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ABSTRACT The results of randomized clinical trials (RCTs) are frequently assessed with the fragility index (FI). Although the information provided by FI may supplement the p value, this indicator presents intrinsic weaknesses and shortcomings. In this article, we establish an analysis of fragility within a broader framework so that it can reliably complement the information provided by the p value. This perspective is named the analysis of strength. We first propose a new strength index (SI), which can be adopted in normal distribution settings. This measure can be obtained for both significance and nonsignificance and is straightforward to calculate, thus presenting compelling advantages over FI, starting from the presence of a threshold. The case of time‐to‐event outcomes is also addressed. Then, beyond the p value, we develop the analysis of strength using likelihood ratios from Royall's statistical evidence viewpoint. A new R package is provided for performing strength calculations, and a simulation study is conducted to explore the behavior of SI and the likelihood‐based indicator empirically across different settings. The newly proposed analysis of strength is applied in the assessment of the results of three recent trials involving the treatment of COVID‐19. [ABSTRACT FROM AUTHOR]

Background With the emergence of new variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, such as the Omicron variant, during the third wave of the coronavirus disease 2019 (COVID-19) pandemic, there was a need to identify useful homeopathic medicines. This study aimed to identify such medicines and their indications using prognostic factor research (PFR). Methods This was an open-label, multi-centred observational study conducted in January 2022, on confirmed COVID-19 cases. The data were collected from integrated COVID Care Centres in Delhi, India, where homeopathic medicines were prescribed along with conventional treatment. Only those cases that met a set of selection criteria were considered for analysis. The likelihood ratio (LR) was calculated for the frequently occurring symptoms of the frequently prescribed medicines. An LR of 1.3 or greater was considered meaningful. Results Out of the 362 COVID-19 cases, 263 cases were selected for analysis after applying selection criteria. Common symptoms included fatigue, cough, sore throat, myalgia and headache. Twenty-one medicines were prescribed, of which nine medicines – Gelsemium sempervirens , Bryonia alba , Hepar sulphuris , Rhus toxicodendron , Pulsatilla nigricans , Arsenicum album , Belladonna , Nux vomica and Phosphorus – were frequently used. By calculating LRs, the study identified meaningful indications for these medicines. Conclusion Homeopathic medicines have shown promising results in the third wave of COVID-19 as an adjunct therapy. The medicines that were used in the first and second waves were found useful in the third wave also, and their indications were analogous to those found in the earlier waves. Certain new indications of some medicines were elicited in this wave, which warrant further research. However, it is important not to restrict to these medicines only and to continue data collection on COVID-19 in future waves for the improvement of the COVID-19 mini-repertory. [ABSTRACT FROM AUTHOR]

Quantitative landslide hazard models provide estimations of the number of landslides per area and time that might be expected in the near future. These models are essential to calculate landslide risk in monetary terms. Although they are very useful tools for managing the activity of unstable slopes, their production calls for a vast amount of spatial and temporal data. Here, we present a case where this was possible producing the quantitative landslide hazard map for the municipality of Loja, Ecuador. It is based on a model that integrates six causal factors (distance to faults, lithology, slope, geomorphology, topographic position index, land use) and a comprehensive multi-temporal inventory of landslides. First, a susceptibility map was generated with a good prediction capability (Area under prediction rate curve, AUPRC: 0.8) combining two widely used and tested probabilistic methods: "Matrix" and "Likelihood ratio". Subsequently, this map was transformed into a hazard map by including the temporal frequency of landslides. The map assesses the annual probability of each pixel to be set in motion within one of these landslides. The preliminary temporal validation of the hazard map indicates that the pixels mobilized during two years after the map production fit reasonably well with our spatio-temporal forecast. The findings emphasize that classical spatial prediction methods, when augmented by robust and extensive data on landslide distribution and activity, can yield hazard models with reliable predictive capabilities. This suggests that in practical applications, models based on relatively simple calculations can provide effective and reliable starting points for managing landslide risks. [ABSTRACT FROM AUTHOR]

Likelihood ratios compare two values (i.e., case rates) in order to illustrate the magnitude of the difference between the two. This ratio increases the confidence one can have in a diagnostic test from a different vantage point than that of sensitivity and specificity. The calculations of likelihood ratios are presented along with a simplified approach. Likelihood ratios are another tool the toxicologist should employ in their understanding of statistics and probability. [ABSTRACT FROM AUTHOR]

Background Clinical utility data on pulmonary nodule (PN) risk stratification biomarkers are lacking. We aimed to determine the incremental predictive value and clinical utility of using an artificial intelligence (AI) radiomics-based computer-aided diagnosis (CAD) tool in addition to routine clinical information to risk stratify PNs among real-world patients. Methods We performed a retrospective cohort study of patients with PNs who underwent lung biopsy. We collected clinical data and used a commercially available AI radiomics-based CAD tool to calculate a Lung Cancer Prediction (LCP) score. We developed logistic regression models to evaluate a well-validated clinical risk prediction model (the Mayo Clinic model) with and without the LCP score (Mayo vs Mayo + LCP) using area under the curve (AUC), risk stratification table, and standardized net benefit analyses. Results Among the 134 patients undergoing PN biopsy, cancer prevalence was 61%. Addition of the radiomics-based LCP score to the Mayo model was associated with increased predictive accuracy (likelihood ratio test, P  =  .012). The AUCs for the Mayo and Mayo + LCP models were 0.58 (95% CI = 0.48 to 0.69) and 0.65 (95% CI = 0.56 to 0.75), respectively. At the 65% risk threshold, the Mayo + LCP model was associated with increased sensitivity (56% vs 38%; P  =  .019), similar false positive rate (33% vs 35%; P  =  .8), and increased standardized net benefit (18% vs -3.3%) compared with the Mayo model. Conclusions Use of a commercially available AI radiomics-based CAD tool as a supplement to clinical information improved PN cancer risk prediction and may result in clinically meaningful changes in risk stratification. [ABSTRACT FROM AUTHOR]

