Dataset Information

Estimating the cumulative risk of false positive cancer screenings.

ABSTRACT: 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.

SUBMITTER: Baker SG

PROVIDER: S-EPMC166156 | biostudies-literature | 2003 Jul

REPOSITORIES: biostudies-literature

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Estimating the cumulative risk of false positive cancer screenings.

Baker Stuart G SG Erwin Diane D Kramer Barnett S BS

BMC medical research methodology 20030703

<h4>Background</h4>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.<h4>Method</h4>By rede ...[more]

PMID: 12841854

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Project description:Screening tests are widely recommended for the early detection of disease among asymptomatic individuals. While detecting disease at an earlier stage has the potential to improve outcomes, screening also has negative consequences, including false positive results which may lead to anxiety, unnecessary diagnostic procedures, and increased healthcare costs. In addition, multiple false positive results could discourage participating in subsequent screening rounds. Screening guidelines typically recommend repeated screening over a period of many years, but little prior research has investigated how often individuals receive multiple false positive test results. Estimating the cumulative risk of multiple false positive results over the course of multiple rounds of screening is challenging due to the presence of censoring and competing risks, which may depend on the false positive risk, screening round, and number of prior false positive results. To address the general challenge of estimating the cumulative risk of multiple false positive test results, we propose a nonhomogeneous multistate model to describe the screening process including competing events. We developed alternative approaches for estimating the cumulative risk of multiple false positive results using this multistate model based on existing estimators for the cumulative risk of a single false positive. We compared the performance of the newly proposed models through simulation studies and illustrate model performance using data on screening mammography from the Breast Cancer Surveillance Consortium. Across most simulation scenarios, the multistate extension of a censoring bias model demonstrated lower bias compared to other approaches. In the context of screening mammography, we found that the cumulative risk of multiple false positive results is high. For instance, based on the censoring bias model, for a high-risk individual, the cumulative probability of at least two false positive mammography results after 10 rounds of annual screening is 40.4.

Project description:ObjectivesAbsence of somatosensory evoked potentials is considered a nearly perfect predictor of poor outcome after cardiac arrest. However, reports of good outcomes despite absent somatosensory evoked potentials and high rates of withdrawal of life-sustaining therapies have raised concerns that estimates of the prognostic value of absent somatosensory evoked potentials may be biased by self-fulfilling prophecies. We aimed to develop an unbiased estimate of the false positive rate of absent somatosensory evoked potentials as a predictor of poor outcome after cardiac arrest.Data sourcesPubMed.Study selectionWe selected 35 studies in cardiac arrest prognostication that reported somatosensory evoked potentials.Data extractionIn each study, we identified rates of withdrawal of life-sustaining therapies and good outcomes despite absent somatosensory evoked potentials. We appraised studies for potential biases using the Quality in Prognosis Studies tool. Using these data, we developed a statistical model to estimate the false positive rate of absent somatosensory evoked potentials adjusted for withdrawal of life-sustaining therapies rate.Data synthesisTwo-thousand one-hundred thirty-three subjects underwent somatosensory evoked potential testing. Five-hundred ninety-four had absent somatosensory evoked potentials; of these, 14 had good functional outcomes. The rate of withdrawal of life-sustaining therapies for subjects with absent somatosensory evoked potential could be estimated in 14 of the 35 studies (mean 80%, median 100%). The false positive rate for absent somatosensory evoked potential in predicting poor neurologic outcome, adjusted for a withdrawal of life-sustaining therapies rate of 80%, is 7.7% (95% CI, 4-13%).ConclusionsAbsent cortical somatosensory evoked potentials do not infallibly predict poor outcome in patients with coma following cardiac arrest. The chances of survival in subjects with absent somatosensory evoked potentials, though low, may be substantially higher than generally believed.

Project description:False-positive mammography results are common. Biennial screening may decrease the cumulative probability of false-positive results across many years of repeated screening but could also delay cancer diagnosis.To compare the cumulative probability of false-positive results and the stage distribution of incident breast cancer after 10 years of annual or biennial screening mammography.Prospective cohort study.7 mammography registries in the National Cancer Institute-funded Breast Cancer Surveillance Consortium.169,456 women who underwent first screening mammography at age 40 to 59 years between 1994 and 2006 and 4492 women with incident invasive breast cancer diagnosed between 1996 and 2006.False-positive recalls and biopsy recommendations stage distribution of incident breast cancer.False-positive recall probability was 16.3% at first and 9.6% at subsequent mammography. Probability of false-positive biopsy recommendation was 2.5% at first and 1.0% at subsequent examinations. Availability of comparison mammograms halved the odds of a false-positive recall (adjusted odds ratio, 0.50 [95% CI, 0.45 to 0.56]). When screening began at age 40 years, the cumulative probability of a woman receiving at least 1 false-positive recall after 10 years was 61.3% (CI, 59.4% to 63.1%) with annual and 41.6% (CI, 40.6% to 42.5%) with biennial screening. Cumulative probability of false-positive biopsy recommendation was 7.0% (CI, 6.1% to 7.8%) with annual and 4.8% (CI, 4.4% to 5.2%) with biennial screening. Estimates were similar when screening began at age 50 years. A non-statistically significant increase in the proportion of late-stage cancers was observed with biennial compared with annual screening (absolute increases, 3.3 percentage points [CI, -1.1 to 7.8 percentage points] for women age 40 to 49 years and 2.3 percentage points [CI, -1.0 to 5.7 percentage points] for women age 50 to 59 years) among women with incident breast cancer.Few women underwent screening over the entire 10-year period. Radiologist characteristics influence recall rates and were unavailable. Most mammograms were film rather than digital. Incident cancer was analyzed in a small sample of women who developed cancer.After 10 years of annual screening, more than half of women will receive at least 1 false-positive recall, and 7% to 9% will receive a false-positive biopsy recommendation. Biennial screening appears to reduce the cumulative probability of false-positive results after 10 years but may be associated with a small absolute increase in the probability of late-stage cancer diagnosis.National Cancer Institute.

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Estimating the cumulative risk of false positive cancer screenings.

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Estimating the cumulative risk of false positive cancer screenings.

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