A simulation study of regression approaches for estimating risk ratios in the presence of multiple confounders

Background Risk ratio is a popular effect measure in epidemiological research. Although previous research has suggested that logistic regression may provide biased odds ratio estimates when the number of events is small and there are multiple confounders, the performance of risk ratio estimation has yet to be examined in the presence of multiple confounders. Methods We conducted a simulation study to evaluate the statistical performance of three regression approaches for estimating risk ratios: (1) risk ratio interpretation of logistic regression coefficients, (2) modified Poisson regression, and (3) regression standardization using logistic regression. We simulated 270 scenarios with systematically varied sample size, the number of binary confounders, exposure proportion, risk ratio, and outcome proportion. Performance evaluation was based on convergence proportion, bias, standard error estimation, and confidence interval coverage. Results With a sample size of 2500 and an outcome proportion of 1%, both logistic regression and modified Poisson regression at times failed to converge, and the three approaches were comparably biased. As the outcome proportion or sample size increased, modified Poisson regression and regression standardization yielded unbiased risk ratio estimates with appropriate confidence intervals irrespective of the number of confounders. The risk ratio interpretation of logistic regression coefficients, by contrast, became substantially biased as the outcome proportion increased. Conclusions Regression approaches for estimating risk ratios should be cautiously used when the number of events is small. With an adequate number of events, risk ratios are validly estimated by modified Poisson regression and regression standardization, irrespective of the number of confounders.

Correlates of COVID-19 Vaccine Hesitancy among People who Inject Drugs in the San Diego-Tijuana Border Region

Background: People who inject drugs (PWID) are vulnerable to acquiring SARS-CoV-2 but their barriers to COVID-19 vaccination are under-studied. We examined correlates of COVID-19 vaccine hesitancy among PWID in the U.S.-Mexico border region, of whom only 7.6% had received ≥one COVID-19 vaccine dose by September, 2021. Methods: Between October, 2020 and September, 2021, participants aged ≥18 years from San Diego, California, USA and Tijuana, Baja California, Mexico who injected drugs within the last month completed surveys and SARS-CoV-2, HIV, and HCV serologic testing. Logistic regressions with robust standard error estimation via generalized estimating equations identified factors associated with COVID-19 vaccine hesitancy, defined as being unsure or unwilling to receive COVID-19 vaccines. Results: Of 393 participants, 127 (32.3%) were vaccine hesitant. Older participants, those with greater food insecurity, and those with greater concern about acquiring SARS-CoV-2 were more willing to be vaccinated. Higher numbers of chronic health conditions, having access to a smart phone or computer, and citing social media as one's most important source of COVID-19 information were independently associated with vaccine hesitancy. COVID-19-related disinformation was independently associated with vaccine hesitancy (adjusted odds ratio: 1.51 per additional conspiracy theory endorsed; 95% confidence interval: 1.31-1.74). Conclusions: Nearly one third of PWID in the San Diego-Tijuana border region reported COVID-19 vaccine hesitancy, which was significantly influenced by exposure to disinformation. Interventions that improve accurate knowledge and trust in COVID-19 vaccines are needed to increase vaccination in this vulnerable population.

The Association Between Sarcopenia and Functional Improvement in Older and Younger Patients Who Completed Inpatient Rehabilitation: A Prospective Cohort Study

Objective: To investigate the association between sarcopenia and functional improvement in patients older and younger than 65 years upon completion of an inpatient rehabilitation program.Design: Prospective cohort study.Participants: Adult consecutive patients who completed the inpatient rehabilitation program at a metropolitan tertiary referral hospital general inpatient rehabilitation unit.Methods: Sarcopenia status was determined using the European Working Group on Sarcopenia in Older People 2 algorithm, using muscle mass measured by BioImpedance Analysis and grip strength. Progress in rehabilitation was measured using change in the Functional Independence Measure and Goal Attainment Scaling score. To investigate the age group by sarcopenia status interaction we used quantile regression models with bootstrapped standard error estimation for functional improvement and linear regression model with robust standard error estimation for GAS score.Results: 257 participants [128 (50%) male, median age 63 years (IQR: 52–72)], 33(13%) with sarcopenia, completed inpatient rehabilitation [median length of stay 16 days (IQR: 11–27.5)]. Participants' median Functional Independence Measure change was 24 (IQR 15–33.5) and mean total Goal Attainment Scaling score was 57.6 (SD 10.2). Adjusting for admission Functional Independence Measure score, the median difference in Functional Independence Measure change between participants with and without sarcopenia was: −4.3 (95% CI: −10.6, 1.9); p = 0.17 in participants 65 years and younger, and 4.6 (95% CI: 1.0, 8.2); p = 0.01 in participants older than 65; age-by-sarcopenia interaction p = 0.02.Conclusions: Unlike younger people, older people with sarcopenia have greater functional improvement in inpatient rehabilitation than those without sarcopenia.

Numerical methods for scattering problems in periodic waveguides

In this paper, we propose new numerical methods for scattering problems in periodic waveguides. Based on [20], the “physically meaningful” solution, which is obtained via the Limiting Absorption Principle (LAP) and is called an LAP solution, is written as an integral of quasi-periodic solutions on a contour. The definition of the contour depends both on the wavenumber and the periodic structure. The contour integral is then written as the combination of finite propagation modes and a contour integral on a small circle. Numerical methods are developed and based on the two representations. Compared with other numerical methods, we do not need the LAP process during numerical approximations, thus a standard error estimation is easily carried out. Based on this method, we also develop a numerical solver for halfguide problems. The method is based on the result that any LAP solution of a halfguide problem can be extended to the LAP solution of a fullguide problem. At the end of this paper, we also give some numerical results to show the efficiency of our numerical methods.

