Confidence intervals for directly standardized rates using mid-p gamma intervals

Kinetics of cardiac troponin and other biomarkers in patients with ST elevation myocardial infarction

Objective

To examine changes in concentration, time-to-peak and the ensuing half-life of cardiac biomarkers in patients with myocardial infarction.

Methods

Blood sampling was performed every third hour within 24 h after percutaneous coronary intervention (PCI) on a cohort of patients with ST elevation myocardial infarction. Cardiac troponin (cTn) was measured by the Dimension Vista, Vitros, Atellica, and Alinity high-sensitivity (hs) cTnI assays, and the Elecsys hs-cTnT assay. Further, creatine kinase (CK), myoglobin, creatine kinase MB (CKMB) and other biomarkers were analyzed.

Results

A total of 36 patients completed blood sampling (median age 60 years, IQR 56.4-66.5 years; seven women, 19.4%). Hs-cTnI measured by the Vitros assay was the first hs-cTn to peak at 9.1 h (95%-CI 6.2-10.1) after PCI and 11.7 h (95%-CI 10.4-14.8) after symptoms onset. There were no notable differences between hs-cTn assays in regard to time-to-peak. Also, Vitros hs-cTnI reached the highest median ratio of concentration to upper reference level of nearly 2,000. The median half-life from peak concentration ranged from 7.6 h for myoglobin (CI 6.8-8.6) to 17.8 h for CK (CI 6.8-8.6). For hs-cTn assays the median T½ ranged from 12.4 h for the Vista hs-cTnI assay (95%-CI 11.0-14.1 h) to 17.3 h for the Elecsys hs-cTnT (95%-CI 14.9-20.8 h).

Conclusions

This study updates knowledge on the kinetics of cardiac biomarkers in current clinical use. There was no notable difference in trajectories, time-to-peak or half-life between hs-cTn assays.

Confidence intervals for prevalence estimates from complex surveys with imperfect assays

There are established methods for estimating disease prevalence with associated confidence intervals for complex surveys with perfect assays, or simple random sample surveys with imperfect assays. We develop and study methods for the complicated case of complex surveys with imperfect assays. The new methods use the melding method to combine gamma intervals for directly standardized rates and established adjustments for imperfect assays by estimating sensitivity and specificity. One of the new methods appears to have at least nominal coverage in all simulated scenarios. We compare our new methods to established methods in special cases (complex surveys with perfect assays or simple surveys with imperfect assays). In some simulations, our methods appear to guarantee coverage, while competing methods have much lower than nominal coverage, especially when overall prevalence is very low. In other settings, our methods are shown to have higher than nominal coverage. We apply our method to a seroprevalence survey of SARS-CoV-2 in undiagnosed adults in the United States between May and July 2020.

Evaluation of four gamma-based methods for calculating confidence intervals for age-adjusted mortality rates when data are sparse

Background

Equal-tailed confidence intervals that maintain nominal coverage (0.95 or greater probability that a 95% confidence interval covers the true value) are useful in interval-based statistical reliability standards, because they remain conservative. For age-adjusted death rates, while the Fay–Feuer gamma method remains the gold standard, modifications have been proposed to streamline implementation and/or obtain more efficient intervals (shorter intervals that retain nominal coverage).

Methods

This paper evaluates three such modifications for use in interval-based statistical reliability standards, the Anderson–Rosenberg, Tiwari, and Fay–Kim intervals, when data are sparse and sample size-based standards alone are overly coarse. Initial simulations were anchored around small populations (P = 2400 or 1200), the median crude all-cause US mortality rate in 2010–2019 (833.8 per 100,000), and the corresponding age-specific probabilities of death. To allow for greater variation in the age-adjustment weights and age-specific probabilities, a second set of simulations draws those at random, while holding the mean number of deaths at 20 or 10. Finally, county-level mortality data by race/ethnicity from four causes are selected to capture even greater variation: all causes, external causes, congenital malformations, and Alzheimer disease.

Results

The three modifications had comparable performance when the number of deaths was large relative to the denominator and the age distribution was as in the standard population. However, for sparse county-level data by race/ethnicity for rarer causes of death, and for which the age distribution differed sharply from the standard population, coverage probability in all but the Fay–Feuer method sometimes fell below 0.95. More efficient intervals than the Fay–Feuer interval were identified under specific circumstances. When the coefficient of variation of the age-adjustment weights was below 0.5, the Anderson–Rosenberg and Tiwari intervals appeared to be more efficient, whereas when it was above 0.5, the Fay–Kim interval appeared to be more efficient.

Conclusions

As national and international agencies reassess prevailing data presentation standards to release age-adjusted estimates for smaller areas or population subgroups than previously presented, the Fay–Feuer interval can be used to develop interval-based statistical reliability standards with appropriate thresholds that are generally applicable. For data that meet certain statistical conditions, more efficient intervals could be considered.

Epidemiological Study of Lung Cancer and Clinical Medication in England from 2001 to 2019

We aimed to explore the epidemiological characteristics and changes of lung cancer and the clinical medication in England from 2001 to 2019. We searched related research using search engine systems such as MEDLINE, PubMed, and PsychINFO. Lung cancer is a serious disease and the prognosis is usually very poor. The overall mortality rate of lung cancer decreased year by year in England from 2001 to 2019, but men, the elderly, and people exposed to polluted air are still more likely to be infected with lung cancer or die as a result, the prevalence and mortality rate of lung cancer in the north of England is significantly higher than that in the south, and the gap is increasing year by year. Lung cancer has changeable risk factors such as quitting smoking and improving air quality, which can effectively reduce the related risk. Paclitaxel, docetaxel, gemcitabine, and vinorelbine are the main drugs for the treatment of lung cancer in England and the treatment of these drugs is beneficial to the survival and quality of life of patients. Men and the elderly are at high risk of lung cancer, which means that lung cancer has obvious gender inequality and age inequality. At the same time, based on the statistical data of lung cancer risk in different regions, it can be concluded that lung cancer also has strong geographical and economic inequality. Changing risk factors and using drugs can effectively reduce the risk of lung cancer and provide effective treatment.