Estimates Of Population Parameters Research Articles

Nonstandard Errors

ABSTRACTIn statistics, samples are drawn from a population in a data‐generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence‐generating process (EGP). We claim that EGP variation across researchers adds uncertainty—nonstandard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for more reproducible or higher rated research. Adding peer‐review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants.

Bayesian Probabilistic Projection of Population Census in the Kingdom of Saudi Arabia

Population census supplies a complete and accurate picture of a country's population and its residents' characteristics. Modeling population growth has been worked upon by different scholars before now with more of a classical approach and less of a Bayesian approach. Therefore, an attempt is made in this work to apply Bayesian probabilistic projection on the usual exponential growth rate model in estimating population parameters and predicting population census in the Kingdom of Saudi Arabia (KSA) across thirteen (13) regions. The obtained data from WorldData and United Nations Population were used for the estimation and projection with the application of appropriate prior, likelihood, and posterior selection through Bayesian inference. This approach is reasonably accurate and well-calibrated with a significant precision of 0.01025 approximately 99% model accuracy for the period due to the estimated population parameters that were used: to make a comparison with the 2019 Population Census of Saudi Arabia which was perfectly closed and to forecast for the next 80 years using out-sample cases.

The importance of independence in unmarked spatial capture–recapture analysis

Wildlife populations can be unmarked, meaning individuals lack distinguishing features for individual identification. Populations may also exhibit non‐independent movements, meaning individuals move together. For populations of either unmarked or non‐independent individuals, models based on spatial capture–recapture (SCR) approaches can be used to estimate abundance, density, and other parameters critical for monitoring, management, and conservation. However, when individuals are both unmarked and non‐independent, few model options are available. One approach has been to apply unmarked models and not address the non‐independence despite unquantified impacts on bias, precision, and the ability to make robust ecological inferences. We conducted a simulation study to quantify the impact of non‐independence on the performance of spatial count (SC) and spatial partial identity models (SPIM) – two SCR‐based unmarked modeling approaches – and used the performance of fully marked and independent SCR as a reference. We varied the levels of non‐independence (aggregation and cohesion), detection probability, and the number of partial identity covariates used to resolve identities in SPIM estimation. We expected abundance estimates to be increasingly biased and precise as aggregation and cohesion increased. Results showed that models indeed became less robust to increasing non‐independence, but importantly suggested that only SPIM could be reliably applied under low levels of cohesion when sufficient partial identity covariates are available. SC yielded consistently biased estimates with poor precision. SCR was consistently robust across combinations of aggregation and cohesion, as expected. We therefore advise against the use of SC models for estimating population parameters when individuals are known to be non‐independent, caution that SPIM may be used under narrow ecological conditions, and encourage continued investigations into sampling design and methods development for estimating populations of unmarked and non‐independent individuals.

On Modification of Some Estimators using Parameters of Auxiliary Information for the Estimation of the Population Coefficient of Variation

Several studies in the theory of sampling survey have established the fact that the use of auxiliary information at the planning and estimation stages helps in enhancing the efficiency of estimators for estimating population parameters like population mean, population variance, standard deviation etc. as compared to the estimators which use not auxiliary information. In the present study, four estimators for estimating the population coefficient of variation of the study variable using auxiliary information were proposed. The properties (Biases and MSEs) of the proposed estimators were derived up to first order of approximation using Taylor series approach. Numerical analysis was conducted to justify the efficiencies of the proposed estimators and the results revealed that the proposed estimators are more efficient than the existing estimators considered in the study.

Is the current therapeutic dosage of nadroparin adequate for neonates and infants under 8 months with thromboembolic disease? a population pharmacokinetic study from a national children's medical center.

