Diagnostic accuracy of active pulmonary tuberculosis screening during detention admission: a systematic review.

Individuals entering incarceration are at high risk for infectious diseases, other ill conditions, and risky behavior. Typically, the status of active pulmonary tuberculosis (PTB) is not known at the time of admission. Early detection and treatment are essential for effective TB control. So far, no study has compared the diagnostic accuracy of various TB screening tools in detention using a network meta-analysis (NMA). We aimed to investigate the diagnostic accuracy of active PTB screening tests upon detention admission. We searched PubMed, Global Index Medicus, the Cochrane Library electronic databases, and grey literature for publications reporting detention TB entry screening in March 2022 and January 2024. Inclusion was non-restrictive regarding time, language, location, reference standards, or screening tests. Eligible study designs comprised comparative, observational, and diagnostic studies. Publications had to report TB screening of individuals entering confinement and provide data for diagnostic accuracy calculations. The QUADAS-2 tool was designed to assess the quality of primary diagnostic accuracy studies. This systematic review was registered with PROSPERO (CRD42022307863) and conducted without external funding. We screened a total of 2,455 records. Despite extensive searching, no studies met our inclusion criteria. However, we identified evidence revealing key differences in screening algorithm application. In conclusion, more diagnostic accuracy data on TB screening algorithms for detention admission worldwide needs to be collected. We recommend that global TB initiatives set up multi-site studies to investigate the diagnostic accuracy of TB screening on admission in low- and high-prevalence criminal justice systems. Further network meta-analyses of these studies could inform policymakers and public health experts to establish or fine-tune TB control in detention settings.

Modeling recent positive selection using identity-by-descent segments.

Recent positive selection can result in an excess of long identity-by-descent (IBD) haplotype segments overlapping a locus. The statistical methods that we propose here address three major objectives in studying selective sweeps: scanning for regions of interest, identifying possible sweeping alleles, and estimating a selection coefficient s. First, we implement a selection scan to locate regions with excess IBD rates. Second, we estimate the allele frequency and location of an unknown sweeping allele by aggregating over variants that are more abundant in an inferred outgroup with excess IBD rate versus the rest of the sample. Third, we propose an estimator for the selection coefficient and quantify uncertainty using the parametric bootstrap. Comparing against state-of-the-art methods in extensive simulations, we show that our methods are more precise at estimating s when s≥0.015. We also show that our 95% confidence intervals contain s in nearly 95% of our simulations. We apply these methods to study positive selection in European ancestry samples from the Trans-Omics for Precision Medicine project. We analyze eight loci where IBD rates are more than four standard deviations above the genome-wide median, including LCT where the maximum IBD rate is 35 standard deviations above the genome-wide median. Overall, we present robust and accurate approaches to study recent adaptive evolution without knowing the identity of the causal allele or using time series data.

Post-selection inference in regression models for group testing data.

We develop a methodology for valid inference after variable selection in logistic regression when the responses are partially observed, that is, when one observes a set of error-prone testing outcomes instead of the true values of the responses. Aiming at selecting important covariates while accounting for missing information in the response data, we apply the expectation-maximization algorithm to compute maximum likelihood estimators subject to LASSO penalization. Subsequent to variable selection, we make inferences on the selected covariate effects by extending post-selection inference methodology based on the polyhedral lemma. Empirical evidence from our extensive simulation study suggests that our post-selection inference results are more reliable than those from naive inference methods that use the same data to perform variable selection and inference without adjusting for variable selection.

Measure of Strength of Evidence for Visually Observed Differences between Subpopulations.

For measuring the strength of visually-observed subpopulation differences, the Population Difference Criterion is proposed to assess the statistical significance of visually observed subpopulation differences. It addresses the following challenges: in high-dimensional contexts, distributional models can be dubious; in high-signal contexts, conventional permutation tests give poor pairwise comparisons. We also make two other contributions: Based on a careful analysis we find that a balanced permutation approach is more powerful in high-signal contexts than conventional permutations. Another contribution is the quantification of uncertainty due to permutation variation via a bootstrap confidence interval. The practical usefulness of these ideas is illustrated in the comparison of subpopulations of modern cancer data.

Most meta-analyses in oral health do not have conclusive and robust results.

