Modeling the impact of novel systemic treatments on lung cancer screening benefits.

BACKGROUND: Since low-dose computed tomography (LDCT) screening was shown to be effective in the National Lung Screening Trial (NLST), novel targeted therapies and immunotherapies for advanced lung cancer have become available. This study investigated the impact of these treatment advances on the expected benefits of LDCT screening. METHODS: A microsimulation model of LDCT screening for high-risk individuals under standard systemic treatments (chemotherapy and radiation therapy) and novel treatments (immunotherapy and targeted therapy) was used. The model assumed a reduction in advanced-stage disease consistent with the NLST, and given the stage at diagnosis, it projected survival. The disease-specific relative mortality reduction (MR) due to LDCT screening was projected in the trial setting and in a population eligible for LDCT screening under the current US Preventive Services Task Force (USPSTF) recommendations. RESULTS: The availability of novel treatments reduced the MR in the LDCT arm of the NLST from 15% to 13.5% and the number of lung cancer deaths prevented from 310 to 224 per 100,000 persons screened. Over 10 years, population LDCT screening based on USPSTF recommendations prevented 374 lung cancer deaths per 100,000 under standard treatments (13.3% MR) and 236 per 100,000 under fully adopted novel treatments (10.6% MR). The number needed to screen to avert one death over 10 years was 270 under standard treatments and 440 under novel treatments. CONCLUSIONS: The transition from standard systemic treatments to novel treatments is expected to reduce the relative and absolute mortality benefits of LDCT screening. Benefit-harm tradeoffs of LDCT screening are likely to change as novel treatments become widespread.

Estimation of Breast Cancer Overdiagnosis in a U.S. Breast Screening Cohort.

BACKGROUND: Mammography screening can lead to overdiagnosis-that is, screen-detected breast cancer that would not have caused symptoms or signs in the remaining lifetime. There is no consensus about the frequency of breast cancer overdiagnosis. OBJECTIVE: To estimate the rate of breast cancer overdiagnosis in contemporary mammography practice accounting for the detection of nonprogressive cancer. DESIGN: Bayesian inference of the natural history of breast cancer using individual screening and diagnosis records, allowing for nonprogressive preclinical cancer. Combination of fitted natural history model with life-table data to predict the rate of overdiagnosis among screen-detected cancer under biennial screening. SETTING: Breast Cancer Surveillance Consortium (BCSC) facilities. PARTICIPANTS: Women aged 50 to 74 years at first mammography screen between 2000 and 2018. MEASUREMENTS: Screening mammograms and screen-detected or interval breast cancer. RESULTS: The cohort included 35 986 women, 82 677 mammograms, and 718 breast cancer diagnoses. Among all preclinical cancer cases, 4.5% (95% uncertainty interval [UI], 0.1% to 14.8%) were estimated to be nonprogressive. In a program of biennial screening from age 50 to 74 years, 15.4% (UI, 9.4% to 26.5%) of screen-detected cancer cases were estimated to be overdiagnosed, with 6.1% (UI, 0.2% to 20.1%) due to detecting indolent preclinical cancer and 9.3% (UI, 5.5% to 13.5%) due to detecting progressive preclinical cancer in women who would have died of an unrelated cause before clinical diagnosis. LIMITATIONS: Exclusion of women with first mammography screen outside BCSC. CONCLUSION: On the basis of an authoritative U.S. population data set, the analysis projected that among biennially screened women aged 50 to 74 years, about 1 in 7 cases of screen-detected cancer is overdiagnosed. This information clarifies the risk for breast cancer overdiagnosis in contemporary screening practice and should facilitate shared and informed decision making about mammography screening. PRIMARY FUNDING SOURCE: National Cancer Institute.

Prostate cancer mortality and metastasis under different biopsy frequencies in North American active surveillance cohorts.

