Two-phase stratified sampling and analysis for predicting binary outcomes.

The two-phase study design is a cost-efficient sampling strategy when certain data elements are expensive and, thus, can only be collected on a sub-sample of subjects. To date guidance on how best to allocate resources within the design has assumed that primary interest lies in estimating association parameters. When primary interest lies in the development and evaluation of a risk prediction tool, however, such guidance may, in fact, be detrimental. To resolve this, we propose a novel strategy for resource allocation based on oversampling cases and subjects who have more extreme risk estimates according to a preliminary model developed using fully observed predictors. Key to the proposed strategy is that it focuses on enhancing efficiency regarding estimation of measures of predictive accuracy, rather than on efficiency regarding association parameters which is the standard paradigm. Towards valid estimation and inference for accuracy measures using the resultant data, we extend an existing semiparametric maximum likelihood ethod for estimating odds ratio association parameters to accommodate the biased sampling scheme and data incompleteness. Motivated by our sampling design, we additionally propose a general post-stratification scheme for analyzing general two-phase data for estimating predictive accuracy measures. Through theoretical calculations and simulation studies, we show that the proposed sampling strategy and post-stratification scheme achieve the promised efficiency improvement. Finally, we apply the proposed methods to develop and evaluate a preliminary model for predicting the risk of hospital readmission after cardiac surgery using data from the Pennsylvania Health Care Cost Containment Council.

Predicting Risk of Colorectal Cancer After Adenoma Removal in a Large Community-Based Setting.

INTRODUCTION: Colonoscopy surveillance guidelines categorize individuals as high or low risk for future colorectal cancer (CRC) based primarily on their prior polyp characteristics, but this approach is imprecise, and consideration of other risk factors may improve postpolypectomy risk stratification. METHODS: Among patients who underwent a baseline colonoscopy with removal of a conventional adenoma in 2004-2016, we compared the performance for postpolypectomy CRC risk prediction (through 2020) of a comprehensive model featuring patient age, diabetes diagnosis, and baseline colonoscopy indication and prior polyp findings (i.e., adenoma with advanced histology, polyp size ≥10 mm, and sessile serrated adenoma or traditional serrated adenoma) with a polyp model featuring only polyp findings. Models were developed using Cox regression. Performance was assessed using area under the receiver operating characteristic curve (AUC) and calibration by the Hosmer-Lemeshow goodness-of-fit test. RESULTS: Among 95,001 patients randomly divided 70:30 into model development (n = 66,500) and internal validation cohorts (n = 28,501), 495 CRC were subsequently diagnosed; 354 in the development cohort and 141 in the validation cohort. Models demonstrated adequate calibration, and the comprehensive model demonstrated superior predictive performance to the polyp model in the development cohort (AUC 0.71, 95% confidence interval [CI] 0.68-0.74 vs AUC 0.61, 95% CI 0.58-0.64, respectively) and validation cohort (AUC 0.70, 95% CI 0.65-0.75 vs AUC 0.62, 95% CI 0.57-0.67, respectively). DISCUSSION: A comprehensive CRC risk prediction model featuring patient age, diabetes diagnosis, and baseline colonoscopy indication and polyp findings was more accurate at predicting postpolypectomy CRC diagnosis than a model based on polyp findings alone.

Long-Term Outcomes in Patients Using Protocol-Directed Active Surveillance for Prostate Cancer.