Objectives: Nadroparin, a low-molecular-weight-heparin is commonly used off-label in neonates and infants for thromboembolic events prevention. However, the recommended dosing regimen often fails to achieve therapeutic target ranges. This study aimed to develop a population pharmacokinetic (PK) model of nadroparin to determine an appropriate dosing regimen for neonates and infants less than 8 months. Methods: A retrospective chart review was conducted on patients treated with nadroparin at Children's Hospital of Fudan University between July 2021 and December 2023. A population PK model was developed using anti-Xa levels, and its predictive performance was evaluated internally. Monte Carlo simulations were performed to design an initial dosing schedule targeting anti-Xa levels between 0.5 and 1IU/mL. Results: A total of 40 neonates and infants aged less than 8 months with gestational age ranging from 25 to 41 weeks treated with nadroparin were enrolled in the study for analysis. A one-compartment PK model with first order absorption and elimination was adequately fitted to the data. Creatinine clearance was identified as a significant factor contributing to inter-individual variability in clearance. The typical population parameter estimates of clearance, distribution volume and absorption rate in this population were 0.211L/h, 1.55L and 0.495h-1, respectively. Our findings suggest that current therapeutic doses of nadroparin (150-200IU/kg q12h) may result in subtherapeutic exposure, thus higher doses might be required. Conclusion: The present study offers the first estimation of PK parameters for nadroparin in preterm or term neonates and infants less than 8 months utilizing the model. Our findings have potential implications for recommending initial personalized dosages, particularly among patient populations exhibiting similar characteristics.

Spatial Variation of Population Density and Age Structure of White-Tailed Deer (Odocoileus virginianus) in a Colombian Moor

Wildlife population density is affected by resource availability, predators and competitors, and anthropogenic stressors.  Its analysis and the factors affecting it are important for conservation, use, or population control.  Based on estimates of density and population parameters, together with niche modeling, previous studies considered that the white-tailed deer population at the Chingaza National Natural Park (NNP) is undergoing exponential growth due to the marginal influence of factors regulating its abundance, such as predators.  The aim of this study was to evaluate the population density and age structure of the white-tailed deer in the Chingaza NNP, Colombia.  To evaluate the population density and age structure of white-tailed deer in the Monterredondo and La Paila sectors of the Chingaza NNP, 10 transects were installed with circular plots in each.  These were visited twice; in each visit, we collected all deer pellet groups, measuring each pellet to estimate its volume.  Population density was calculated using the Eberhardt and Van Etten method and the semi-automated PELLET procedure using FSC and FAR techniques; the age structure was estimated by k-means and fuzzy clustering analyses. With the FAR technique, the estimated deer density was 2.09 ind/km2 in Monterredondo and 0.94 ind/km2 in La Paila.  With PELLET, these values were 0.37 ± 0.4 ind/km2 for Monterredondo and 0.16 ± 0.2 ind/km2 for La Paila.  The proportion of age classes (adult, juvenile, and fawn) was 1:0.29:0 for Monterredondo and 1:0.57:0.04 for La Paila.  The population density of white-tailed deer in two sectors of the Chingaza NNP is not homogeneous, but shows temporal and spatial variations.  Population density has decreased versus reports from previous studies in this same area, with the highest deer abundance recorded in 2004, mostly in the Monterredondo sector.  The proportion of age classes suggests that the population is stable, as the largest proportion of individuals corresponds to the adult stage

A Generalized Family of Ratio-Product Exponential Type Estimator Using Power Transformation

Usage of Auxiliary information has been widely addressed in literature with a view of enhancing the precision of the estimators of  population parameters. this paper reviewed research works of some exponential and ratio type estimators for estimating the population  parameters using the population mean of an auxiliary variable. In this research, a generalized family of ratio-product type estimator of population mean as well as the coefficient of kurtosis of two auxiliary variables x and z under stratified random sampling has been  proposed using power transformation and exponential techniques. The Bias and MSE of proposed estimator have been derived up to  second order of approximation. The conditions in which the proposed estimator is better than existing estimators were obtained. The  empirical study shows that the proposed estimator is more efficient than existing estimators in reference to the validation statistics  employed. 

Geometric mean approach to measure Composite Index

Composite Index (CI) combines measures of indicators on economic, social, environmental variables, government initiatives, wellbeing of health, psychological, satisfaction of basic needs, social/cultural, etc. However, existing measures of CIs suffer from limitations. The paper proposes CI measure as geometric mean (GM), which avoids scaling and choosing of weights. It helps in better comparison of countries across time and space and satisfies desired properties like monotonically increasing continuous function, constant elasticity, identification of key areas, reduction of level of substitutability, formation of chain indices to show growth of CI of a country, and is less affected by outliers. The index with wide applicability can be used for ranking, classification of countries and facilitates computation of mean and variance of a sample of countries. In addition, the index satisfies time-reversal test, helps to identify the critical areas and find contribution of the component indicators, estimation of population parameters and testing statistical hypothesis. Separate indices for domains can be constructed by considering indicators relevant to a domain. Thus, it is an improvement over the existing methods. Future studies suggested.