BACKGROUND: Active surveillance (AS) is an accepted means of managing low-risk prostate cancer. Because of the rarity of downstream events, data from existing AS cohorts cannot yet address how differences in surveillance intensity affect metastasis and mortality. This study projected the comparative benefits of different AS schedules in men diagnosed with prostate cancer who had Gleason score (GS) ≤6 disease and risk profiles similar to those in North American AS cohorts. METHODS: Times of GS upgrading were simulated based on AS data from the University of Toronto, Johns Hopkins University, the University of California at San Francisco, and the Canary Pass Active Surveillance Cohort. Times to metastasis and prostate cancer death, informed by models from the Scandinavian Prostate Cancer Group 4 trial, were projected under biopsy surveillance schedules ranging from watchful waiting to annual biopsies. Outcomes included the risk of metastasis, the risk of death, remaining life-years (LYs), and quality-adjusted LYs. RESULTS: Compared with watchful waiting, AS biopsies reduced the risk of prostate cancer metastasis and prostate cancer death at 20 years by 1.4% to 3.3% and 1.0% to 2.4%, respectively; and 5-year biopsies reduced the risk of metastasis and prostate cancer death by 1.0% to 2.4% and 0.6% to 1.6%, respectively. There was little difference between annual and 5-year biopsy schedules in terms of LYs (range of differences, 0.04-0.16 LYs) and quality-adjusted LYs (range of differences, -0.02 to 0.09 quality-adjusted LYs). CONCLUSIONS: Among men diagnosed with GS ≤6 prostate cancer, obtaining a biopsy every 3 or 4 years appears to be an acceptable alternative to more frequent biopsies. Reducing surveillance intensity for those who have a low risk of progression reduces the number of biopsies while preserving the benefit of more frequent schedules.

Radiation-Induced Breast Cancer Incidence and Mortality From Digital Mammography Screening: A Modeling Study.

BACKGROUND: Estimates of risk for radiation-induced breast cancer from mammography screening have not considered variation in dose exposure or diagnostic work-up after abnormal screening results. OBJECTIVE: To estimate distributions of radiation-induced breast cancer incidence and mortality from digital mammography screening while considering exposure from screening and diagnostic mammography and dose variation among women. DESIGN: 2 simulation-modeling approaches. SETTING: U.S. population. PATIENTS: Women aged 40 to 74 years. INTERVENTION: Annual or biennial digital mammography screening from age 40, 45, or 50 years until age 74 years. MEASUREMENTS: Lifetime breast cancer deaths averted (benefits) and radiation-induced breast cancer incidence and mortality (harms) per 100,000 women screened. RESULTS: Annual screening of 100,000 women aged 40 to 74 years was projected to induce 125 breast cancer cases (95% CI, 88 to 178) leading to 16 deaths (CI, 11 to 23), relative to 968 breast cancer deaths averted by early detection from screening. Women exposed at the 95th percentile were projected to develop 246 cases of radiation-induced breast cancer leading to 32 deaths per 100,000 women. Women with large breasts requiring extra views for complete examination (8% of population) were projected to have greater radiation-induced breast cancer risk (266 cancer cases and 35 deaths per 100,000 women) than other women (113 cancer cases and 15 deaths per 100,000 women). Biennial screening starting at age 50 years reduced risk for radiation-induced cancer 5-fold. LIMITATION: Life-years lost from radiation-induced breast cancer could not be estimated. CONCLUSION: Radiation-induced breast cancer incidence and mortality from digital mammography screening are affected by dose variability from screening, resultant diagnostic work-up, initiation age, and screening frequency. Women with large breasts may have a greater risk for radiation-induced breast cancer. PRIMARY FUNDING SOURCE: Agency for Healthcare Research and Quality, U.S. Preventive Services Task Force, National Cancer Institute.

Is the closest facility the one actually used? An assessment of travel time estimation based on mammography facilities.

BACKGROUND: Characterizing geographic access depends on a broad range of methods available to researchers and the healthcare context to which the method is applied. Globally, travel time is one frequently used measure of geographic access with known limitations associated with data availability. Specifically, due to lack of available utilization data, many travel time studies assume that patients use the closest facility. To examine this assumption, an example using mammography screening data, which is considered a geographically abundant health care service in the United States, is explored. This work makes an important methodological contribution to measuring access--which is a critical component of health care planning and equity almost everywhere. METHOD: We analyzed one mammogram from each of 646,553 women participating in the US based Breast Cancer Surveillance Consortium for years 2005-2012. We geocoded each record to street level address data in order to calculate travel time to the closest and to the actually used mammography facility. Travel time between the closest and the actual facility used was explored by woman-level and facility characteristics. RESULTS: Only 35% of women in the study population used their closest facility, but nearly three-quarters of women not using their closest facility used a facility within 5 min of the closest facility. Individuals that by-passed the closest facility tended to live in an urban core, within higher income neighborhoods, or in areas where the average travel times to work was longer. Those living in small towns or isolated rural areas had longer closer and actual median drive times. CONCLUSION: Since the majority of US women accessed a facility within a few minutes of their closest facility this suggests that distance to the closest facility may serve as an adequate proxy for utilization studies of geographically abundant services like mammography in areas where the transportation networks are well established.

