A Bayesian test for seasonality in medical data

In medical research, the results from seasonality analyses provide valuable information that eventually can help to clarify the etiology of poorly understood diseases. We present a Bayesian procedure for the analysis of seasonal variation in medical data. The method is a Bayesian version of a frequentist test that performs very well. Statistical seasonality analyses of medical data often involve a short time series, 12 observations with small amplitude and small sample size. Among the specialized procedures already developed for such analyses, only one is Bayesian; the method we present in this paper appears to be the second such Bayesian procedure. Easy to understand and apply, the method is versatile because it can be used to analyze different types of seasonal variation. We illustrate the procedure’s application with two examples of real data.

Prevalence of SARS-CoV-2 Antibodies from a one-year National Serosurveillance of Kenyan Blood Transfusion Donors

In tropical Africa, SARS-CoV-2 epidemiology is poorly described because of lack of access to testing and weak surveillance systems. Since April 2020, we followed SARS-CoV-2 seroprevalence in plasma samples across the Kenya National Blood Transfusion Service. We developed an IgG ELISA against full length spike protein. Validated in locally-observed, PCR-positive COVID-19 cases and in pre-pandemic sera, sensitivity was 92.7% and sensitivity was 99.0%. Using sera from 9,922 donors, we estimated national seroprevalence of SARS-CoV-2 antibodies at 4.3% in April-June 2020 and 9.1% in August-September 2020. The second COVID-19 wave peaked in November 2020. Here we estimate national seroprevalence in early 2021. Between January 3 and March 15, 2021, we collected 3,062 samples from donors aged 16-64 years. Among 3,018 samples that met our study criteria 1,333 were seropositive (crude seroprevalence 44.2%, 95% CI 42.4-46.0%). After Bayesian test-performance adjustment and population weighting to represent the national population distribution, the national estimate of seroprevalence was 48.5% (95% CI 45.2-52.1%). Seroprevalence varied little by age or sex but was higher in Nairobi, the capital city, and lower in two rural regions. Almost half of Kenyan adult donors had evidence of past SARS-CoV-2 infection by March 2021. Although high, the estimate is corroborated by other population-specific estimates in country. Between March and June, 2% of the population were vaccinated against COVID-19 and the country experienced a third epidemic wave. Natural infection is outpacing vaccine delivery substantially in Africa, and this reality needs to be considered as objectives of the vaccine programme are set.

Bayesian Inference for the A/B Test: Example Applications with R and JASP

Popular in business, psychology, and the analysis of clinical trial data, the A/B test refers to a comparison between two proportions. Here we discuss two Bayesian A/B tests that allow users to monitor the uncertainty about a difference in two proportions as data accumulate over time. We emphasize the advantage of assigning a dependent prior distribution to the proportions (i.e., assigning a prior to the log odds ratio). This dependent-prior approach has been implemented in the open-source statistical software programs R and JASP. Several examples demonstrate how JASP can be used to apply this Bayesian test and interpret the results.

Assessing the lifetime performance index of weighted Lomax distribution based on progressive type-II censoring scheme for bladder cancer

In this paper, Health-related quality of life has not been adequately measured in bladder cancer. A recently developed reliable and disease-specific quality of life instrument (Bladder Cancer Index, BCI) was used to measure. Progressive type II censoring schemes have potential usefulness in practice where budget constraints in place or there is a necessity for the speedy test. To test the process capability, the lifetime performance index [Formula: see text] is widely recommended for evaluating the performance of the product’s lifetime and evaluating the lifetime performance index [Formula: see text] for the three-parameter Weighted-Lomax distribution (WLx) under progressive type-II censoring sample for a lower specification limit ([Formula: see text]). The statistical inference concerning [Formula: see text] is conducted via obtaining the maximum likelihood of [Formula: see text] on the base of progressive type-II censoring. The asymptotic normal distribution of the MLE of [Formula: see text] and the confidence interval is proposed. Moreover, the hypothesis testing of [Formula: see text] for evaluating the lifetime performance of WLx data is conducted. Also, assuming the conjugate prior distribution and squared error loss function, this study constructs a Bayes estimator of [Formula: see text]. The Bayes estimator of [Formula: see text]is then utilized to develop a credible interval in the condition of known [Formula: see text]. Moreover, we propose a Bayesian test to assess the lifetime performance of products. We also propose a Bayesian test to assess the lifetime performance of products. Finally, two examples are given, one of them is considering a real life data of the remission times of bladder cancer patients in endurance lifetime test and the other is a simulated example to illustrate the usage of the proposed procedure.

The radial acceleration relation and its emergent nature

We review some of our recent results about the Radial Acceleration Relation (RAR) and its interpretation as either a fundamental or an emergent law. The former interpretation is in agreement with a class of modified gravity theories that dismiss the need for dark matter in galaxies (MOND in particular). Our most recent analysis, which includes refinements on the priors and the Bayesian test for compatibility between the posteriors, confirms that the hypothesis of a fundamental RAR is rejected at more than 5σ from the very same data that was used to infer the RAR.

Facial expression recognition using best tree RD-LGP encoded features and HMM

Hand and face gestures enable deaf people to communicate in their daily lives rather than speaking. This paper describes a hidden Markov model (HMM)-based framework for face sign recognition and detection. The observation vectors used to characterize the states of the HMM are obtained using the best tree local gradient pattern (LGP) encoded features. Each face gesture is modeled as a five-state HMM. The problem of facial expression classification is posed as a composite seven-classes multi-hypothesis Bayesian test. The likelihood ratio test showed that the overall recognition rate for the proposed model is higher than the HMM-local binary pattern descriptor by 6.4%. The overall recognition rate is enhanced by 8.6% using the discrete wavelet packet best tree decomposition filter as a pre-processing noise removal tool. In addition, the overall recognition rate ranges from 84.3%, for the seven classes Bayesian test, to 100%, for lower number of classes depending upon the type of the face gesture. The proposed face expression algorithm reduces significantly the computational complexity of previous HMM-based face expression recognition systems, and still preserve the recognition rate.