In order to enhance the reliability and accuracy of speaker recognition in courtrooms, and facilitate the transformation of scientific evaluation paradigm for courtroom voice analysis methods and processes, a novel method for automatic speaker recognition in courtrooms based on an improved emphasized channel attention propagation aggregation time delay neural network (ECAPA-TDNN) architecture was proposed. This method integrates spatial attention mechanism, channel attention mechanism, and multi-head attention mechanism to enhance the accuracy and generalization capability of the model. The network model utilized a fusion of spectrogram and gammatone frequency cepstral coefficients (GFCC), selecting the one with the best training performance as the input. The trained neural network was employed as a deep feature extractor, followed by evaluating the strength of speech evidence using a likelihood ratio quantification evaluation system specifically designed for courtroom evidence. Experimental results demonstrate that on the VoxCeleb1 dataset, the Cllr value is 0. 156, outperforming the previously published literature on automatic speaker recognition systems in courtrooms. On the Chinese zhaishell dataset, the false acceptance rate and false rejection rate both reach zero, with a minimum likelihood ratio supporting the homogeneity hypothesis of 1. 72 × 106 and a maximum likelihood ratio supporting the heterogeneity hypothesis of 5. 83 × 10-21. Consequently, this method further enhances the reliability and accuracy of the recognition system, providing robust support for the conclusion of evaluating speech evidence in courtrooms. [ABSTRACT FROM AUTHOR]

Audio forensics in the criminal justice system always remains significant support. Voice prints are of great interest to investigators as partial evidence of illegal activities. However, with rapid technological advancements, people with illicit intentions often utilize different voice changers as anti-forensic weapons and disguise their voices electronically while performing unlawful activities. Several automated machine learning-based techniques are adopted in true speaker identification. Our proposed analytical framework compares traditional forensic laboratory methods with automated deep learning methods. The analysis is conducted based on the likelihood of acoustic parameters, including similarities and dissimilarities. For this, we have collected our own data set, consisting of 105 speech recordings, 35 of which were from females and 70 from males. To determine the robustness of our framework on our collected dataset, we created disguised voices using electronic and non-electronic methods. Our proposed deep learning model outperforms machine learning and automatic lab-based speaker identification systems with 95 % test accuracy. The model also correctly identifies 97 % of the real audio samples and 75 % of fake ones. The results demonstrate that our CNN model is more effective in detecting disguised voices than other forensic methods in our framework. To further evaluate the performance, we also compared our proposed forensic speaker identification model with other state-of-the-art methods and found it to be superior. [ABSTRACT FROM AUTHOR]

The persistent issue of wrongful convictions in the United States emphasizes the need for scrutiny and improvement of the criminal justice system. While statistical methods for the evaluation of forensic evidence, including glass, fingerprints, and deoxyribonucleic acid, have significantly contributed to solving intricate crimes, there is a notable lack of national‐level standards to ensure the appropriate application of statistics in forensic investigations. We discuss the obstacles in the application of statistics in court and emphasize the importance of making statistical interpretation accessible to non‐statisticians, especially those who make decisions about potentially innocent individuals. We investigate the use and misuse of statistical methods in crime investigations, in particular the likelihood ratio approach. We further describe the use of graphical models, where hypotheses and evidence can be represented as nodes connected by arrows signifying association or causality. We emphasize the advantages of special graph structures, such as object‐oriented Bayesian networks and chain event graphs, which allow for the concurrent examination of evidence of various nature. [ABSTRACT FROM AUTHOR]

The development of probabilistic genotyping (PG) systems to quantitatively analyze DNA mixture samples has been transformative in forensic science. TrueAllele® Casework (TA) and STRmix™ (STRmix) are the two most widely used PG systems in the United States. The two systems were challenged with 48 two‐, three‐, and four‐person mock casework samples, for a total of 152 likelihood ratio (LR) comparisons. TA and STRmix converged on the same result (supportive, non‐supportive, or inconclusive) for ~91% of contributor‐specific comparisons. Where moderate or substantial differences in log(LR) values were observed, 9% affected the conclusion of the reference association to the mixture. The PG systems exhibited high correlations for estimated contributor‐specific template quantities (~92%) and log(LR)s produced (>88%). When the log(LR)s for only low‐template contributors (<100 pg) were compared, the R2 value dropped to ~68% and the difference became statistically significant. Of the 14 contributor comparisons where the conclusion differed, two were contradictory (supportive vs. non‐supportive) and 12 were either inconclusive versus non‐supportive or inconclusive versus supportive. The differing results were likely due to dissimilarities in the mixture input file as STRmix uses a lab‐defined analytical threshold (AT) and TA models to 10 RFUs for each electropherogram. When 7 of the 14 mixtures were reanalyzed by STRmix using a 10 RFU AT, the log(LR)s for the low‐template contributors became more similar to TAs. This study shows that while both systems may produce accurate and calibrated LRs, their results can deviate, especially for low‐template, degraded contributors, and the deviation is generally predictable. [ABSTRACT FROM AUTHOR]

Two-sample location-scale refers to a model that permits a pair of standardised random variables to have a common base distribution. Function-based hypothesis testing in these models refers to formal tests based on distributions functions, or direct transformations thereof, that would help decide whether or not two samples come from some location-scale family of distributions. For uncensored data, an approach of testing based on plug-in empirical likelihood (PEL) is carried out with sample means and standard deviations as the plug-ins. The method extends to censored data, where censoring adjusted moment estimators provide the requisite plug-ins. The large sample null distribution of the PEL statistic is derived. Since it is not distribution free, a two-sample location-scale appropriate resampling is employed to obtain thresholds needed for the testing. Numerical studies are carried out to investigate the performance of the proposed method. Real examples are presented for both the uncensored and censored cases. [ABSTRACT FROM AUTHOR]