A joint model for multistate disease processes and random informative observation times, with applications to electronic medical records data.

Multistate models are used to characterize individuals' natural histories through diseases with discrete states. Observational data resources based on electronic medical records pose new opportunities for studying such diseases. However, these data consist of observations of the process at discrete sampling times, which may either be pre-scheduled and non-informative, or symptom-driven and informative about an individual's underlying disease status. We have developed a novel joint observation and disease transition model for this setting. The disease process is modeled according to a latent continuous-time Markov chain; and the observation process, according to a Markov-modulated Poisson process with observation rates that depend on the individual's underlying disease status. The disease process is observed at a combination of informative and non-informative sampling times, with possible misclassification error. We demonstrate that the model is computationally tractable and devise an expectation-maximization algorithm for parameter estimation. Using simulated data, we show how estimates from our joint observation and disease transition model lead to less biased and more precise estimates of the disease rate parameters. We apply the model to a study of secondary breast cancer events, utilizing mammography and biopsy records from a sample of women with a history of primary breast cancer.

Breast cancer in female survivors of Wilms tumor: a report from the national Wilms tumor late effects study.

BACKGROUND: The standard treatment of pulmonary metastases in patients with Wilms tumor (WT) includes 12-gray radiotherapy (RT) to the entire chest. To the authors' knowledge, the risk of breast cancer (BC) in a large cohort of female survivors of WT has not previously been reported. METHODS: A total of 2492 female participants in National Wilms Tumor Studies 1 through 4 (1969-1995) were followed from age 15 years through the middle of 2013 for incident BC. The median age at the time of last contact was 27.3 years. The authors calculated cumulative risk at age 40 years (CR40), hazard ratios (HR) by Cox regression, standardized incidence ratios (SIRs) relative to US population rates, and 95% confidence intervals (95% CIs). RESULTS: The numbers of survivors with invasive BC divided by the numbers at risk were 16 of 369 (CR40, 14.8% [95% CI, 8.7-24.5]) for women who received chest RT for metastatic WT, 10 of 894 (CR40, 3.1% [95% CI, 1.3-7.41]) for those who received only abdominal RT, and 2 of 1229 (CR40, 0.3% [95% CI, 0.0-2.3]) for those who received no RT. The SIRs for these 3 groups were 27.6 (95% CI, 16.1-44.2) based on 5010 person-years (PY) of follow-up, 6.0 (95% CI, 2.9-11.0) based on 13,185 PY of follow-up, and 2.2 (95% CI, 0.3-7.8) based on 13,560 PY of follow-up, respectively. The risk was high regardless of the use of chest RT among women diagnosed with WT at age ≥10 years, with 9 of 90 women developing BC (CR40, 13.5% [95% CI, 5.6-30.6]; SIR, 23.6 [95% CI, 10.8-44.8] [PY, 1463]). CONCLUSIONS: Female survivors of WT who were treated with chest RT had a high risk of developing early BC, with nearly 15% developing invasive disease by age 40 years. Current guidelines that recommend screening only those survivors who received ≥20 Gy of RT to the chest might be reevaluated.

Learning about pain through observation: the role of pain-related fear.

Observational learning may contribute to development and maintenance of pain-related beliefs and behaviors. The current study examined whether observation of video primes could impact appraisals of potential back stressing activities, and whether this relationship was moderated by individual differences in pain-related fear. Participants viewed a video prime in which back-stressing activity was associated with pain and injury. Both before and after viewing the prime, participants provided pain and harm ratings of standardized movements drawn from the Photograph of Daily Activities Scale (PHODA). Results indicated that observational learning occurred for participants with high levels of pain-related fear but not for low fear participants. Specifically, following prime exposure, high fear participants showed elevated pain appraisals of activity images whereas low fear participants did not. High fear participants appraised the PHODA-M images as significantly more harmful regardless of prime exposure. The findings highlight individual moderators of observational learning in the context of pain.

Projecting the Impact of Multi-Cancer Early Detection on Late-Stage Incidence Using Multi-State Disease Modeling.