Importance: Outcomes from protocol-directed active surveillance for favorable-risk prostate cancers are needed to support decision-making. Objective: To characterize the long-term oncological outcomes of patients receiving active surveillance in a multicenter, protocol-directed cohort. Design, Setting, and Participants: The Canary Prostate Active Surveillance Study (PASS) is a prospective cohort study initiated in 2008. A cohort of 2155 men with favorable-risk prostate cancer and no prior treatment were enrolled at 10 North American centers through August 2022. Exposure: Active surveillance for prostate cancer. Main Outcomes and Measures: Cumulative incidence of biopsy grade reclassification, treatment, metastasis, prostate cancer mortality, overall mortality, and recurrence after treatment in patients treated after the first or subsequent surveillance biopsies. Results: Among 2155 patients with localized prostate cancer, the median follow-up was 7.2 years, median age was 63 years, 83% were White, 7% were Black, 90% were diagnosed with grade group 1 cancer, and median prostate-specific antigen (PSA) was 5.2 ng/mL. Ten years after diagnosis, the incidence of biopsy grade reclassification and treatment were 43% (95% CI, 40%-45%) and 49% (95% CI, 47%-52%), respectively. There were 425 and 396 patients treated after confirmatory or subsequent surveillance biopsies (median of 1.5 and 4.6 years after diagnosis, respectively) and the 5-year rates of recurrence were 11% (95% CI, 7%-15%) and 8% (95% CI, 5%-11%), respectively. Progression to metastatic cancer occurred in 21 participants and there were 3 prostate cancer-related deaths. The estimated rates of metastasis or prostate cancer-specific mortality at 10 years after diagnosis were 1.4% (95% CI, 0.7%-2%) and 0.1% (95% CI, 0%-0.4%), respectively; overall mortality in the same time period was 5.1% (95% CI, 3.8%-6.4%). Conclusions and Relevance: In this study, 10 years after diagnosis, 49% of men remained free of progression or treatment, less than 2% developed metastatic disease, and less than 1% died of their disease. Later progression and treatment during surveillance were not associated with worse outcomes. These results demonstrate active surveillance as an effective management strategy for patients diagnosed with favorable-risk prostate cancer.

Dietary Patterns and Risk of Gleason Grade Progression among Men on Active Surveillance for Prostate Cancer: Results from the Canary Prostate Active Surveillance Study.

Modifiable lifestyle factors, such as following a healthy dietary pattern may delay or prevent prostate cancer (PCa) progression. However, few studies have evaluated whether following specific dietary patterns after PCa diagnosis impacts risk of disease progression among men with localized PCa managed by active surveillance (AS). 564 men enrolled in the Canary Prostate Active Surveillance Study, a protocol-driven AS study utilizing a pre-specified prostate-specific antigen monitoring and surveillance biopsy regimen, completed a food frequency questionnaire (FFQ) at enrollment and had ≥ 1 surveillance biopsy during follow-up. FFQs were used to evaluate adherence to the Dietary Guidelines for Americans (Healthy Eating index (HEI))-2015, alternative Mediterranean Diet (aMED), and Dietary Approaches to Stop Hypertension (DASH) dietary patterns. Multivariable-adjusted hazards ratios (HRs) and 95% confidence intervals were estimated using Cox proportional hazards models. During a median follow-up of 7.8 years, 237 men experienced an increase in Gleason score on subsequent biopsy (grade reclassification). Higher HEI-2015, aMED or DASH diet scores after diagnosis were not associated with significant reductions in the risk of grade reclassification during AS. However, these dietary patterns have well-established protective effects on chronic diseases and mortality and remain a prudent choice for men with prostate cancer managed by AS.

Constructing time-invariant dynamic surveillance rules for optimal monitoring schedules.

Dynamic surveillance rules (DSRs) are sequential surveillance decision rules informing monitoring schedules in clinical practice, which can adapt over time according to a patient's evolving characteristics. In many clinical applications, it is desirable to identify and implement optimal time-invariant DSRs, where the parameters indexing the decision rules are shared across different decision points. We propose a new criterion for DSRs that accounts for benefit-cost tradeoff during the course of disease surveillance. We develop two methods to estimate the time-invariant DSRs optimizing the proposed criterion, and establish asymptotic properties for the estimated parameters of biomarkers indexing the DSRs. The first approach estimates the optimal decision rules for each individual at every stage via regression modeling, and then estimates the time-invariant DSRs via a classification procedure with the estimated time-varying decision rules as the response. The second approach proceeds by optimizing a relaxation of the empirical objective, where a surrogate function is utilized to facilitate computation. Extensive simulation studies are conducted to demonstrate the superior performances of the proposed methods. The methods are further applied to the Canary Prostate Active Surveillance Study (PASS).

