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Purpose: We assessed the extent to which language, speech, and fine/gross motor skills in preschoolers with developmental language disorder (DLD; also referred to as specific language impairment) predicted language outcome two years later.Method: Participants with DLD (n = 15) and typical development (TD; n = 14) completed language, speech, and fine/gross motor assessments annually, beginning as 4- to 5-year-olds (Year 1 timepoint) and continuing through 6 to 7 years of age (Year 3 timepoint). We performed Pearson correlation and hierarchical regression analyses to examine the relative contributions of Year 1 language, speech, and motor skills to Year 3 language outcome in each group.Result: Among children with DLD, Year 1 fine/gross motor scores positively correlated with Year 3 language scores, uniquely explaining 40% of the variance in language outcomes. Neither Year 1 language, speech-sound, nor speech-motor scores predicted language outcome in this group. Among children with TD, only Year 1 language predicted language outcome.Conclusion: This small longitudinal study reveals that, among preschoolers with DLD, certain early fine/gross motor deficits predict persistent language impairment. Future research that includes larger sample sizes and motor tasks that incorporate complex sequencing will enhance the understanding of the relationship between language, speech, and motor skills; specifically, whether certain motor deficits simply co-occur with language deficits or whether they are tied to DLD through shared impairments in sequential learning mechanisms.

This study used a meta-analysis to examine the relationship between authenticity and tourist loyalty. A total of 144 effect sizes and 27,986 accumulated sample sizes from 68 independent empirical s...

One of the key aspects of designing a clinical trial is sample size estimation. While the statistical power improves with the increased sample size, one has to be considerate about the ethical, sci...

Author(s): Zheng, Bang Quan | Advisor(s): Bentler, Peter M | Abstract: Does Reweighted Least Squares (RLS) perform better in small samples than maximum likelihood (ML) for mean and covariance structure? ML statistics in covariance structure analysis are based on the asymptotic normality assumption; however, actual applications of structural equation modeling (SEM) in social and behavioral science research usually involve small samples. It has been found that chi-square tests often incorrectly over-reject the null hypothesis: Σ=Σ(θ), because when sample is small the sample covariance matrix becomes ill-conditioned and entails unstable estimates. In certain SEM models, the vector of parameter must contain both means, variances and covariances. Yet, whether RLS also works in mean and covariance structure remains unexamined. This research is an extended examination of reweighted least squares in mean and covariance structure. Specifically, we replace biased covariance matrix in traditional GLS function (Browne, 1974) with the unbiased sample covariance matrix that derives from ML estimation. Moreover, under the assumption of multivariate normality, a Monte Carlo simulation study was carried out to examine the statistical performance as compared with ML methods in different sample sizes. Based on empirical rejection frequencies and empirical averages of test statistic, this study shows that RLS performs much better than ML in mean and covariance structure models when sample sizes are small.

The classical two-sample t-test is not robust to skewed populations, and the large sample approximation has low accuracy for finite sample sizes. This paper presents two new types of tests, the TCF...

Mansour Adam Mahmoud,1 Md Ashraful Islam,2 Malaz Ahmed,3 Randa Bashir,3 Romisa Ibrahim,3 Shahd Al-Nemiri,3 Ethar Babiker,3 Neven Mutasim,3 Sultan Othman Alolayan,1 Sultan Al Thagfan,1 Samrein BM Ahmed,4 Ibrahim Sales,5 Mohamed Azmi Hassali6 ,†Yaser Alahmadi,1 Mirghani A Yousif3 1Department of Clinical & Hospital Pharmacy, College of Pharmacy, Taibah University, Al-Madinah Al-Munawarah, Saudi Arabia; 2Department of Pharmacy Practice, College of Clinical Pharmacy, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia; 3Department of Clinical Pharmacy & Pharmacy Practice, Faculty of Pharmacy, University of Gezira, Wad Madani, Sudan; 4College of Medicine, University of Sharjah, Sharjah, United Arab Emirates; 5Department of Clinical Pharmacy, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia; 6Discipline of Social and Administrative Pharmacy, School of Pharmaceutical Sciences, Universiti Sains Malaysia, Penang, Malaysia†Mohamed Azmi Hassali passed away on July 16, 2021Correspondence: Mansour Adam MahmoudDepartment of Clinical and Hospital Pharmacy, College of Pharmacy, Al-Madinah Al-Munawarrah, Saudi ArabiaEmail Mammm.99@gmail.comObjective: The aim of this study was to validate the Arabic version of General Medication Adherence Scale (GMAS) in Sudanese patients with type 2 diabetes mellitus (T2DM).Methods: A 3-month cross-sectional study was conducted among patients with T2DM at Al-Daraja Health Center, located in Wad Medani, Sudan. A convenient sample of patients was selected, and the study sample size was calculated using the item response ratio. Factorial, known group, and construct validities were determined. Internal consistency and reliability were also determined.Results: Responses were provided by 500 patients. The average medication adherence score was 30 (median 31). The normed fit index (NFI) was 0.950, the comparative fit index (CFI) was 0.963, the incremental fit index (IFI) was 0.963, and the root-mean-square error of approximation (RMSEA) was 0.071. The results from these fit indices indicated a good model. Factorial, known group and construct validities were all established. A significant association was found between adherence score and age (P = 0.03) since a larger proportion of older patients were found to have high adherence compared to patients in other age groups. The reliability (α) of the questionnaire was 0.834.Conclusion: The Arabic version of GMAS was validated in Sudanese patients with T2DM making it a suitable scale to be used in this population.Keywords: medication adherence, patient compliance, medication persistence, chronic illness, Sudan

