Estimated quadratic inference function for correlated failure time data.

An estimated quadratic inference function method is proposed for correlated failure time data with auxiliary covariates. The proposed method makes efficient use of the auxiliary information for the incomplete exposure covariates and preserves the property of the quadratic inference function method that requires the covariates to be completely observed. It can improve the estimation efficiency and easily deal with the situation when the cluster size is large. The proposed estimator which minimizes the estimated quadratic inference function is shown to be consistent and asymptotically normal. A chi-squared test based on the estimated quadratic inference function is proposed to test hypotheses about the regression parameters. The small-sample performance of the proposed method is investigated through extensive simulation studies. The proposed method is then applied to analyze the Study of Left Ventricular Dysfunction (SOLVD) data as an illustration.

Combining evidence from clinical trials in conditional or accelerated approval.

Conditional (European Medicines Agency) or accelerated (U.S. Food and Drug Administration) approval of drugs allows earlier access to promising new treatments that address unmet medical needs. Certain post-marketing requirements must typically be met in order to obtain full approval, such as conducting a new post-market clinical trial. We study the applicability of the recently developed harmonic mean χ 2 -test to this conditional or accelerated approval framework. The proposed approach can be used both to support the design of the post-market trial and the analysis of the combined evidence provided by both trials. Other methods considered are the two-trials rule, Fisher's criterion and Stouffer's method. In contrast to some of the traditional methods, the harmonic mean χ 2 -test always requires a post-market clinical trial. If the p -value from the pre-market clinical trial is ≪ 0.025 , a smaller sample size for the post-market clinical trial is needed than with the two-trials rule. For illustration, we apply the harmonic mean χ 2 -test to a drug which received conditional (and later full) market licensing by the EMA. A simulation study is conducted to study the operating characteristics of the harmonic mean χ 2 -test and two-trials rule in more detail. We finally investigate the applicability of these two methods to compute the power at interim of an ongoing post-market trial. These results are expected to aid in the design and assessment of the required post-market studies in terms of the level of evidence required for full approval.

A first approach to human biometeorology research in Brazil: a systematic review and meta-analysis.

This systematic review aims to give an overview of the diversity of research areas related to human biometeorology in Brazil. The main focus of this paper addresses research trends, represented by published papers with national and international authorship, main contributions and shortcomings, as well as challenges and prospects of research in this area of study. An extensive literature search was conducted in the Scopus, Web of Science, and Science Direct databases so as to identify relevant publication output up to July 2021 related to the research area. The screening resulted in 96 studies chosen for full-text reading. Overall, results indicated a reduced amount of articles on the subject matter published internationally, with noticeable gaps in research in some regions of the country, such as the Amazon region and in the Brazilian Midwest region. Research gaps in relevant areas have been identified with limited output in the climate dimensions of tourism, vector-borne diseases, mortality and morbidity in urban centers. Such gaps should further encourage researchers to engage in research focused on those areas.

Nonparametric Clustering of Mixed Data Using Modified Chi-Squared Tests.

We propose a non-parametric method to cluster mixed data containing both continuous and discrete random variables. The product space of the continuous and discrete sample space is transformed into a new product space based on adaptive quantization on the continuous part. Detection of cluster patterns on the product space is determined locally by using a weighted modified chi-squared test. Our algorithm does not require any user input since the number of clusters is determined automatically by data. Simulation studies and real data analysis results show that our proposed method outperforms the benchmark method, AutoClass, in various settings.

Prevalence of evidence of inconsistency and its association with network structural characteristics in 201 published networks of interventions.