• An effective strategy to improve face recognition of low-quality facial images. • Validation of this strategy under forensically relevant challenging scenarios. • Cleaned versions of the BFW and Adience datasets are provided. • A new verification protocol for the Quis-Campi dataset. In forensic facial comparison, questioned-source images are usually captured in uncontrolled environments, with non-uniform lighting, and from non-cooperative subjects. The poor quality of such material usually compromises their value as evidence in legal proceedings. On the other hand, in forensic casework, multiple images of the person of interest are usually available. In this paper, we propose to aggregate deep neural network embeddings from various images of the same person to improve the performance in forensic comparison of facial images. We observe significant performance improvements, especially for low-quality images. Further improvements are obtained by aggregating embeddings of more images and by applying quality-weighted aggregation. We demonstrate the benefits of this approach in forensic evaluation settings with the development and validation of common-source likelihood ratio systems and report improvements in C llr both for CCTV images and for social media images. [ABSTRACT FROM AUTHOR]

In a recent contribution, we developed a family of cumulative sum-based change-point tests in the context of a random coefficient autoregressive model of order 1. In the current paper, we complement the results in that contribution by studying the (maximally selected) likelihood ratio statistic. We show that this has power versus breaks occurring even as close as periods from the beginning/end of sample; moreover, the use of quasi-maximum likelihood-based estimates yields better power properties, with the added bonus of being nuisance-free. Our test statistic has the same distribution—of the Darling–Erdős type—irrespective of whether the data are stationary or not, and can therefore be applied with no prior knowledge of this. Our simulations show that our test has very good power and, when applying a suitable correction to the asymptotic critical values, the correct size. We illustrate the usefulness and generality of our approach through applications to economic and epidemiological time series. [ABSTRACT FROM AUTHOR]

In forensic DNA laboratories, it is important to conduct internal validations of the commercially available kits of short tandem repeat (STR) loci and to investigate their individual and combined effectiveness. This study aims to report on a comparative investigation of the forensic kits used in our laboratory and their combinations in analysing low-copy-number (LCN) human DNA samples. We used five partly overlapping multiplex kits with different marker configurations from different manufacturers: the NGM SelectTM PCR Amplification Kit, NGM DetectTM, the GlobalFilerTM Amplification Kit (Applied BiosystemTM, Foster City, CA, USA), the PowerPlex® Fusion 6C System (Promega Co., Madison, WI, USA) and the Investigator® 24plex QS Kit (Qiagen GmbH, Hilden, Germany). The efficacy of the kits was scrutinised by specific criteria, such as allelic dropout rate, the individually calculated Likelihood Ratio (LR) of consensus profiles and the LR value of the composite profile produced by the combined profiles of two kits. According to the results, the pairing of PowerPlex® Fusion 6C System and Investigator® 24plex QS produced the lowest, while the pairing of the NGM DetectTM and GlobalFilerTM kits provided the highest LR value. In summary, our study is meant to aid the selection of the optimal kit combination for samples of different qualities. [ABSTRACT FROM AUTHOR]

The purpose of this study is to examine the effect of missing data imputation methods, namely regression imputation (RI), multiple imputation (MI) and k-nearest neighbor (kNN) on differential item functioning (DIF). In this regard, the datasets used in the research were created by deleting some of the data via the missing completely at random mechanism from the complete datasets obtained from 600 students in Türkiye, the United Kingdom, the USA, New Zealand and Australia, who answered booklets 14 and 15 from the PISA 2018 science literacy test. Data imputation was applied to the datasets through missing data using RI, MI and kNN methods and DIF analysis was performed on all datasets in terms of language and gender variables via Lord's χ2 method, Raju's area measurement method and item response theory likelihood ratio method. DIF results from the complete datasets were taken as a reference and they were compared with the results from other datasets. As a result of the research, values close to 10% of accurate imputation were achieved in the RI method depending on language and gen-der variables. In MI and kNN methods, results closest to the complete datasets were obtained at a rate of 5% depending on the language variable. In the MI method, inaccurate results were obtained in all proportions in terms of the gender variable. For the gender variable, the kNN method gave accurate results at rates of 5% and 10%. [ABSTRACT FROM AUTHOR]

A large number of variants identified through clinical genetic testing in disease susceptibility genes, are of uncertain significance (VUS). Following the recommendations of the American College of Medical Genetics and Genomics (ACMG) and Association for Molecular Pathology (AMP), the frequency in case-control datasets (PS4 criterion), can inform their interpretation. We present a novel case-control likelihood ratio-based method that incorporates gene-specific age-related penetrance. We demonstrate the utility of this method in the analysis of simulated and real datasets. In the analyses of simulated data, the likelihood ratio method was more powerful compared to other methods. Likelihood ratios were calculated for a case-control dataset of BRCA1 and BRCA2 variants from the Breast Cancer Association Consortium (BCAC), and compared with logistic regression results. A larger number of variants reached evidence in favor of pathogenicity, and a substantial number of variants had evidence against pathogenicity - findings that would not have been reached using other case-control analysis methods. Our novel method provides greater power to classify rare variants compared to classical case-control methods. As an initiative from the ENIGMA Analytical Working Group, we provide user-friendly scripts and pre-formatted excel calculators for implementation of the method for rare variants in BRCA1, BRCA2 and other high-risk genes with known penetrance.

In forensic DNA laboratories, it is important to conduct internal validations of the commercially available kits of short tandem repeat (STR) loci and to investigate their individual and combined effectiveness. This study aims to report on a comparative investigation of the forensic kits used in our laboratory and their combinations in analysing low-copy-number (LCN) human DNA samples. We used five partly overlapping multiplex kits with different marker configurations from different manufacturers: the NGM SelectTM PCR Amplification Kit, NGM DetectTM, the GlobalFilerTM Amplification Kit (Applied BiosystemTM, Foster City, CA, USA), the PowerPlex® Fusion 6C System (Promega Co., Madison, WI, USA) and the Investigator® 24plex QS Kit (Qiagen GmbH, Hilden, Germany). The efficacy of the kits was scrutinised by specific criteria, such as allelic dropout rate, the individually calculated Likelihood Ratio (LR) of consensus profiles and the LR value of the composite profile produced by the combined profiles of two kits. According to the results, the pairing of PowerPlex® Fusion 6C System and Investigator® 24plex QS produced the lowest, while the pairing of the NGM DetectTM and GlobalFilerTM kits provided the highest LR value. In summary, our study is meant to aid the selection of the optimal kit combination for samples of different qualities.