Proportionality Adjusted Ratio-Type Calibration Estimators of Population Mean Under Stratified Sampling

The estimation accuracy of estimators of finite population parameters in sample surveys can be enhanced by taking advantage of existing auxiliary information using the calibration approach. The present study developed the calibration estimators of population mean utilizing known auxiliary information. The properties of the recommended estimators have been studied. The performance of the suggested estimators has also been investigated with the help of a simulation study and compared with the estimators given by Clement [2] and Khare et al. [11].

Widespread Deviant Patterns of Heterozygosity in Whole-Genome Sequencing Due to Autopolyploidy, Repeated Elements, and Duplication.

Most population genomic tools rely on accurate single nucleotide polymorphism (SNP) calling and filtering to meet their underlying assumptions. However, genomic complexity, resulting from structural variants, paralogous sequences, and repetitive elements, presents significant challenges in assembling contiguous reference genomes. Consequently, short-read resequencing studies can encounter mismapping issues, leading to SNPs that deviate from Mendelian expected patterns of heterozygosity and allelic ratio. In this study, we employed the ngsParalog software to identify such deviant SNPs in whole-genome sequencing (WGS) data with low (1.5×) to intermediate (4.8×) coverage for four species: Arctic Char (Salvelinus alpinus), Lake Whitefish (Coregonus clupeaformis), Atlantic Salmon (Salmo salar), and the American Eel (Anguilla rostrata). The analyses revealed that deviant SNPs accounted for 22% to 62% of all SNPs in salmonid datasets and approximately 11% in the American Eel dataset. These deviant SNPs were particularly concentrated within repetitive elements and genomic regions that had recently undergone rediploidization in salmonids. Additionally, narrow peaks of elevated coverage were ubiquitous along all four reference genomes, encompassed most deviant SNPs, and could be partially associated with transposons and tandem repeats. Including these deviant SNPs in genomic analyses led to highly distorted site frequency spectra, underestimated pairwise FST values, and overestimated nucleotide diversity. Considering the widespread occurrence of deviant SNPs arising from a variety of sources, their important impact in estimating population parameters, and the availability of effective tools to identify them, we propose that excluding deviant SNPs from WGS datasets is required to improve genomic inferences for a wide range of taxa and sequencing depths.

Joint cosmological and gravitational-wave population inference using dark sirens and galaxy catalogues

In the absence of numerous gravitational-wave detections with confirmed electromagnetic counterparts, the “dark siren” method has emerged as a leading technique of gravitational-wave cosmology. The method allows redshift information of such events to be inferred statistically from a catalogue of potential host galaxies. Due to selection effects, dark siren analyses necessarily depend on the mass distribution of compact objects and the evolution of their merger rate with redshift. Informative priors on these quantities will impact the inferred posterior constraints on the Hubble constant (H 0). It is thus crucial to vary these unknown distributions during an H 0 inference. This was not possible in earlier analyses due to the high computational cost, restricting them to either excluding galaxy catalogue information, or fixing the gravitational-wave population mass distribution and risking introducing bias to the H 0 measurement. This paper introduces a significantly enhanced version of the Python package gwcosmo, which allows joint estimation of cosmological and compact binary population parameters. This thereby ensures the analysis is now robust to a major source of potential bias. The gravitational-wave events from the Third Gravitational-Wave Transient Catalogue are reanalysed with the GLADE+ galaxy catalogue, and an updated, more reliable measurement of H 0 =  69+12 -7 km s-1 Mpc-1 is found (maximum a posteriori probability and 68% highest density interval). This improved method will enable cosmological analyses with future gravitational-wave detections to make full use of the information available (both from galaxy catalogues and the compact binary population itself), leading to promising new independent bounds on the Hubble constant.