BACKGROUND: Network meta-analysis (NMA) has attracted growing interest in evidence-based medicine. Consistency between different sources of evidence is fundamental to the reliability of the NMA results. The purpose of the present study was to estimate the prevalence of evidence of inconsistency and describe its association with different NMA characteristics. METHODS: We updated our collection of NMAs with articles published up to July 2018. We included networks with randomised clinical trials, at least four treatment nodes, at least one closed loop, a dichotomous primary outcome, and available arm-level data. We assessed consistency using the design-by-treatment interaction (DBT) model and testing all the inconsistency parameters globally through the Wald-type chi-squared test statistic. We estimated the prevalence of evidence of inconsistency and its association with different network characteristics (e.g., number of studies, interventions, intervention comparisons, loops). We evaluated the influence of the network characteristics on the DBT p-value via a multivariable regression analysis and the estimated Pearson correlation coefficients. We also evaluated heterogeneity in NMA (consistency) and DBT (inconsistency) random-effects models. RESULTS: We included 201 published NMAs. The p-value of the design-by-treatment interaction (DBT) model was lower than 0.05 in 14% of the networks and lower than 0.10 in 20% of the networks. Networks including many studies and comparing few interventions were more likely to have small DBT p-values (less than 0.10), which is probably because they yielded more precise estimates and power to detect differences between designs was higher. In the presence of inconsistency (DBT p-value lower than 0.10), the consistency model displayed higher heterogeneity than the DBT model. CONCLUSIONS: Our findings show that inconsistency was more frequent than what would be expected by chance, suggesting that researchers should devote more resources to exploring how to mitigate inconsistency. The results of this study highlight the need to develop strategies to detect inconsistency (because of the relatively high prevalence of evidence of inconsistency in published networks), and particularly in cases where the existing tests have low power.

Semiparametric regression based on quadratic inference function for multivariate failure time data with auxiliary information.

This paper deals with statistical inference procedure of multivariate failure time data when the primary covariate can be measured only on a subset of the full cohort but the auxiliary information is available. To improve efficiency of statistical inference, we use quadratic inference function approach to incorporate the intra-cluster correlation and use kernel smoothing technique to further utilize the auxiliary information. The proposed method is shown to be more efficient than those ignoring the intra-cluster correlation and auxiliary information and is easy to implement. In addition, we develop a chi-squared test for hypothesis testing of hazard ratio parameters. We evaluate the finite-sample performance of the proposed procedure via extensive simulation studies. The proposed approach is illustrated by analysis of a real data set from the study of left ventricular dysfunction.

Power difference in a χ2 test vs generalized linear mixed model in the presence of missing data - a simulation study.

BACKGROUND: Longitudinal randomized controlled trials (RCTs) often aim to test and measure the effect of treatment between arms at a single time point. A two-sample χ2 test is a common statistical approach when outcome data are binary. However, only complete outcomes are used in the analysis. Missing responses are common in longitudinal RCTs and by only analyzing complete data, power may be reduced and estimates could be biased. Generalized linear mixed models (GLMM) with a random intercept can be used to test and estimate the treatment effect, which may increase power and reduce bias. METHODS: We simulated longitudinal binary RCT data to compare the performance of a complete case χ2 test to a GLMM in terms of power, type I error, relative bias, and coverage under different missing data mechanisms (missing completely at random and missing at random). We considered how the baseline probability of the event, within subject correlation, and dropout rates under various missing mechanisms impacted each performance measure. RESULTS: When outcome data were missing completely at random, both χ2 and GLMM produced unbiased estimates; however, the GLMM returned an absolute power gain up to from 12.0% as compared to the χ2 test. When outcome data were missing at random, the GLMM yielded an absolute power gain up to 42.7% and estimates were unbiased or less biased compared to the χ2 test. CONCLUSIONS: Investigators wishing to test for a treatment effect between treatment arms in longitudinal RCTs with binary outcome data in the presence of missing data should use a GLMM to gain power and produce minimally unbiased estimates instead of a complete case χ2 test.

Testing against umbrella or tree orderings for binomial proportions with an adaptation of an insect resistance case.

Alternative hypotheses for order restrictions, such as umbrella or inverse umbrella (a.k.a tree) orderings, have been studied extensively in the literature, although less so when the studied response for each individual is the presence or absence of the event of interest. Two families of test statistics for solving the problem of testing against an umbrella or a tree ordering when the responses are binomial proportions are studied in this work and their asymptotic distributions are derived. A simulation study is conducted to compare the empirical power of some members of the derived families of test statistics with competing approaches. The methodology developed here was driven by an applied problem arising in stored products research where despite universal mortality in the case of doses of 1000 ppm of the insecticide phosphine, unexpected survival was noted at higher doses.

Demographic Seasonality.