Sickle cell disease (SCD) resulting from a mutation of the β-globin gene results in sickle deformation of the red blood cell with consequent vaso-occlusion and intravascular hemolysis. SCD results in substantial morbidity, with impaired quality of life and premature mortality. Comprehensive and supportive care, disease modifying therapies and treatments with curative intent are each associated with asymmetrical costs, burden of care, and impact on survival and quality of life. There is thus a considerable decisional dilemma regarding treatment among patients and caregivers. The objective of this review is to evaluate the literature regarding quantitative and qualitative studies of patient preferences in therapy for SCD. Numerous survey-based studies have been performed evaluating SCD patients’ treatment preferences. These studies are limited, however, as they are purely descriptive in nature with limited quantitative information on the relative value of treatment alternatives. Time trade-off and standard gamble studies and health state utility studies have also been utilized to quantify patient utility especially for curative hematopoietic cell transplant. However, these studies suffer from inaccurate assumptions regarding patient preferences. Qualitative studies have garnered the patient and caregiver perspective. Qualitative studies may be limited by selective and purposive sampling, and lack of representativeness due to sample size.

Purpose Prostate cancer (PCa) is the most common non-skin cancer in men worldwide and more than 80% of men with PCa also have histo-anatomical findings of benign prostate hyperplasia (BPH). It is well documented that BPH develops in the transition zone (TZ), whereas 80–85% of PCa originates in the peripheral zone (PZ) of the prostate. Possible causal links between both disease entities are controversially discussed in the current literature. Some studies have reported that larger prostates have a decreased incidence of PCa compared to smaller prostates. The purpose of this systematic review is to comprehensively summarize studies analyzing any association between prostate gland volume and incidence of PCa. Methods A thorough literature review was performed between 01.01.1990 through 02.28.2020 using PubMed and applying the “PRISMA” guidelines. Inclusion and exclusion criteria were defined. Results Our systematic review found 41 articles reporting an inverse (negative) relationship between prostate gland volume and incidence of prostate cancer. Sample sizes ranged from 114 to 6692 patients in these single institutional and multi-institutional studies. Thirty-nine (95%) of the 41 articles showed a statistically significant inverse relationship. In our search, no study was found showing a positive correlation between BPH size and the incidence of PCa. Conclusion To our knowledge, this is the first systematic review on the important clinical question of interaction between prostate size and the incidence of PCa. The results are demonstrating an inverse relationship, and therefore reveal strong evidence that large prostates may be protective of PCa when compared to smaller prostates.

The win odds is a distribution-free method of comparing locations of distributions of two independent random variables. Introduced as a method for analyzing hierarchical composite endpoints, it is well suited to be used in the analysis of ordinal scale endpoints in COVID-19 clinical trials. For a single outcome, we provide power and sample size calculation formulas for the win odds test. We also provide an implementation of the win odds analysis method for a single ordinal outcome in a commonly used statistical software to make the win odds analysis fully reproducible.

The case-control design is known for its efficiency at a fixed sample size when the disease under study is rare. Such a design is usually carried out for the inference of disease odds ratio in the ...

Advancing clock times by 1 h in the spring to daylight savings time and setting clock times back 1 h in the autumn to standard time disrupts circadian timing, sleep and skilled motor behavior such as driving an automobile. It is unknown if endurance performance is impacted by daylight savings transition (DST). The natural experiment described here examined whether exposure to a DST in the 10 h prior to the start of a marathon race was associated with a different mean completion time compared to participants who ran the same course but were unexposed to a recent DST. The primary outcome was the average running time of finishers of United States marathons that were completed on either spring-DST or autumn-DST days in the years 2000-2018. Comparisons were made to results from the same marathon held in a different year that was not run on a DST day. Data were obtained from the public data base marathonguide.com/results. Analysis of the primary outcome used paired samples t-tests weighted by sample size. Spring and autumn data were analyzed separately. Eighteen spring and 29 autumn marathons met the inclusion criteria. Compared to control marathons, the weighted spring-DST performance was worse by 12.3 min (4.1%; P < .001) and equal to a moderate standardized effect size of 0.57 while autumn-DST was trivially worse by 1.4 min (0.5%), which was equivalent to an effect size of 0.13. Ambient temperatures for the DST and control races did not differ for either the spring (10.6 vs. 8.9℃; P = .212) or autumn marathons (7.6 vs. 9.3℃; P = .131). Within the limitations of a natural experiment research design, it is concluded that the findings support worse running performance in marathon races held in the spring on the day of transition to daylight savings time when there is a forced circadian change and sleep loss.

As the most widely used tool for assessing dietary intake, the validity of food frequency questionnaires (FFQs) should be evaluated before application. A comprehensive search of the PubMed and Web of Science databases was conducted for publications from January 2000 to April 1, 2020. Pooled estimates were calculated for correlation coefficients and mean differences for energy and 61 nutrients between FFQs and standard methods. The literature search identified 130 articles that included 21,494 participants. Subgroup analyses according to the number of administrations of the reference method, sample size, administration methods, FFQ items, reference periods, quality of the studies, gender, and regions were also performed. We conducted a meta-analysis by summarizing the available evidence to comprehensively assess the validity of FFQs stratified by the reference method type (24-hour recall (24HRs) and food records (FRs). We also performed subgroup analyses to examine the impact on the final summary estimates. After a meta-analysis of the FFQs’ validity correlation coefficients of the included studies, this study showed that the range (median) of the validity coefficients of the 24HRs as reference methods was 0.220–0.770 (0.416), and for the FRs, it was 0.173–0.735 (0.373), which indicated that FFQs were suitable to assess the overall dietary intake in nutritional epidemiological studies. The results of the subgroup analysis showed that the number of administrations of the reference method, administration mode, number of items, reference periods, sample size, and gender mainly affected the validity correlation of FFQs. Supplemental data for this article is available online at https://doi.org/10.1080/10408398.2021.1966737 .