2529. Population pharmacokinetic models for cefepime and enmetazobactam derived from pooled Phase 1 to Phase 3 clinical studies

Abstract Background The investigational β-lactam/β-lactamase inhibitor combination of cefepime (FEP)/enmetazobactam (ENM) met criteria for non-inferiority and superiority compared to piperacillin/tazobactam in a phase 3 clinical trial of adult patients with complicated urinary tract infections (cUTI)/acute pyelonephritis (AP). In this study, a population pharmacokinetic (PK) model is presented that describes FEP and ENM plasma PK in adult cUTI/AP patients. Methods Plasma PK samples were analyzed from participants receiving FEP (n=588) and ENM (n=649) in three phase 1 (n=132), and single phase 2 (n=43) and phase 3 (n=488) clinical trials. Population PK parameters were assessed and the relationship between different covariates and model parameters were investigated. The effect of infection on disposition was assessed by testing disease covariates on fixed and random effects for all PK parameters. Results A two-compartment model with linear clearance of FEP (5.95 L/h) and ENM (7.68 L/h) from the central compartment best described the PK of both compounds. Common covariates for both compounds were de-indexed estimated glomerular filtration rate (eGFR), age, and body weight while ENM PK covariates also included cUTI infection and sex. Simulations showed that the only covariate requiring dose adjustment was renal function. The final model simultaneously described FEP and ENM plasma PK, which enabled estimates of the correlation of the random effects between PK parameters. Clearances and volumes of distribution (Vd) were strongly correlated within individuals between the two compounds (correlation coefficients >0.9). Variability was higher in cUTI patients although differences in mean ENM or FEP exposures between healthy subjects and cUTI patients were negligible. The total Vd was 16.9 L for FEP and 20.6 L for ENM. Clearance of both agents was found to depend on de-indexed eGFR (covariate coefficients ≥0.83), suggesting that renal filtration is the predominant clearance mechanism. The terminal elimination half-life was 2.2 h for FEP and 2.0 h for ENM. Final population PK model population parameter estimates for cefepime and enmetazobactam determined from the pooled phase 1 to phase 3 clinical studies Conclusion The population PK models for FEP and ENM predict exposures in cUTI patients, including subjects with renal impairment. These models support Monte-Carlo simulations and target attainment estimations in adult cUTI/AP patients. Disclosures Adam Belley, PhD, Allecra Therapeutics SAS: Advisor/Consultant Patrick Velicitat, MD, Allecra Therapeutics SAS: Salary

On the methodological framework of composite index under complex surveys and its application in development of food consumption index for India

Indices are created by consolidating multidimensional data into a single representative measure known as an index, using a fundamental mathematical model. Most present indices are essentially the averages or weighted averages of the variables under study, ignoring multicollinearity among the variables, with the exception of the existing Ordinary Least Squares (OLS) estimator based OLS-PCA index methodology. Many existing surveys adopt survey designs that incorporate survey weights, aiming to obtain a representative sample of the population while minimizing costs. Survey weights play a crucial role in addressing the unequal probabilities of selection inherent in complex survey designs, ensuring accurate and representative estimates of population parameters. However, the existing OLS-PCA based index methodology is designed for simple random sampling and is incapable of incorporating survey weights, leading to biased estimates and erroneous rankings that can result in flawed inferences and conclusions for survey data. To address this limitation, we propose a novel Survey Weighted PCA (SW-PCA) based Index methodology, tailored for survey-weighted data. SW-PCA incorporates survey weights, facilitating the development of unbiased and efficient composite indices, improving the quality and validity of survey-based research. Simulation studies demonstrate that the SW-PCA based index outperforms the OLS-PCA based index that neglects survey weights, indicating its higher efficiency. To validate the methodology, we applied it to a Household Consumer Expenditure Survey (HCES), NSS 68th Round survey data to construct a Food Consumption Index for different states of India. The result was significant improvements in state rankings when survey weights were considered. In conclusion, this study highlights the crucial importance of incorporating survey weights in index construction from complex survey data. The SW-PCA based Index provides a valuable solution, enhancing the accuracy and reliability of survey-based research, ultimately contributing to more informed decision-making.