The seasonality of demographic data has been of great interest. It depends mainly on the climatic conditions, and the findings may vary from study to study. Commonly, the studies are based on monthly data. The population at risk plays a central role. For births or deaths over short periods, the population at risk is proportional to the lengths of the months. Hence, one must analyze the number of births (and deaths) per day. If one studies the seasonality of multiple maternities, the population at risk is the total monthly number of confinements and the number of multiple maternities in a given month must be compared with the monthly number of all maternities. Consequently, when one considers the monthly rates of multiple maternities, the monthly number of births is eliminated and one obtains an unaffected seasonality measure of the rates. In general, comparisons between the seasonality of different data sets presuppose standardization of the data to indices with common means, mainly 100. If one assumes seasonality as 'non-flatness' throughout a year, a chi-squared test would be an option, but this test calculates only the heterogeneity and the same test statistic can be obtained for data sets with extreme values occurring in consecutive months or in separate months. Hence, chi-squared tests for seasonality are weak because of this arbitrariness and cannot be considered a model test. When seasonal models are applied, one must pay special attention to how well the applied model fits the data. If the goodness of fit is poor, nonsignificant models obtained can erroneously lead to statements that the seasonality is slight, although the observed seasonal fluctuations are marked. In this study, we investigate how the application of seasonal models can be applied to different demographic variables.

Decomposing Pearson's χ2 test: A linear regression and its departure from linearity.

In case-control genetic association studies, a standard practice is to perform the Cochran-Armitage (CA) trend test under the assumption of the additive model because of its robustness. We could even identify situations in which it outperformed the analysis model consistent with the underlying inheritance mode. In this article, we analytically reveal the statistical basis that leads to the phenomenon. By elucidating the origin of the CA trend test as a linear regression model, we decompose Pearson's χ2 -test statistic into two components-one is the CA trend test statistic that measures the goodness of fit of the linear regression model, and the other measures the discrepancy between data and the linear regression model. Under this framework, we show that the additive coding scheme, as well as the multiplicative coding scheme, increases the coefficient of determination of the regression model by increasing the spread of data points. We also obtain the conditions under which the CA trend test statistic equals the MAX statistic and Pearson's χ2 -test statistic.

An approach of identifying differential nucleosome regions in multiple samples.

BACKGROUND: Nucleosome plays a role in transcriptional regulation through occluding the binding of proteins to DNA sites. Nucleosome occupancy varies among different cell types. Identification of such variation will help to understand regulation mechanism. The previous researches focused on the methods for two-sample comparison. However, a multiple-sample comparison (n ≥ 3) is necessary, especially in studying development and cancer. METHODS: Here, we proposed a Chi-squared test-based approach, named as Dimnp, to identify differential nucleosome regions (DNRs) in multiple samples. Dimnp is designed for sequenced reads data and includes the modules of both calling nucleosome occupancy and identifying DNRs. RESULTS: We validated Dimnp on dataset of the mutant strains in which the modifiable histone residues are mutated into alanine in Saccharomyces cerevisiae. Dimnp shows a good capacity (area under the curve > 0.87) compared with the manually identified DNRs. Just by one time, Dimnp is able to identify all the DNRs identified by two-sample method Danpos. Under a deviation of 40 bp, the matched DNRs are above 60% between Dimnp and Danpos. With Dimnp, we found that promoters and telomeres are highly dynamic upon mutating the modifiable histone residues. CONCLUSIONS: We developed a tool of identifying the DNRs in multiple samples and cell types. The tool can be applied in studying nucleosome variation in gradual change in development and cancer.

Differentiating the Cochran-Armitage Trend Test and Pearson's χ2 Test: Location and Dispersion.

In genetic case-control association studies, a standard practice is to perform the Cochran-Armitage (CA) trend test with 1 degree-of-freedom (d.f.) under the assumption of an additive model. However, when the true genetic model is recessive or near recessive, it is outperformed by Pearson's χ2 test with 2 d.f. In this article, we analytically reveal the statistical basis that leads to the phenomenon. First, we show that the CA trend test examines the location shift between the case and control groups, whereas Pearson's χ2 test examines both the location and dispersion shifts between the two groups. Second, we show that under the additive model, the effect of location deviation outweighs that of the dispersion deviation and vice versa under a near recessive model. Therefore, Pearson's χ2 test is a more robust test than the CA trend test, and it outperforms the latter when the mode of inheritance evolves to the recessive end.

Studies of the Seasonal Pattern of Multiple Maternities.