Asymptotic properties of robust regression estimators are well known. However, it is not always clear what is the best strategy for confidence intervals and hypothesis testing when the sample size is not very large, since the distribution of residuals coming from robust estimates has unknown properties for small samples. In the present work we propose an analysis of various strategies for estimating the variance-covariance matrix of the S estimators at the variation of n and p, considering different �� functions. An adaptive correction strategy is proposed. In addition to the simulation study, an example on a benchmark dataset is shown.

Li Niu,&ast; Yan Liu,&ast; Xin Wang School of Medicine, Jiujiang University, Jiujiang, Jiangxi, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Xin Wang Email wangxin0072@126.comBackground: Migrants are one of the most vulnerable populations facing many health issues. Inadequate health care access and unequal insurance are the most challenging. This study aimed to construct a nomogram to predict the risk of hospitalization forgone among internal migrants in China.Methods: We analyzed the 2014 Mobile Population Social Integration and Mental Health Survey (MPSIMHS) launched by National Health and Family Planning Commission. Using the Probability Proportionate to Size Sampling method (PPS), MPSIMHS sampled from eight cities (districts) with a total sample size of 15,999. Of total 589 patients who were diagnosed with hospitalization requirement, 116 forwent their hospitalization, 473 had no forgone. Demographics, socioeconomic status, and health conditions were analyzed using univariate analysis and multivariate logistic regression. A nomogram was built and validated by applying bootstrap resampling.Results: After model selection, gender, age group, marital status, migration range, insurance (having NRMI), and self-evaluated health were chosen into the nomogram to predict the risk of hospitalization forgone. The nomogram that predicted the risk of hospitalization forgone was validated for discrimination and calibration using bootstrap resampling. The calibration curves illustrated optimal agreement between the actual and predicted probabilities of the nomogram. The value of C-index from bootstrap was 0.80 (95% CI: 0.76– 0.85).Conclusion: This study identified some possible factors contributing to migrant’s hospitalization forgone: being single, male and middle-aged, having fixed health insurance, and having bad or great self-evaluated health. By integrating significant and easy-to-get prognostic factors, a nomogram was developed to estimate an individual patient’s risk of hospitalization forgone, which might have practical utility and the potential to assist clinicians in making hospitalization recommendations.Keywords: urbanization, internal migrant, hospitalization forgone, nomogram, China

The present study had a dual objective: (1) to present and validate a predictive model of standing posture in the sagittal plane, joint torques and support forces for a smartphone user built from biomechanical principles; (2) propose risk scales for joint torques and reaction forces based on simulations in order to use them into the musculoskeletal disorders prevention. Comparison of the modelled data with experimental measurements (400 tested postures with sample size verification) for calling and texting tasks highlights the model's ability to correctly estimate posture and reaction forces on the ground. The model was able to provide estimates of the range of variation of each parameter for a wide range of environmental conditions as a function of the user body mass index (setting between 12.5 and 50). Joint torques risk scales have been constructed, especially for shoulder and elbow, to characterise the risks incurred by the users. Practitioner summary: The proposed model enables the postures, joint torques and reaction forces to be estimated from subject's body mass index and environmental configuration without resorting to experimentation, which is relevant in industry. This approach allows the proposition of new scales based on joint torques to reinforce the recommendations for MSDs prevention. Abbreviations: BMI: body mass index; LUBA: postural loading on the upper body assessment; MSDs: musculoskeletal disorders; RULA: rapid upper limb assessment; WHO: World Health Organization.

The estimation of a large covariance matrix is challenging when the dimension p is large relative to the sample size n. Common approaches to deal with the challenge have been based on thresholding ...

In the recent review published in 2019, Malinovsky and Albert conjectured analytical formulae of the optimal sample sizes for the modified Dorfman and Sterret group testing schemes and verified the validity of the formulae numerically within a broad range. However, the authors pointed out that they were unable to give a rigorous proof of the stated conjectures. In this note, we affirm and refine their hypotheses analitically thereby filling in the present gap.

Background The purpose of the meta-analysis is to evaluate the accuracy of extracellular vesicles (EV) miRNAs in the diagnosis of Non-small cell lung cancer (NSCLC). Methods All eligible studies were searched electronically online database. STATA15.0, Meta-disc14.0 and Review Manager 5.2 software were used to perform all statistical analysis. Results The analysis included 16 articles searched electronically online database and a total of 70 studies. Pooled sensitivity (SE) and specificity (SP), positive predictive value (PPV) and negative predictive value (NPV) were 0.77 (95% CI: 0.72-0.80), 0.83(95% CI: 0.78-0.86), 0.88 (95% CI: 0.86-0.90) and 0.63 (95% CI: 0.58-0.68), respectively. The overall diagnostic odds ratio (DOR) were 16(95% CI: 11-21) and the area under the curve (AUC) was 0.86 (95% CI: 0.83-0.89). Threshold analysis showed there was no threshold effect (P=0.11). Meta-regression suggested specimen, publication year and sample size might be the major source of heterogeneity. Subgroup analysis hinted that combined test had better diagnostic accuracy for NSCLC than alone. Conclusion Circulating EV miRNAs are significantly associated with NSCLC diagnosis, especially metastatic NSCLC, followed by early NSCLC, whose AUC values were 0.90 and 0.88, respectively. 3 of EV miRNAs might act as biomarkers for metastatic NSCLC diagnosis, and 10 for early stage of NSCLC. EV miR-21, miR-210, and miR-1290 might be associated with PK13/AKT related pathway and be the valuable diagnostic biomarkers for NSCLC. Besides, multitudinous EV miRNAs combined to diagnose NSCLC show a higher diagnostic value than alone.