Evaluation of Harms Reporting in U.S. Cancer Screening Guidelines.

BACKGROUND: Cancer screening should be recommended only when the balance between benefits and harms is favorable. This review evaluated how U.S. cancer screening guidelines reported harms, within and across organ-specific processes to screen for cancer. OBJECTIVE: To describe current reporting practices and identify opportunities for improvement. DESIGN: Review of guidelines. SETTING: United States. PATIENTS: Patients eligible for screening for breast, cervical, colorectal, lung, or prostate cancer according to U.S. guidelines. MEASUREMENTS: Information was abstracted on reporting of patient-level harms associated with screening, diagnostic follow-up, and treatment. The authors classified harms reporting as not mentioned, conceptual, qualitative, or quantitative and noted whether literature was cited when harms were described. Frequency of harms reporting was summarized by organ type. RESULTS: Harms reporting was inconsistent across organ types and at each step of the cancer screening process. Guidelines did not report all harms for any specific organ type or for any category of harm across organ types. The most complete harms reporting was for prostate cancer screening guidelines and the least complete for colorectal cancer screening guidelines. Conceptualization of harms and use of quantitative evidence also differed by organ type. LIMITATIONS: This review considers only patient-level harms. The authors did not verify accuracy of harms information presented in the guidelines. CONCLUSION: The review identified opportunities for improving conceptualization, assessment, and reporting of screening process-related harms in guidelines. Future work should consider nuances associated with each organ-specific process to screen for cancer, including which harms are most salient and where evidence gaps exist, and explicitly explore how to optimally weigh available evidence in determining net screening benefit. Improved harms reporting could aid informed decision making, ultimately improving cancer screening delivery. PRIMARY FUNDING SOURCE: National Cancer Institute.

Treatment in the absence of disease reclassification among men on active surveillance for prostate cancer.

BACKGROUND: Maintaining men on active surveillance for prostate cancer can be challenging. Although most men who eventually undergo treatment have experienced clinical progression, a smaller subset elects treatment in the absence of disease reclassification. This study sought to understand factors associated with treatment in a large, contemporary, prospective cohort. METHODS: This study identified 1789 men in the Canary Prostate Cancer Active Surveillance Study cohort enrolled as of 2020 with a median follow-up of 5.6 years. Clinical and demographic data as well as information on patient-reported quality of life and urinary symptoms were used in multivariable Cox proportional hazards regression models to identify factors associated with the time to treatment RESULTS: Within 4 years of their diagnosis, 33% of men (95% confidence interval [CI], 30%-35%) underwent treatment, and 10% (95% CI, 9%-12%) were treated in the absence of reclassification. The most significant factor associated with any treatment was an increasing Gleason grade group (adjusted hazard ratio [aHR], 14.5; 95% CI, 11.7-17.9). Urinary quality-of-life scores were associated with treatment without reclassification (aHR comparing "mostly dissatisfied/terrible" with "pleased/mixed," 2.65; 95% CI, 1.54-4.59). In a subset analysis (n = 692), married men, compared with single men, were more likely to undergo treatment in the absence of reclassification (aHR, 2.63; 95% CI, 1.04-6.66). CONCLUSIONS: A substantial number of men with prostate cancer undergo treatment in the absence of clinical changes in their cancers, and quality-of-life changes and marital status may be important factors in these decisions. LAY SUMMARY: This analysis of men on active surveillance for prostate cancer shows that approximately 1 in 10 men will decide to be treated within 4 years of their diagnosis even if their cancer is stable. These choices may be related in part to quality-or-life or spousal concerns.

Evaluating the Outcomes of Active Surveillance in Grade Group 2 Prostate Cancer: Prospective Results from the Canary PASS Cohort.