BACKGROUND: Downstaging-reduction in late-stage incidence-has been proposed as an endpoint in randomized trials of multi-cancer early detection (MCED) tests. How downstaging depends on test performance and follow-up has been studied for some cancers but is understudied for cancers without existing screening and for MCED tests that include these cancer types. METHODS: We develop a model for cancer natural history that can be fit to registry incidence patterns under minimal inputs and can be estimated for solid cancers without existing screening. Fitted models are combined to project downstaging in MCED trials given sensitivity for early- and late-stage cancers. We fit models for 12 cancers using incidence data from the Surveillance, Epidemiology, and End Results program and project downstaging in a simulated trial under variable preclinical latencies and test sensitivities. RESULTS: A proof-of-principle lung cancer model approximated downstaging in the National Lung Screening Trial. Given published stage-specific sensitivities for 12 cancers, we projected downstaging ranging from 21% to 43% across plausible preclinical latencies in a hypothetical 3-screen MCED trial. Late-stage incidence reductions manifest soon after screening begins. Downstaging increases with longer early-stage latency or higher early-stage test sensitivity. CONCLUSIONS: Even short-term MCED trials could produce substantial downstaging given adequate early-stage test sensitivity. IMPACT: Modeling the natural histories of cancers without existing screening facilitates analysis of novel MCED products and trial designs. The framework informs expectations of MCED impact on disease stage at diagnosis and could serve as a building block for designing trials with late-stage incidence as the primary endpoint.

Fitting and interpreting continuous-time latent Markov models for panel data.

Multistate models characterize disease processes within an individual. Clinical studies often observe the disease status of individuals at discrete time points, making exact times of transitions between disease states unknown. Such panel data pose considerable modeling challenges. Assuming the disease process progresses accordingly, a standard continuous-time Markov chain (CTMC) yields tractable likelihoods, but the assumption of exponential sojourn time distributions is typically unrealistic. More flexible semi-Markov models permit generic sojourn distributions yet yield intractable likelihoods for panel data in the presence of reversible transitions. One attractive alternative is to assume that the disease process is characterized by an underlying latent CTMC, with multiple latent states mapping to each disease state. These models retain analytic tractability due to the CTMC framework but allow for flexible, duration-dependent disease state sojourn distributions. We have developed a robust and efficient expectation-maximization algorithm in this context. Our complete data state space consists of the observed data and the underlying latent trajectory, yielding computationally efficient expectation and maximization steps. Our algorithm outperforms alternative methods measured in terms of time to convergence and robustness. We also examine the frequentist performance of latent CTMC point and interval estimates of disease process functionals based on simulated data. The performance of estimates depends on time, functional, and data-generating scenario. Finally, we illustrate the interpretive power of latent CTMC models for describing disease processes on a dataset of lung transplant patients. We hope our work will encourage wider use of these models in the biomedical setting.

Pulmonary disease after treatment for Wilms tumor: a report from the national wilms tumor long-term follow-up study.

PURPOSE: This study was undertaken to evaluate the incidence of pulmonary disease among patients treated with radiation therapy (RT) for pulmonary metastases (PM) from Wilms tumor (WT). PATIENTS AND METHODS: We reviewed records of 6,449 patients treated on National Wilms Tumor Studies-1, -2, -3, and -4 whose flow sheets or annual status reports documented one of several pulmonary conditions. Cases were fully evaluable if pulmonary function test (PFT) results were available, pulmonary fibrosis was identified on a chest radiograph or was listed as the primary or a contributing factor to death. Partially evaluable cases were those for whom PFT results could not be obtained. We evaluated the relationship between RT factors and the occurrence of pulmonary disease using hazard ratios (HRs) and cumulative incidence, treating death as a competing risk. RESULTS: Sixty-four fully evaluable and 16 partially evaluable cases of pulmonary disease were identified. The cumulative incidence of pulmonary disease at 15 years since WT diagnosis was 4.0% (95% confidence interval [CI] 2.6-5.4%) among fully evaluable and 4.8% (95% CI 3.3-6.4%) among fully and partially evaluable patients who received lung RT for PM at initial diagnosis. Rates of pulmonary disease were substantially higher among those who received lung RT for PM present at initial diagnosis or relapse compared to those who received no RT or only abdominal RT (HR 30.2, 95% CI 16.9-53.9). CONCLUSION: The risk of pulmonary disease must be considered in evaluating the risk:benefit ratio of lung RT for the management of PM from WT.