The seasonality of population data has been of great interest in demographic studies. When seasonality is analyzed, the population at risk plays a central role. In a study of the monthly number of births and deaths, the population at risk is the product of the size of the population and the length of the month. Usually, the population can be assumed to be constant, and consequently, the population at risk is proportional to the length of the month. Hence, the number of cases per day has to be analyzed. If one studies the seasonal variation in twin or multiple maternities, the population at risk is the total number of monthly confinements, and the study should be based on the rates of the multiple maternities. Consequently, if one considers monthly twinning rates, the monthly number of birth data is eliminated and one obtains an unaffected seasonality measure of the twin maternities. The strength of the seasonality is measured by a chi-squared test or by the standard deviation. When seasonal models are applied, one must pay special attention to how well the model fits the data. If the goodness of fit is poor, it can erroneously result in a statement that the seasonality is slight, although the observed seasonal fluctuations are marked.

Comparison of tests of contingency tables.

We explore the use of bootstrap for testing independence of two categorical variables. We develop a theoretical justification for bootstrapping a contingency table and provide more accurate inference for small sample sizes. We also study the effect of equalized marginals on tests of independence. The small sample properties of the proposed and existing tests of independence are examined using Monte Carlo simulations. It is shown that the Fisher exact test and the Chi-squared test with continuity correction are very conservative and cannot be recommended to test independence with small sample sizes.

Testing conditional independence in sets of I × J tables by means of moment and correlation score tests with application to HPV vaccine.

A new testing approach is described for improving statistical tests of independence in sets of tables stratified on one or more relevant factors in case of categorical (nominal or ordinal) variables. Common tests of independence that exploit the ordinality of one of the variables use a restricted-alternative approach. A different, relaxed-null method is presented. Specifically, the M-moment score tests and the correlation tests are introduced. Using multinomial-Poisson homogeneous modeling theory, it is shown that these tests are computationally and conceptually simple, and simulation results suggest that they can perform better than other common tests of conditional independence. To illustrate, the proposed tests are used to better understand the human papillomavirus type-specific infection by exploring the intention to vaccinate. Copyright © 2016 John Wiley & Sons, Ltd.

Testing for independence in J×K contingency tables with complex sample survey data.

The test of independence of row and column variables in a (J×K) contingency table is a widely used statistical test in many areas of application. For complex survey samples, use of the standard Pearson chi-squared test is inappropriate due to correlation among units within the same cluster. Rao and Scott (1981, Journal of the American Statistical Association 76, 221-230) proposed an approach in which the standard Pearson chi-squared statistic is multiplied by a design effect to adjust for the complex survey design. Unfortunately, this test fails to exist when one of the observed cell counts equals zero. Even with the large samples typical of many complex surveys, zero cell counts can occur for rare events, small domains, or contingency tables with a large number of cells. Here, we propose Wald and score test statistics for independence based on weighted least squares estimating equations. In contrast to the Rao-Scott test statistic, the proposed Wald and score test statistics always exist. In simulations, the score test is found to perform best with respect to type I error. The proposed method is motivated by, and applied to, post surgical complications data from the United States' Nationwide Inpatient Sample (NIS) complex survey of hospitals in 2008.

Improving the Hosmer-Lemeshow goodness-of-fit test in large models with replicated Bernoulli trials.

The Hosmer-Lemeshow (HL) test is a commonly used global goodness-of-fit (GOF) test that assesses the quality of the overall fit of a logistic regression model. In this paper, we give results from simulations showing that the type I error rate (and hence power) of the HL test decreases as model complexity grows, provided that the sample size remains fixed and binary replicates (multiple Bernoulli trials) are present in the data. We demonstrate that a generalized version of the HL test (GHL) presented in previous work can offer some protection against this power loss. These results are also supported by application of both the HL and GHL test to a real-life data set. We conclude with a brief discussion explaining the behavior of the HL test, along with some guidance on how to choose between the two tests. In particular, we suggest the GHL test to be used when there are binary replicates or clusters in the covariate space, provided that the sample size is sufficiently large.

A Case-Control Study of the Relationship Between Genetic Polymorphism and Cretinism in Xinjiang.