PURPOSE: Active surveillance (AS) for grade group (GG) 2 patients is not yet well defined. We sought to compare clinical outcomes of men with GG1 and GG2 prostate cancer undergoing AS in a large prospective North American cohort. MATERIALS AND METHODS: Participants were prospectively enrolled in an AS study with protocol-directed followup at 10 centers in the U.S. and Canada. We evaluated time from diagnosis to biopsy grade reclassification and time to treatment. In men treated after initial surveillance, adverse pathology and recurrence were also analyzed. RESULTS: At diagnosis, 154 (9%) had GG2 and 1,574 (91%) had GG1. Five-year reclassification rates were similar between GG2 and GG1 (30% vs 37%, p=0.11). However, more patients with GG2 were treated at 5 years (58% vs 34%, p <0.001) and GG at diagnosis was associated with time to treatment (HR=1.41; p=0.01). Treatment rates were similar in patients who reclassified during AS, but in patients who did not reclassify, those diagnosed with GG2 underwent definitive treatment more often than GG1 (5-year treatment rates 52% and 12%, p <0.0001). In participants who underwent radical prostatectomy after initial surveillance, the adjusted risk of adverse pathology was similar (HR=1.26; p=0.4). Biochemical recurrence within 3 years of treatment for GG2 and GG1 patients was 6% for both groups. CONCLUSIONS: In patients on AS, the rate of definitive treatment is higher after an initial diagnosis of GG2 than GG1. Adverse pathology after radical prostatectomy and short-term biochemical recurrence after definitive treatment were similar between GG2 and GG1.

Impact of Prostate Health Index Results for Prediction of Biopsy Grade Reclassification During Active Surveillance.

PURPOSE: We assessed whether Prostate Health Index results improve prediction of grade reclassification for men on active surveillance. METHODS AND MATERIALS: We identified men in Canary Prostate Active Surveillance Study with Grade Group 1 cancer. Outcome was grade reclassification to Grade Group 2+ cancer. We considered decision rules to maximize specificity with sensitivity set at 95%. We derived rules based on clinical data (R1) vs clinical data+Prostate Health Index (R3). We considered an "or"-logic rule combining clinical score and Prostate Health Index (R4), and a "2-step" rule using clinical data followed by risk stratification based on Prostate Health Index (R2). Rules were applied to a validation set, where values of R2-R4 vs R1 for specificity and sensitivity were evaluated. RESULTS: We included 1,532 biopsies (n = 610 discovery; n = 922 validation) among 1,142 men. Grade reclassification was seen in 27% of biopsies (23% discovery, 29% validation). Among the discovery set, at 95% sensitivity, R2 yielded highest specificity at 27% vs 17% for R1. In the validation set, R3 had best performance vs R1 with Δsensitivity = -4% and Δspecificity = +6%. There was slight improvement for R3 vs R1 for confirmatory biopsy (AUC 0.745 vs R1 0.724, ΔAUC 0.021, 95% CI 0.002-0.041) but not for subsequent biopsies (ΔAUC -0.012, 95% CI -0.031-0.006). R3 did not have better discrimination vs R1 among the biopsy cohort overall (ΔAUC 0.007, 95% CI -0.007-0.020). CONCLUSIONS: Among active surveillance patients, using Prostate Health Index with clinical data modestly improved prediction of grade reclassification on confirmatory biopsy and did not improve prediction on subsequent biopsies.

Associations between Genetic Variants and Blood Biomarkers of One-Carbon Metabolism in Postmenopausal Women from the Women's Health Initiative Observational Study.