Estimating stage-specific sensitivity for cancer screening tests.

OBJECTIVES: When evaluating potential new cancer screening modalities, estimating sensitivity, especially for early-stage cases, is critical. There are methods to approximate stage-specific sensitivity in asymptomatic populations, both in the prospective (active screening) and retrospective (stored specimens) scenarios. We explored their validity via a simulation study. METHODS: We fit natural history models to lung and ovarian cancer screening data that permitted estimation of stage-specific (early/late) true sensitivity, defined as the probability subjects screened in the given stage had positive tests. We then ran simulations, using the fitted models, of the prospective and retrospective scenarios. Prospective sensitivity by stage was estimated as screen-detected divided by screen-plus interval-detected cancers, where stage is defined as stage at detection. Retrospective sensitivity by stage was estimated based on cancers detected within specified windows before clinical diagnosis with stage defined as stage at clinical diagnosis. RESULTS: Stage-specific true sensitivities estimated by the lung cancer natural history model were 47% (early) and 63% (late). Simulation results for the prospective setting gave estimated sensitivities of 81% (early) versus 62% (late). In the retrospective scenario, early/late sensitivity estimates were 35%/57% (1-year window) and 27%/49% (2-year window). In the prospective scenario, most subjects with negative early-stage screens presented as other than early-stage interval cases. Results were similar for ovarian cancer, with estimated prospective sensitivity much greater than true sensitivity for early stage, 84% versus 25%. CONCLUSIONS: Existing methods for approximating stage-specific sensitivity in both prospective and retrospective scenarios are unsatisfactory; improvements are needed before they can be considered to be reliable.

Racial disparities in prostate cancer mortality: a model-based decomposition of contributing factors.

To investigate the relative contributions of natural history and clinical interventions to racial disparities in prostate cancer mortality in the United States, we extended a model that was previously calibrated to Surveillance, Epidemiology, and End Results (SEER) incidence rates for the general population and for Black men. The extended model integrated SEER data on curative treatment frequencies and cancer-specific survival. Starting with the model for all men, we replaced up to 9 components with corresponding components for Black men, projecting age-standardized mortality rates for ages 40-84 years at each step. Based on projections in 2019, the increased frequency of developing disease, more aggressive tumor features, and worse cancer-specific survival in Black men diagnosed at local-regional and distant stages explained 38%, 34%, 22%, and 8% of the modeled disparity in mortality. Our results point to intensified screening and improved care in Black men as priority areas to achieve greater equity.

Risk of cancer versus risk of cancer diagnosis? Accounting for diagnostic bias in predictions of breast cancer risk by race and ethnicity.

OBJECTIVES: Cancer risk prediction may be subject to detection bias if utilization of screening is related to cancer risk factors. We examine detection bias when predicting breast cancer risk by race/ethnicity. METHODS: We used screening and diagnosis histories from the Breast Cancer Surveillance Consortium to estimate risk of breast cancer onset and calculated relative risk of onset and diagnosis for each racial/ethnic group compared with non-Hispanic White women. RESULTS: Of 104,073 women aged 40-54 receiving their first screening mammogram at a Breast Cancer Surveillance Consortium facility between 2000 and 2018, 10.2% (n = 10,634) identified as Asian, 10.9% (n = 11,292) as Hispanic, and 8.4% (n = 8719) as non-Hispanic Black. Hispanic and non-Hispanic Black women had slightly lower screening frequencies but biopsy rates following a positive mammogram were similar across groups. Risk of cancer diagnosis was similar for non-Hispanic Black and White women (relative risk vs non-Hispanic White = 0.90, 95% CI 0.65 to 1.14) but was lower for Asian (relative risk = 0.70, 95% CI 0.56 to 0.97) and Hispanic women (relative risk = 0.82, 95% CI 0.62 to 1.08). Relative risks of disease onset were 0.78 (95% CI 0.68 to 0.88), 0.70 (95% CI 0.59 to 0.83), and 0.95 (95% CI 0.84 to 1.09) for Asian, Hispanic, and non-Hispanic Black women, respectively. CONCLUSIONS: Racial/ethnic differences in mammography and biopsy utilization did not induce substantial detection bias; relative risks of disease onset were similar to or modestly different than relative risks of diagnosis. Asian and Hispanic women have lower risks of developing breast cancer than non-Hispanic Black and White women, who have similar risks.

Test sensitivity in a prospective cancer screening program: A critique of a common proxy measure.