BayeSED-GALAXIES. I. Performance Test for Simultaneous Photometric Redshift and Stellar Population Parameter Estimation of Galaxies in the CSST Wide-field Multiband Imaging Survey

The forthcoming Chinese Space Station Telescope (CSST) wide-field multiband imaging survey will produce seven-band photometric spectral energy distributions (SEDs) for billions of galaxies. The effective extraction of astronomical information from these massive data sets of SEDs relies on the techniques of SED synthesis (or modeling) and SED analysis (or fitting). We evaluate the performance of the latest version of the BayeSED code combined with SED models with increasing complexity for simultaneously determining the photometric redshifts and stellar population parameters of galaxies in this survey. By using an empirical statistics–based mock galaxy sample without SED modeling errors, we show that the random observational errors in photometries are more important sources of errors than the parameter degeneracies and Bayesian analysis method and tool. By using a Horizon-AGN hydrodynamical simulation–based mock galaxy sample with SED modeling errors about the star formation histories (SFHs) and dust attenuation laws (DALs), the simple typical assumptions lead to significantly worse parameter estimation with CSST photometries only. SED models with more flexible (or complicated) forms of SFH/DAL do not necessarily lead to better estimation of redshift and stellar population parameters. We discuss the selection of the best SED model by means of Bayesian model comparison in different surveys. Our results reveal that Bayesian model comparison with Bayesian evidence may favor SED models with different complexities when using photometries from different surveys. Meanwhile, the SED model with the largest Bayesian evidence tends to give the best performance of parameter estimation, which is clearer for photometries with higher discriminative power.

Constructing efficient strata boundaries in stratified sampling using survey cost

For maximum precision in population parameter estimation under the Stratified sampling design, the optimum strata boundaries (OSB) could be constructed based on a continuous study variable rather than a set of categorical variables. If constructed optimally, the OSB results in homogenous units within each stratum leading to optimal stratum sample sizes (OSS) as well. The OSB and OSS may not remain optimum if the problem is considered in terms of a fixed total sample size, especially when a survey design involves a fixed budget. This article suggests a methodology for computing the OSB and OSS when the per unit stratum measurement costs for the survey or its probability density function are known. To plan for such a stratified survey, we demonstrate a design-based stratification empirically by using Wave 18 of the HILDA Survey general release dataset where we estimate the mean level of Gamma-distributed annual total disposable income in Australia, which could potentially be an important variable for policy decision-making. We also provide numerical illustrations for hypothetical study variables that follow exponential and right-triangular distributions respectively. The findings indicate that the suggested method is satisfactory in the sense that it is either more efficient or relatively comparable with other methods aimed at improving the accuracy of population parameter estimates. The proposed technique has been implemented in the updated stratifyR package.

Population pharmacokinetic modeling of multiple-dose intravenous fosfomycin in critically ill patients during continuous venovenous hemodialysis

The aim of this study was to investigate the pharmacokinetics of multiple-dose intravenous (i.v.) fosfomycin in critically ill patients during continuous venovenous hemodialysis (CVVHD). Non-compartmental analysis and population pharmacokinetic modeling were used to simulate different dosing regimens. We evaluated 15 critically ill patients with renal insufficiency and CVVHD undergoing anti-infective treatment with fosfomycin in our ICU. Five grams of fosfomycin were administered for 120 min every 6 h. Plasma concentrations were determined with and without CVVHD. Pharmacokinetic analysis and simulations were performed using non-linear mixed effects modelling (NONMEM). A two-compartment model with renal and dialysis clearance was most accurate in describing the pharmacokinetics of i.v. fosfomycin during CVVHD. Population parameter estimates were 18.20 L and 20.80 L for the central and peripheral compartment volumes, and 0.26 L/h and 5.08 L/h for renal and intercompartmental clearance, respectively. Urinary creatinine clearance (CLCR) represented a considerable component of renal clearance. Central compartment volume increased over time after the first dose. For patients with CLCR > 50 (90) mL/min and CVVHD, dosage should be increased to ≥ 15 (16) grams of i.v. fosfomycin across three (four) daily doses. Individual CLCR must be considered when dosing i.v. fosfomycin in critically ill patients during CVVHD.

Smoothing of Estimators of Population mean using Calibration Technique with Sample Errors

The challenges bedeviling the performance of estimators of population parameters in survey samples as a result of measurement and nonresponse errors are of great concern to researchers and users of statistics. This study suggests new estimators and adopts the calibration approach in smoothing the existing and proposed estimators for optimal performance. We have proposed improved estimators for estimating the finite population mean under stratified random sampling in three different situations: first, the properties of the estimators are considered under nonresponse, then the study of the estimators for measurement errors and in the last case, the estimators are examined in the presence of both measurement and nonresponse errors simultaneously. Expressions for mean square errors are obtained for the suggested estimators. Empirical study has been carried out with two real datasets to validate the theoretical underpinnings of this study.