Background and aim The role of needle-based confocal laser endomicroscopy (nCLE) in the diagnosis of pancreatic cystic lesions (PCLs) remains controversial. This study aimed to systematically evaluate the diagnostic accuracy and adverse effects of nCLE in the detection of pathological subtypes in patients with PCLs. Material and methods We conducted a comprehensive literature search using MEDLINE, EMBASE, and Cochrane for identifying studies that reported the use of nCLE for PCLs diagnosis (dated prior to 10 October 2020). Studies with a sample size >10 were included. We used the QUADAS-2 criteria for quality evaluation. We first extracted the diagnostic rates and the information on adverse events (AEs) from the studies; then used STATA15.0 to calculate the variables, draw forest plots and summary receiver operating characteristic (SROC) curves; and finally, we completed subgroup analyses to explore the heterogeneity. Results Overall, 299 article titles were identified after an initial search, and ten studies with 547 individuals with PCLs were included in the analysis. The pooled sensitivity, specificity, positive likelihood ratio, and pooled negative likelihood ratio of nCLE in detecting gastric disorders were 90%, 96%, 20.4, and 0.11, respectively. The pooled sensitivity and specificity showed a substantial heterogeneity. An ROC curve was constructed with an area under the curve (AUC) of 0.94. The overall AEs rate of pancreatitis was 2.7%. Conclusions We showed that nCLE had a relatively high sensitivity and specificity for diagnosing PCLs with a relatively low rate of AEs occurring. We suggest that nCLE has good diagnostic accuracy for PCLs.

In cluster randomized trials (CRTs) groups rather than individuals are randomized to different interventions. Individuals’ responses within clusters are commonly more similar than those across clusters. This dependency introduces complexity when calculating the number of clusters required to reach a specified statistical power for nominal significance levels and effect sizes. Current CRTs’ sample size estimation approaches rely on asymptotic-based formulae or Monte Carlo methods. We propose a new Monte Carlo procedure which is based on the potential outcomes framework. By explicitly defining the causal estimand, the data generating, the sampling, and the treatment assignment mechanisms, this procedure allows for sample size calculations in a broad range of study designs including sample size calculations in finite and infinite populations. It can also address financial and administrative considerations by allowing for unequal allocation of clusters. The R package CRTsampleSearch implements the method and we provide examples for using this package.

The two one-sided tests (TOST) procedure is the common method for assessing bioequivalence between two different drugs or formulations of the same drug. Several solutions to Behrens-Fisher problem ...

This meta-analysis aimed to examine the association between the mode of delivery and dyspareunia worldwide. Epidemiologic studies evaluating the link between the mode of delivery and dyspareunia (published up to July 2019) were included in this research. These studies were selected by searching several databases such as MEDLINE, ClinicalTraial.gov, PubMed, Web of Science, Scopus and Google Scholar and considering the list of selected articles extracted from references. Then, meta-analyses, subgroup analyses and meta-regressions of the studies were conducted to evaluate the association between the mode of delivery and dyspareunia. In this study, 23 out of 1099 articles were identified and used in the final analysis. Dyspareunia differed according to the mode of delivery although this difference was not statistically significant. In terms of the mode of delivery, a difference was found between instrumental vaginal delivery and caesarean section, but it was not significant. It seems that more studies with a larger sample size should be considered to determine the difference.

Roza Yagudina,1 Andrey Kulikov,1 Vyacheslav Serpik,1 Alex Borodin,2 Irina Vygodchikova3 1Department of Organization of Medical Provision and Pharmacoeconomics, I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia; 2Plekhanov Russian University of Economics, Moscow, Russia; 3Department of Differential Equations and Mathematical Economics, Saratov State University, Saratov, RussiaCorrespondence: Alex Borodin Tel +7 926 6469990Fax +7 916 7008865Email aib-2004@yandex.ruPurpose: Stimulating cost reduction of pharmaceutical companies to optimize the structure of distribution of patients by the level of treatment costs in various programs.Patients and Methods: In this article, we rise up the issues of pharmacoeconomic modeling related to the description of the patient flows in the pharmacoeconomic model and methods to determining the course dose of drugs under the restriction of integer computations. We established two possible ways of distributing patients through treatment regimens in pharmacoeconomic models, also analyzed the effects of simultaneous and uniform entry of patients into the model. Also, we considered the limitations and possibilities of calculations based on the active substance and packaging, as well as the transition factor of the remainder of the drug in the next time period.Results: A mathematical model of the analysis of the system assessment of patients by the level of risk of abandoning a healthy lifestyle in connection with the growing problems of the difficult-to-control process is developed. The use of a rational data convolution mode allowed us to obtain a criterion for the optimality of the process and a logical point of stability of the pharmaceutical company by rationally applying treatment methods according to established standards (percentage base). This approach makes it possible to influence the management of private clinics through clear ideas on the algorithms for prescribing drugs in each group of patients and their zoning in the vector recovery mode.Conclusion: Initial data and sample size: 552 measurements of the intervals of changes in the subject’s indicators in seconds (smoothing and scaling the data to the level of the base (analytical) period or the final (barrier) period). Regular use of this approach makes it possible to reserve the resources of the body of a healthy and physically active person in a timely manner for a very reliable functioning of all body systems, taking into account the dosed intake of prescribed drugs and the conditions of comfortable (decent) maintenance of patients during the course of treatment according to the method chosen by the doctor.Keywords: pharmacoeconomic modelling, pharmacoeconomic model, number of patients, patient flow, patient distribution, budget impact analysis, purchase requisition