The exponentially modified Gaussian (EMG) distribution is adopted in many fields such as psychology, chromatographic peaks, and cell biology. In this article, we focus on the test for the parameters of the EMG distribution. The primary test statistic is the classical likelihood ratio. Although the EMG distribution is an asymmetric one, we proved that the logarithm of the likelihood ratio is asymptotically distributed as χ 2 , that is, the Wilks phenomenon. We employ Q–Q plots to illustrate the sample sizes needed for the asymptotic distribution as a good approximation to the null distribution of the loglikelihood ratio for different parameters. The available approximate χ 2 distribution for the loglikelihood ratio is guaranteed by a relatively large sample size. Hence, when the sample size is small, the approximate χ 2 distribution is inapplicable, since the EMG distribution is asymmetric. For the small sample size, we propose a parametric bootstrap algorithm, which is demonstrated to be successful by the simulation studies. An application of our method to a real data example shows that the test is satisfactory. [ABSTRACT FROM AUTHOR]

This paper studies spin glass to paramagnetic transition in the Spherical Sherrington–Kirkpatrick model with ferromagnetic Curie-Weiss interaction with coupling constant J and inverse temperature β . The disorder of the system is represented by a general Wigner matrix. We confirm a conjecture of Baik and Lee (Stat Phys 165(2):185–224, 2016; Ann Henri Poincaré 18(6):1867–1917, 2017), that the critical window of temperatures for this transition is β = 1 + b N - 1 / 3 log N with b ∈ R . The limiting distribution of the scaled free energy is Gaussian for negative b and a weighted linear combination of independent Gaussian and Tracy–Widom components for positive b. In the special case where the Wigner matrix is from the Gaussian Orthogonal or Unitary Ensemble, we describe the triple point transition between spin glass, paramagnetic, and ferromagnetic regimes in a critical window for (β , J) around the triple point (1, 1): the Tracy–Widom component is replaced by the one parameter family of deformations described by Bloemendal and Virág [9]. [ABSTRACT FROM AUTHOR]

AbstractIn this paper, we develop two procedures for identifying the dynamic trends in the parameters of length-biased lognormal distribution based on likelihood ratio and modified information criterion. These methods mainly consider the test of the existence of the change point and provide the maximum likelihood estimation of the change point when the change point exists. In addition, the asymptotic distribution of test statistic based on likelihood ratio is derived as an extreme value distribution, while the asymptotic distribution of test statistic based on modified information criterion is derived as a chi-square distribution. And the consistency of parameter estimation is proved. Simulations are conducted to study the performance of the proposed method in terms of power, coverage probabilities and average sizes of confidence sets. The proposed methods are applied to a real data to illustrate the detecting procedures. [ABSTRACT FROM AUTHOR]

Internal quality control in clinical chemistry laboratories are based on analyzing samples of stable control materials among the patient samples. The control results are interpreted by using quality control rules that usually are designed to detect systematic errors. The best rules have a high probability of error detection (Ped), i.e. to detect the maximal allowable (critical) systematic error and a low probability of false rejection (Pfr, false alarm). In this work we show that quality control rules can be represented by points on a ROC curve which appears when Ped is plotted against Pfrand only the control limit is varied. Further, we introduce a new method for choosing the optimal control limit, analogous to choosing the optimal operating point on the ROC curve of a diagnostic test. This decision needs knowledge of the pretest probability of a critical systematic error, the benefit of detecting it when it occurs and the cost of false alarm. The ROC curve analysis showed that if rules based on N = 2 are used, mean rules outperform Westgard rules because the ROC curve of the mean rules was lying above the ROC curves of the Westgard rules. A mean rule also had a lower maximum expected increase in the number of unacceptable patient results reported during the presence of an out-of-control error condition (Max E(NUF)) than comparable Westgard rules. [ABSTRACT FROM AUTHOR]

Background Air pollution is positively associated with some childhood cancers, whereas greenness is inversely associated with some adult cancers. The interplay between air pollution and greenness in childhood cancer etiology is unclear. We estimated the association between early-life air pollution and greenness exposure and childhood cancer in Texas (1995 to 2011). Methods We included 6101 cancer cases and 109 762 controls (aged 0 to 16 years). We linked residential birth address to census tract annual average fine particulate matter <2.5 µg/m³ (PM2.5) and Normalized Difference Vegetation Index (NDVI). We estimated odds ratios (ORs) and 95% confidence intervals (CIs) between PM2.5/NDVI interquartile range increases and cancer. We assessed statistical interaction between PM2.5 and NDVI (likelihood ratio tests). Results Increasing residential early-life PM2.5 exposure was associated with all childhood cancers (OR = 1.10, 95% CI = 1.06 to 1.15), lymphoid leukemias (OR = 1.15, 95% CI = 1.07 to 1.23), Hodgkin lymphomas (OR = 1.27, 95% CI = 1.02 to 1.58), non-Hodgkin lymphomas (OR = 1.24, 95% CI = 1.02 to 1.51), ependymoma (OR = 1.27, 95% CI = 1.01 to 1.60), and others. Increasing NDVI exposure was inversely associated with ependymoma (0- to 4-year-old OR = 0.75, 95% CI = 0.58 to 0.97) and medulloblastoma (OR = 0.75, 95% CI = 0.62 to 0.91) but positively associated with malignant melanoma (OR = 1.75, 95% CI = 1.23 to 2.47) and Langerhans cell histiocytosis (OR = 1.56, 95% CI = 1.07 to 2.28). There was evidence of statistical interaction between NDVI and PM2.5 (P  < .04) for all cancers. Conclusion Increasing early-life exposure to PM2.5 increased the risk of childhood cancers. NDVI decreased the risk of 2 cancers yet increased the risk of others. These findings highlight the complexity between PM2.5 and NDVI in cancer etiology. [ABSTRACT FROM AUTHOR]