The true sensitivity of a cancer screening test, defined as the frequency with which the test returns a positive result if the cancer is present, is a key indicator of diagnostic performance. Given the challenges of directly assessing test sensitivity in a prospective screening program, proxy measures for true sensitivity are frequently reported. We call one such proxy empirical sensitivity, as it is given by the observed ratio of screen-detected cancers to the sum of screen-detected and interval cancers. In the setting of the canonical three-state Markov model for progression from preclinical onset to clinical diagnosis, we formulate a mathematical relationship for how empirical sensitivity varies with the screening interval and the mean preclinical sojourn time and identify conditions under which empirical sensitivity exceeds or falls short of true sensitivity. In particular, when the inter-screening interval is short relative to the mean sojourn time, empirical sensitivity tends to exceed true sensitivity, unless true sensitivity is high. The Breast Cancer Surveillance Consortium (BCSC) has reported an estimate of 0.87 for the empirical sensitivity of digital mammography. We show that this corresponds to a true sensitivity of 0.82 under a mean sojourn time of 3.6 years estimated based on breast cancer screening trials. However, the BCSC estimate of empirical sensitivity corresponds to even lower true sensitivity under more contemporary, longer estimates of mean sojourn time. Consistently applied nomenclature that distinguishes empirical sensitivity from true sensitivity is needed to ensure that published estimates of sensitivity from prospective screening studies are properly interpreted.

Melanoma literacy among the general population of three western US states.

Melanoma is a significant cause of cancer death, despite being detectable without specialized or invasive technologies. Understanding barriers to preventive behaviors such as skin self-examination (SSE) could help to define interventions for increasing the frequency of early detection. To determine melanoma knowledge and beliefs across three high-incidence US states, 15,000 surveys were sent to a population-representative sample. We aimed to assess (1) melanoma literacy (i.e., knowledge about melanoma risks, attitudes, and preventive behaviors) and (2) self-reported SSE and its association with melanoma literacy, self-efficacy, and belief in the benefits of SSE. Of 2326 respondents, only 21.2% provided responses indicating high knowledge of melanoma, and 62.8% reported performing an SSE at any time in their lives. Only 38.3% and 7.3% reported being "fairly" or "very" confident about doing SSE, respectively. SSE performance among respondents was most strongly associated with higher melanoma knowledge, higher self-efficacy, and personal history of melanoma. Melanoma literacy among survey respondents was modest, with greater literacy associated with a higher likelihood of reported preventive behavior. This assessment establishes a baseline and provides guidance for public health campaigns designed to increase prevention and early detection of this lethal cancer.

Treatments and outcomes for end-stage renal disease following Wilms tumor.

BACKGROUND: Little is known about treatment outcomes for children who have end-stage renal disease (ESRD) after treatment for Wilms tumor (WT). METHODS: Time-to-transplant, graft failure, and survival outcomes were examined for 173 children enrolled on the National Wilms Tumor Study who developed ESRD. RESULTS: Fifty-five patients whose ESRD resulted from progressive bilateral WT (PBWT) experienced high early mortality from WT that limited their opportunity for transplant (47% at 5 years) and survival (44% at 10 years) in comparison to population controls. The 118 patients whose ESRD was due to other causes (termed "chronic kidney disease"), many of whom had WT-associated congenital anomalies, had transplant (77% at 5 years) and survival (73% at 10 years) outcomes no worse than those for population controls. Graft failure following transplant was comparable for the two groups. Minority children had twice the median time to transplant as non-Hispanic whites and twice the mortality rates, also reflecting population trends. CONCLUSIONS: In view of the continuing high mortality in patients with ESRD, and the dramatic improvement in outlook following kidney transplantation, re-evaluation of current guidelines for a 2-year delay in transplant following WT treatment may be warranted.

Cancer Modeling as Learning Experience.

Cancer modeling has become an accepted method for generating evidence about comparative effectiveness and cost-effectiveness of candidate cancer control policies across the continuum of care. Models of early detection policies require inputs concerning disease natural history and screening test performance, which are often subject to considerable uncertainty. Model validation against an external data source can increase confidence in the reliability of assumed or calibrated inputs. When a model fails to validate, this presents an opportunity to revise these inputs, thereby learning new information about disease natural history or diagnostic performance that could both enhance the model results and inform real-world practices. We discuss the conditions necessary for validly drawing conclusions about specific inputs such as diagnostic performance from model validation studies. Doing so requires being able to faithfully replicate the validation study in terms of its design and implementation and being alert to the problem of non-identifiability, which could lead to explanations for failure to validate other than those identified. See related article by Rutter et al., p. 775.

