Confidence Intervals For Quantiles Research Articles

CLL Incidence Based-Mortality By Gender: Is There a Change in the Probability of Dying from CLL Today Among Men and Women?

CLL Incidence Based-Mortality by Gender: Is there a change in the probability of dying from CLL today among men and women? Background: About one-fourth of newly diagnosed cases of leukemia are CLL. The lifetime risk of developing CLL is approximately 1 in 175 (0.57%) for the average person. Men are slightly more at risk than women are. We looked at the incidence-based mortality rates (probability of dying from) of CLL by gender in adult populations in the United States from 2000-2019 via SEER.gov database. Methods: We used Incidence -Based Mortality SEER Research Data, 17 Registries, Nov 2021 Sub (2000-2019), which provides the broadest geographic coverage for incidence-based mortality rates and is roughly 26.5% of the US population (based on the 2020 Census) for the calculation of incidence rates of CLL. SEER*Stat 8.4.1 was used to obtain age-adjusted rates for different age-groups, races and genders. We used Join-point software, version 5.0.2. by the National Cancer Institute, annually to create log-linear time trends. There are no P-values noted for the CI as Joinpoint does not currently produce a test statistic or p-value for Empirical Quantile confidence intervals. Results: Fig 1: CLL Incidence Based Mortality Rate per 100,000 and age-adjusted to the 2000 US standard population for males and females 2000-2019 Our study showed increasing incidence based mortality rates, with higher rates in males compared to females. We also noted significant Joinpoints in 2002, 2005 and 2012 for males and2003, and 2010 for females. It is of note that at the Joinpoints, the slope for the rate significantly changes or drops. The Average Annual Percent Change (AAPC) for males and females were 10.64%*(95% CI 9.95 to 11.53) and 10.09%*( 95% CI 9.41 to 11.44) Conclusion: Our study reveals that the slope of incidence-based mortality has significantly reduced over the years from 2000-2019. This means the probability of dying from CLL has also significantly dropped over the years. This pattern points to changes in guidelines or therapy that have significantly impacted the probability of dying from CLL. This suggests that more investigation is warranted to understand the underlying causes for the trends appreciated.

Incidence-Based Mortality Rates for Chronic Myeloid Leukemia (CML) in Adult US Populations By Gender and Race: The Aftermath of Tyrosine Kinase Inhibitors (TKIs)

Incidence-Based Mortality Rates For Chronic Myeloid Leukemia (CML) In Adult US Populations by Gender and Race: The Aftermath of Tyrosine Kinase Inhibitors (TKIs) Background: Chronic Myeloid Leukemia (CML) is one of the leading causes of Leukemia in North America and world wide. According to the American Cancer Society, 1 in 526 Americans will develop CML during their lifetime. CML accounts for about 15% of all new cases of leukemia. Estimates for new cases diagnosed in the United States in 2023 is about 8,930, with roughly 42% of these new cases in females and 58% in males. We looked at the incidence based mortality rates (probability of dying from) of CML by gender and race in adult populations in the United States from 2000-2019 via SEER.gov database. Methods: We used Incidence -Based Mortality SEER Research Data, 17 Registries, Nov 2021 Sub (2000-2019), which provides the broadest geographic coverage for incidence-based mortality rates and is roughly 26.5% of the US population (based on the 2020 Census) for the calculation of incidence rates of CML. SEER*Stat 8.4.1 was used to obtain age-adjusted rates for different age-groups, races and genders. We used Join-point software, version 5.0.2. by the National Cancer Institute, annually to create log-linear time trends. There are no P-values noted for the CI as Joinpoint does not currently produce a test statistic or p-value for Empirical Quantile confidence intervals. Results: Fig 1: CML Incidence Based Mortality Rate per 100,000 and age-adjusted to the 2000 US standard population for males and females 2000-2019 We identified a total of 1,183,747,806 diagnosed cases of CML between 2000-2019 among males and females in the United States. A dainty rise in the number of diagnosed cases was appreciated over the years, with 52,890,308 cases diagnosed in the year 2000 and 64,920,828 in the year 2019. The trend for CML among males show an increasing Incidence Based Mortality between 2000-2019 with an Average Annual Percent Change(AAPC) of 4.69%*CI 3.40 to 5.95), and 5.85%*(CI 4.54 to 8.35) in females. The AAPC for Whites, Black, American Indian/Alaska native and Asian or Pacific Islander were 5.51%* (95% CI 4.72 to 6.59), 2.03%*(95% CI 0.64 to 3.79), 3.12% (-2.15 to 10.78), and 2.42%*(95% CI 0.22 to 6.05) respectively. Conclusion: The denouement of our study shows a significant high rate of change in increasing incidence based mortality rate of CML in the US adult population from ages 20 years and older prior to 2002.The rate of change significantly drops after 2002, as noted in males, females and whites. Incidence based mortality was also noted to be significantly higher among males compared to females. Regarding race, the trend shows higher rates among whites and blacks compared to Asians or American Indians. The evidence points to some changes or events in or around the year 2002 that have significantly impacted the probability of dying from CML in the adult populations in the United States. This significant change coincides with the approval of Tyrosine Kinase inhibitors, with Imatinib (Gleevec) being the first one approved in 2001.

