly suited to tests that can be conducted on stored specimens in the control arm, such as stored blood for multicancer detection (MCD) tests. Methods We simulated hypothetical MCD screening trials to compare power and sample size for the standard vs IE analysis. Under two assumptions that we detail herein, we projected the IE analysis for 3 existing screening trials (National Lung Screening Trial [NLST], Minnesota Colon Cancer Control Study [MINN-FOBT-A], and Prostate, Lung, Colorectal, Ovarian Cancer Screening Trial—colorectal component [PLCO-CRC]). Results Compared with the standard analysis for the 3 existing trials, the IE design could have reduced cancer-specific mortality P values 6-fold (NLST), 33-fold (MINN-FOBT-A), or 260 000-fold (PLCO-CRC) or, alternately, reduced sample size (90% power) by 25% (NLST), 47% (MINN-FOBT-A), or 63% (PLCO-CRC). For potential MCD trial designs requiring 100 000 subjects per arm to achieve 90% power for multicancer mortality for the standard analysis, the IE analysis achieves 90% power for only 37 500-50 000 per arm, depending on assumptions concerning control-arm test-positives. Conclusions Testing stored specimens in the control arm of screening trials to conduct the IE analysis could substantially increase power to reduce sample size or accelerate trials and could provide particularly strong power gains for MCD tests. [ABSTRACT FROM AUTHOR]

Conventional lung cancer screening eligibility criteria exclude many high-benefit persons. This study evaluated whether alternative simple criteria could better identify persons who would derive high benefit fromlung cancer screening. Visual Abstract. The Development and Performance of Alternative Criteria for Lung Cancer Screening: Conventional lung cancer screening eligibility criteria exclude many high-benefit persons. This study evaluated whether alternative simple criteria could better identify persons who would derive high benefit fromlung cancer screening. Background: The recommendation for lung cancer screening (LCS) developed by the U.S. Preventive Services Task Force (USPSTF) may exclude some high-benefit people. Objective: To determine whether alternative criteria can identify these high-benefit people. Design: Model-based projections. Setting: United States. Participants: People from the 1997–2014 National Health Interview Survey (NHIS) to develop alternative criteria using fast-and-frugal tree algorithms and from the 2014–2018 NHIS and the 2022 Behavioral Risk Factor Surveillance System for comparisons of USPSTF criteria versus alternative criteria. Measurements: Life-years gained from LCS were estimated using the life-years gained from screening computed tomography (LYFS-CT) model. "High-benefit" was defined as gaining an average of at least 16.2 days of life from 3 annual screenings, which reflects high lung cancer risk and substantial life gains if lung cancer is detected by screening. Results: The final alternative criteria were 1) people who smoked any amount each year for at least 40 years, or 2) people aged 60 to 80 years with at least 40 pack-years of smoking. The USPSTF and alternative criteria selected similar numbers of people for LCS. Compared with the USPSTF criteria, the alternative criteria had higher sensitivity (91% vs. 78%; P < 0.001) and specificity (86% vs. 84%; P < 0.001) for identifying high-benefit people. For racial and ethnic minorities, the alternative criteria provided greater gains in sensitivity than the USPSTF criteria (Black: 83% vs. 56% [ P < 0.001]; Hispanic: 95% vs. 73% [ P  = 0.086]; Asian: 94% vs. 68% [ P  = 0.171]) at similar specificity. The alternative criteria identify high-risk, high-benefit groups excluded by the USPSTF criteria (those with a smoking duration of ≥40 years but <20 pack-years and a quit history of >15 years), many of whom are members of racial and ethnic minorities. Limitation: The results were based on model projections. Conclusion: These results suggest that simple alternative LCS criteria can identify substantially more high-benefit people, especially in some racial and ethnic groups. Primary Funding Source: U.S. Department of Veterans Affairs Lung Precision Oncology Program. [ABSTRACT FROM AUTHOR]