Evaluation of Prostate Cancer Screening Strategies in a Low-Resource, High-risk Population in the Bahamas.

Importance: The benefit of prostate-specific antigen screening may be greatest in high-risk populations, including men of African descent in the Caribbean. However, organized screening may not be sustainable in low- and middle-income countries. Objective: To evaluate the expected population outcomes and resource use of conservative prostate-specific antigen screening programs in the Bahamas. Design Setting and Participants: Prostate cancer incidence from GLOBOCAN and prostate-specific antigen screening data for 4300 men from the Bahamas were used to recalibrate 2 decision analytical models previously used to study prostate-specific antigen screening for Black men in the United States. Data on age and results obtained from prostate-specific antigen screening tests performed in Nassau from 2004 to 2018 and in Freeport from 2013 to 2018 were used. Data were analyzed from January 15, 2021, to March 23, 2022. Interventions: One or 2 screenings for men aged 45 to 60 years and conservative criteria for biopsy (prostate-specific antigen level >10 ng/mL) and curative treatment (Gleason score ≥8) were modeled. Categories of Gleason scores were 6 or lower, 7, and 8 or higher, with higher scores indicating higher risk of cancer progression and death. Main Outcomes and Measures: Projected numbers of tests and biopsies, prostate cancer (over)diagnoses, lives saved, and life-years gained owing to screening from 2022 to 2040. Results: In this decision analytical modeling study, screening histories from 4300 men (median age, 54 years; range, 13-101 years) tested between 2004 and 2018 at 2 sites in the Bahamas were used to inform the models. Screening once at 60 years of age was projected to involve 40 000 to 42 000 tests (range between models) and prevent 500 to 600 of 10 000 to 14 000 prostate cancer deaths. Screening at 50 and 60 years doubled the number of tests but increased lives saved by only 15% to 16%. Among onetime strategies, screening once at 60 years of age involved the fewest tests per life saved (74-84 tests) and curative treatments per life saved (1.2-2.8 treatments). Conclusions and Relevance: The findings of this decision analytical modeling study of prostate cancer screening in the Bahamas suggest that limited screening offered modest benefits that varied with screening ages and number of tests. The results can be combined with data on capacity constraints and evaluated relative to competing national public health priorities.

Risk factors for end stage renal disease in non-WT1-syndromic Wilms tumor.

PURPOSE: We assessed risk factors for end stage renal disease in patients with Wilms tumor without known WT1 related syndromes. We hypothesized that patients with characteristics suggestive of a WT1 etiology (early onset, stromal predominant histology, intralobar nephrogenic rests) would have a higher risk of end stage renal disease due to chronic renal failure. We predicted a high risk of end stage renal disease due to progressive bilateral Wilms tumor in patients with metachronous bilateral disease. MATERIALS AND METHODS: End stage renal disease was ascertained in 100 of 7,950 nonsyndromic patients enrolled in a National Wilms Tumor Study during 1969 to 2002. Risk factors were evaluated with cumulative incidence curves and proportional hazard regressions. RESULTS: The cumulative incidence of end stage renal disease due to chronic renal failure 20 years after Wilms tumor diagnosis was 0.7%. For end stage renal disease due to progressive bilateral Wilms tumor the incidence was 4.0% at 3 years after diagnosis in patients with synchronous bilateral Wilms tumor and 19.3% in those with metachronous bilateral Wilms tumor. For end stage renal disease due to chronic renal failure stromal predominant histology had a HR of 6.4 relative to mixed (95% CI 3.4, 11.9; p<0.001), intralobar rests had a HR of 5.9 relative to no rests (95% CI 2.0, 17.3; p=0.001), and Wilms tumor diagnosis at less than 24 months had a HR of 1.7 relative to 24 to 48 months and 2.8 relative to greater than 48 months (p=0.003 for trend). CONCLUSIONS: Metachronous bilateral Wilms tumor is associated with high rates of end stage renal disease due to surgery for progressive Wilms tumor. Characteristics associated with a WT1 etiology markedly increased the risk of end stage renal disease due to chronic renal failure despite the low risk in non-WT1 syndromic cases overall.