ISCEV standard pattern reversal VEP development: paediatric reference limits from 649 healthy subjects

PurposeTo establish the extent of agreement for ISCEV standard reference pattern reversal VEPs (prVEPs) acquired at three European centres, to determine any effect of sex, and to establish reference intervals from birth to adolescence.MethodsPrVEPs were recorded from healthy reference infants and children, aged 2 weeks to 16 years, from three centres using closely matched but non-identical protocols. Amplitudes and peak times were modelled with orthogonal quadratic and sigmoidal curves, respectively, and two-sided limits, 2.5th and 97.5th centiles, estimated using nonlinear quantile Bayesian regression. Data were compared by centre and by sex using median quantile confidence intervals. The ‘critical age’, i.e. age at which P100 peak time ceased to shorten, was calculated.ResultsData from the three centres were adequately comparable. Sex differences were not clinically meaningful. The pooled data showed rapid drops in P100 peak time which stabilised by 27 and by 34 weeks for large and small check widths, respectively. Post-critical-age reference limits were 87–115 ms and 96–131 ms for large and small check widths, respectively. Amplitudes varied markedly and reference limits for all ages were 5–57 μV and 3.5–56 μV for large and small check widths, respectively.ConclusionsPrVEP reference data could be combined despite some methodology differences within the tolerances of the ISCEV VEP Standard, supporting the clinical benefit of ISCEV Standards. Comparison with historical data is hampered by lack of minimum reporting guidelines. The reference data presented here could be validated or transformed for use elsewhere.

Assessment of the in vivo genotoxic potential of three smoke flavoring primary product mixtures.

Smoke flavorings are mixtures generated from wood pyrolysis that are filtered to remove tar and are often considered healthier alternatives to conventional smoking processes. While the latter is mostly unregulated, smoke-flavoring primary products (SFPPs) are undergoing the 10-year required re-evaluation in the European Union (EU). To comply with recent smoke flavor guidance, in vivo micronucleus studies in rats and transgenic rodent (TGR) mutation assays in Muta™Mice were conducted on three SFPPs. For most studies, typical limit doses were exceeded to comply with regulatory requests. Exposure to SFPPs by oral gavage did not result in significant increases in bone marrow micronucleus formation. Except for one group, exposure to SFPPs via feed for 28 days did not result in significant increases in mutant frequency (MF) in the glandular stomach or liver. One group exposed to a maximal feasible dietary dose of 50,000 ppm (>10,000 mg/kg bodyweight per day) exhibited a statistically significant increase in liver MF; however, the MF in all mice in this group were within the historical vehicle control 95% quantile confidence intervals and therefore not considered biologically relevant. Based on estimates of human dietary exposure to each SFPP, the margin of exposure (MOE) values in the TGR assays exceed 10,000. The MOE for one unintentionally present constituent, 2,5(H)-furanone, also exceeds 10,000. Collectively, these data indicate that these SFPPs pose no genotoxic risk and are safe alternatives to conventional smoking.

The use of fractional order statistics for estimating nonparametric confidence intervals for quantiles of the fatigue damage computed in service random loadings

This paper deals with the statistical variability of fatigue damage in random loadings as caused by the inherent randomness of the loading. It examines the use of a nonparametric method based on fractional order statistics as a tool for constructing confidence intervals for the quantile of fatigue damage, regardless of the knowledge of the damage probability distribution. The approach here discussed relies on a statistical method existing in the literature. After a short review of this method, with also an emphasis on possible advantages and limitations, the paper presents a numerical benchmark, followed by a practical application to time-histories measured on a mountain-bike in off-road tracks. The paper concludes with some critical remarks and an outlook.