BACKGROUND: Genetic variation in one-carbon metabolism may affect nutrient concentrations and biological functions. However, data on genetic variants associated with blood biomarkers of one-carbon metabolism in US postmenopausal women are limited, and whether these associations were affected by the nationwide folic acid (FA) fortification program is unclear. OBJECTIVES: We investigated associations between genetic variants and biomarkers of one-carbon metabolism using data from the Women's Health Initiative Observational Study. METHODS: In 1573 non-Hispanic White (NHW) and 282 Black/African American, American Indian/Alaska Native, Asian/Pacific Islander, and Hispanic/Latino women aged 50-79 y, 288 nonsynonymous and tagging single-nucleotide variants (SNVs) were genotyped. RBC folate, plasma folate, pyridoxal-5'-phosphate (PLP), vitamin B-12, homocysteine, and cysteine concentrations were determined in 12-h fasting blood. Multivariable linear regression tested associations per variant allele and for an aggregated genetic risk score. Effect modifications before, during, and after nationwide FA fortification were examined. RESULTS: After correction for multiple comparisons, among NHW women, 5,10-methylenetetrahydrofolate reductase (MTHFR) rs1801133 (677C→T) variant T was associated with lower plasma folate (-13.0%; 95% CI: -17.3%, -8.6%) and higher plasma homocysteine (3.5%; 95% CI: 1.7%, 5.3%) concentrations. Other associations for nonsynonymous SNVs included DNMT3A rs11695471 (T→A) with plasma PLP; EHMT2 rs535586 (G→A), TCN2 rs1131603 (L349S A→G), and TCN2 rs35838082 (R188W G→A) with plasma vitamin B-12; CBS rs2851391 (G→A) with plasma homocysteine; and MTHFD1 rs2236224 (G→A) and rs2236225 (R653Q G→A) with plasma cysteine. The influence of FA fortification on the associations was limited. Highest compared with lowest quartiles of aggregated genetic risk scores from SNVs in MTHFR and MTRR were associated with 14.8% to 18.9% lower RBC folate concentrations. Gene-biomarker associations were similar in women of other races/ethnicities. CONCLUSIONS: Our findings on genetic variants associated with several one-carbon metabolism biomarkers may help elucidate mechanisms of maintaining B vitamin status in postmenopausal women.

Re-calibrating pure risk integrating individual data from two-phase studies with external summary statistics.

Accurate risk assessment is critical in clinical decision-making. It entails the projected risk based on a risk prediction model agreeing with the observed risk in the target cohort. However, the model often over- or under-estimates the risk. Building a new model for the target cohort would be ideal but costly. It is therefore of great interest to recalibrate an existing model for the target cohort. Existing methods have been proposed to recalibrate the model by leveraging the disease incidence rates from the target cohort. However, they assume the same covariate distribution across cohorts and when the assumption is violated, the recalibrated model can be substantially biased. Further, recalibration is also complicated by the two-phase sampling design that is commonly used for developing risk prediction models. In this paper, we develop a weighted estimating-equation approach accounting for the two-phase design and combine it with a weighted empirical likelihood that leverages the summary information on both disease incidence rates and covariates from the target cohort. We provide a resampling-based inference procedure. Our extensive simulation results show that using the summary information from the target population, the proposed recalibration method yields nearly unbiased risk estimates under a wide range of scenarios. An application to a colorectal cancer study also illustrates that the proposed method yields a well-calibrated model in the target cohort.

New weighting methods when cases are only a subset of events in a nested case-control study.

Nested case control (NCC) is a sampling method widely used for developing and evaluating risk models with expensive biomarkers on large prospective cohort studies. In a typical NCC design, biomarker values are obtained on a subcohort, where cases consist of all the events (subjects who experience the event during the follow-up). However, when the number of events is not small, due to the cost and limited availability of biospecimen, one may select only a subset of events as cases. We refer to such a variation as the untypical NCC. Unfortunately, existing inverse probability weighted (IPW) estimators for the untypical NCC are biased, and they only focus on relative risk parameters under the proportional hazards (PH) model. In this manuscript, we propose new weighting methods that produce consistent IPW estimators for not only relative risk parameters but also several metrics that evaluate a risk model's predictive performance. We also provide the inference procedure via perturbation resampling, which captures all the variance and between-subject covariance induced by the sampling processes for both case and control selections. In addition, our methods are not limited to the PH model, and they can be applied to the time-specific generalized linear model. Under the typical NCC design, our new weights are equivalent to the weight proposed by Samuelsen; under the untypical NCC, the IPW estimators using our weights have smaller bias and variance than the existing methods. We will demonstrate this improved performance via both analytical and numerical investigations.

Association between post-treatment circulating biomarkers of inflammation and survival among stage II-III colorectal cancer patients.