Background: Cretinism is a subtype of congenital hypothyroidism, an endocrine disorder resulting from inadequate thyroid hormone production or receptor deficiency. Genetic abnormalities play a major role in the development of thyroid dysfunction. Methods: We recruited 183 participants with cretinism and 119 healthy participants from the Xinjiang Uyghur Autonomous Region and randomly selected 29 tag single nucleotide polymorphisms (tSNPs) in TSHB, PAX8, TPO, NKX2-5, and TSHR in all participants. We compared genotype and allele frequencies between cases and controls utilizing the chi-squared test, logistic regression analysis, and haplotype analysis. Results: Using the chi-squared test, a single SNP was found to be associated with cretinism (recessive model: rs3754363, OR = 0.46, 95% CI = 0.27-0.80, P = 0.00519; genotype model: P = 0.01677). We stratified neurological, myxedematous, and mixed type and determined that another SNP was associated with a higher risk when comparing myxedematous type to the neurological type (rs2277923). Conclusion: rs3754363 has a statistically significant protective effect on people with cretinism, while rs2277923 may play a greater role in promoting the development of neurocretinism.

Analyzing Perceived Psychological and Social Stress of University Students: A Machine Learning Approach.

The COVID-19 pandemic has worsened the psychological and social stress levels of university students due to physical illness, enhanced dependence on mobile devices and internet, a lack of social activities, and home confinement. Therefore, early stress detection is crucial for their successful academic performance and mental well-being. The advent of machine learning (ML)-based prediction models can have a crucial impact in predicting stress at its early stages and taking necessary steps for the well-being of individuals. This study aims to develop a reliable machine learning-based prediction model for perceived stress prediction and validate the model using real-world data collected through an online survey among 444 university students from different ethnicity. The machine learning models were built using supervised machine learning algorithms. Principal Component Analysis (PCA) and the chi-squared test were employed as feature reduction techniques. Moreover, Grid Search Cross-Validation (GSCV) and Genetic Algorithm (GA) were employed for hyperparameter optimization (HPO). According to the findings, around 11.26% of individuals were identified with high levels of social stress. In comparison, approximately 24.10% of people were found to be suffering from extremely high psychological stress, which is quite alarming for students' mental health. Furthermore, the prediction results of the ML models demonstrated the most remarkable accuracy (80.5%), precision (1.000), F1 score (0.890), and recall value (0.826). The Multilayer Perceptron model was shown to have the maximum accuracy when combined with PCA as a feature reduction approach and GSCV for HPO. The convenience sampling technique used in this study only considers self-reported data, which may have biased results and lack generalizability. Future research should consider a large sample of data and focus on tracking long-term impacts with coping strategies and interventions. The results of this study can be used to develop strategies to mitigate adverse effects of the overuse of mobile devices and promote student well-being during pandemics and other stressful situations.

Occupational injuries among non-standard workers in the Taiwan construction industry.

INTRODUCTION: Global changes in the labor force have led to an increase in non-standard employment (NSE) workers, particularly apparent in the construction industry. These workers have a higher risk of occupational injury and negative health-related outcomes. METHOD: In this study, relevant literature and the database for construction accidents are examined to identify the classification of NSE in the Taiwan construction industry. Accident reports from 2000 to 2018 are extracted from case reports of the Northern Occupational Safety and Health Center of Taiwan. Pearson's chi-squared test are then employed to analyze a total of 1,612 occupational fatality cases in the construction industry to explore the differences in occupational injuries between NSE and standard employment (SE). Further, characteristics of occupational injuries for different types of NSE in the construction industry are analyzed. RESULTS: The NSE occupational injury rate for older workers over 60 years old is higher, especially for self-employed workers taking on technical work. NSE workers are more expected to suffer occupational fatalities in the small-scale, non-public, and repair projects. Occupational injuries involving self-employed and temporary agency workers are clearly regionally concentrated. Temporary agency workers involved in occupational injuries are most engaged in non-technical work and movement for worker motion with their unfamiliarity with the worksite. Most enterprises did not perform safety management on construction sites for occupational injuries involving NSE workers, especially for self-employed workers. CONCLUSIONS: The results show that the hazard characteristics of NSE workers are clearly different from SE workers. NSE workers face inferior job security and protection, especially for self-employed workers. PRACTICAL APPLICATIONS: The results can be used to establish effective occupational safety management policies and programs more efficiently.