OBJECTIVES: In meta-analyses with few studies, between-study heterogeneity is poorly estimated. The Hartung and Knapp (HK) correction and the prediction intervals can account for the uncertainty in estimating heterogeneity and the range of effect sizes we may encounter in future trials, respectively. The aim of this study was to assess the reported use of the HK correction in oral health meta-analyses and to compare the published reported results and interpretation i) to those calculated using eight heterogeneity estimators and the HK adjustment ii) and to the prediction intervals (PIs). METHODS: We sourced systematic reviews (SRs) published between 2021 and 2023 in eighteen leading specialty and general dental journals. We extracted study characteristics at the SR and meta-analysis level and re-analyzed the selected meta-analyses via the random-effects model and eight heterogeneity estimators, with and without the HK correction. For each meta-analysis, we re-calculated the overall estimate, the P-value, the 95 % confidence interval (CI) and the PI. RESULTS: We analysed 292 meta-analyses. The median number of primary studies included in meta-analysis was 8 (interquartile range [IQR] = [5.75-15] range: 3-121). Only 3/292 meta-analyses used the HK adjustment and 12/292 reported PIs. The percentage of statistically significant results that became non-significant varied across the heterogeneity estimators (7.45 %- 16.59 %). Based on the PIs, >60 % of meta-analyses with statistically significant results are likely to change in the future and >40 % of the PIs included the opposite pooled effect. CONCLUSIONS: The precision and statistical significance of the pooled estimates from meta-analyses with at least three studies is sensitive to the HK correction, the heterogeneity variance estimator, and the PIs. CLINICAL SIGNIFICANCE: Uncertainty in meta-analyses estimates should be considered especially when a small number of trials is available or vary notably in their precision. Misinterpretation of the summary results can lead to ineffective interventions being applied in clinical practice.

How estimating nuisance parameters can reduce the variance (with consistent variance estimation).

We often estimate a parameter of interest ψ $$ \psi $$ when the identifying conditions involve a finite-dimensional nuisance parameter θ ∈ ℝ d $$ \theta \in {\mathbb{R}} $$ . Examples from causal inference are inverse probability weighting, marginal structural models and structural nested models, which all lead to unbiased estimating equations. This article presents a consistent sandwich estimator for the variance of estimators ψ ^ $$ \hat{\psi} $$ that solve unbiased estimating equations including θ $$ \theta $$ which is also estimated by solving unbiased estimating equations. This article presents four additional results for settings where θ ^ $$ \hat{\theta} $$ solves (partial) score equations and ψ $$ \psi $$ does not depend on θ $$ \theta $$ . This includes many causal inference settings where θ $$ \theta $$ describes the treatment probabilities, missing data settings where θ $$ \theta $$ describes the missingness probabilities, and measurement error settings where θ $$ \theta $$ describes the error distribution. These four additional results are: (1) Counter-intuitively, the asymptotic variance of ψ ^ $$ \hat{\psi} $$ is typically smaller when θ $$ \theta $$ is estimated. (2) If estimating θ $$ \theta $$ is ignored, the sandwich estimator for the variance of ψ ^ $$ \hat{\psi} $$ is conservative. (3) A consistent sandwich estimator for the variance of ψ ^ $$ \hat{\psi} $$ . (4) If ψ ^ $$ \hat{\psi} $$ with the true θ $$ \theta $$ plugged in is efficient, the asymptotic variance of ψ ^ $$ \hat{\psi} $$ does not depend on whether θ $$ \theta $$ is estimated. To illustrate we use observational data to calculate confidence intervals for (1) the effect of cazavi versus colistin on bacterial infections and (2) how the effect of antiretroviral treatment depends on its initiation time in HIV-infected patients.

Risk Factors for Unplanned Higher-Level Re-Amputation and Mortality after Lower Extremity Amputation in Chronic Limb-Threatening Ischemia.