We address sampling design of population pharmacokinetics (popPK) experiments in the context of two ongoing phase 2b efficacy trials that evaluate the efficacy of VRC01 (vs. placebo) in reducing the rate of HIV infection among 4625 participants. Blood samples are collected at up to 22 study visits from all participants for immediate HIV diagnosis as the primary trial outcome, and stored for future outcome-dependent marker measurements. A key secondary objective of the trials is to evaluate correlates of prevention efficacy among a sub-cohort of VRC01 recipients in terms of whether the current value of VRC01 serum concentration is associated with the instantaneous rate of HIV infection. To accomplish this, concentrations on a daily grid are estimated via non-linear mixed effects popPK modeling of observed 4-weekly concentrations. Given the impracticality of measuring concentrations in all stored blood samples, we devised a simulation-based sampling design framework to evaluate the impact of sub-cohort sample sizes (m) and sampling schemes of time-points on the accuracy and precision of the popPK model parameters. We accounted for specific study schedules and heterogeneity in participants’ characteristics and study adherence patterns. We found that with m = 120, reasonably unbiased and consistent estimates of most fixed and random effect terms could be obtained without complete sampling of all 22 time-points, even under low study adherence (about half of the 4-weekly visits missing per participant). The described simulation framework is not only novel in its application to popPK sampling design for studying correlates of prevention efficacy in a subcohort of the parent trial, but also flexible in accommodating real-life study setup options, and can be generalized to other single- or multiple-dose PK sampling design settings.

Qualitative research sample size determination has always been a contentious and confusing issue. Studies are often vague when explaining the processes and justifications that have been used to det...

Introduction: Occupational balance and satisfaction with daily occupations are two central concepts in occupational science. The aim of the present study was therefore to explore associations between different aspects of occupational balance and satisfaction with daily occupations. A second aim was to explore whether different aspects of occupational balance predicted satisfaction with daily occupations 12 months later. Methods: The present study had an exploratory correlational longitudinal pilot study design and was part of a project in which the Tree Theme Method was evaluated for patients with depression and/or anxiety disorders. Data collected directly after the intervention (n=107) and 12 months later (n=84) were used in the present study. The Occupational Balance Questionnaire (OBQ) and the Satisfaction with Daily Occupations (SDO) were used, and binary logistic regressions were used to explore associations between them. Results: Statistically significant associations were found between the following aspects of occupational balance: Balance between work, home, family, leisure, rest, and sleep; Having neither too much nor too little to do during a regular week; Satisfaction with time spent in rest, recovery, and sleep and satisfaction with daily occupations directly after the intervention. The occupational balance item Balance between energy-giving/energy-taking occupations directly after the intervention predicted satisfaction with daily occupations 12 months later. Conclusion: This study provides some support for the assumption about an association between occupational balance and satisfaction with daily occupations. Studies with larger sample sizes and in other cultures are recommended to confirm the presence of these associations and/or to find other associations. (Less)

Bayesian estimation of multilevel structural equation models (MLSEMs) offers advantages in terms of sample size requirements and computational feasibility, but does require careful specification of the prior distribution especially for the random effects variance parameters. The traditional “non-informative” conjugate choice of an inverse-Gamma prior with small hyperparameters has been shown time and again to be problematic. In this paper, we investigate alternative, more robust prior distributions for the doubly latent categorical multilevel model. In contrast to multilevel models without latent variables, MLSEMs have multiple random effects variance parameters both for the multilevel structure and for the latent variable structure. It is therefore even more important to construct reasonable priors for these parameters. We find that, although the robust priors outperform the traditional inverse-Gamma prior, their hyperparameters do require careful consideration.

To examine the measurement model of the Tokophobia Severity Scale and consider issues of statistical power and sample size from the original instrument development study.Fear of childbirth (FoC) and tokophobia represents an area of increasing concern within perinatal mental health research and clinical practice. Existing measures of the FoC have been criticised due to either measurement inconsistencies, difficulties in scoring or practical clinical application. Attempting to address these limitations, researchers developed the Tokophobia Severity Scale (TSS). A fundamental assumption underpinning the use of the TSS is unidimensionality, however this assertion may have been based on sub-optimal sample size and approach taken to factor structure determination.Parallel analysis (PA), principal components analysis (PCA), exploratory factor analysis (EFA), power analysis and sample size calculation using a reconstruction of the original dataset from published summary data.Following replication of the original PCA, a three-factor model was found to offer a significantly better fit to data than a unidimensional model. Power analysis suggested the original study was underpowered.The TSS remains a promising tool but assumptions regarding its measurement model are based on an inadequate sample size. Sample sizes for a sufficiently powered study indicated.