MLE-Based Parameter Estimation for Four-Parameter Exponential Gamma Distribution and Asymptotic Variance of Its Quantiles

The choice of a probability distribution function and confidence interval of estimated design values have long been of interest in flood frequency analysis. Although the four-parameter exponential gamma (FPEG) distribution has been developed for application in hydrology, its maximum likelihood estimation (MLE)-based parameter estimation method and asymptotic variance of its quantiles have not been well documented. In this study, the MLE method was used to estimate the parameters and confidence intervals of quantiles of the FPEG distribution. This method entails parameter estimation and asymptotic variances of quantile estimators. The parameter estimation consisted of a set of four equations which, after algebraic simplification, were solved using a three dimensional Levenberg-Marquardt algorithm. Based on sample information matrix and Fisher’s expected information matrix, derivatives of the design quantile with respect to the parameters were derived. The method of estimation was applied to annual precipitation data from the Weihe watershed, China and confidence intervals for quantiles were determined. Results showed that the FPEG was a good candidate to model annual precipitation data and can provide guidance for estimating design values.

A Comparison of Moderate and Extreme ERA‐5 Daily Precipitation With Two Observational Data Sets

AbstractA comparison of moderate to extreme daily precipitation from the ERA‐5 reanalysis by the European Centre for Medium‐Range Weather Forecasts against two observational gridded data sets, EOBS and CMORPH, is presented. We assess the co‐occurrence of precipitation days and compare the full precipitation distributions. The co‐occurrence is quantified by the hit rate. An extended generalized Pareto distribution (EGPD) is fitted to the positive precipitation distribution at every grid point and confidence intervals of quantiles compared. The Kullback–Leibler divergence is used to quantify the distance between the entire EGPDs obtained from ERA‐5 and the observations. For days exceeding the local 90th percentile, the mean hit rate is 65% between ERA‐5 and EOBS (over Europe) and 60% between ERA‐5 and CMORPH (globally). Generally, we find a decrease of the co‐occurrence with increasing precipitation intensity. The agreement between ERA‐5 and EOBS is weaker over the southern Mediterranean region and Iceland compared to the rest of Europe. Differences between ERA‐5 and CMORPH are smallest over the oceans. Differences are largest over NW America, Central Asia, and land areas between 15°S and 15°N. The confidence intervals on quantiles are overlapping between ERA‐5 and the observational data sets for more than 80% of the grid points on average. The intensity comparisons indicate an excellent agreement between ERA‐5 and EOBS over Germany, Ireland, Sweden, and Finland, and a disagreement over areas where EOBS uses sparse input stations. ERA‐5 and CMORPH precipitation intensity agree well over the midlatitudes and disagree over the tropics.

Correction to ‘Distribution‐free Approximate Methods for Constructing Confidence Intervals for Quantiles’

One way of evaluating the quality of a simulation study methodology is to estimate the Monte Carlo Error (MCE). The formula for this calculation was incorrectly implemented in Nagaraja & Nagaraja (2020). The correct formula that replaces equation (24) on page 96 of the original paper is as follows. The MCE was estimated for the confidence interval coverage probability calculations, and those results have been updated in Tables 29–56 of the Supporting Information. No conclusions changed due to this correction. Supporting info item Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

Confidence Intervals for Quantiles in Ranked Set Sampling

This article concerns the problem of interval estimation for the population quantiles in ranked set sampling. Some intervals are developed using asymptotic normality of nonparametric quantile estimator and/or resampling methods. The proposed procedures are evaluated in terms of coverage rate and average length. Some comparisons with analogous intervals in simple random sampling are also made. Finally, a medical data set is used to illustrate application of the intervals.

Distribution‐free Approximate Methods for Constructing Confidence Intervals for Quantiles

SummaryQuantile estimation is important for a wide range of applications. While point estimates based on one or two order statistics are common, constructing confidence intervals around them, however, is a more difficult problem. This paper has two goals. First, it surveys the numerous distribution‐free methods for constructing approximate confidence intervals for quantiles. These techniques can be divided roughly into four categories: using a pivotal quantity, resampling, interpolation, and empirical likelihood methods. Second, a method based on the pivotal quantity that has received limited attention in the past is extended. Comprehensive simulation studies are used to compare performance across methods. The proposed method is simple and performs similarly to linear interpolation methods and a smoothed empirical likelihood method. While the proposed method has slightly wider interval widths, it can be calculated for more extreme quantiles even when there are few observations.

Constructing quantile confidence intervals using extended simple random sample in finite populations

ABSTRACTThis paper constructs quantile confidence intervals based on extended simple random sample (SRS) from a finite population, where ranks of population units are all known. Extended simple random sample borrows additional information from unmeasured observations in the population by conditioning on the population ranks of the measured units in SRS. The confidence intervals are improved using Rao-Blackwell theorem over the conditional distribution of sample ranks given the measured sample units. Empirical evidence shows that the proposed confidence intervals have shorter lengths than confidence intervals constructed from an SRS sample.