It is frequently observed in practice that the Wald statistic gives a poor assessment of the statistical significance of a variance component. This paper provides detailed analytic insight into the phenomenon by way of two simple models, which point to an atypical geometry as the source of the aberration. The latter can in principle be checked numerically to cover situations of arbitrary complexity, such as those arising from elaborate forms of blocking in an experimental context, or models for longitudinal or clustered data. The salient point, echoing Dickey (2020) , is that a suitable likelihood-ratio test should always be used for the assessment of variance components. [ABSTRACT FROM AUTHOR]

Neyman–Pearson lemma establishes the most powerful tests for simple hypotheses, inducing the uniformly most powerful (UMP) tests for one-sided hypotheses on one-parameter models. For general hypotheses, there is no the UMP test without restrictions, but the classical UMP unbiased tests are too restricted and complex to easily apply. Hence, we create the simple UMP tests under much weaker restrictions than unbiasedness, assuming that the random samples (data) come from parametric models with continuous distributions. [ABSTRACT FROM AUTHOR]

Two probabilistic genotyping (PG) programs, STRMix™ and TrueAllele™, were used to assess the strength of the same item of DNA evidence in a federal criminal case, with strikingly different results. For STRMix, the reported likelihood ratio in favor of the non-contributor hypothesis was 24; for TrueAllele it ranged from 1.2 million to 16.7 million, depending on the reference population. This case report seeks to explain why the two programs produced different results and to consider what the difference tells us about the reliability and trustworthiness of these programs. It uses a locus-by-locus breakdown to trace the differing results to subtle differences in modeling parameters and methods, analytic thresholds, and mixture ratios, as well as TrueAllele's use of an ad hoc procedure for assigning LRs at some loci. These findings illustrate the extent to which PG analysis rests on a lattice of contestable assumptions, highlighting the importance of rigorous validation of PG programs using known-source test samples that closely replicate the characteristics of evidentiary samples. The article also points out misleading aspects of the way STRMix and TrueAllele results are routinely presented in reports and testimony and calls for clarification of forensic reporting standards to address those problems.

Abstract Background In clinical practice, Measurement of estimated glomerular filtration rates (eGFR) is the gold standard assessing renal function the glomerular filtration rate often estimated from plasma creatinine. Several studies have shown Cystatin C based eGFR (Cys C) to be a better parameter for the diagnosis of impaired renal function. Cystatin C based eGFR has been proposed as a potential renal function marker but its use in HIV&AIDS patients has not been well evaluated. Methods A cross sectional study was carried out on 914 HIV&AIDS patients on antiretroviral therapy (ART) attending Mildmay Uganda for care and treatment between January to March 2015. Serum Cystatin C based eGFR was measured using the particle enhanced immunoturbidimetric assay. Creatinine was analyzed using enzymatic Creatinine PAP method and creatinine clearance was calculated according to C&G. Results The sensitivity of Cystatin C based eGFR was 15.1% (95% CI = 8.4, 24) with specificity 99.3% (95% CI = 98- 99.7). The positive and negative predictive values were 70.0% (95% CI 45.7–88.1) and 91.2% (95% CI 98.11–92.94) respectively. The positive likelihood ratio was 18.81 and negative likelihood ratio was 0.85. Cystatin C based eGFR had diagnostic accuracy of 90.7 and area under curve was 0.768. Conclusion Cystatin C based eGFR exhibited a high specificity and a high positive likelihood ratio in diagnosis of kidney disease among HIV&AIDS patients. Cystatin C based eGFR can be used as a confirmatory test.

Familial searching is a useful technique in a forensic investigation. Using genetic information, it is possible to identify individuals, determine familial relationships, and obtain racial/ethnic information. The total number of shared alleles (TNSA) and likelihood ratio (LR) methods have traditionally been used, and novel data-mining classification methods have recently been applied here as well. However, it is difficult to apply these methods to identify familial relationships above the third degree (e.g., uncle-nephew and first cousins). Therefore, we propose to apply a stacking ensemble machine learning algorithm to improve the accuracy of familial relationship identification. Using real data analysis, we obtain superior relationship identification results when applying metaclassifiers with a stacking algorithm rather than applying traditional TNSA or LR methods and data mining techniques. [ABSTRACT FROM AUTHOR]

Background: In clinical practice, Measurement of estimated glomerular filtration rates (eGFR) is the gold standard assessing renal function the glomerular filtration rate often estimated from plasma creatinine. Several studies have shown Cystatin C based eGFR (Cys C) to be a better parameter for the diagnosis of impaired renal function. Cystatin C based eGFR has been proposed as a potential renal function marker but its use in HIV&AIDS patients has not been well evaluated. Methods: A cross sectional study was carried out on 914 HIV&AIDS patients on antiretroviral therapy (ART) attending Mildmay Uganda for care and treatment between January to March 2015. Serum Cystatin C based eGFR was measured using the particle enhanced immunoturbidimetric assay. Creatinine was analyzed using enzymatic Creatinine PAP method and creatinine clearance was calculated according to C&G. Results: The sensitivity of Cystatin C based eGFR was 15.1% (95% CI = 8.4, 24) with specificity 99.3% (95% CI = 98- 99.7). The positive and negative predictive values were 70.0% (95% CI 45.7–88.1) and 91.2% (95% CI 98.11–92.94) respectively. The positive likelihood ratio was 18.81 and negative likelihood ratio was 0.85. Cystatin C based eGFR had diagnostic accuracy of 90.7 and area under curve was 0.768. Conclusion: Cystatin C based eGFR exhibited a high specificity and a high positive likelihood ratio in diagnosis of kidney disease among HIV&AIDS patients. Cystatin C based eGFR can be used as a confirmatory test. [ABSTRACT FROM AUTHOR]