The Costa Rica Vaccine Trial (CVT) was a randomized clinical trial conducted between 2004 and 2010, which randomized 7466 women aged 18 to 25 to receive the bivalent HPV-16/18 vaccine or control Hepatitis-A vaccine. Participants were followed for 4 years with cross-over vaccination at the study end. In 2010 the long term follow-up (LTFU) study was initiated to evaluate the 10-year impact of HPV-16/18 vaccination, determinants of the immune response, and HPV natural history in a vaccinated population. Herein, the rationale, design and methods of the LTFU study are described, which actively follows CVT participants in the HPV-arm 6 additional years at biennial intervals (3 additional study visits for 10 years of total follow-up), or more often if clinically indicated. According to the initial commitment, women in the Hepatitis-A arm were offered HPV vaccination at cross-over; they were followed 2 additional years and exited from the study. 92% of eligible CVT women accepted participation in LTFU. To provide underlying rates of HPV acquisition and cervical disease among unvaccinated women to compare with the HPV-arm during LTFU, a new unvaccinated control group (UCG) of women who are beyond the age generally recommended for routine vaccination was enrolled, and will be followed by cervical cancer screening over 6 years. To form the UCG, 5000 women were selected from a local census, of whom 2836 women (61% of eligible women) agreed to participate. Over 90% of participants complied with an interview, blood and cervical specimen collection. Evaluation of comparability between the original (Hepatitis-A arm of CVT) and new (UCG) control groups showed that womens characteristics, as well as their predicted future risk for cervical HPV acquisition, were similar, thus validating use of the UCG. LTFU is poised to comprehensively address many important questions related to long-term effects of prophylactic HPV vaccines.

Multicancer detection (MCD) tests use a single, easily obtainable biospecimen, such as blood, to screen for more than one cancer concurrently. MCD tests can potentially be used to improve early cancer detection, including cancers that currently lack effective screening methods. However, these tests have unknown and unquantified benefits and harms. MCD tests differ from conventional cancer screening tests in that the organ responsible for a positive test is unknown, and a broad diagnostic workup may be necessary to confirm the location and type of underlying cancer. Among two prospective studies involving greater than 16,000 individuals, MCD tests identified those who had some cancers without currently recommended screening tests, including pancreas, ovary, liver, uterus, small intestine, oropharyngeal, bone, thyroid, and hematologic malignancies, at early stages. Reported MCD test sensitivities range from 27% to 95% but differ by organ and are lower for early stage cancers, for which treatment toxicity would be lowest and the potential for cure might be highest. False reassurance from a negative MCD result may reduce screening adherence, risking a loss in proven public health benefits from standard‐of‐care screening. Prospective clinical trials are needed to address uncertainties about MCD accuracy to detect different cancers in asymptomatic individuals, whether these tests can detect cancer sufficiently early for effective treatment and mortality reduction, the degree to which these tests may contribute to cancer overdiagnosis and overtreatment, whether MCD tests work equally well across all populations, and the appropriate diagnostic evaluation and follow‐up for patients with a positive test. [ABSTRACT FROM AUTHOR]

For Hispanic women, the Breast Cancer Risk Assessment Tool (BCRAT; "Gail Model") combines 1990-1996 breast cancer incidence for Hispanic women with relative risks for breast cancer risk factors from non-Hispanic white (NHW) women. BCRAT risk projections have never been comprehensively evaluated for Hispanic women. We compared the relative risks and calibration of BCRAT risk projections for 6,353 Hispanic to 128,976 NHW postmenopausal participants aged 50 and older in the Women's Health Initiative (WHI). Calibration was assessed by the ratio of the number of breast cancers observed with that expected by the BCRAT (O/E). We re-evaluated calibration for an updated BCRAT that combined BCRAT relative risks with 1993-2007 breast cancer incidence that is contemporaneous with the WHI. Cox regression was used to estimate relative risks. Discriminatory accuracy was assessed using the concordance statistic (AUC). In the WHI Main Study, the BCRAT underestimated the number of breast cancers by 18% in both Hispanics (O/E = 1.18, P = 0.06) and NHWs (O/E = 1.18, P < 0.001). Updating the BCRAT improved calibration for Hispanic women (O/E = 1.08, P = 0.4) and NHW women (O/E = 0.98, P = 0.2). For Hispanic women, relative risks for number of breast biopsies (1.71 vs. 1.27, P = 0.03) and age at first birth (0.97 vs. 1.24, P = 0.02) differed between the WHI and BCRAT. The AUC was higher for Hispanic women than NHW women (0.63 vs. 0.58, P = 0.03). Updating the BCRAT with contemporaneous breast cancer incidence rates improved calibration in the WHI. The modest discriminatory accuracy of the BCRAT for Hispanic women might improve by using risk factor relative risks specific to Hispanic women.