Parametric inference for quantile event times with adjustment for covariates on competing risks data

ABSTRACTWe propose parametric inferences for quantile event times with adjustment for covariates on competing risks data. We develop parametric quantile inferences using parametric regression modeling of the cumulative incidence function from the cause-specific hazard and direct approaches. Maximum likelihood inferences are developed for estimation of the cumulative incidence function and quantiles. We develop the construction of parametric confidence intervals for quantiles. Simulation studies show that the proposed methods perform well. We illustrate the methods using early stage breast cancer data.

A comparative study on nonparametric estimation procedures for survival quantiles

In survival or reliability data analysis, it is often useful to estimate the quantiles of the lifetime distribution, such as the median time to failure. Different nonparametric methods can construct confidence intervals for the quantiles of the lifetime distributions, some of which are implemented in commonly used statistical software packages. We here investigate the performance of different interval estimation procedures under a variety of settings with different censoring schemes. Our main objectives in this paper are to (i) evaluate the performance of confidence intervals based on the transformation approach commonly used in statistical software, (ii) introduce a new density-estimation-based approach to obtain confidence intervals for survival quantiles, and (iii) compare it with the transformation approach. We provide a comprehensive comparative study and offer some useful practical recommendations based on our results. Some numerical examples are presented to illustrate the methodologies developed.

Confidence intervals for quantiles from histograms and other grouped data

We introduce methods to obtain confidence intervals for quantiles when only grouped data are available. Our preferred method is to approximate the underlying density using the Generalized Lambda Distribution to estimate the quantiles and variance of the estimators. We compare this method with a frequency approximation approach and another method that uses a linear interpolation approximation of the density. Simulations show that intervals with excellent coverage can be found for a wide number of distributions, including those that are highly skewed distributions. We also apply our methods to real data sets highlighting how intervals can be computed from just histograms.

Assessment of the Concrete Compressive Strength in Existing Structures Based on Core Test Results

Evaluation of the concrete compressive strength in existing structures is an important problem, which is associated with structural reliability estimation as well as a quality control procedure. In accordance with a new concept of EN 13791, reported by T.A.Harrison, one of the main targets of the standard is to determine not a class, but in-situ characteristic concrete compressive strength. Hereby proposed criterion for the estimation of the in-situ characteristic concrete compressive strength is based on the non-parametric confidence interval for quantile. This criterion was verified by the both Monte Carlo simulation and test results under the real concrete structures.

On the distribution-free confidence intervals and universal bounds for quantiles based on joint records

ABSTRACTIn this paper, we consider three distribution-free confidence intervals for quantiles given joint records from two independent sequences of continuous random variables with a common continuous distribution function. The coverage probabilities of these intervals are compared. We then compute the universal bounds of the expected widths of the proposed confidence intervals. These results naturally extend to any number of independent sequences instead of just two. Finally, the proposed confidence intervals are applied for a real data set to illustrate the practical usefulness of the procedures developed here.

Confidence intervals for quantiles based on samples of random sizes

On the basis of failure times of a sample of random size N of iid continuous random variables, we consider the estimation problem of population quantiles of the same distribution. Based on order statistics, confidence intervals for quantile intervals are introduced. Confidence intervals for the difference of quantiles are also investigated. Exact expressions for the coverage probabilities of these intervals are derived and computed numerically. A biometric data set representing the duration of remission of 20 Leukemia patients is used to illustrate the results developed here.

L-Moment-Based Regional Frequency Analysis of Annual Extreme Precipitation and its Uncertainty Analysis

Estimation of precipitation amounts associated with different return periods is an important task for the planning and design of many types of infrastructures. In this study, regional frequency analysis based on L-moments is proposed to estimate the annual maximum daily precipitation quantiles in the Taihu basin, China. The Generalized Extreme Value (GEV) distribution is used to describe the frequency distributions of extreme rainfall events. At-site frequency analysis results based on L-moments are compared with those obtained from regional analysis. The 95% confidence intervals of estimated precipitation quantiles are calculated using Monte Carlo simulations (MCS). Uncertainty assessment results indicate that regional analysis is more robust and more accurate than at-site analysis. Furthermore, when conducting regional frequency analysis, the estimation of precipitation quantile confidence intervals can be simplified by assuming normality for the MCS results. The variation of the precipitation quantiles’ sample statistics for different return periods is expressed as a function of the return period. The proposed methods are useful for the Taihu Basin and are recommended for other regions.

On the Performance of Confidence Intervals for Quantiles

On the Performance of Confidence Intervals for Quantiles

Nonparametric confidence intervals for quantiles based on a modified ranked set sampling

Nonparametric confidence intervals for quantiles based on a modified ranked set sampling