BACKGROUND: Biomarker studies on colorectal cancer (CRC) prognosis are limited to pre-diagnostic or pre-operative measures. Post-treatment biomarkers are not well understood for their associations with CRC survival. METHODS: We included 306 eligible incident stage II-III CRC cases from the population-based Seattle Colon Cancer Family Registry. Concentrations of C-reactive protein (CRP), interleukin-6 (IL-6), monocyte chemoattractant protein-1 (MCP-1), adiponectin, and leptin were measured using post-treatment plasma samples. Adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) for all-cause and CRC-specific mortality were calculated using Cox proportional hazard models. RESULTS: Elevated levels of CRP, IL-6, MCP-1, and adiponectin were significantly associated with a higher risk of all-cause mortality within 10 years post blood draw with HRs (95% CI) of 1.32 (1.10-2.59), 2.72 (2.07-3.56), 1.97 (1.18-3.28) and 1.71 (1.14-2.58), respectively. IL-6 and adiponectin had a dose-response effect (Ptrend < 0.0001). For CRC-specific mortality, we observed positive associations for CRP (HR = 1.75, 95% CI: 1.2-2.56), IL-6 (HR = 5.02, 95% CI: 2.92-8.59), MCP-1 (HR = 3.78, 95% CI: 1.41-10.08), and adiponectin (HR = 3.16, 95% CI: 1.27-7.86), and inverse association for leptin (HR = 0.44, 95% CI: 0.29-0.68) within the first year of blood draw, whereas the association for IL-6 remained statistically significant over 10 years. CONCLUSION: Our results support the role of chronic inflammation in CRC progression and suggested several post-treatment inflammatory biomarkers, particularly IL-6, are promising prognostic markers for stage II-III CRC patients.

Use of the MyProstateScore Test to Rule Out Clinically Significant Cancer: Validation of a Straightforward Clinical Testing Approach.

PURPOSE: The MyProstateScore test was validated for improved detection of clinically significant (grade group ≥2) prostate cancer relative to prostate specific antigen based risk calculators. We sought to validate an optimal MyProstateScore threshold for clinical use in ruling out grade group ≥2 cancer in men referred for biopsy. MATERIALS AND METHODS: Biopsy naïve men provided post-digital rectal examination urine prior to biopsy. MyProstateScore was calculated using the validated, locked multivariable model including only serum prostate specific antigen, urinary prostate cancer antigen 3 and urinary TMPRSS2:ERG. The MyProstateScore threshold approximating 95% sensitivity for grade group ≥2 cancer was identified in a training cohort, and performance was measured in 2 external validation cohorts. We assessed the 1) overall biopsy referral population and 2) population meeting guideline based testing criteria (ie, prostate specific antigen 3-10, or <3 with suspicious digital rectal examination). RESULTS: Validation cohorts were prospectively enrolled from academic (977 patients, median prostate specific antigen 4.5, IQR 3.1-6.0) and community (548, median prostate specific antigen 4.9, IQR 3.7-6.8) settings. In the overall validation population (1,525 patients), 338 men (22%) had grade group ≥2 cancer on biopsy. The MyProstateScore threshold of 10 provided 97% sensitivity and 98% negative predictive value for grade group ≥2 cancer. MyProstateScore testing would have prevented 387 unnecessary biopsies (33%), while missing only 10 grade group ≥2 cancers (3.0%). In 1,242 patients meeting guideline based criteria, MyProstateScore ≤10 provided 96% sensitivity and 97% negative predictive value, and would have prevented 32% of unnecessary biopsies, missing 3.7% of grade group ≥2 cancers. CONCLUSIONS: In a large, clinically pertinent biopsy referral population, MyProstateScore ≤10 provided exceptional sensitivity and negative predictive value for ruling out grade group ≥2 cancer. This straightforward secondary testing approach would reduce the use of more costly and invasive procedures after screening with prostate specific antigen.

Developing and evaluating risk prediction models with panel current status data.

