Practice In Statistical Analysis Research Articles

Application of Minimum Bayes Factor to A Balanced Two-Way ANOVA With Random Effects

It is a common practice in statistical analysis to draw conclusions based on significance. P-values often reflect the probability of incorrectly concluding that a null hypothesized model is true; they do not provide information about other types of error that are also important for interpreting statistical results. Standard model selection criteria and test procedures are often inappropriate for comparing models with different numbers of random effects, due to constraints on the parameter space of the variance components. In this paper, we focused on a minimum Bayes factor proposed by Held and Ott (2018) and applied it to a balanced two way analysis of variance (ANOVA) with random effects under three cases namely: Case 1: both factors are fixed; Case 2: both factors are random; Case 3: factor A is fixed and factor B is random. We realized that in all the three cases, considered the Bayes factor indicates weak evidence against the null hypothesis of zero variability in the effects of the levels of the factors as well as the interactions. This result is due to the conservative nature of the minimum Bayes factor

Application of Minimum Bayes Factor to a Balanced Two-Way Anova with Random Effects

It is a common practice in statistical analysis to draw conclusions based on significance. P-values often reflect the probability of incorrectly concluding that a null hypothesized model is true; they do not provide information about other types of error that are also important for interpreting statistical results. Standard model selection criteria and test procedures are often inappropriate for comparing models with different numbers of random effects, due to constraints on the parameter space of the variance components. In this paper, we focused on a minimum Bayes factor proposed by Held and Ott (2018) and applied it to a balanced two way analysis of variance (ANOVA) with random effects under three cases namely: Case 1: both factors are fixed; Case 2: both factors are random; Case 3: factor A is fixed and factor B is random. We realized that in all the three cases, considered the Bayes factor indicates weak evidence against the null hypothesis of zero variability in the effects of the levels of the factors as well as the interactions. This result is due to the conservative nature of the minimum Bayes factor.

Application of Minimum Bayes Factor to a Balanced Two-Way Anova with Random Effects

It is a common practice in statistical analysis to draw conclusions based on significance. P-values often reflect the probability of incorrectly concluding that a null hypothesized model is true; they do not provide information about other types of error that are also important for interpreting statistical results. Standard model selection criteria and test procedures are often inappropriate for comparing models with different numbers of random effects, due to constraints on the parameter space of the variance components. In this paper, we focused on a minimum Bayes factor proposed by Held and Ott (2018) and applied it to a balanced two way analysis of variance (ANOVA) with random effects under three cases namely: Case 1: both factors are fixed; Case 2: both factors are random; Case 3: factor A is fixed and factor B is random. We realized that in all the three cases, considered the Bayes factor indicates weak evidence against the null hypothesis of zero variability in the effects of the levels of the factors as well as the interactions. This result is due to the conservative nature of the minimum Bayes factor.

Systematic review of statistical methods for safety data in malaria chemoprevention in pregnancy trials

BackgroundDrug safety assessments in clinical trials present unique analytical challenges. Some of these include adjusting for individual follow-up time, repeated measurements of multiple outcomes and missing data among others. Furthermore, pre-specifying appropriate analysis becomes difficult as some safety endpoints are unexpected. Although existing guidelines such as CONSORT encourage thorough reporting of adverse events (AEs) in clinical trials, they provide limited details for safety data analysis. The limited guidelines may influence suboptimal analysis by failing to account for some analysis challenges above. A typical example where such challenges exist are trials of anti-malarial drugs for malaria prevention during pregnancy. Lack of proper standardized evaluation of the safety of antimalarial drugs has limited the ability to draw conclusions about safety. Therefore, a systematic review was conducted to establish the current practice in statistical analysis for preventive antimalarial drug safety in pregnancy.MethodsThe search included five databases (PubMed, Embase, Scopus, Malaria in Pregnancy Library and Cochrane Central Register of Controlled Trials) to identify original English articles reporting Phase III randomized controlled trials (RCTs) on anti-malarial drugs for malaria prevention in pregnancy published from January 2010 to July 2019.ResultsEighteen trials were included in this review that collected multiple longitudinal safety outcomes including AEs. Statistical analysis and reporting of the safety outcomes in all the trials used descriptive statistics; proportions/counts (n = 18, 100%) and mean/median (n = 2, 11.1%). Results presentation included tabular (n = 16, 88.9%) and text description (n = 2, 11.1%). Univariate inferential methods were reported in most trials (n = 16, 88.9%); including Chi square/Fisher’s exact test (n = 12, 66.7%), t test (n = 2, 11.1%) and Mann–Whitney/Wilcoxon test (n = 1, 5.6%). Multivariable methods, including Poisson and negative binomial were reported in few trials (n = 3, 16.7%). Assessment of a potential link between missing efficacy data and safety outcomes was not reported in any of the trials that reported efficacy missing data (n = 7, 38.9%).ConclusionThe review demonstrated that statistical analysis of safety data in anti-malarial drugs for malarial chemoprevention in pregnancy RCTs is inadequate. The analyses insufficiently account for multiple safety outcomes potential dependence, follow-up time and informative missing data which can compromise anti-malarial drug safety evidence development, based on the available data.