Purpose To investigate if children with cerebral palsy have sustained attendance and involvement in physical activities after completing physical activity interventions. Methods The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed. Seven databases were searched for the period 2001–2020 with hand-searching of pertinent reference lists. Criteria for study inclusion were participants aged 0–18 years and ≥50% with cerebral palsy; follow-up ≥1 month beyond completion of the physical activity intervention; and measurement of attendance and/or involvement in any physical activity post-intervention. Study selection, data extraction, and risk of bias assessments (Physiotherapy Evidence Database (PEDro) or tool for non-randomised studies) were completed independently by paired reviewers. Results were compiled by narrative synthesis. Results Thirteen studies were included (11 randomised controlled trials (RCTs), two non-randomised case series; intervention sample sizes: 6–34). All study participants had cerebral palsy and were aged 4–16.7 years. PEDro scores for the RCTs ranged from 5 to 10; 10 did not blind one or more therapist, participant, or assessor. Two case series showed high risk of bias. Twelve studies reported on attendance, with positive changes in three studies. At 4–14 weeks post-intervention, two studies demonstrated positive changes were maintained. Four studies included involvement outcomes; one reporting positive changes in physical activity involvement four weeks after intervention completion. Conclusions Physical activity attendance may be influenced by physical activity interventions in the short term, but more robust research designs are required to investigate whether gains can be sustained. Activity involvement, which may influence ongoing participation, is under-researched. Implications for Rehabilitation • Positive changes in attendance and involvement following physical activity interventions appear short term at best. • Physical activity interventions should have longer follow-up periods to determine the effect on sustained physical activity participation. • Careful selection and reporting of attendance and involvement outcome measures is required. • The optimal physical activity intervention to increase attendance or involvement in physical activities remains uncertain.

This study investigates the effects of sample size and sample prevalence on cellular automata (CA) simulation of urban growth. We take the CA models based on an artificial neural network (ANN), log...

Traditional psychometric modeling focuses on observed categorical item responses, which can over-simplify the respondent cognitive response process. A further weakness is that analysis of ordinal responses has been primarily limited to a single substantive trait at one time point. We propose a significant expansion of this modeling framework to account for complex response processes across multiple waves of data collection using the beneficial item response tree framework. This study proposes a novel model, the longitudinal IRTree, for response processes in longitudinal studies, and investigates whether the response style changes are proportional to changes in the substantive trait of interest. A simulation study demonstrates adequate item parameter recovery in a Bayesian framework, especially with larger sample sizes of 2000. The longitudinal change parameters were recovered similarly well, with improved recovery using informative priors over default priors in Mplus. The empirical application demonstrates that relatively stable observed scores are due to a decrease in response styles offsetting an increase in the latent trait of interest.

Among health care professionals, dentists are more prone to infection due to their direct contact with blood and saliva. This study highlights the level of compliance with infection control measures among dental students; and linked this to their knowledge and attitudes regarding infection control measures and proper vaccinations. The study is a multi-centre analytical cross-sectional survey. The sample size was 969 clinical years' students from governmental and private dental colleges in Sudan and Saudi Arabia. A pre-coded, pre-tested, and anonymous questionnaire consisted of seven categories of knowledge, attitude, awareness, and practices, including 40 questions, was answered. The study has linked knowledge, attitude, and practice in dental clinics with possible COVID-19 transmission in dental clinics. The results showed that the use of some Personal Protective Equipment was low in Sudan and moderate in Saudi Arabia (statistically significant, p < 0.001). Moderate percentage of Hepatitis B vaccination status, with low percentage of Post HBV serology, was reported (p < 0.001). The percentages of major causes of percutaneous and/or eye exposure/injury, for examples, were as follows: anesthesia (33.3-62.7%, p 0.006) and suture needle (17.5-39.4%, p 0.037). In conclusion, dental students in the present study had good knowledge and positive attitudes regarding infection control in general, but showed moderate compliance with the recommended infection control guidelines.

This work discourages using the logistic regression (LR) model for estimative purposes when the sample size is small. We propose a new model called the “Robust Bayesian Logistic (RBL) model” that minimizes bias in the estimated logistic regression coefficients when the sample size is small, and covariate corruption is suspected. For the prior specification in the proposed RBL model, all the logistic regression coefficients are assigned independent Student’s t-distribution with the location parameter 0, scale parameter 1, and degree of freedom 7 for the constant term and degree of freedom of 1 for all other regression coefficients. In our experimental study, the proposed RBL model outperforms the Logistic Regression (LR) model by having a lower mean squared error (MSE) in the regression coefficients estimated for all the sample sizes considered. The proposed RBL model has a lower standard deviation than the LR model for all the estimated regression coefficients on the real-life dataset. We suggest that the proposed RBL model be considered for logistic modeling since it generates stable, consistent, and reliable estimates, especially when the sample size is small. The proposed RBL model is a fully Bayesian method implemented in the R environment using the RJAGS package.

