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Multicancer detection (MCD) tests use blood specimens to detect preclinical cancers. A major concern is overdiagnosis, the detection of preclinical cancer on screening that would not have developed into symptomatic cancer in the absence of screening. Because overdiagnosis can lead to unnecessary and harmful treatments, its quantification is important. A key metric is the screen overdiagnosis fraction (SOF), the probability of overdiagnosis at screen detection. Estimating SOF is notoriously difficult because overdiagnosis is not observed. This estimation is more challenging with MCD tests because short‐term results are needed as the technology is rapidly changing. To estimate average SOF for a program of yearly MCD tests, I introduce a novel method that requires at least two yearly MCD tests given to persons having a wide range of ages and applies only to cancers for which there is no conventional screening. The method assumes an exponential distribution for the sojourn time in an operational screen‐detectable preclinical cancer (OPC) state, defined as once screen‐detectable (positive screen and work‐up), always screen‐detectable. Because this assumption appears in only one term in the SOF formula, the results are robust to violations of the assumption. An SOF plot graphs average SOF versus mean sojourn time. With lung cancer screening data and synthetic data, SOF plots distinguished small from moderate levels of SOF. With its unique set of assumptions, the SOF plot would complement other modeling approaches for estimating SOF once sufficient short‐term observational data on MCD tests become available. [ABSTRACT FROM AUTHOR]

Objective Many cancer screening trials involve a screening programme of one or more screenings with follow-up after the last screening. Usually a maximum follow-up time is selected in advance. However, during the follow-up period there is an opportunity to report the results of the trial sooner than planned. Early reporting of results from a randomized screening trial is important because obtaining a valid result sooner translates into health benefits reaching the general population sooner. The health benefits are reduction in cancer deaths if screening is found to be beneficial and more screening is recommended, or avoidance of unnecessary biopsies, work-ups and morbidity if screening is not found to be beneficial and the rate of screening drops. Methods Our proposed method for deciding if results from a cancer screening trial should be reported earlier in the follow-up period is based on considerations involving postscreening noise. Postscreening noise (sometimes called dilution) refers to cancer deaths in the follow-up period that could not have been prevented by screening: (1) cancer deaths in the screened group that occurred after the last screening in subjects whose cancers were not detected during the screening program and (2) cancer deaths in the control group that occurred after the time of the last screening and whose cancers would not have been detected during the screening programme had they been randomized to screening (the number of which is unobserved). Because postscreening noise increases with follow-up after the last screening, we propose early reporting at the time during the follow-up period when postscreening noise first starts to overwhelm the estimated effect of screening as measured by a z-statistic. This leads to a confidence interval, adjusted for postscreening noise, that would not change substantially with additional follow-up. Details of the early reporting rule were refined by simulation, which also accounts for multiple looks. Results For the re-analysis of the Health Insurance Plan trial for breast cancer screening and the Mayo Lung Project for lung cancer screening, estimates and confidence intervals for the effect of screening on cancer mortality were similar on early reporting and later. Conclusion The proposed early reporting rule for a cancer screening trial with post-screening follow-up is a promising method for making results from the trial available sooner, which translates into health benefits (reduction in cancer deaths or avoidance of unnecessary morbidity) reaching the population sooner. [ABSTRACT FROM AUTHOR]

Objectives: When evaluating screening for the early detection of cancer, it is important to estimate both harms and benefits. One common harm is a false-positive (FP), which is a positive screening result, perhaps followed by an invasive test, with no cancer detected on the diagnostic work-up or within a specified time period. An important goal is to estimate the risk of at least one FP, which we call the cumulative risk of an FP, if persons took a regimen of various screening tests, as is commonly recommended. The estimation is complicated because the data come from a study in which subjects are offered various screening tests in rounds with some missing tests in most subjects. Previous methods for estimating cumulative risk of FPs with a single type of test are not directly applicable, so a new approach was developed. Methods: The tests were ordered by appearance, where the last test was either the first FP (analogous to a failure time) or the last test taken with no FPs having occurred on that test or previously (analogous to a censoring time). We applied a Kaplan-Meier approach for survival analysis with the innovation that the hazard for a first FP for a given test depends on the type of test and number of previous tests of that type which were taken. Results: The method is illustrated with data from the screening arm of the randomized Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial. With an FP defined as a diagnostic work-up in the absence of cancer (or advanced adenoma) within three years, the probability of at least one FP among 14 tests in men was 60.5% with 95% confidence interval of (59.3%, 61.6%). Conclusion: A simple estimate is proposed for the probability of at least one FP if persons took a regimen of multiple screening tests of different types. The methodology is useful for summarizing the burden of multiphasic screening programmes. [ABSTRACT FROM AUTHOR]