Panel current status data arise frequently in biomedical studies when the occurrence of a particular clinical condition is only examined at several prescheduled visit times. Existing methods for analyzing current status data have largely focused on regression modeling based on commonly used survival models such as the proportional hazards model and the accelerated failure time model. However, these procedures have the limitations of being difficult to implement and performing sub-optimally in relatively small sample sizes. The performance of these procedures is also unclear under model misspecification. In addition, no methods currently exist to evaluate the prediction performance of estimated risk models with panel current status data. In this paper, we propose a simple estimator under a general class of nonparametric transformation (NPT) models by fitting a logistic regression working model and demonstrate that our proposed estimator is consistent for the NPT model parameter up to a scale multiplier. Furthermore, we propose nonparametric estimators for evaluating the prediction performance of the risk score derived from model fitting, which is valid regardless of the adequacy of the fitted model. Extensive simulation results suggest that our proposed estimators perform well in finite samples and the regression parameter estimators outperform existing estimators under various scenarios. We illustrate the proposed procedures using data from the Framingham Offspring Study.

Biomarker evaluation under imperfect nested case-control design.

The nested case-control (NCC) design has been widely adopted as a cost-effective sampling design for biomarker research. Under the NCC design, markers are only measured for the NCC subcohort consisting of all cases and a fraction of the controls selected randomly from the matched risk sets of the cases. Robust methods for evaluating prediction performance of risk models have been derived under the inverse probability weighting framework. The probabilities of samples being included in the NCC cohort can be calculated based on the study design ``a previous study'' or estimated non-parametrically ``a previous study''. Neither strategy works well due to model mis-specification and the curse of dimensionality in practical settings where the sampling does not entirely follow the study design or depends on many factors. In this paper, we propose an alternative strategy to estimate the sampling probabilities based on a varying coefficient model, which attains a balance between robustness and the curse of dimensionality. The complex correlation structure induced by repeated finite risk set sampling makes the standard resampling procedure for variance estimation fail. We propose a perturbation resampling procedure that provides valid interval estimation for the proposed estimators. Simulation studies show that the proposed method performs well in finite samples. We apply the proposed method to the Nurses' Health Study II to develop and evaluate prediction models using clinical biomarkers for cardiovascular risk.

Cancer screening in the U.S. through the COVID-19 pandemic, recovery, and beyond.

COVID-19 has proved enormously disruptive to the provision of cancer screening, which does not just represent an initial test but an entire process, including risk detection, diagnostic follow-up, and treatment. Successful delivery of services at all points in the process has been negatively affected by the pandemic. There is a void in empirical high-quality evidence to support a specific strategy for administering cancer screening during a pandemic and its resolution phase, but several pragmatic considerations can help guide prioritization efforts. Targeting guideline-eligible people who have never been screened, or those who are significantly out of date with screening, has the potential to maximize benefits now and into the future. Disruptions to care due to the pandemic could represent an unparalleled opportunity to reassess early detection programs towards an explicit, thoughtful, and just prioritization of populations historically experiencing cancer disparities. By focusing screening services on populations that have the most to gain, and by careful and deliberate planning for the period following the pandemic, we can positively affect cancer outcomes for all.

Evaluating longitudinal markers under two-phase study designs.

Little attention has been given to the design of efficient studies to evaluate longitudinal biomarkers. Measuring longitudinal markers on an entire cohort is cost prohibitive and, especially for rare outcomes such as cancer, may be infeasible. Thus, methods for evaluation of longitudinal biomarkers using efficient and cost-effective study designs are needed. Case cohort (CCH) and nested case-control (NCC) studies allow investigators to evaluate biomarkers rigorously and at reduced cost, with only a small loss in precision. In this article, we develop estimators of several measures to evaluate the accuracy and discrimination of predicted risk under CCH and NCC study designs. We use double inverse probability weighting (DIPW) to account for censoring and sampling bias in estimation and inference procedures. We study the asymptotic properties of the proposed estimators. To facilitate inference using two-phase longitudinal data, we develop valid resampling-based variance estimation procedures under CCH and NCC. We evaluate the performance of our estimators under CCH and NCC using simulation studies and illustrate them on a NCC study within the hepatitis C antiviral long-term treatment against cirrhosis (HALT-C) clinical trial. Our estimators and inference procedures perform well under CCH and NCC, provided that the sample size at the time of prediction (effective sample size) is reasonable. These methods are widely applicable, efficient, and cost-effective and can be easily adapted to other study designs used to evaluate prediction rules in a longitudinal setting.