The amount of real-world data (RWD) available from sources other than randomized-controlled trials (RCTs) has grown ultra-rapidly in recent years. It provides the impetus for generating substantial evidence of effectiveness and safety from both RCTs and RWD to accelerate medical product development. Especially in the areas of unmet needs, the conduct of fully powered RCTs is generally infeasible because of their sizes, duration, cost, or ethical constraints. The unique challenges in such areas include a small patient population, heterogeneity in disease presentation, and a lack of established endpoints. However, merging information from disparate sources is an intricate task. The value of the Bayesian framework has gained more recognition due to its flexibility in calibrating uncertainty and handling data heterogeneity, and its inherent updating process ideal for synthesizing information. Meanwhile, propensity score, as a powerful tool in causal inference, can be used in various ways to adjust for confounders. As a newly emerging data borrowing strategy in a regulatory setting, integrating propensity scores in a Bayesian setting not only utilizes the strengths from Bayesian models but also minimizes bias from external data borrowing. These methods potentially allow information sharing among data sources, provide more reliable estimates when the sample size is small, and improve the efficiency of treatment effect estimation. In this paper, we will review the recent development of methods incorporating propensity score for evidence synthesis under the Bayesian framework, and discuss different examples of incorporating external data with or without RCTs, as well as the recommendations for reporting in clinical studies.

Factor mixture modeling (FMM) has been increasingly used in behavioral and social sciences to capture underlying population heterogeneity. This Monte Carlo simulation study investigated a common is...

Perhaps the most frequently asked question concerning sampling is, ‘What sample size do we need?’. The answer to this question is influenced by many factors. Here, we consider the progressively typ...

In parametric estimates, the maximum likelihood estimation method is the most popular method widely used in the social sciences and psychology, although it is biased in situations where sample sizes are small or the data are heavily censored. Therefore, the main objective of this research is to improve this estimation method using the Runge-Kutta technique. The improved method was applied to derive the estimators of the shape scale family parameters and compare them with Bayesian estimators based on the informative and kernel priors, via Monte Carlo simulation. The simulation results showed that the improved maximum likelihood estimation method is highly efficient and outperforms the Bayesian method for different sample sizes. Finally, from a future perspective, the proposed model could be important for analyzing real data sets including data on COVID-19 deaths in Egypt, for potential comparative studies with other countries.

Objectives. Depression is known to be associated with a variety of occupational factors. The aim of this study was to meta-analyze the prevalence of depressive disorder in industrial workers. Methods. Three databases indexing abstracts of articles were selected and searched until August 2020: PubMed, Web of Science and Scopus. For each study, the sample size and number of depression events were extracted, and after extracting these data, the random effects method was used to assess the prevalence. I2 and χ2 values were used to investigate the heterogeneity. Results. The prevalence of depressive disorder in industrial workers is 21%. The prevalence of depressive disorder in men and women industrial workers is 23 and 28%, respectively. The prevalence of depressive disorder in Asia, Europe and America is equal to 22, 18 and 20%, respectively. The result of the heterogeneity test showed that the heterogeneity is high. Conclusion. The prevalence of depression in industrial workers is higher than in the general population. These differences can be due to the working conditions of industrial workers. Therefore, the work environment and the promotion of occupational health can play an important role in preventing depression.

To estimate the thermal properties of plumbene under different temperatures, a plumbene sheet is developed by multi-scale modeling. Plumbene sheets of variable sample sizes are also investigated to...

Introduction: Precision medicine is impacting clinical practice and drug development in oncology, promoting notable changes in the design of early-phase cancer trials. There is increasing pressure ...

Anticholinergic drugs and behavioral interventions are effective methods for the treatment of OAB and UUI. This randomized controlled, prospective, and quasi-experimental study determined the effect of healthy lifestyle behavior training, based on the Health Promotion Model (HPM), on the treatment of women with Overactive Bladder (OAB). The study sample included intervention and control groups with a sample size of 100 women diagnosed with OAB who received pharmacological treatment and agreed to participate in the study. The data were collected using an Introductory Information Form, Urinary Diary, OAB Questionnaire (OAB-V8), King’s Health Questionnaire (KHQ), Healthy Life Style Behavior Scale II (HLSB II), Brief Symptom Inventory (BSI), and 24-hour Pad Test. The intervention group was administered a 45-minute training program based on Pender’s HPM. There was a statistically significantly higher level of decrease in urgency (z = −3,259;p = 0,001), nocturia (z = −3,691;p < 0,001), urge urinary incontinence (z = −2,391;p = 0,017), and urinary frequency (χ2 = 17,420;p < 0,001) in the intervention group during the posttest period. The study found a significant decrease in the total posttest scores of the women in the intervention group on the OAB-V8 (t = −6.955;p < 0.001), KHQ (t = −5.354;p < 0.001), and BSI (t = −6.463;p < 0.001) scales whereas a statistically significant increase was found in their total score on the HLSB II (t = 9.139;p < 0.001) scale. The study concluded that HLSB training, which was prepared based on HPM, reduced OAB symptoms among women, improved the quality of their lives, changed their HLSBs, and positively affected their psychological symptoms.

The idea that medical treatment costs and outcomes might be connected is not new. Likewise, as long as researchers have been designing clinical trials and public opinion polls, there has been inter...

In stepped wedge cluster randomised trials (SW-CRTs), clusters of subjects are randomly assigned to sequences, where they receive a specific order of treatments. Compared to conventional cluster ra...

Asymptotic approaches are traditionally used to calculate confidence intervals for intraclass correlation coefficient in a clustered binary study. When sample size is small to medium, or correlation or response rate is near the boundary, asymptotic intervals often do not have satisfactory performance with regard to coverage. We propose using the importance sampling method to construct the profile confidence limits for the intraclass correlation coefficient. Importance sampling is a simulation based approach to reduce the variance of the estimated parameter. Four existing asymptotic limits are used as statistical quantities for sample space ordering in the importance sampling method. Simulation studies are performed to evaluate the performance of the proposed accurate intervals with regard to coverage and interval width. Simulation results indicate that the accurate intervals based on the asymptotic limits by Fleiss and Cuzick generally have shorter width than others in many cases, while the accurate intervals based on Zou and Donner asymptotic limits outperform others when correlation and response rate are close to their boundaries.