Extreme-mass-ratio inspirals (EMRIs) are significant observational targets for spaceborne gravitational wave detectors, namely, LISA, Taiji, and Tianqin, which involve the inspiral of stellar-mass compact objects into massive black holes (MBHs) with a mass range of approximately 10 4 ∼ 10 7 M ⊙ . EMRIs are estimated to produce long-lived gravitational wave signals with more than 10 5 cycles before plunge, making them an ideal laboratory for exploring the strong-gravity properties of the spacetimes around the MBHs, stellar dynamics in galactic nuclei, and properties of the MBHs itself. However, the complexity of the waveform model, which involves the superposition of multiple harmonics, as well as the high-dimensional and large-volume parameter space, make the fully coherent search challenging. In our previous work, we proposed a 10-dimensional search using Particle Swarm Optimization (PSO) with local maximization over the three initial angles. In this study, we extend the search to an 8-dimensional PSO with local maximization over both the three initial angles and the angles of spin direction of the MBH, where the latter contribute a time-independent amplitude to the waveforms. Additionally, we propose a 7-dimensional PSO search by using a fiducial value for the initial orbital frequency and shifting the corresponding 8-dimensional Time Delay Interferometry responses until a certain lag returns the corresponding 8-dimensional log-likelihood ratio's maximum. The reduced dimensionality likelihoods enable us to successfully search for EMRI signals with a duration of 0.5 years and signal-to-noise ratio of 50 within a wider search range than our previous study. However, the ranges used by both the LISA Data Challenge (LDC) and Mock LISA Data Challenge (MLDC) to generate their simulated signals are still wider than the those we currently employ in our direct searches. Consequently, we discuss further developments, such as using a hierarchical search to narrow down the search ranges of certain parameters and applying Graphics Processing Units to speed up the code. These advances aim to improve the efficiency, accuracy, and generality of the EMRI search algorithm. [ABSTRACT FROM AUTHOR]

In resource-limited settings, alternatives to HIV viral load testing may be necessary to monitor the health of people living with HIV. We assessed the utility of self-report antiretroviral therapy (ART) to screen for HIV viral load among persons who inject drugs in Hai Phong Vietnam, and consider differences by recent methamphetamine use. From 2016 to 2018 we recruited PWID through cross sectional surveys and collected self-report ART adherence and HIV viral load to estimate sensitivity, specificity, positive and negative predictive values (PPV, NPV) and likelihood ratios (LR+, LR-) for self-reported ART adherence as a screening test for HIV viral load. We used three HIV viral load thresholds: < 1000, 500 and 250 copies/mL; laboratory-confirmed HIV viral load was the gold standard. Among 792 PWID recruited, PPV remained above 90% regardless of recent methamphetamine use with slightly higher PPV among those not reporting recent methamphetamine use. The results remained consistent across all three HIV viral load thresholds. Our findings suggest that when HIV viral load testing is not possible, self-reported ART adherence may inform decisions about how to prioritize HIV viral load testing among PWID. The high PPV values suggest self-reported high ART adherence indicates likely HIV viral suppression, irrespective of methamphetamine use. [ABSTRACT FROM AUTHOR]

In a two‐way additive analysis of variance (ANOVA) model, we consider the problem of testing for homogeneity of both row and column effects against their simultaneous ordering. The error variances are assumed to be heterogeneous with unbalanced samples in each cell. Two simultaneous test procedures are developed—the first one using the likelihood ratio test (LRT) statistics of two independent hypotheses and another based on the consecutive pairwise differences of estimators of effects. The parametric bootstrap (PB) approach is used to find critical points of both the tests and the asymptotic accuracy of the bootstrap is established. An extensive simulation study shows that the proposed tests achieve the nominal size and have very good power performance. The robustness of the tests is also analyzed under deviation from normality. An "R" package is developed and shared on "GitHub" for ease of implementation of users. The proposed tests are illustrated using a real data set on the mortality due to alcoholic liver disease and it is shown that age and gender have a significant impact on the increasing incidence of mortality. [ABSTRACT FROM AUTHOR]

Background: Alpha-1 acid glycoprotein (AGP) may support a clinical diagnosis of feline infectious peritonitis (FIP). In this study, we assessed the analytical and diagnostic performances of a novel ELISA method to measure feline AGP. Methods: AGP was measured in sera and effusions from cats with FIP (n = 20) or with other diseases (n = 15). Precision was calculated based on the coefficient of variation (CV) of repeated testing, and accuracy was calculated by linearity under dilution (LUD). Results: The test is precise (intra-assay CVs: <6.0% in individual samples, <15.0% in pooled samples; inter-assay CVs <11.0% and <15.0%) and accurate (serum LUD r2: 0.995; effusion LUD r2: 0.950) in serum and in effusions. AGP is higher in cats with FIP than in other cats in both serum (median: 1968, I-III interquartile range: 1216–3371 μg/mL and 296, 246–1963 μg/mL; p = 0.009) and effusion (1717, 1011–2379 μg/mL and 233, 165–566 μg/mL; p < 0.001). AGP discriminates FIP from other diseases (area under the receiver operating characteristic curve: serum, 0.760; effusion, 0.877), and its likelihood ratio is high (serum: 8.50 if AGP > 1590 μg/mL; effusion: 3.75 if AGP > 3780 μg/mL). Conclusion: This ELISA method is precise and accurate. AGP in serum and in effusions is a useful diagnostic marker for FIP. [ABSTRACT FROM AUTHOR]

The diagnostic accuracy of multiple biomarkers in medical research is crucial for detecting diseases and predicting patient outcomes. An optimal method for combining these biomarkers is essential to maximize the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC). Although the optimality of the likelihood ratio has been proven by Neyman and Pearson, challenges persist in estimating the likelihood ratio, primarily due to the estimation of multivariate density functions. In this study, we propose a non‐parametric approach for estimating multivariate density functions by utilizing Smoothing Spline density estimation to approximate the full likelihood function for both diseased and non‐diseased groups, which compose the likelihood ratio. Simulation results demonstrate the efficiency of our method compared to other biomarker combination techniques under various settings for generated biomarker values. Additionally, we apply the proposed method to a real‐world study aimed at detecting childhood autism spectrum disorder (ASD), showcasing its practical relevance and potential for future applications in medical research. [ABSTRACT FROM AUTHOR]