Evolution of Studies on Orthopaedic Surgery Regarding Cost Management Tools: A Systematic Literature Review

Objective: The subject of hospital management is relevant given the importance of these institutions in the social and economic sphere, as well as for their managers, considering that in the hospital context, which involves high costs, scarce resources, pressure for quality and good service, information on costs and performance is useful for efficient management. This systematic literature review (SLR) aims to extend knowledge of the evidence linking costing mechanisms to the performance results of health institutions, analysing articles written in the last 10 years dealing with surgery in the specialization of orthopaedics, filling an important gap in the literature. Methods: Rigorous criteria for selection of articles is followed by use of the PRISMA meta-analysis, which gives an important overview of the types of studies made, as well as classification according to the cost management tools, leading to mapping costing methods and performance analysis. Results: Studies were classified by measurement methods, costs of decision-making and control and elements of strategic cost management, showing that the greatest number of studies originate in the USA (69, 23%) and use the variable of statistical analysis (53, 85%) in the majority of strategic management methods. As for types of surgical procedures, arthroplasty leads the ranking, accounting for 34, 62% of studies. Conclusions: Predominance of the use of standard costing as the method of measurement, use of control methods for decision-making and a tendency towards the practice of statistical analysis as a variable of the elements of strategic cost management, with studies of replacement surgery (hip, knee and shoulder) being most prominent. This study provides managers and public policy-makers with challenges and indicators of efficiency and effectiveness for better performance in orthopaedic surgical processes.

Videogame interventions and spatial ability interactions.

Numerous research studies have been conducted on the use of videogames as tools to improve one’s cognitive abilities. While meta-analyses and qualitative reviews have provided evidence that some aspects of cognition such as spatial imagery are modified after exposure to videogames, other evidence has shown that matrix reasoning measures of fluid intelligence do not show evidence of transfer from videogame training. In the current work, we investigate the available evidence for transfer specifically to nonverbal intelligence and spatial ability measures, given recent research that these abilities may be most sensitive to training on cognitive and working memory tasks. Accordingly, we highlight a few studies that on the surface provide evidence for transfer to spatial abilities, but a closer look at the pattern of data does not reveal a clean interpretation of the results. We discuss the implications of these results in relation to research design and statistical analysis practices.

Viral suppression in HIV studies: Combining times to suppression and rebound

In HIV-1 clinical trials the interest is often to compare how well treatments suppress the HIV-1 RNA viral load. The current practice in statistical analysis of such trials is to define a single ad hoc composite event which combines information about both the viral load suppression and the subsequent viral rebound, and then analyze the data using standard univariate survival analysis techniques. The main weakness of this approach is that the results of the analysis can be easily influenced by minor details in the definition of the composite event. We propose a straightforward alternative endpoint based on the probability of being suppressed over time, and suggest that treatment differences be summarized using the restricted mean time a patient spends in the state of viral suppression. A nonparametric analysis is based on methods for multiple endpoint studies. We demonstrate the utility of our analytic strategy using a recent therapeutic trial, in which the protocol specified a primary analysis using a composite endpoint approach.

Statistics: Reasoning on Uncertainty, and the Insignificance of Testing Null

The practice of statistical analysis and inference in ecology is critically reviewed. The dominant doctrine of null hypothesis significance testing (NHST) continues to be applied ritualistically and mindlessly. This dogma is based on superficial understanding of elementary notions of frequentist statistics in the 1930s, and is widely disseminated by influential textbooks targeted at biologists. It is characterized by silly null hypotheses and mechanical dichotomous division of results being “significant” (P < 0.05) or not. Simple examples are given to demonstrate how distant the prevalent NHST malpractice is from the current mainstream practice of professional statisticians. Masses of trivial and meaningless “results” are being reported, which are not providing adequate quantitative information of scientific interest. The NHST dogma also retards progress in the understanding of ecological systems and the effects of management programmes, which may at worst contribute to damaging decisions in conservation biology. In the beginning of this millennium, critical discussion and debate on the problems and shortcomings of NHST has intensified in ecological journals. Alternative approaches, like basic point and interval estimation of effect sizes, likelihood-based and information theoretic methods, and the Bayesian inferential paradigm, have started to receive attention. Much is still to be done in efforts to improve statistical thinking and reasoning of ecologists and in training them to utilize appropriately the expanded statistical toolbox. Ecologists should finally abandon the false doctrines and textbooks of their previous statistical gurus. Instead they should more carefully learn what leading statisticians write and say, collaborate with statisticians in teaching, research, and editorial work in journals.

Using spirometry results in occupational medicine and research: Common errors and good practice in statistical analysis and reporting

Spirometry appears to be a simple and inexpensive method to measure disorders of the respiratory tract. In reality however, a simple spirometry test requires knowledge and skill to correctly conduct and evaluate the test and its results. This review addresses common misunderstandings in using, evaluating and reporting spirometry results in Occupational Health practice, clinical medicine and research. Results of spirometry need to be evaluated in relation to reference values. The factory medical officer has to decide first whether the test was technically correctly executed and is acceptable for medical interpretation. The next step is to compare results of the individual to published reference values. A 10% reduction of reference values for North Indians and Pakistanis and a 12 to 13% reduction for South Indians is recommended when Caucasian reference tables are used. In occupational health practice the worker's spirometry performance over time needs to be considered. Common errors in reporting summarized results, for instance from groups of workers, are the incorrect use of tests of significance and incorrect presentation of aggregated spirometry results. The loss of respiratory function is recommended as an indicator of difference between two groups. That way, early changes in function can seen without waiting for a drop of function below the usually used 80%-of-predicted limit. This procedure increases the sensitivity of medical surveillance. In research the more precise Lower Limit of Normal should be calculated and used. Correct reference equations, good patient coaching, decision on the technical quality (acceptability) of each spirometry test and critical re-evaluation of the machine's readout<b> </b> are essential parts of a correct spirometry test. A good understanding how results are calculated is crucial for further statistical evaluation.