A method to adjust a prior distribution in Bayesian second-level fMRI analysis

Agreement test of P value versus Bayes factor for sample means comparison: analysis of articles from the Angle Orthodontist journal

BACKGROUND

Researchers are cautioned against misinterpreting the conventional P value, especially while implementing the popular t test. Therefore, this study evaluated the agreement between the P value and Bayes factor (BF₀₁) results obtained from a comparison of sample means in published orthodontic articles.

METHODS

Data pooling was undertaken using the modified PRISMA flow diagram. Per the inclusion criteria applied to The Angle Orthodontist journal for a two-year period (November 2016 to September 2018), all articles that utilised the t test for statistical analysis were selected. The agreement was evaluated between the P value and Bayes factor set at 0.05 and 1, respectively. The percentage of agreement and Kappa coefficient were calculated. Plotting of effect size against P value and BF₀₁ was analysed.

RESULTS

From 265 articles, 82 utilised the t test. Of these, only 37 articles met the inclusion criteria. The study identified 793 justifiable t tests (438 independent-sample and 355 dependent-sample t tests) for which the agreement percentage and Kappa coefficient were found to be 93.57% and 0.87, respectively. However, when anecdotal evidence (1/3 < BF₀₁ < 3) was considered, almost half of the studies missed statistical significance. Furthermore, two-thirds of the significantly reported P values (0.01 < P < 0.05; 30 independent-sample and 20 dependent-sample t tests) showed only anecdotal evidence (1/3 < BF₀₁ < 1). Moreover, BF₀₁ indicated moderate evidence (BF₀₁ > 3) for approximately one-third of the total studies, with nonsignificant P values (P > 0.05). Furthermore, accompanying the P values, the effect sizes, especially for studies with independent-sample t tests, were very high with a strong potential to show substantive significance. Although it is best to extend the statistical calculation of a doubted P value (just below 0.05), especially for orthodontic innovation, orthodontists may reach a balanced decision relying on cephalometric measurements.

CONCLUSIONS

The Kappa coefficient indicated perfect agreement between the two methods. BF₀₁ restricted this judgement to approximately half of them, with two-thirds of these studies showing nonsignificant P values. Simple extensions of statistical calculations, especially effect size and BF_01, can be useful and should be considered when finalising statistical analyses, especially for orthodontic studies without cephalometric analysis.

Bayesian Modeling of COVID-19 to Classify the Infection and Death Rates in a Specific Duration: The Case of Algerian Provinces

COVID-19 causes acute respiratory illness in humans. The direct consequence of the spread of the virus is the need to find appropriate and effective solutions to reduce its spread. Similar to other countries, the pandemic has spread in Algeria, with noticeable variation in mortality and infection rates between regions. We aimed to estimate the proportion of people who died or became infected with SARS-CoV-2 in each provinces using a Bayesian approach. The estimation parameters were determined using a binomial distribution along with an a priori distribution, and the results had a high degree of accuracy. The Bayesian model was applied during the third wave (1 January–15 August 2021), in all Algerian’s provinces. For spatial analysis of duration, geographical maps were used. Our findings show that Tissemsilt, Ain Defla, Illizi, El Taref, and Ghardaia (Mean = 0.001) are the least affected provinces in terms of COVID-19 mortality. The results also indicate that Tizi Ouzou (Mean = 0.0694), Boumerdes (Mean = 0.0520), Annaba (Mean = 0.0483), Tipaza (Mean = 0.0524), and Tebessa (Mean = 0.0264) are more susceptible to infection, as they were ranked in terms of the level of corona infections among the 48 provinces of the country. Their susceptibility seems mainly due to the population density in these provinces. Additionally, it was observed that northeast Algeria, where the population is concentrated, has the highest infection rate. Factors affecting mortality due to COVID-19 do not necessarily depend on the spread of the pandemic. The proposed Bayesian model resulted in being useful for monitoring the pandemic to estimate and compare the risks between provinces. This statistical inference can provide a reasonable basis for describing future pandemics in other world geographical areas.

A Novel Method to Use Coordinate Based Meta-Analysis to Determine a Prior Distribution for Voxelwise Bayesian Second-Level fMRI Analysis

Previous research showed that employing results from meta-analyses of relevant previous fMRI studies can improve the performance of voxelwise Bayesian second-level fMRI analysis. In this process, prior distributions for Bayesian analysis can be determined by information acquired from the meta-analyses. However, only image-based meta-analysis, which is not widely accessible to fMRI researchers due to the lack of shared statistical images, was tested in the previous study, so the applicability of the prior determination method proposed by the previous study might be limited. In the present study, whether determining prior distributions based on coordinate-based meta-analysis, which is widely accessible to researchers, can also improve the performance of Bayesian analysis, was examined. Three different types of coordinate-based meta-analyses, BrainMap and Ginger ALE, and NeuroQuery, were tested as information sources for prior determination. Five different datasets addressing three task conditions, i.e., working memory, speech, and face processing, were analyzed via Bayesian analysis with a meta-analysis informed prior distribution, Bayesian analysis with a default Cauchy prior adjusted for multiple comparisons, and frequentist analysis with familywise error correction. The findings from the aforementioned analyses suggest that use of coordinate-based meta-analysis also significantly enhanced performance of Bayesian analysis as did image-based meta-analysis.

Representative Sampling of the Via Assessment Suite for Adults

Character strengths have become a popular topic in personality research. A set of questionnaires has recently been developed as measures of character strengths: the VIA Inventory of Strengths-Revised, two 96-item short forms of that instrument, and two new measures called the Global Assessment of Character Strengths and Signature Strengths Survey. Collectively, these are referred to as the VIA Assessment Suite for Adults. Prior research has supported the reliability and validity of these measures. The current study extended those findings through a demographically stratified sample of 1,765 U.S. resident adults. Results indicated the scores were interchangeable across all three versions of the VIA-IS, irrespective of whether the items are all positively keyed or a mix of positive and negative items. In addition, the VIA-IS-R factor structure is also consistent with a previously identified three-factor model for the strengths. By freeing residual covariances, a model was developed for which adequate fit was replicable. This provided the foundation for demonstrating measurement invariance. The present study also explored differences in strengths across demographic categories and evaluated various approaches to identifying key (signature) strengths for the respondents. Recommendations on the use of the different instruments are provided.