Background: The factors associated with unplanned higher-level re-amputation (UHRA) and one-year mortality among patients with chronic limb-threatening ischemia (CLTI) after lower extremity amputation are poorly understood. Methods: This was a single-center retrospective study of patients who underwent amputations for CLTI between 2014 and 2017. Unadjusted bivariate analyses and adjusted odds ratios (AOR) from logistic regression models were used to assess associations between pre-amputation risk factors and outcomes (UHRA and one-year mortality). Results: We obtained data on 203 amputations from 182 patients (median age 65 years [interquartile range (IQR) 57, 75]; 70.7% males), including 118 (58.1%) toe, 20 (9.9%) transmetatarsal (TMA), 37 (18.2%) below-knee (BKA), and 28 (13.8%) amputations at or above the knee. Median follow-up was 285 days (IQR 62, 1348). Thirty-six limbs (17.7%) had a UHRA, and the majority of these (72.2%) were following index forefoot amputations. Risk factors for UHRA included non-ambulatory status (AOR 6.74, 95% confidence interval (CI) 1.74-26.18; p < 0.10) and toe pressure < 30 mm Hg (AOR 4.89, 95% CI 1.52-15.78; p < 0.01). One-year mortality was 17.2% (n = 32), and risk factors included coronary artery disease (AOR 3.93, 95% CI 1.56-9.87; p < 0.05), congestive heart failure (AOR 4.90, 95% CI 1.96-12.29; p = 0.001), end-stage renal disease (AOR 7.54, 95% CI 3.10-18.34; p < 0.001), and non-independent ambulation (AOR 4.31, 95% CI 1.20-15.49; p = 0.03). Male sex was associated with a reduced odds of death at 1 year (AOR 0.37, 95% CI 0.15-0.89; p < 0.05). UHRA was not associated with one-year mortality. Conclusions: Rates of UHRA after toe amputations and TMA are high despite revascularization and one-year mortality is high among patients with CLTI requiring amputation.

For a proper use of frequentist inferential statistics in public health.

As widely noted in the literature and by international bodies such as the American Statistical Association, severe misinterpretations of P-values, confidence intervals, and statistical significance are sadly common in public health. This scenario poses serious risks concerning terminal decisions such as the approval or rejection of therapies. Cognitive distortions about statistics likely stem from poor teaching in schools and universities, overly simplified interpretations, and - as we suggest - the reckless use of calculation software with predefined standardized procedures. In light of this, we present a framework to recalibrate the role of frequentist-inferential statistics within clinical and epidemiological research. In particular, we stress that statistics is only a set of rules and numbers that make sense only when properly placed within a well-defined scientific context beforehand. Practical examples are discussed for educational purposes. Alongside this, we propose some tools to better evaluate statistical outcomes, such as multiple compatibility or surprisal intervals or tuples of various point hypotheses. Lastly, we emphasize that every conclusion must be informed by different kinds of scientific evidence (e.g., biochemical, clinical, statistical, etc.) and must be based on a careful examination of costs, risks, and benefits.

Alternatives to the P value: connotations of significance.

The statistical significance of a clinical trial analysis result is determined by a mathematical calculation and probability based on null hypothesis significance testing. However, statistical significance does not always align with meaningful clinical effects; thus, assigning clinical relevance to statistical significance is unreasonable. A statistical result incorporating a clinically meaningful difference is a better approach to present statistical significance. Thus, the minimal clinically important difference (MCID), which requires integrating minimum clinically relevant changes from the early stages of research design, has been introduced. As a follow-up to the previous statistical round article on P values, confidence intervals, and effect sizes, in this article, we present hands-on examples of MCID and various effect sizes and discuss the terms statistical significance and clinical relevance, including cautions regarding their use.

Reporting of confidence intervals, achievement of intended sample size, and adjustment for multiple primary outcomes in randomised trials of physical therapy interventions: an analysis of 100 representatively sampled trials.

BACKGROUND: The physical therapy profession has made efforts to increase the use of confidence intervals due to the valuable information they provide for clinical decision-making. Confidence intervals indicate the precision of the results and describe the strength and direction of a treatment effect measure. OBJECTIVES: To determine the prevalence of reporting of confidence intervals, achievement of intended sample size, and adjustment for multiple primary outcomes in randomised trials of physical therapy interventions. METHODS: We randomly selected 100 trials published in 2021 and indexed on the Physiotherapy Evidence Database. Two independent reviewers extracted the number of participants, any sample size calculation, and any adjustments for multiple primary outcomes. We extracted whether at least one between-group comparison was reported with a 95 % confidence interval and whether any confidence intervals were interpreted. RESULTS: The prevalence of use of confidence intervals was 47 % (95 % CI 38, 57). Only 6 % of trials (95 % CI: 3, 12) both reported and interpreted a confidence interval. Among the 100 trials, 59 (95 % CI: 49, 68) calculated and achieved the required sample size. Among the 100 trials, 19 % (95 % CI: 13, 28) had a problem with unadjusted multiplicity on the primary outcomes. CONCLUSIONS: Around half of trials of physical therapy interventions published in 2021 reported confidence intervals around between-group differences. This represents an increase of 5 % from five years earlier. Very few trials interpreted the confidence intervals. Most trials reported a sample size calculation, and among these most achieved that sample size. There is still a need to increase the use of adjustment for multiple comparisons.