Background The field of cancer biomarker development has been evolving rapidly. New developments both in the biologic and statistical realms are providing increasing opportunities for evaluation of markers for both early detection and diagnosis of cancer. Purpose To review the major conceptual and methodological issues in cancer biomarker evaluation, with an emphasis on recent developments in statistical methods together with practical recommendations. Methods We organized this review by type of study: preliminary performance, retrospective performance, prospective performance and cancer screening evaluation. Results For each type of study, we discuss methodologic issues, provide examples and discuss strengths and limitations. Conclusion Preliminary performance studies are useful for quickly winnowing down the number of candidate markers; however their results may not apply to the ultimate target population, asymptomatic subjects. If stored specimens from cohort studies with clinical cancer endpoints are available, retrospective studies provide a quick and valid way to evaluate performance of the markers or changes in the markers prior to the onset of clinical symptoms. Prospective studies have a restricted role because they require large sample sizes, and, if the endpoint is cancer on biopsy, there may be bias due to overdiagnosis. Cancer screening studies require very large sample sizes and long follow-up, but are necessary for evaluating the marker as a trigger of early intervention. [ABSTRACT FROM AUTHOR]

Background: In recent years there has been increased interest in evaluating breast cancer screening using data from before-and-after studies in multiple geographic regions. One approach, not previously mentioned, is the paired availability design. The paired availability design was developed to evaluate the effect of medical interventions by comparing changes in outcomes before and after a change in the availability of an intervention in various locations. A simple potential outcomes model yields estimates of efficacy, the effect of receiving the intervention, as opposed to effectiveness, the effect of changing the availability of the intervention. By combining estimates of efficacy rather than effectiveness, the paired availability design avoids confounding due to different fractions of subjects receiving the interventions at different locations. The original formulation involved short-term outcomes; the challenge here is accommodating long-term outcomes. Methods: The outcome is incident breast cancer deaths in a time period, which are breast cancer deaths that were diagnosed in the same time period. We considered the plausibility of the basic five assumptions of the paired availability design and propose a novel analysis to accommodate likely violations of the assumption of stable screening effects. Results: We applied the paired availability design to data on breast cancer screening from six counties in Sweden. The estimated yearly change in incident breast cancer deaths per 100,000 persons ages 40-69 (in most counties) due to receipt of screening (among the relevant type of subject in the potential outcomes model) was -9 with 95% confidence interval (-14, -4) or (-14, - 5), depending on the sensitivity analysis. Conclusion: In a realistic application, the extended paired availability design yielded reasonably precise confidence intervals for the effect of receiving screening on the rate of incident breast cancer death. Although the assumption of stable preferences may be questionable, its impact will be small if there is little screening in the first time period. However, estimates may be substantially confounded by improvements in systemic therapy over time. Therefore the results should be interpreted with care. [ABSTRACT FROM AUTHOR]

Studies the central role of receiver operating characteristic curves in evaluating tests for the early detection of cancer. Reduction of cancer mortality through screening; Use of a randomized controlled trial in the evaluation of the benefits of cancer screening; Types of errors involved in tests for the early detection of cancer. [ABSTRACT FROM AUTHOR]

Background: When evaluating cancer screening it is important to estimate the cumulative risk of false positives from periodic screening. Because the data typically come from studies in which the number of screenings varies by subject, estimation must take into account dropouts. A previous approach to estimate the probability of at least one false positive in n screenings unrealistically assumed that the probability of dropout does not depend on prior false positives. Method: By redefining the random variables, we obviate the unrealistic dropout assumption. We also propose a relatively simple logistic regression and extend estimation to the expected number of false positives in n screenings. Results: We illustrate our methodology using data from women ages 40 to 64 who received up to four annual breast cancer screenings in the Health Insurance Program of Greater New York study, which began in 1963. Covariates were age, time since previous screening, screening number, and whether or not a previous false positive occurred. Defining a false positive as an unnecessary biopsy, the only statistically significant covariate was whether or not a previous false positive occurred. Because the effect of screening number was not statistically significant, extrapolation beyond 4 screenings was reasonable. The estimated mean number of unnecessary biopsies in 10 years per woman screened is .11 with 95% confidence interval of (.10, .12). Defining a false positive as an unnecessary work-up, all the covariates were statistically significant and the estimated mean number of unnecessary work-ups in 4 years per woman screened is .34 with 95% confidence interval (.32, .36). Conclusion: Using data from multiple cancer screenings with dropouts, and allowing dropout to depend on previous history of false positives, we propose a logistic regression model to estimate both the probability of at least one false positive and the expected number of false positives associated with n cancer screenings. The methodology can be used for both informed decision making at the individual level, as well as planning of health services. [ABSTRACT FROM AUTHOR]

