Methods to Estimate the Comparative Effectiveness of Clinical Strategies that Administer the Same Intervention at Different Times

Clinical guidelines that rely on observational data due to the absence of data from randomized trials benefit when the observational data or its analysis emulates trial data or its analysis. In this paper, we review a methodology for emulating trials that compare the effects of different timing strategies, that is, strategies that vary the frequency of delivery of a medical intervention or procedure. We review trial emulation for comparing (i) single applications of the procedure at different times, (ii) fixed schedules of application, and (iii) schedules adapted to the evolving clinical characteristics of the patients. For illustration, we describe an application in which we estimate the effect of surveillance colonoscopies in patients who had an adenoma detected during the Norwegian Colorectal Cancer Prevention (NORCCAP) trial

Mortality in Norway and Sweden during the COVID-19 pandemic

Background: Norway and Sweden are similar countries in terms of socioeconomics and health care. Norway implemented extensive COVID-19 measures, such as school closures and lockdowns, whereas Sweden did not. Aims: To compare mortality in Norway and Sweden, two similar countries with very different mitigation measures against COVID-19. Methods: Using real-world data from national registries, we compared all-cause and COVID-19-related mortality rates with 95% confidence intervals (CI) per 100,000 person-weeks and mortality rate ratios (MRR) comparing the five preceding years (2015–2019) with the pandemic year (2020) in Norway and Sweden. Results: In Norway, all-cause mortality was stable from 2015 to 2019 (mortality rate 14.6–15.1 per 100,000 person-weeks; mean mortality rate 14.9) and was lower in 2020 than from 2015 to 2019 (mortality rate 14.4; MRR 0.97; 95% CI 0.96–0.98). In Sweden, all-cause mortality was stable from 2015 to 2018 (mortality rate 17.0–17.8; mean mortality rate 17.1) and similar to that in 2020 (mortality rate 17.6), but lower in 2019 (mortality rate 16.2). Compared with the years 2015–2019, all-cause mortality in the pandemic year was 3% higher due to the lower rate in 2019 (MRR 1.03; 95% CI 1.02–1.04). Excess mortality was confined to people aged ⩾70 years in Sweden compared with previous years. The COVID-19-associated mortality rates per 100,000 person-weeks during the first wave of the pandemic were 0.3 in Norway and 2.9 in Sweden. Conclusions: All-cause mortality in 2020 decreased in Norway and increased in Sweden compared with previous years. The observed excess deaths in Sweden during the pandemic may, in part, be explained by mortality displacement due to the low all-cause mortality in the previous year

Overdiagnosis in breast cancer screening: the importance of length of observation period and lead time

PMCID: PMC3706885This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

An investigation of the apparent breast cancer epidemic in France: screening and incidence trends in birth cohorts

<p>Abstract</p> <p>Background</p> <p>Official descriptive data from France showed a strong increase in breast-cancer incidence between 1980 to 2005 without a corresponding change in breast-cancer mortality. This study quantifies the part of incidence increase due to secular changes in risk factor exposure and in overdiagnosis due to organised or opportunistic screening. Overdiagnosis was defined as non progressive tumours diagnosed as cancer at histology or progressive cancer that would remain asymptomatic until time of death for another cause.</p> <p>Methods</p> <p>Comparison between age-matched cohorts from 1980 to 2005. All women residing in France and born 1911-1915, 1926-1930 and 1941-1945 are included. Sources are official data sets and published French reports on screening by mammography, age and time specific breast-cancer incidence and mortality, hormone replacement therapy, alcohol and obesity. Outcome measures include breast-cancer incidence differences adjusted for changes in risk factor distributions between pairs of age-matched cohorts who had experienced different levels of screening intensity.</p> <p>Results</p> <p>There was an 8-fold increase in the number of mammography machines operating in France between 1980 and 2000. Opportunistic and organised screening increased over time. In comparison to age-matched cohorts born 15 years earlier, recent cohorts had adjusted incidence proportion over 11 years that were 76% higher [95% confidence limits (CL) 67%, 85%] for women aged 50 to 64 years and 23% higher [95% CL 15%, 31%] for women aged 65 to 79 years. Given that mortality did not change correspondingly, this increase in adjusted 11 year incidence proportion was considered as an estimate of overdiagnosis.</p> <p>Conclusions</p> <p>Breast cancer may be overdiagnosed because screening increases diagnosis of slowly progressing non-life threatening cancer and increases misdiagnosis among women without progressive cancer. We suggest that these effects could largely explain the reported "epidemic" of breast cancer in France. Better predictive classification of tumours is needed in order to avoid unnecessary cancer diagnoses and subsequent procedures.</p

Increasingly strong reduction in breast cancer mortality due to screening

Item does not contain fulltextBACKGROUND: Favourable outcomes of breast cancer screening trials in the 1970s and 1980s resulted in the launch of population-based service screening programmes in many Western countries. We investigated whether improvements in mammography and treatment modalities have had an influence on the effectiveness of breast cancer screening from 1975 to 2008. METHODS: In Nijmegen, the Netherlands, 55,529 women received an invitation for screening between 1975 and 2008. We designed a case-referent study to evaluate the impact of mammographic screening on breast cancer mortality over time from 1975 to 2008. A total number of 282 breast cancer deaths were identified, and 1410 referents aged 50-69 were sampled from the population invited for screening. We estimated the effectiveness by calculating the odds ratio (OR) indicating the breast cancer death rate for screened vs unscreened women. RESULTS: The breast cancer death rate in the screened group over the complete period was 35% lower than in the unscreened group (OR=0.65; 95% CI=0.49-0.87). Analysis by calendar year showed an increasing effectiveness from a 28% reduction in breast cancer mortality in the period 1975-1991 (OR=0.72; 95% CI=0.47-1.09) to 65% in the period 1992-2008 (OR=0.35; 95% CI=0.19-0.64). CONCLUSION: Our results show an increasingly strong reduction in breast cancer mortality over time because of mammographic screening

Machine learning-based prediction of breast cancer growth rate in-vivo

BackgroundDetermining the rate of breast cancer (BC) growth in vivo, which can predict prognosis, has remained elusive despite its relevance for treatment, screening recommendations and medicolegal practice. We developed a model that predicts the rate of in vivo tumour growth using a unique study cohort of BC patients who had two serial mammograms wherein the tumour, visible in the diagnostic mammogram, was missed in the first screen.MethodsA serial mammography-derived in vivo growth rate (SM-INVIGOR) index was developed using tumour volumes from two serial mammograms and time interval between measurements. We then developed a machine learning-based surrogate model called Surr-INVIGOR using routinely assessed biomarkers to predict in vivo rate of tumour growth and extend the utility of this approach to a larger patient population. Surr-INVIGOR was validated using an independent cohort.ResultsSM-INVIGOR stratified discovery cohort patients into fast-growing versus slow-growing tumour subgroups, wherein patients with fast-growing tumours experienced poorer BC-specific survival. Our clinically relevant Surr-INVIGOR stratified tumours in the discovery cohort and was concordant with SM-INVIGOR. In the validation cohort, Surr-INVIGOR uncovered significant survival differences between patients with fast-growing and slow-growing tumours.ConclusionOur Surr-INVIGOR model predicts in vivo BC growth rate during the pre-diagnostic stage and offers several useful applications