Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Methods for calculating credible intervals for ratios of beta distributions with application to relative risks of death during the second plague pandemic.

ABSTRACT: Employing historical records we are able to estimate the risk of premature death during the second plague pandemic, and identify the Black Death and pestis secunda epidemics. We show a novel method of calculating Bayesian credible intervals for a ratio of beta distributed random variables and use this to quantify uncertainty of relative risk estimates for these two epidemics which we consider in a 2 × 2 contingency table framework.

SUBMITTER: Bekker-Nielsen Dunbar M

PROVIDER: S-EPMC6386284 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Methods for calculating credible intervals for ratios of beta distributions with application to relative risks of death during the second plague pandemic.

Bekker-Nielsen Dunbar Maria M Finnie Thomas J R TJR Sloane Barney B Hall Ian M IM

PloS one 20190222 2

Employing historical records we are able to estimate the risk of premature death during the second plague pandemic, and identify the Black Death and pestis secunda epidemics. We show a novel method of calculating Bayesian credible intervals for a ratio of beta distributed random variables and use this to quantify uncertainty of relative risk estimates for these two epidemics which we consider in a 2 × 2 contingency table framework. ...[more]

PMID: 30794569

Similar Datasets

Fine-mapping within eQTL Credible Intervals by E-CROPseq

Project description:Precise identification of causal variants within credible intervals of eQTL associations is needed to identify regulatory GWAS variants. We show that CROPseq, namely multiplex CRISPR-Cas9 genome editing combined with single cell RNAseq, is a viable strategy for fine mapping regulatory SNPs. Mutations were induced nearby 67 SNPs in three genes, two of which, rs2251039 and rs17523802, significantly altered CISD1 and PARK7 expression, respectively, and overlap with chromatin accessibility peaks.

2019-11-28 | GSE140553 | GEO

Methods for calculating confidence and credible intervals for the residual between-study variance in random effects meta-regression models.

Project description:Meta-regression is becoming increasingly used to model study level covariate effects. However this type of statistical analysis presents many difficulties and challenges. Here two methods for calculating confidence intervals for the magnitude of the residual between-study variance in random effects meta-regression models are developed. A further suggestion for calculating credible intervals using informative prior distributions for the residual between-study variance is presented.Two recently proposed and, under the assumptions of the random effects model, exact methods for constructing confidence intervals for the between-study variance in random effects meta-analyses are extended to the meta-regression setting. The use of Generalised Cochran heterogeneity statistics is extended to the meta-regression setting and a Newton-Raphson procedure is developed to implement the Q profile method for meta-analysis and meta-regression. WinBUGS is used to implement informative priors for the residual between-study variance in the context of Bayesian meta-regressions.Results are obtained for two contrasting examples, where the first example involves a binary covariate and the second involves a continuous covariate. Intervals for the residual between-study variance are wide for both examples.Statistical methods, and R computer software, are available to compute exact confidence intervals for the residual between-study variance under the random effects model for meta-regression. These frequentist methods are almost as easily implemented as their established counterparts for meta-analysis. Bayesian meta-regressions are also easily performed by analysts who are comfortable using WinBUGS. Estimates of the residual between-study variance in random effects meta-regressions should be routinely reported and accompanied by some measure of their uncertainty. Confidence and/or credible intervals are well-suited to this purpose.

| S-EPMC4160560 | biostudies-other

Dynamic classification using credible intervals in longitudinal discriminant analysis.

Project description:Recently developed methods of longitudinal discriminant analysis allow for classification of subjects into prespecified prognostic groups using longitudinal history of both continuous and discrete biomarkers. The classification uses Bayesian estimates of the group membership probabilities for each prognostic group. These estimates are derived from a multivariate generalised linear mixed model of the biomarker's longitudinal evolution in each of the groups and can be updated each time new data is available for a patient, providing a dynamic (over time) allocation scheme. However, the precision of the estimated group probabilities differs for each patient and also over time. This precision can be assessed by looking at credible intervals for the group membership probabilities. In this paper, we propose a new allocation rule that incorporates credible intervals for use in context of a dynamic longitudinal discriminant analysis and show that this can decrease the number of false positives in a prognostic test, improving the positive predictive value. We also establish that by leaving some patients unclassified for a certain period, the classification accuracy of those patients who are classified can be improved, giving increased confidence to clinicians in their decision making. Finally, we show that determining a stopping rule dynamically can be more accurate than specifying a set time point at which to decide on a patient's status. We illustrate our methodology using data from patients with epilepsy and show how patients who fail to achieve adequate seizure control are more accurately identified using credible intervals compared to existing methods.

| S-EPMC5655752 | biostudies-literature

Believability of relative risks and odds ratios in abstracts: cross sectional study.