In order to identify suspects in digital forensics, Forensic Voice Comparison (FVC) entails comparing trace voices with known suspect voice samples. The FVC uses semi-automatic approaches to identify speakers using acoustic features. Two Australian datasets such as forensic evaluation dataset 1 & English dataset 2 were used. The current research on FVC samples is noise-reduced using a stationary noise reduction algorithm within a likelihood ratio-based framework. To demonstrate the power of knowledge representation, acoustic features like Mel Frequency Cepstral Coefficients(MFCC) and Linear Predictive Cepstral Coefficients (LPCC) were extracted. The suspect is then identified by classifying the model using the Generative Adversarial Network(GAN) and Artificial Neural Network (ANN). The accuracy is 80% & 90% for MFCC for dataset 1 & 2. Similarly, accuracy is 20% & 40% for LPCC for dataset 1 & 2. The overall comparison of acoustic features with two different datasets the MFCC performs better accurate results for Australian English dataset 2 with varying sample sizes. [ABSTRACT FROM AUTHOR]

The ultimate performance of machine learning algorithms for classification tasks is usually measured in terms of the empirical error probability (or accuracy) using a testing dataset, although these algorithms are optimized through the minimization of a typically different—more convenient—loss function using a training set. For classification tasks, this loss function is often the negative log-loss that yields the well-known cross-entropy risk that is typically better behaved (in terms of numerical behavior) than the 0-1 loss. Conventional studies on the generalization error do not usually take into account the underlying mismatch between losses at training and testing phases. In this work, we introduce a theoretical analysis based on a pointwise probably approximately correct (PAC) approach over the generalization gap considering the mismatch of testing on the accuracy metric and training on the negative log-loss, referred to as PACMAN. Building on the fact that the resulting mismatch can be written as a likelihood ratio, concentration inequalities can be used to obtain insights into the generalization gap in terms of PAC bounds, which depend on some meaningful information-theoretic quantities. An analysis of the obtained bounds and a comparison with available results in the literature is also provided. [ABSTRACT FROM AUTHOR]

The application of nanomaterials in various fields has both benefits and risks. While the unique physico-chemical properties of nanomaterials are valuable for diverse applications, they may also alter how nanomaterials are taken up in the body, thus affecting their potential toxicity to humans. To ensure their safety, the potential risk of nanomaterials needs to be assessed. The risk of a given nanomaterial is typically evaluated through in vitro and in vivo studies. The H2020 NanoInformaTIX project, that is conducted under the European Union's Horizon 2020 research and innovation programme is focusing on the study of nanomaterials. One of its objectives includes the identification of nanomaterial toxicity, of which the first step consists of the detection of a nanomaterial dose-response relationship. In this paper, we focus on this step, which can be done for a given nanomaterial toxicity endpoint of interest, using statistical tests for a monotone dose-response trend, i.e., monotone relationship between the increasing concentration of the nanomaterial and the toxicity endpoint. The significance of the monotone trend is tested using a likelihood ratio test and t-tests. An R package and a Shiny App were developed to perform the analysis. As an illustration, data extracted from the H2020 NanoInformaTIX platform is presented as a case study. This data consists of genetic toxicity studies with DNA strand breaks as the toxicity endpoint of interest. Significant trends were found for 12 out of 26 nanomaterials after adjusting for multiplicity by applying the False discovery rate (FDR) method. [ABSTRACT FROM AUTHOR]

Objective A detection method with high efficiency and accuracy is urgently needed in clinical work. The purpose of our study was to determine the diagnostic accuracy of the Xpert MTB/RIF assay for intestinal tuberculosis (ITB). Methods We searched PubMed and 4 other databases from their establishment to July 19, 2022, for published essays of diagnostic performance in which Xpert MTB/RIF was used to test patients with clinically suspected ITB. An assessment of the quality of the selected literature was conducted using QUADAS-2. We built forest plots by MetaDiSc software. Results The pooled Xpert MTB/RIF sensitivity was 48%, and the specificity was 99%. Moreover, the positive likelihood ratio for ITB diagnosis was 21.61. The negative likelihood ratio was 0.54. There were substantial variations between the study estimates of sensitivity (I 2 = 87.6%) and specificity (I 2 = 82.4%). Conclusion Intestinal TB is detected with limited diagnostic sensitivity by Xpert MTB/RIF but with high specificity. An Xpert-positive result may facilitate the rapid identification of ITB cases. Nevertheless, a negative result has less certainty in excluding the disease. [ABSTRACT FROM AUTHOR]

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Fingerprints with similar morphological characteristics but from different individuals can lead to errors in individual identification, especially when dealing with large databases containing millions of fingerprints. To address this issue and enhance the accuracy of similar fingerprint identification, the use of the likelihood ratio (LR) model for quantitative evaluation of fingerprint evidence has emerged as an effective research method. In this study, the LR fingerprint evidence evaluation model was established by using mathematical statistical methods, such as parameter estimation and hypothesis testing. This involved various steps, including database construction, scoring, fitting, calculation, and visual evaluation. Under the same-source conditions, the optimal parameter methods selected by different number of minutiae are gamma and Weibull distribution, while normal, Weibull, and lognormal distributions were the fitting parameters selected for minutiae configurations. The fitting parameters selected by different number of minutiae under different-source conditions are lognormal distribution, and the parameter methods selected for different minutiae configurations include Weibull, gamma, and lognormal distributions. The results of the LR model showed increased accuracy as the number of minutiae increased, indicating strong discriminative and corrective power. However, the accuracy of the LR evaluation based on different configurations was comparatively lower. In addition, the LR models with different numbers of minutiae outperformed those with different minutiae configurations. Our study shows that the use of LR models based on parametric methods is favoured in reducing the risk of fingerprint evidence misidentification, improving the quantitative assessment methods of fingerprint evidence, and promoting fingerprint identification from experience to science. Key points Likelihood ratio (LR) method based on parameter estimation was applied to scientific evaluation of fingerprint evidence with excellent discriminatory and calibration capabilities. Both the number of minutiae and configuration of minutiae have significant effects on the score-based LR method. Fingerprints from the same source contain many different patterns of deformation. Databases containing 10 million fingerprints from different sources have been used for building the LR model. [ABSTRACT FROM AUTHOR]

