Nonparametric and semiparametric inference on quantile lost lifespan

A new summary measure for time-to-event data, termed lost lifespan, is proposed in which the existing concept of reversed percentile residual life, or percentile inactivity time, is recast to show that it can be used for routine analysis to summarize life lost. The lost lifespan infers the distribution of time lost due to experiencing an event of interest before some specified time point. An estimating equation approach is adopted to avoid estimation of the probability density function of the underlying time-to-event distribution to estimate the variance of the quantile estimator. A K-sample test statistic is proposed to test the ratio of quantile lost lifespans. Simulation studies are performed to assess finite properties of the proposed statistic in terms of coverage probability and power. The concept of life lost is then extended to a regression setting to analyze covariate effects on the quantiles of the distribution of the lost lifespan under right censoring. An estimating equation, variance estimator, and minimum dispersion statistic for testing the significance of regression parameters are proposed and evaluated via simulation studies. The proposed approach reveals several advantages over existing methods for analyzing time-to-event data, which is illustrated with a breast cancer dataset from a Phase III clinical trial conducted by the National Surgical Adjuvant Breast and Bowel Project. Public Health Significance: The analysis of time-to-event data can provide important information about new treatments and therapies, particularly in clinical trial settings. The methods provided in this dissertation will allow public health researchers to analyze effectiveness of new treatments in terms of a new summary measure, life loss. In addition to providing statistical advantages over existing methods, analyzing time-to-event data in terms of the lost lifespan provides a more straightforward interpretation beneficial to clinicians, patients, and other stakeholders

Dietary Fat and Fatty Acid Intake in Nulliparous Women: Associations with Preterm Birth and Distinctions by Maternal BMI

Background: Evidence documenting whether diet quality, particularly dietary fatty acids, is associated with preterm birth (PTB) is limited. Objective: The aim was to measure associations between dietary fatty acid intake prior to pregnancy, specifically n-3 (ɷ-3) PUFAs and odds of PTB in US women and determine if associations differed by prepregnancy BMI. Methods: We designed a secondary analysis of dietary intake in nulliparous women enrolled in a longitudinal cohort (NCT01322529). Participants completed an FFQ, modified to assess detailed PUFA intake, during the 3 mo preceding pregnancy. Inclusion in this analytic cohort required total energy intake within 2 SDs of the group mean. Prepregnancy BMI was categorized as underweight, normal, overweight, or obese. The primary exposure was estimated intake of EPA and DHA (combined EPA+DHA), in the context of a recommended intake of 250 mg. The primary outcome was PTB (<37 wk). Adjusted regression models controlled for maternal factors relevant to PTB and evaluated associations with PUFAs. Interaction terms estimated effect modification of BMI. A false discovery rate (FDR) correction accounted for multiple comparisons. Results: Median daily intake of combined EPA+DHA in 7365 women was 70 mg (IQR: 32, 145 mg). A significant interaction term indicated the effects of EPA+DHA on odds of PTB were different for different BMI categories (P < 0.01). Specifically, higher intake of combined EPA+DHA was nominally associated with reduced odds of PTB in women with underweight (OR: 0.67; 95% CI: 0.46-0.98) and normal BMI (OR: 0.87; 95% CI: 0.78-0.96), yet was associated with increased odds of overweight BMI (OR: 1.21; 95% CI: 1.02-1.44). Associations remained significant after FDR correction. Conclusions: Based on a cohort of US women designed to identify predictors of adverse pregnancy outcomes, dietary intake of combined EPA+DHA was considerably lower than recommended. Associations between intake of these recommended n-3 fatty acids and risk of PTB differ by maternal BMI

Heart rate variability as a marker of recovery from critical illness in children.

ObjectivesThe purpose of this study was to Identify whether changes in heart rate variability (HRV) could be detected as critical illness resolves by comparing HRV from the time of pediatric intensive care unit (PICU) admission with HRV immediately prior to discharge. We also sought to demonstrate that HRV derived from electrocardiogram (ECG) data from bedside monitors can be calculated in critically-ill children using a real-time, streaming analytics platform.MethodsThis was a retrospective, observational pilot study of 17 children aged 0 to 18 years admitted to the PICU of a free-standing, academic children's hospital. Three time-domain measures of HRV were calculated in real-time from bedside monitor ECG data and stored for analysis. Measures included: root mean square of successive differences between NN intervals (RMSSD), percent of successive NN interval differences above 50 ms (pNN50), and the standard deviation of NN intervals (SDNN).ResultsHRV values calculated from the first and last 24 hours of PICU stay were analyzed. Mixed effects models demonstrated that all three measures of HRV were significantly lower during the first 24 hours compared to the last 24 hours of PICU admission (pConclusionHRV was significantly lower in the first 24 hours compared to the 24 hours preceding PICU discharge, after resolution of critical illness. This demonstrates that it is feasible to detect changes in HRV using an automated, streaming analytics platform. Continuous tracking of HRV may serve as a marker of recovery in critically ill children