Project description:ObjectiveTo compare the distribution of P values in abstracts of randomised controlled trials with that in observational studies, and to check P values between 0.04 and 0.06.DesignCross sectional study of all 260 abstracts in PubMed of articles published in 2003 that contained "relative risk" or "odds ratio" and reported results from a randomised trial, and random samples of 130 abstracts from cohort studies and 130 from case-control studies. P values were noted or calculated if unreported.Main outcome measuresPrevalence of significant P values in abstracts and distribution of P values between 0.04 and 0.06.ResultsThe first result in the abstract was statistically significant in 70% of the trials, 84% of cohort studies, and 84% of case-control studies. Although many of these results were derived from subgroup or secondary analyses, or biased selection of results, they were presented without reservations in 98% of the trials. P values were more extreme in observational studies (P < 0.001) and in cohort studies than in case-control studies (P = 0.04). The distribution of P values around P = 0.05 was extremely skewed. Only five trials had 0.05 < or = P < 0.06, whereas 29 trials had 0.04 < or = P < 0.05. I could check the calculations for 27 of these trials. One of four non-significant results was significant. Four of the 23 significant results were wrong, five were doubtful, and four could be discussed. Nine cohort studies and eight case-control studies reported P values between 0.04 and 0.06, but in all 17 cases P < 0.05. Because the analyses had been adjusted for confounders, these results could not be checked.ConclusionsSignificant results in abstracts are common but should generally be disbelieved.

| S-EPMC1523498 | biostudies-literature

Fine-mapping within eQTL credible intervals by expression CROP-seq.

Project description:The majority of genome-wide association study (GWAS)-identified SNPs are located in noncoding regions of genes and are likely to influence disease risk and phenotypes by affecting gene expression. Since credible intervals responsible for genome-wide associations typically consist of ≥100 variants with similar statistical support, experimental methods are needed to fine map causal variants. We report here a moderate-throughput approach to identifying regulatory GWAS variants, expression CROP-seq, which consists of multiplex CRISPR-Cas9 genome editing combined with single-cell RNAseq to measure perturbation in transcript abundance. Mutations were induced in the HL60/S4 myeloid cell line nearby 57 SNPs in three genes, two of which, rs2251039 and rs35675666, significantly altered CISD1 and PARK7 expression, respectively, with strong replication and validation in single-cell clones. The sites overlap with chromatin accessibility peaks and define causal variants for inflammatory bowel disease at the two loci. This relatively inexpensive approach should be scalable for broad surveys and is also implementable for the fine mapping of individual genes.

| S-EPMC7334875 | biostudies-literature

Sex- and age-specific reference intervals for diagnostic ratios reflecting relative activity of steroidogenic enzymes and pathways in adults.

Project description:ObjectiveDiagnostic ratios calculated from urinary steroid hormone metabolites are used as a measure for the relative activity of steroidogenic enzymes or pathways in the clinical investigation of steroid metabolism disorders. However, population-based sex- and age-specific reference intervals and day-night differences in adults are lacking.MethodsSixty-five diagnostic ratios were calculated from steroid metabolites measured by GC-MS in day- and night-time and in 24-hour urine from 1128 adults recruited within the Swiss Kidney Project on Genes in Hypertension (SKIPOGH), a population-based, multicenter cohort study. Differences related to sex, age and day- and night-time were evaluated and reference curves in function of age and sex were modelled by multivariable linear mixed regression for diagnostic ratios and were compared to values from the literature.ResultsMost ratios had sex- and age-specific relationships. For each ratio, percentiles were plotted in function of age and sex in order to create reference curves and sex- and age-specific reference intervals derived from 2.5th and 97.5th percentiles were obtained. Most ratios reflected a higher enzyme activity during the day compared to the night.ConclusionsSex- and age-specific references for 24 hours, day and night urine steroid metabolite ratios may help distinguishing between health and disease when investigating human disorders affecting steroid synthesis and metabolism. The day-night differences observed for most of the diagnostic ratios suggest a circadian rhythm for enzymes involved in human steroid hormones metabolism.

| S-EPMC8266106 | biostudies-literature

Second primary cancer after female breast cancer: Familial risks and cause of death.