Background: Because randomized cancer screening trials are very expensive, observational cancer screening studies can play an important role in the early phases of screening evaluation. Periodic screening evaluation (PSE) is a methodology for estimating the reduction in population cancer mortality from data on subjects who receive regularly scheduled screens. Although PSE does not require assumptions about natural history of cancer it requires other assumptions, particularly progressive detection -- the assumption that once a cancer is detected by a screening test, it will always be detected by the screening test. Methods: We formulate a simple version of PSE and show that it leads to an upper bound on screening efficacy if the progressive detection assumption does not hold (and any effect of birth cohort is minimal) To determine if the upper bound is reasonable, for three randomized screening trials, we compared PSE estimates based only on screened subjects with PSE estimates based on all subjects. Results: In the three randomized screening trials, PSE estimates based on screened subjects gave fairly close results to PSE estimates based on all subjects. Conclusion: PSE has promise for obtaining an upper bound on the reduction in population cancer mortality rates based on observational screening data. If the upper bound estimate is found to be small and any birth cohort effects are likely minimal, then a definitive randomized trial would not be warranted. [ABSTRACT FROM AUTHOR]

Background: If intervention A bests B in one randomized trial, and B bests C in another randomized trial, can one conclude that A is better than C? The problem was motivated by the planning of a randomized trial, where A is spiral-CT screening, B is x-ray screening, and C is no screening. On its surface, this would appear to be a straightforward application of the transitive principle of logic. Methods: We extended the graphical approach for omitted binary variables that was originally developed to illustrate Simpson's paradox, applying it to hypothetical, but plausible scenarios involving lung cancer screening, treatment for gastric cancer, and antibiotic therapy for clinical pneumonia. Results: Graphical illustrations of the three examples show different ways the transitive fallacy for randomized trials can arise due to changes in an unobserved or unadjusted binary variable. In the most dramatic scenario, B bests C in the first trial, A bests B in the second trial, but C bests A at the time of the second trial. Conclusion: Even with large sample sizes, combining results from a previous randomized trial of B versus C with results from a new randomized trial of A versus B will not guarantee correct inference about A versus C. A three-arm trial of A, B, and C would protect against this problem and should be considered when the sequential trials are performed in the context of changing secular trends in important omitted variables such as therapy in cancer screening trials. [ABSTRACT FROM AUTHOR]

Background: The evaluation of randomized trials for cancer screening involves special statistical considerations not found in therapeutic trials. Although some of these issues have been discussed previously, we present important recent and new methodologies. Methods: Our emphasis is on simple approaches. Results: We make the following recommendations: (1) Use death from cancer as the primary endpoint, but review death records carefully and report all causes of death (2) Use a simple "causal" estimate to adjust for nonattendance and contamination occurring immediately after randomization (3) Use a simple adaptive estimate to adjust for dilution in follow-up after the last screen Conclusion: The proposed guidelines combine recent methodological work on screening endpoints and noncompliance/contamination with a new adaptive method to adjust for dilution in a study where follow-up continues after the last screen. These guidelines ensure good practice in the design and analysis of randomized trials of cancer screening. [ABSTRACT FROM AUTHOR]

Sometimes the only data available for evaluating screening for the early detection and treatment of cancer come from studies lacking a randomized control group. To evaluate these data, various approaches have been proposed. They include constructing a model to estimate the duration of preclinical cancer and the sensitivity of the screen, formulating a model to estimate rates of cancer mortality, and using a nonrandomized control group to estimate age-specific incidence and mortality rates. These methods have various limitations. Either the estimated quantities are insufficient for a complete evaluation, strong parametric assumptions are required, or an assumption must be made that the nonrandomized control group yields unbiased estimates of cancer incidence. To bypass some of these limitations, a new method has been developed for analyzing screening data without a randomized control group. The method is called periodic screening evaluation (PSE) because it requires data from periodic screens rather than a single screen. Using weakly structured parametric models, PSE estimates the effect of screening on cancer mortality, using only data from persons offered screening PSE's major innovation is a technique for estimating age-specific cancer incidence using older screened persons as controls for younger screened persons. The limitations of PSE are often preferable to those associated with other methods. PSE's principal limitations are (a) use of a nonrandomized control group to estimate case-fatality rates in the absence of screening and (b) an assumption that, given a person's age, the year of birth adds no reformation for predicting cancer incidence PSE was applied to data on persons offered screening for the early detection and treatment of breast cancer in which a randomized control group not offered screening was available for validation. Using only data from persons offered screening, the probability of averting dearth from cancer due to starting periodic screening at various ages was estimated. Confidence intervals were large. Nevertheless, a fivefold increase in sample size, which probably would have been feasible without the constraints of a randomized trial would likely have yielded adequate precision. For validation purposes the cumulative number of observed deaths from cancer in the randomized control group was compared with the estimated cumulative number based on applying PSE only to women offered screening. The agreement was good. [ABSTRACT FROM AUTHOR]

A letter to the editor is presented in response to the article "Modelling the cumulative risk of a false positive screening test" in the 2010 issue.

A letter to the editor is presented in response to the article "A semiparametric censoring bias model for estimating the cumulative risk of a false-positive screening test under dependent censoring," by R. A. Hubbard and D. L. Miglioretti that was published in the 2013 issue.