Introduction: The likelihood ratio (LR) can be an efficient means of distinguishing various relationships in forensic fields. However, traditional list-based methods for derivation and presentation of LRs in distant or complex relationships hinder code editing and software programming. This paper proposes an approach for a unified formula for LRs, in which differences in participants' genotype combinations can be ignored for specific identification. This formula could reduce the difficulty of by-hand coding, as well as running time of largesample-size simulation. Methods: The approach is first applied to a problem of kinship identification in which at least one of the participants is alleged to be inbred. This can be divided into two parts: i) the probability of different identical by descent (IBD) states according to the alleged kinship; and ii) the ratio of the probability that specific genotype combination can be detected assuming the alleged kinship exists between the two participants to the similar probability assuming that they are unrelated, for each state. For the probability, there are usually recognized results for common identification purposes. For the ratio, subscript letters representing IBD alleles of individual A's alleles are used to eliminate differences in genotype combinations between the two individuals and to obtain a unified formula for the ratio in each state. The unification is further simplified for identification cases in which it is alleged that both of the participants are outbred. Verification is performed to show that the results obtained with the unified and list-form formulae are equivalent. Results: A series of unified formulae are derived for different identification purposes, based on which an R package named KINSIMU has been developed and evaluated for use in large-size simulations for kinship analysis. Comparison between the package with two existing tools indicated that the unified approach presented here is more convenient and time-saving with respect to the coding process for computer applications compared with the list-based approach, despite appearing more complicated. Moreover, the method of derivation could be extended to other identification problems, such as those with different hypothesis sets or those involving multiple individuals. Conclusion: The unified approach of LR calculation can be beneficial in kinship identification field. [ABSTRACT FROM AUTHOR]

Swarm intelligence (SI) methods are nature-inspired metaheuristics for global optimization that exploit a coordinated stochastic search strategy by a group of agents. Particle swarm optimization (PSO) is an established SI method that has been applied successfully to the optimization of rugged high-dimensional likelihood functions, a problem that represents the main bottleneck across a variety of gravitational wave (GW) data analysis challenges. We present results from the first application of PSO to one of the most difficult of these challenges, namely the search for the Extreme Mass Ratio Inspiral (EMRI) in data from future spaceborne GW detectors such as LISA, Taiji, or Tianqin. We use the standard Generalized Likelihood Ratio Test formalism, with the minimal use of restrictive approximations, to search 6 months of simulated LISA data and quantify the search depth, signal-to-noise ratio (SNR), and breadth, within the ranges of the EMRI parameters, that PSO can handle. Our results demonstrate that a PSO-based EMRI search is successful for a search region ranging over ≳10σ for the majority of parameters and ≳200σ for one, with σ being the SNR-dependent Cramer–Rao lower bound on the parameter estimation error and 30 ≤ SNR ≤ 50 . This is in the vicinity of the search ranges that the current hierarchical schemes can identify. Directions for future improvement, including computational bottlenecks to be overcome, are identified. [ABSTRACT FROM AUTHOR]

The field of forensic statistics offers a unique hierarchical data structure in which a population is composed of several subpopulations of sources and a sample is collected from each source. This subpopulation structure creates an additional layer of complexity. Hence, the data has a hierarchical structure in addition to the existence of underlying subpopulations. Finite mixtures are known for modeling heterogeneity; however, previous parameter estimation procedures assume that the data is generated through a simple random sampling process. We propose using a semi‐supervised mixture modeling approach to model the subpopulation structure which leverages the fact that we know the collection of samples came from the same source, yet an unknown subpopulation. A simulation study and a real data analysis based on famous glass datasets and a keystroke dynamic typing data set show that the proposed approach performs better than other approaches that have been used previously in practice. [ABSTRACT FROM AUTHOR]

It has been argued in forensic science that the empirical validation of a forensic inference system or methodology should be performed by replicating the conditions of the case under investigation and using data relevant to the case. This study demonstrates that the above requirement for validation is also critical in forensic text comparison (FTC); otherwise, the trier-of-fact may be misled for their final decision. Two sets of simulated experiments are performed: one fulfilling the above validation requirement and the other overlooking it, using mismatch in topics as a case study. Likelihood ratios (LRs) are calculated via a Dirichlet-multinomial model, followed by logistic-regression calibration. The derived LRs are assessed by means of the log-likelihood-ratio cost, and they are visualized using Tippett plots. Following the experimental results, this paper also attempts to describe some of the essential research required in FTC by highlighting some central issues and challenges unique to textual evidence. Any deliberations on these issues and challenges will contribute to making a scientifically defensible and demonstrably reliable FTC available. [ABSTRACT FROM AUTHOR]

Data-driven most powerful tests are statistical hypothesis decision-making tools that deliver the greatest power against a fixed null hypothesis among all corresponding data-based tests of a given size. When the underlying data distributions are known, the likelihood ratio principle can be applied to conduct most powerful tests. Reversing this notion, we consider the following questions. (a) Assuming a test statistic, say T, is given, how can we transform T to improve the power of the test? (b) Can T be used to generate the most powerful test? (c) How does one compare test statistics with respect to an attribute of the desired most powerful decision-making procedure? To examine these questions, we propose one-to-one mapping of the term "most powerful" to the distribution properties of a given test statistic via matching characterization. This form of characterization has practical applicability and aligns well with the general principle of sufficiency. Findings indicate that to improve a given test, we can employ relevant ancillary statistics that do not have changes in their distributions with respect to tested hypotheses. As an example, the present method is illustrated by modifying the usual t-test under nonparametric settings. Numerical studies based on generated data and a real-data set confirm that the proposed approach can be useful in practice. [ABSTRACT FROM AUTHOR]