Project description:BackgroundWith continuous increases in survival rates following breast cancer (BC) diagnosis, the challenge of multiple primary cancers has become an issue. The data on familial risk of SPCs after BC diagnosis and the related mortality in BC patients are scarce.MethodsA total of 87 752 female BC patients were followed for SPC diagnoses and records of death. Relative risks (RRs) of SPC in BC patients who had first-degree relatives (parents or siblings) affected by the same cancer were compared to the patients without family history. Causes of death were compared between patients with and without SPC.ResultsAfter a median follow-up of 5 years, 14 952 BC patients developed SPCs, among which 10 280 (68.8%) had first-degree relatives diagnosed with cancer. Familial risks were significant for 14 site-specific SPCs, and the highest risk was for second ovarian cancer (RR = 6.28, 95%CI: 4.50-8.75), compared to those without family history (1.49, 1.34-1.65). In patients with SPC, SPC was the main cause of death, including diverse cancers and BC in approximately equal proportions.ConclusionsFamily history contributed to the excess number of patients with SPCs, and SPC was the leading cause of death in patients with SPC. Taking family history at diagnosis of BC may provide warning signs with regard to possible subsequent SPCs and may offer possibilities for counseling, intervention and management.

| S-EPMC6346247 | biostudies-literature

Acceleration of plague outbreaks in the second pandemic.

Project description:Historical records reveal the temporal patterns of a sequence of plague epidemics in London, United Kingdom, from the 14th to 17th centuries. Analysis of these records shows that later epidemics spread significantly faster ("accelerated"). Between the Black Death of 1348 and the later epidemics that culminated with the Great Plague of 1665, we estimate that the epidemic growth rate increased fourfold. Currently available data do not provide enough information to infer the mode of plague transmission in any given epidemic; nevertheless, order-of-magnitude estimates of epidemic parameters suggest that the observed slow growth rates in the 14th century are inconsistent with direct (pneumonic) transmission. We discuss the potential roles of demographic and ecological factors, such as climate change or human or rat population density, in driving the observed acceleration.

| S-EPMC7959508 | biostudies-literature

How smoking affects the proportion of deaths attributable to obesity: assessing the role of relative risks and weight distributions.

Project description:Although ever-smokers make up the majority of the older adult population in the USA, they are often excluded from studies examining the impact of obesity on mortality. Understanding how smoking and obesity interact is critical to assessing the proportion of deaths attributable to obesity.Nationally representative sample of the non-institutionalised population of the USA. Baseline data were drawn from the National Health and Nutrition Examination Survey, 1988-1994 and 1999-2004.US adults aged 50-74 (n=9835).We used Cox models to estimate the mortality risks of obesity by smoking status. All-cause mortality was assessed prospectively through 31 December 2006 (n=1243 deaths). Maximum body mass index (BMI) was specified as the key exposure variable. We also calculated population attributable fractions (PAFs) by smoking status and investigated differences in PAFs in a decomposition analysis.The HR associated with a one-unit increment in BMI beyond 25.0 kg/m(2) was 1.057 for never-smokers (95% CI 1.033 to 1.082; p<0.001), 1.036 for former smokers (95% CI 1.015 to 1.059; p<0.01) and 1.024 for current smokers (95% CI 0.997 to 1.052).We estimated that 19.8% of deaths were attributable to excess weight. The PAFs were 31.9, 20.4 and 11.3 for never-smokers, former and current smokers, respectively. The difference in PAFs between never-smokers and current smokers was almost entirely explained by the difference in HRs.The proportion of deaths attributable to obesity is nearly 3 times as high among never-smokers compared with current smokers. This finding is consistent with the fact that smokers are subject to significant competing risks. Analyses that exclude smokers are likely to substantially overestimate the proportion of deaths attributable to obesity in the USA.

| S-EPMC4769428 | biostudies-other