P-values and confidence intervals for weighted log-rank tests under truncated binomial design based on clustered medical data.

The randomization design employed to gather the data is the basis for the exact distributions of the permutation tests. One of the designs that is frequently used in clinical trials to force balance and remove experimental bias is the truncated binomial design. The exact distribution of the weighted log-rank class of tests for censored cluster medical data under the truncated binomial design is examined in this paper. For p-values in this class, a double saddlepoint approximation is developed using the truncated binomial design. With the right censored cluster data, the saddlepoint approximation's speed and accuracy over the normal asymptotic make it easier to invert the weighted log-rank tests and find nominal 95% confidence intervals for the treatment effect.

Nonparametric estimation of measurement uncertainty arising from sampling.

BACKGROUND: Increasingly, measurement uncertainty has been used by pure and applied analytical chemistry to ensure decision-making in commercial transactions and technical-scientific applications. Until recently, it was considered that measurement uncertainty boiled down to analytical uncertainty; however, over the last two decades, uncertainty arising from sampling has also been considered. However, the second version of the EURACHEM guide, published in 2019, assumes that the frequency distribution is approximately normal or can be normalized through logarithmic transformations, without treating data that deviate from the normality. RESULTS: Here, six examples (four from Eurachem guide) were treated by classical ANOVA and submitted to an innovative nonparametric approach for estimating the uncertainty contribution arising from sampling. Based on bootstrapping method, confidence intervals were used to guarantee metrological compatibility between the uncertainty ratios arising from the results of the traditional parametric tests and the unprecedented proposed nonparametric methodology. SIGNIFICANCE AND NOVELTY: The present study proposed an innovative methodology for covering this gap in the literature based on nonparametric statistics (NONPANOVA) using the median absolute deviation concepts. Supplementary material based on Excel spreadsheets was developed, assisting users in the statistical treatment of their real examples.

Prehospital early warning scores for adults with suspected sepsis: the PHEWS observational cohort and decision-analytic modelling study.

Background: Guidelines for sepsis recommend treating those at highest risk within 1 hour. The emergency care system can only achieve this if sepsis is recognised and prioritised. Ambulance services can use prehospital early warning scores alongside paramedic diagnostic impression to prioritise patients for treatment or early assessment in the emergency department. Objectives: To determine the accuracy, impact and cost-effectiveness of using early warning scores alongside paramedic diagnostic impression to identify sepsis requiring urgent treatment. Design: Retrospective diagnostic cohort study and decision-analytic modelling of operational consequences and cost-effectiveness. Setting: Two ambulance services and four acute hospitals in England. Participants: Adults transported to hospital by emergency ambulance, excluding episodes with injury, mental health problems, cardiac arrest, direct transfer to specialist services, or no vital signs recorded. Interventions: Twenty-one early warning scores used alongside paramedic diagnostic impression, categorised as sepsis, infection, non-specific presentation, or other specific presentation. Main outcome measures: Proportion of cases prioritised at the four hospitals; diagnostic accuracy for the sepsis-3 definition of sepsis and receiving urgent treatment (primary reference standard); daily number of cases with and without sepsis prioritised at a large and a small hospital; the minimum treatment effect associated with prioritisation at which each strategy would be cost-effective, compared to no prioritisation, assuming willingness to pay £20,000 per quality-adjusted life-year gained. Results: Data from 95,022 episodes involving 71,204 patients across four hospitals showed that most early warning scores operating at their pre-specified thresholds would prioritise more than 10% of cases when applied to non-specific attendances or all attendances. Data from 12,870 episodes at one hospital identified 348 (2.7%) with the primary reference standard. The National Early Warning Score, version 2 (NEWS2), had the highest area under the receiver operating characteristic curve when applied only to patients with a paramedic diagnostic impression of sepsis or infection (0.756, 95% confidence interval 0.729 to 0.783) or sepsis alone (0.655, 95% confidence interval 0.63 to 0.68). None of the strategies provided high sensitivity (> 0.8) with acceptable positive predictive value (> 0.15). NEWS2 provided combinations of sensitivity and specificity that were similar or superior to all other early warning scores. Applying NEWS2 to paramedic diagnostic impression of sepsis or infection with thresholds of > 4, > 6 and > 8 respectively provided sensitivities and positive predictive values (95% confidence interval) of 0.522 (0.469 to 0.574) and 0.216 (0.189 to 0.245), 0.447 (0.395 to 0.499) and 0.274 (0.239 to 0.313), and 0.314 (0.268 to 0.365) and 0.333 (confidence interval 0.284 to 0.386). The mortality relative risk reduction from prioritisation at which each strategy would be cost-effective exceeded 0.975 for all strategies analysed. Limitations: We estimated accuracy using a sample of older patients at one hospital. Reliable evidence was not available to estimate the effectiveness of prioritisation in the decision-analytic modelling. Conclusions: No strategy is ideal but using NEWS2, in patients with a paramedic diagnostic impression of infection or sepsis could identify one-third to half of sepsis cases without prioritising unmanageable numbers. No other score provided clearly superior accuracy to NEWS2. Research is needed to develop better definition, diagnosis and treatments for sepsis. Study registration: This study is registered as Research Registry (reference: researchregistry5268). Funding: This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: 17/136/10) and is published in full in Health Technology Assessment; Vol. 28, No. 16. See the NIHR Funding and Awards website for further award information.