The Skewed-Elliptical Log-Linear Birnbaum–Saunders Alpha-Power Model

In this paper, the skew-elliptical sinh-alpha-power distribution is developed as a natural follow-up to the skew-elliptical log-linear Birnbaum–Saunders alpha-power distribution, previously studied in the literature. Special cases include the ordinary log-linear Birnbaum–Saunders and skewed log-linear Birnbaum–Saunders distributions. As shown, it is able to surpass the ordinary sinh-normal models when fitting data sets with high (above the expected with the sinh-normal) degrees of asymmetry. Maximum likelihood estimation is developed with the inverse of the observed information matrix used for standard error estimation. Large sample properties of the maximum likelihood estimators such as consistency and asymptotic normality are established. An application is reported for the data set previously analyzed in the literature, where performance of the new distribution is shown when compared with other proposed alternative models.

The Influence of Internal and External Factors on NPF And NPL

<p><em>Indonesia has two types of bank, islamic banking and conventional banking. In their activities, banks are often facing any risks, named financing risk (NPF) in the islamic banking and credit risk (NPL) in the conventional banking. Based on data by OJK, the value of NPF is always higher than NPL. However, in January-August 2020 the NPF tended to decrease while the NPL tended to increase, even indicating a movement that would excited the NPF value. Therefore, it's necessary to the research of the factors that influence both NPF and NPL, including the internal and external conditions of the bank. The data that used as reference is the secondary data from OJK of 10 both islamic and conventional commercial banks from the first quarter of 2019 to the third quarter of 2020. Furthermore, the data is analyzed with panel model fixed effect data analysis with the robust standard error estimation method and panels corrected standard error (PCSE cross-sectional SUR). By using 5% of significance level, this research results that NPF is only significantly and positively influenced by FDR. However, NPL is significantly and negatively affected by the inflation and ROA, also significantly and positively influenced by CAR, LDR, and BOPO.</em></p>

Biomass estimates derived from sector subsampling of 360° spherical images

Efficient subsampling designs reduce forest inventory costs by focusing sampling efforts on more variable forest attributes. Sector subsampling is an efficient and accurate alternative to big basal area factor (big BAF) sampling to estimate the mean basal area to biomass ratio. In this study, we apply sector subsampling of spherical images to estimate aboveground biomass and compare our image-based estimates with field data collected from three early spacing trials on western Newfoundland Island in eastern Canada. The results show that sector subsampling of spherical images produced increased sampling errors of 0.3–3.4 per cent with only about 60 trees measured across 30 spherical images compared with about 4000 trees measured in the field. Photo-derived basal area was underestimated because of occluded trees; however, we implemented an additional level of subsampling, collecting field-based basal area counts, to correct for bias due to occluded trees. We applied Bruce’s formula for standard error estimation to our three-level hierarchical subsampling scheme and showed that Bruce’s formula is generalizable to any dimension of hierarchical subsampling. Spherical images are easily and quickly captured in the field using a consumer-grade 360° camera and sector subsampling, including all individual tree measurements, were obtained using a custom-developed python software package. The system is an efficient and accurate photo-based alternative to field-based big BAF subsampling.

Molecular modelling studies on thiazole-based α-Glucosidase Inhibitor using docking and CoMFA, CoMSIA and HQSAR

Aims and Objective: The biological dataset was retrieved from two series of α-glucosidase inhibitors synthesized by Rahim et al. and Taha et al. and consisted of a total 46 (forty-six) α-glucosidase inhibitors. Methods: The α-glucosidase inhibitory IC50 values (µM; performed against α-glucosidase from Saccharomyces cerevisiae) were converted into negative logarithmic units (pIC50). The CoMFA and CoMSIA models were created utilizing 37 as the training set and externally validated utilizing 9 as a test set. The CoMFA models MMFF94 were generated and ranging from 3.4661 to 5.2749 using leave-one-out PLS analysis cross-validated correlation coefficient q2 0.787 a high non-crossvalidated correlation coefficient r2 0.819 with a low standard error estimation (SEE) 0.041, F value 1316.074 and r2pred 0.996. Results: The steric and electrostatic fields contributions were 0.507 and 0.493, respectively. The CoMSIA model q2 0.805, r 2 0.833 was attained, (SEE) 0.065, F value 520.302 and r2pred 0.990. Contribution of steric, electrostatic, hydrophobic, donor and acceptor fields were 0.151, 0.268, 0.223, 0.234, 0.124 respectively. Conclusion: The HQSAR model of training set exhibits significant cross-validated correlation coefficient q2 0.800 and noncross-validated correlation coefficient r2 0.943.

Determination of Optimum Sampling Fraction for Bootstrap Resampling

Lately, there has been much discussion on the bootstrap resampling method, both as a way of estimating standard error and as a way of improving estimations with access to only one sample. However, little is found in literature discussing the size the bootstrap sample should take. This study aims to determine the existence of an optimum sampling fraction for resampling, analysing different estimators and number of resamples. An optimum fraction exists if, and only if, for every estimator and every amount of resamples, a fraction (or region) performs better in every population. Ten random populations were created by adding together different normal, Poisson and exponential distributions such that their means and variances are diverse. A Monte Carlo simulation with ten thousand iterations was done, taking random systematic samples from the populations and from these, bootstrap samples to estimate the mean, variance and respective standard errors. Results show the inexistence of a single optimum fraction. However, it does point to an optimum region for standard error estimation above 37.5%.