Non‐invasive mark–resight surveys for brown bears: Incorporating spatial information to improve landscape‐scale monitoring of density and distribution

Abstract Effective wildlife monitoring and management are reliant on unbiased estimators of population parameters. Most standard survey approaches ignore spatial pattern in the data although spatial relationships can be leveraged to produce more efficient designs for future surveys, thereby reducing project cost. Landscape‐scale surveys for brown bears are notoriously expensive and difficult to complete, leading to the development of a non‐invasive mark–resight aerial survey approach as a practical alternative to more commonly employed approaches for estimating brown bear density across large areas. Here we extend that model to accommodate spatial covariate information and explicitly model residual spatial autocorrelation. Using brown bear survey data from northwestern Alaska, USA from 2015 to 2021, we demonstrate how our extension can be useful in exploring ecological factors related to spatial variation in bear density and detection probability. We found that detection probability was higher in more rugged terrain, and occurrence probability was higher at mid‐elevations where denning habitat was more prevalent and in areas with more salmon streams. The descriptive model of residual autocorrelation confirmed that additional unmeasured factors were associated with bear distribution. While these findings were not unexpected, they are very useful when considering future survey effort. Using this information, we explored a hypothetical redesign of the 2021 brown bear survey that removed 50% of the survey subunits with elevation values below the mean, where bears were least likely to occur. We demonstrated that the resulting estimates were unbiased, suggesting that adopting the spatial approach to analysis could reduce overall project costs by ~20%–30% going forward. Overall, our work indicates that directly accounting for spatial pattern within the non‐invasive mark–resight framework has substantive advantages over the original non‐spatial approach and may be useful in increasing the amount of information available for brown bear populations.

Interpreting Randomized Controlled Trials.

This article describes rationales and limitations for making inferences based on data from randomized controlled trials (RCTs). We argue that obtaining a representative random sample from a patient population is impossible for a clinical trial because patients are accrued sequentially over time and thus comprise a convenience sample, subject only to protocol entry criteria. Consequently, the trial's sample is unlikely to represent a definable patient population. We use causal diagrams to illustrate the difference between random allocation of interventions within a clinical trial sample and true simple or stratified random sampling, as executed in surveys. We argue that group-specific statistics, such as a median survival time estimate for a treatment arm in an RCT, have limited meaning as estimates of larger patient population parameters. In contrast, random allocation between interventions facilitates comparative causal inferences about between-treatment effects, such as hazard ratios or differences between probabilities of response. Comparative inferences also require the assumption of transportability from a clinical trial's convenience sample to a targeted patient population. We focus on the consequences and limitations of randomization procedures in order to clarify the distinctions between pairs of complementary concepts of fundamental importance to data science and RCT interpretation. These include internal and external validity, generalizability and transportability, uncertainty and variability, representativeness and inclusiveness, blocking and stratification, relevance and robustness, forward and reverse causal inference, intention to treat and per protocol analyses, and potential outcomes and counterfactuals.

Abundance, Distribution, and Habitat Preference of Syngnathid Species in Sabaudia Lake (Tyrrhenian Sea)

Syngnathids are considered flagship species of estuarine and coastal environments. However, most of the Mediterranean species are still classified as data-deficient (DD) at a global level according to the IUCN Red List of Threatened Species. In central Italy, several areas host potentially suitable habitats for syngnathids but have not been previously reported in the literature; the estimation of population parameters and habitat partitioning at these sites may help to assess their conservation status. In this study, we investigated the abundance, distribution, and habitat preferences of sympatric populations of Hippocampus hippocampus, H. guttulatus, Syngnathus abaster, and Nerophis ophidion in Sabaudia Lake (Tyrrhenian Sea, Italy). While confirming the primary importance of a healthy coastal habitat, we retrieved hints about species ecology and habitat use. The species distribution in the study area highlights the role of habitat complexity in supporting local populations of these sensitive species.