Representative risk estimation is fundamental to clinical decision-making. However, risks are often estimated from non-representative epidemiologic studies, which usually under-represent minorities. Model-based methods use population registries to improve external validity of risk estimation but assume hazard ratios are generalisable from samples to the target finite population. 'Pseudoweighting' methods improve representativeness of studies by using an external probability-based survey as the reference, but the resulting estimators can be biased due to propensity model misspecification and inefficient due to highly variable pseudoweights or small sample sizes of minorities in the cohort and/or survey. We propose a two-step pseudoweighting procedure that post-stratifies the event rates among age/race/sex strata in the pseudoweighted cohort to the population rates, to produce efficient and robust pure risk estimation (i.e. a cause-specific absolute risk in the absence of competing events). For developing an all-cause mortality risk model representative for the USA, our findings suggest that hazard ratios for minorities are not generalisable, and that surveys can have inadequate numbers of events for minorities. Post-stratification on event rates is crucial for obtaining reliable risk estimation for minority subgroups. [ABSTRACT FROM AUTHOR]

Background: Personal history of cancer is an independent risk factor for lung cancer but is omitted from existing lung cancer screening eligibility criteria. In this study, we assess the lung cancer risk among cancer survivors and discuss potential implications for screening. Methods: This was a retrospective, secondary analysis of data from the Surveillance, Epidemiology and End Results (SEER) registry and the MD Anderson Cancer Center (MDACC). We estimated the standardized incidence ratios (SIRs) for lung cancer by site of first primary cancer using data from SEER. We assessed the lung cancer risk among head and neck cancer survivors from MDACC using cumulative incidence and compared the risk ratios (RR) by individuals' screening eligibility status. Results: Other than first primary lung cancer (SIR: 5.10, 95% CI: 5.01–5.18), cancer survivors in SEER with personal history of head and neck cancer (SIR: 3.71, 95% CI: 3.63–3.80) had the highest risk of developing second primary lung cancer, followed by bladder (SIR: 1.86, 95% CI: 1.81–1.90) and esophageal cancers (SIR: 1.78, 95% CI: 1.61–1.96). Head and neck cancer survivors had higher risk to develop lung cancer compared to the National Lung Screening Trial's subjects, (781 vs. 572 per 100,000 person‐years, respectively). Head and neck cancer survivors ineligible for lung cancer screening seen at MDACC had significantly higher lung cancer risk than head and neck cancer survivors from SEER (RR: 1.9, p < 0.001). Conclusion: Personal history of cancer, primarily head and neck cancer, is an independent risk factor for lung cancer and may be considered as an eligibility criterion in future lung cancer screening recommendations. [ABSTRACT FROM AUTHOR]

Background: This report quantifies counteracting effects of quit‐years and concomitant aging on lung cancer risk, especially on exceeding 15 quit‐years, when the US Preventive Services Task Force (USPSTF) recommends curtailing lung‐cancer screening. Methods: Cox models were fitted to estimate absolute lung cancer risk among Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO) and National Lung Screening Trial (NLST) participants who ever smoked. Absolute lung cancer risk and gainable years of life from screening for individuals aged 50 to 80 in the US‐representative National Health Interview Survey (NHIS) 2015–2018 who ever smoked were projected. Relaxing USPSTF recommendations to 20/25/30 quit‐years versus augmenting USPSTF criteria with individuals whose estimated gain in life expectancy from screening exceeded 16.2 days according to the Life Years From Screening‐CT (LYFS‐CT) prediction model was compared. Results: Absolute lung cancer risk increased by 8.7%/year (95% CI, 7.7%–9.7%; p <.001) as individuals aged beyond 15 quit‐years in the PLCO, with similar results in NHIS and NLST. For example, mean 5‐year lung cancer risk for those aged 65 years with 15 quit‐years = 1.47% (95% CI, 1.35%–1.59%) versus 1.76% (95% CI, 1.62%–1.90%) for those aged 70 years with 20 quit‐years in the PLCO. Removing the quit‐year criterion would make 4.9 million more people eligible and increase the proportion of preventable lung cancer deaths prevented (sensitivity) from 63.7% to 74.2%. Alternatively, augmentation using LYFS‐CT would make 1.7 million more people eligible while increasing the lung cancer death sensitivity to 74.0%. Conclusions: Because of aging, absolute lung cancer risk increases beyond 15 quit‐years, which does not support exemption from screening or curtailing screening once it has been initiated. Compared with relaxing the USPSTF quit‐year criterion, augmentation using LYFS‐CT could prevent most of the deaths at substantially superior efficiency, while also preventing deaths among individuals who currently smoke with low intensity or long duration. In two cohorts, lung cancer risk increased with time since quitting smoking because of concomitant aging, calling into question the US Preventive Services Task Force (USPSTF) lung cancer screening guidelines which restrict screening to individuals with ≤15 quit‐years. Augmenting USPSTF eligibility criteria with individuals estimated to gain the most days of life based on the Life Years From Screening‐CT model of benefit from attending lung cancer screening could prevent lung cancer deaths more efficiently and fairly than relaxing the quit‐year criteria. [ABSTRACT FROM AUTHOR]