U.S. breast cancer incidence has been changing, as have distributions of risk factors, including body mass index (BMI), age at menarche, age at first live birth, and number of live births. Using data for U.S. women from large nationally representative surveys, we estimated risk factor distributions from 1980 to 2008. To estimate ecologic associations with breast cancer incidence, we fitted Poisson models to age- and calendar year-specific incidence data from the NCI's Surveillance, Epidemiology and End Results registries from 1980 to 2011. We then assessed the proportion of incidence attributable to specific risk factors by comparing incidence from models that only included age and calendar period as predictors with models that additionally included age- and cohort-specific categorized mean risk factors. Analyses were stratified by age and race. Ecologic associations usually agreed with previous findings from analytic epidemiology. From 1980 to 2011, compared with the risk factor reference level, increased BMI was associated with 7.6% decreased incidence in women ages 40 to 44 and 2.6% increased incidence for women ages 55 to 59. Fewer births were associated with 22.2% and 3.99% increased incidence in women ages 40 to 44 and 55 to 59 years, respectively. Changes in age at menarche and age at first live birth in parous women did not significantly impact population incidence from 1980 to 2011. Changes in BMI and number of births since 1980 significantly impacted U.S. breast cancer incidence. Quantifying long-term impact of risk factor trends on incidence is important to understand the future breast cancer burden and inform prevention efforts. .
We investigate a method to estimate the combined effect of multiple continuous/ordinal mediators on a binary outcome: 1) fit a structural equation model with probit link for the outcome and identity/probit link for continuous/ordinal mediators, 2) predict potential outcome probabilities, and 3) compute natural direct and indirect effects. Step 2 involves rescaling the latent continuous variable underlying the outcome to address residual mediator variance/covariance. We evaluate the estimation of risk-difference- and risk-ratio-based effects (RDs, RRs) using the ML, WLSMV and Bayes estimators in Mplus. Across most variations in path-coefficient and mediator-residual-correlation signs and strengths, and confounding situations investigated, the method performs well with all estimators, but favors ML/WLSMV for RDs with continuous mediators, and Bayes for RRs with ordinal mediators. Bayes outperforms WLSMV/ML regardless of mediator type when estimating RRs with small potential outcome probabilities and in two other special cases. An adolescent alcohol prevention study is used for illustration.
The demography, survival, and motor phenotypes of amyotrophic lateral sclerosis (ALS) patients have been rarely described in Hispanic countries. The clinical characteristics and survival of a series of Mexican ALS patients are described. Mexican patients with definite ALS were included in a five-year retrospective longitudinal study. Their demographic and clinical features, cumulative survival rates, and independent predictive factors for survival were analysed. Sixty-one definite ALS patients were included. The median follow-up period was 35 months (range 12-108 months). Males were predominant (1.8: 1), the mean age at onset was 47.5 ± 10.5 years, and the median interval from onset to diagnosis was 12 months. Spinal onset occurred in 66% of patients. Upper motor neuron phenotype was predominant in 53% of patients. The overall mean survival from onset was 68.6 months, and from diagnosis was 57.8 months. Longer survival was determined in patients aged ≤ 40 years (54.7 months) compared with other age groups (p = 0.006). In conclusion, the clinical heterogeneity, male predominance, and survival rates in our sample are consistent with those of other studies. Patients in this series had a younger age at onset and a clear trend toward longer survival compared with those of other population studies.
We present a validated risk stratification tool for unplanned readmissions following open-VHR. Future studies should determine if implementation of our CRS optimizes safety and reduces readmission rates in open-VHR patients.
Although covariate measurement error is likely the norm rather than the exception, methods for handling covariate measurement error in propensity score methods have not been widely investigated. We consider a multiple imputation-based approach that uses an external calibration sample with information on the true and mismeasured covariates, Multiple Imputation for External Calibration (MI-EC), to correct for the measurement error, and investigate its performance using simulation studies. As expected, using the covariate measured with error leads to bias in the treatment effect estimate. In contrast, the MI-EC method can eliminate almost all the bias. We confirm that the outcome must be used in the imputation process to obtain good results, a finding related to the idea of congenial imputation and analysis in the broader multiple imputation literature. We illustrate the MI-EC approach using a motivating example estimating the effects of living in a disadvantaged neighborhood on mental health and substance use outcomes among adolescents. These results show that estimating the propensity score using covariates measured with error leads to biased estimates of treatment effects, but when a calibration data set is available, MI-EC can be used to help correct for such bias.
Big Data are of increasing importance in a variety of areas, especially in the biosciences. There is an emerging critical need for Big Data tools and methods, because of the potential impact of advancements in these areas. Importantly, statisticians and statistical thinking have a major role to play in creating meaningful progress in this arena. We would like to emphasize this point in this special issue, as it highlights both the dramatic need for statistical input for Big Data analysis and for a greater number of statisticians working on Big Data problems. We use the field of statistical neuroimaging to demonstrate these points. As such, this paper covers several applications and novel methodological developments of Big Data tools applied to neuroimaging data.
Capping completes the closure of parenteral drug products in the final packaging container and is critical in maintaining an integral seal to ensure product quality. Residual seal force (RSF) is considered the sole quantifiable attribute for measuring seal goodness and potentially enables non-subjective, consistent setting of cappers across manufacturing sites. However, the consistency and reliability of RSF measurement and data have been scarcely reported, and the relationship between RSF and container closure integrity (CCI) remains poorly understood. Here, we present a large data set generated from a commercial capper and the results from a laboratory capper of glass vials and rubber stoppers with aluminum caps. All RSF values exhibited significant variability. We evaluated three potential sources of variability: the capper, the RSF Tester, and the components. We determined that the capper and Tester are not main sources. Dimensional tolerances of the packaging components were the root cause. This study correlated RSF with CCI (via helium leakage) although CCI is not sensitive to RSF; CCI was maintained even for loosely capped vials with no measurable RSF. This was attributed to the stoppers two sealing surfaces: the valve seal and the land seal. A methodology capable of differentiating the two seals functions demonstrated that vials with only the valve seal always passed leakage testing, while vials with only the land seal failed CCI at low RSF values. This observation allows proposal of a low RSF limit that is safe even when the valve seal is defective. Statistical analysis of commercial capping data, with the input of sample size, allowed the relationship between RSFs low limit and an allowable failing rate to be established. Overall, despite the inherent variability of RSF, this study shows that it is a feasible parameter for capping process quantification and demonstrates the potential of RSF measurement in capper setup.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.