Magnetic Resonance Imaging for the Detection of High Grade Cancer in the Canary Prostate Active Surveillance Study.

PURPOSE: We investigated the ability of prostate magnetic resonance imaging to detect Gleason Grade Group 2 or greater cancer in a standardized, multi-institutional active surveillance cohort. MATERIALS AND METHODS: We evaluated men enrolled in Canary Prostate Active Surveillance Study with Gleason Grade Group less than 2 and who underwent biopsy within 12 months of multiparametric magnetic resonance imaging. Our primary outcome was biopsy reclassification to Gleason Grade Group 2 or greater. We evaluated the performance of magnetic resonance imaging PI-RADS® score and clinical factors. Multivariable logistic regression models were fit with magnetic resonance imaging and clinical factors and used to perform receiver operating curve analyses. RESULTS: There were 361 participants with 395 prostate magnetic resonance imaging studies with a median followup of 4.1 (IQR 2.0-7.6) years. Overall 108 (27%) biopsies showed reclassification. Defining positive magnetic resonance imaging as PI-RADS 3-5, the negative predictive value and positive predictive value for detecting Gleason Grade Group 2 or greater cancer was 83% (95% CI 76-90) and 31% (95% CI 26-37), respectively. PI-RADS was significantly associated with reclassification (PI-RADS 5 vs 1 and 2 OR 2.71, 95% CI 1.21-6.17, p=0.016) in a multivariable model but did not improve upon a model with only clinical factors (AUC 0.768 vs 0.762). In 194 fusion biopsies higher grade cancer was found in targeted cores in 21 (11%) instances, while 25 (13%) had higher grade cancer in the systematic cores. CONCLUSIONS: This study adds the largest cohort data to the body of literature for magnetic resonance imaging in active surveillance, recommending systematic biopsy in patients with negative magnetic resonance imaging and the inclusion of systematic biopsy in patients with positive magnetic resonance imaging.

African American Race is Not Associated with Risk of Reclassification during Active Surveillance: Results from the Canary Prostate Cancer Active Surveillance Study.

PURPOSE: In a large, prospective, multi-institutional active surveillance cohort we evaluated whether African American men are at higher risk for reclassification. MATERIALS AND METHODS: The Canary PASS (Prostate Active Surveillance Study) is a protocol driven, active surveillance cohort with a prespecified prostate specific antigen and surveillance biopsy regimen. Men included in this study had Gleason Grade Group 1 or 2 disease at diagnosis and fewer than 5 years between diagnosis and enrollment, and had undergone 1 or more surveillance biopsies. The reclassification risk, defined as an increase in the Gleason score on subsequent biopsy, was compared between African American and Caucasian American men using Cox proportional hazards models. In the subset of men who underwent delayed prostatectomy the rate of adverse pathology findings, defined as pT3a or greater disease, or Gleason Grade Group 3 or greater, was compared in African American and Caucasian American men. RESULTS: Of the 1,315 men 89 (7%) were African American and 1,226 (93%) were Caucasian American. There was no difference in the treatment rate in African American and Caucasian American men. In multivariate models African American race was not associated with the risk of reclassification (HR 1.16, 95% CI 0.78-1.72). Of the 441 men who underwent prostatectomy after a period of active surveillance the rate of adverse pathology was similar in those who were African American and Caucasian American (46% vs 47%, p=0.99). CONCLUSIONS: Of men on active surveillance who followed a standardized protocol of regular prostate specific antigen testing and biopsy those who were African American were not at increased risk for pathological reclassification while on active surveillance, or for adverse pathology findings at prostatectomy. Active surveillance appears to be an appropriate management strategy for African American men with favorable risk prostate cancer.