We thank the opportunity offered by editors for this discussion and the discussants for their insightful comments and thoughtful contributions. We also want to congratulate Kallus (2020) for his inspiring work in improving the efficiency of policy learning by retargeting. Motivated from the discussion in Dukes and Vansteelandt (2020), we first point out interesting connections and distinctions between our work and Kallus (2020) in Section 1. In particular, the assumptions and sources of variation for consideration in these two papers lead to different research problems with different scopes and focuses. In Section 2, following the discussions in Li et al. (2020); Liang and Zhao (2020), we also consider the efficient policy evaluation problem when we have some data from the testing distribution available at the training stage. We show that under the assumption that the sample sizes from training and testing are growing in the same order, efficient value function estimates can deliver competitive performance. We further show some connections of these estimates with existing literature. However, when the growth of testing sample size available for training is in a slower order, efficient value function estimates may not perform well anymore. In contrast, the requirement of the testing sample size for DRITR is not as strong as that of efficient policy evaluation using the combined data. Finally, we highlight the general applicability and usefulness of DRITR in Section 3.

Phase II clinical trials investigate whether a new drug or treatment has sufficient evidence of effectiveness against the disease under study. Two-stage designs are popular for phase II since they can stop in the first stage if the drug is ineffective. Investigators often face difficulties in determining the target response rates, and adaptive designs can help to set the target response rate tested in the second stage based on the number of responses observed in the first stage. Popular adaptive designs consider two alternate response rates, and they generally minimise the expected sample size at the maximum uninterested response rate. Moreover, these designs consider only futility as the reason for early stopping and have high expected sample sizes if the provided drug is effective. Motivated by this problem, we propose an adaptive design that enables us to terminate the single-arm trial at the first stage for efficacy and conclude which alternate response rate to choose. Comparing the proposed design with a popular adaptive design from literature reveals that the expected sample size decreases notably if any of the two target response rates are correct. In contrast, the expected sample size remains almost the same under the null hypothesis.

Planned missing data designs allow researchers to have highly-powered studies by testing only a fraction of the traditional sample size. In two-method measurement planned missingness designs, resea...

Li Li,1,* Xuemei Zhong,1,* Aifang Zheng,1 Chen JianKun,2,3 Ayiguzali A Budukadeer,1 Paierda Aini,1 Maimaitiaili Tuerxun,1 Mukeremu Yasen,1 Tao Ma,1 Jie Ren,1 Rukeyamu Semaiti,1 Chengxin Xie,1 Feifei Li,1 Mireban Rexiati,1 Lifeng Tang,1 Gulanbaier Abudurexiti,1 Dayong Zheng,1 Ji-Qiang Li1– 3 1Department of Respiratory and Critical Care Medicine, The First People’s Hospital of Kashi Prefecture, Kashi, Xinjiang, People’s Republic of China; 2The Second Clinical College, Guangzhou University of Chinese Medicine, Guangzhou, People’s Republic of China; 3The Third Comprehensive Department, The Second Affiliated Hospital, Guangzhou University of Chinese Medicine (Guangdong Provincial Hospital of Chinese Medicine), Guangzhou, People’s Republic of China*These authors contributed equally to this workCorrespondence: Dayong ZhengDepartment of Respiratory and Critical Care Medicine, The First People’s Hospital of Kashi Prefecture, 120 Yingbin Road, Kashi City, Xinjiang, 844000, People’s Republic of ChinaTel +13570029702Email zdy525679@163.comJi-Qiang LiThe Second Clinical College, Guangzhou University of Chinese Medicine, 232 Waihuan East Road, Guangzhou University City, Panyu District of Guangzhou, Guangzhou, 510120, People’s Republic of ChinaTel +13527675982Email Ljq525679@126.comObjective: To investigate the prevalence and the risk factors of COPD in the Uyghur population in the Kashi region.Methods: From August 2018 to March 2019, we recruited participants of Uyghur ethnicity and aged ≥ 40 years old from the Kashi region using a combined cluster sampling and random sampling method. We collected potential risk factors using questionnaire, and conduced lung function using a portable pulmonary function instrument.Results: A total of 2963 participants were included in this analysis, of whom 1268 were males and 1695 were females. There were 504 participants with COPD, generating a prevalence of 17.01%. With the increase of age, the prevalence of COPD in different genders increased significantly. The results of different regions were χ2= 627.89, p < 0.01, indicating significant differences in the prevalence in different regions. Among them, based on the existing survey data, it is speculated that Shache county has the highest crude prevalence, but the sample size needs to be further expanded. The participants with high age, smoking, lower BMI, high waist circumference, systolic blood pressure, fried cooking and barbecue share more COPD than those who partake of fruits and vegetables in this study.Conclusion: The prevalence of COPD among the Uyghur population in the Kashi region is higher than the national rural average. Among them, high age, smoking, low BMI, high waist circumference, high systolic blood pressure, cooking methods that may be stir-fried and deep-fried barbecue are risk factors for COPD, and vegetable and fruit intake may be a protective factor for COPD.Keywords: Uyghur population, chronic obstructive pulmonary disease, prevalence, risk factors, epidemiology study

Researchers designing a clinical trial to demonstrate superiority or non-inferiority of a new treatment to an established control face an important choice: to conduct a randomized controlled trial ...