Using observational data to estimate an upper bound on the reduction in cancer mortality due to periodic screening

Baker, Stuart G.; Erwin, Diane; Kramer, Barnett S.; Prorok, Philip C.

doi:10.1186/1471-2288-3-4

Cited by 9 publications

(11 citation statements)

References 14 publications

(22 reference statements)

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“…The ultimate goal of a screening program in cancer is to reduce cancer mortality. Reduction in cancer mortality is discussed in Baker et al (2003), Flanders and Longini (1990) and Chen et al (1997) in the context of evaluating periodic screening.…”

Section: Discussionmentioning

confidence: 99%

Bayesian Inference for the Lead Time in Periodic Cancer Screening

Rosner

Broemeling

2007

Biometrics

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This article develops a probability distribution for the lead time in periodic cancer screening examinations. The general aim is to allow statistical inference for a screening program's lead time, the length of time the diagnosis is advanced by screening. The program's lead time is distributed as a mixture of a point mass and a piecewise continuous distribution. Simulation studies using the HIP (Health Insurance Plan for Greater New York) study's data provide estimates of different characteristics of a screening program under different screening frequencies. The components of this mixture represent two aspects of screening's benefit, namely, a reduction in the number of interval cases and the extent by which screening advanced the age of diagnosis. We present estimates of these two measures for participants in a breast cancer screening program. We also provide the mean, mode, variance, and density curve of the program's lead time. The model can provide policy makers with important information regarding the screening period, frequency, and the endpoints that may serve as surrogates for the benefit to women who take part in a periodic screening program. Though the study focuses on breast cancer screening, it is also applicable to other kinds of chronic disease.

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Section: Discussionmentioning

confidence: 99%

Bayesian Inference for the Lead Time in Periodic Cancer Screening

Rosner

Broemeling

2007

Biometrics

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“…Although there is substantial evidence suggesting an association between screening sensitivity, the transition probability density into the preclinical state, and the age at diagnosis, few researchers have modeled such relationships or explored quantitatively the changing pattern of these parameters across different age groups. Baker (1998) and Baker et al (2003) discuss evaluating periodic screening without modeling natural history or estimating sensitivity. Nevertheless, precise estimates of age‐dependent sensitivity and the transition probability into the preclinical state should be of great value to those who set health‐care policy, particularly regarding the initial age and frequency of periodic screening exams.…”

Section: Introductionmentioning

confidence: 99%

MLE and Bayesian Inference of Age‐Dependent Sensitivity and Transition Probability in Periodic Screening

2005

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SummaryThis article extends previous probability models for periodic breast cancer screening examinations. The specific aim is to provide statistical inference for age dependence of sensitivity and the transition probability from the disease free to the preclinical state. The setting is a periodic screening program in which a cohort of initially asymptomatic women undergo a sequence of breast cancer screening exams. We use age as a covariate in the estimation of screening sensitivity and the transition probability simultaneously, both from a frequentist point of view and within a Bayesian framework. We apply our method to the Health Insurance Plan of Greater New York study of female breast cancer and give age-dependent sensitivity and transition probability density estimates. The inferential methodology we develop is also applicable when analyzing studies of modalities for early detection of other types of progressive chronic diseases.

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“…Besides this methodology based on the paired availability design, one could also analyze observational screening data using the method of periodic screening evaluation [21]. However this method requires regular screenings, data on the number of cancers detected on screening and in the intervals between screenings, and follow-up of subjects detected with cancer.…”

Section: Discussionmentioning

confidence: 99%

Comparing breast cancer mortality rates before-and-after a change in availability of screening in different regions: Extension of the paired availability design

Baker

Kramer

Prorok

2004

BMC Med Res Methodol

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BackgroundIn 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.MethodsThe 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.ResultsWe 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.ConclusionIn 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.

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Using observational data to estimate an upper bound on the reduction in cancer mortality due to periodic screening

Cited by 9 publications

References 14 publications

Bayesian Inference for the Lead Time in Periodic Cancer Screening

Bayesian Inference for the Lead Time in Periodic Cancer Screening

MLE and Bayesian Inference of Age‐Dependent Sensitivity and Transition Probability in Periodic Screening

Comparing breast cancer mortality rates before-and-after a change in availability of screening in different regions: Extension of the paired availability design

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