Additional file 2: Table S2. of An updated re-analysis of the mortality risk from nasopharyngeal cancer in the National Cancer Institute formaldehyde worker cohort study

a NCI FA cohort, RR analysis using highest peak FA exposure (ppm), asymptotic estimation. b NCI FA cohort, RR analysis using average intensity of FA exposure (ppm), asymptotic estimation. c NCI FA cohort, RR analysis using cumulative FA exposure (ppm-years), asymptotic estimation. d NCI FA cohort, RR analysis using duration of FA exposure (years), asymptotic estimation. (DOCX 54 kb

Additional file 4: Table S4. of An updated re-analysis of the mortality risk from nasopharyngeal cancer in the National Cancer Institute formaldehyde worker cohort study

a Observed deaths and interval RRs or β coefficients by peak FA exposure, asymptotic estimation. b Observed deaths and interval RRs or β coefficients by average FA intensity exposure, asymptotic estimation. c Observed deaths and RRs or β coefficients by cumulative FA exposure, asymptotic estimation. d Observed deaths and RRs or β coefficients by duration of FA exposure, asymptotic estimation. (DOCX 66 kb

Impact of Coal Mining on Self-Rated Health among Appalachian Residents

Objective. To determine the impact of coal mining, measured as the number of coal mining-related facilities nearby one’s residence or employment in an occupation directly related to coal mining, on self-rated health in Appalachia. Methods. Unadjusted and adjusted ordinal logistic regression models calculated odds ratio estimates and associated 95% confidence intervals for the probability of having an excellent self-rated health response versus another response. Covariates considered in the analyses included number of coal mining-related facilities nearby one’s residence and employment in an occupation directly related to coal mining, as well as potential confounders age, sex, BMI, smoking status, income, and education. Results. The number of coal mining facilities near the respondent’s residence was not a statistically significant predictor of self-rated health. Employment in a coal-related occupation was a statistically significant predictor of self-rated health univariably; however, after adjusting for potential confounders, it was no longer a significant predictor. Conclusions. Self-rated health does not seem to be associated with residential proximity to coal mining facilities or employment in the coal industry. Future research should consider additional measures for the impact of coal mining

Correction: Patterns and Trends in Accidental Poisoning Deaths: Pennsylvania's Experience 1979-2014.

[This corrects the article DOI: 10.1371/journal.pone.0151655.]

Analysis og gene expression in prokaryotic and eukaryotic model organisms by proteomic gel-based separation tools

This PhD thesis showed the applicability of a gel-based proteomic separation tool, 2-D electrophoresis in three independent projects. Supplemented with results obtained using different techniques the proteomic studies enabled a global imaging of proteoms in the studied biological systems. Comparing total proteoms of E. coli 61 protein changes were identified and connected with the development of the bacterial population in the presence of an antibiotic compound, erythromycin. This classic proteomic approach included sample extraction, optimization of its 2D separation followed by 2D gel analysis and protein identification by MS methods. A disadvantage of this work was an enourmously large amount of data to be analyzed by computer analysis. For the study of membrane proteom of B. subtilis during a pH induced stress, on the other hand, a modification of isolation techniques for membrane and membrane associated proteins was required first to improve the subsequent protein separation by 2-D electrophoresis. The optimalization of protein extraction included changes in detergents used for protein solubilization and a prolongation of time periods in the protein solubilization protocol. 5 relevant protein changes were then described that play a role in the bacterial response to pH stress. The proteins were..

PA Age-Specific Accidental Poisoning Mortality Rates Per 100,000 by Race and Sex, 2010–2014.

<p>PA Age-Specific Accidental Poisoning Mortality Rates Per 100,000 by Race and Sex, 2010–2014.</p