Sepsis is a life-threatening condition in which an abnormal response to infection causes heart, lung or kidney failure. People with sepsis need urgent treatment. They need to be prioritised at the emergency department rather than waiting in the queue. Paramedics attempt to identify people with possible sepsis using an early warning score (based on simple measurements, such as blood pressure and heart rate) alongside their impression of the patient's diagnosis. They can then alert the hospital to assess the patient quickly. However, an inaccurate early warning score might miss cases of sepsis or unnecessarily prioritise people without sepsis. We aimed to measure how accurately early warning scores identified people with sepsis when used alongside paramedic diagnostic impression. We collected data from 71,204 people that two ambulance services transported to four different hospitals in 2019. We recorded paramedic diagnostic impressions and calculated early warning scores for each patient. At one hospital, we linked ambulance records to hospital records and identified who had sepsis. We then calculated the accuracy of using the scores alongside diagnostic impression to diagnose sepsis. Finally, we used modelling to predict how many patients (with and without sepsis) paramedics would prioritise using different strategies based on early warning scores and diagnostic impression. We found that none of the currently available early warning scores were ideal. When they were applied to all patients, they prioritised too many people. When they were only applied to patients whom the paramedics thought had infection, they missed many cases of sepsis. The NEWS2, score, which ambulance services already use, was as good as or better than all the other scores we studied. We found that using the NEWS2, score in people with a paramedic impression of infection could achieve a reasonable balance between prioritising too many patients and avoiding missing patients with sepsis.

Estimating the size of a closed population by modeling latent and observed heterogeneity.

The paper extends the empirical likelihood (EL) approach of Liu et al. to a new and very flexible family of latent class models for capture-recapture data also allowing for serial dependence on previous capture history, conditionally on latent type and covariates. The EL approach allows to estimate the overall population size directly rather than by adding estimates conditional to covariate configurations. A Fisher-scoring algorithm for maximum likelihood estimation is proposed and a more efficient alternative to the traditional EL approach for estimating the non-parametric component is introduced; this allows us to show that the mapping between the non-parametric distribution of the covariates and the probabilities of being never captured is one-to-one and strictly increasing. Asymptotic results are outlined, and a procedure for constructing profile likelihood confidence intervals for the population size is presented. Two examples based on real data are used to illustrate the proposed approach and a simulation study indicates that, when estimating the overall undercount, the method proposed here is substantially more efficient than the one based on conditional maximum likelihood estimation, especially when the sample size is not sufficiently large.

Point estimation, confidence intervals, and P-values for optimal adaptive two-stage designs with normal endpoints.

Due to the dependency structure in the sampling process, adaptive trial designs create challenges in point and interval estimation and in the calculation of P-values. Optimal adaptive designs, which are designs where the parameters governing the adaptivity are chosen to maximize some performance criterion, suffer from the same problem. Various analysis methods which are able to handle this dependency structure have already been developed. In this work, we aim to give a comprehensive summary of these methods and show how they can be applied to the class of designs with planned adaptivity, of which optimal adaptive designs are an important member. The defining feature of these kinds of designs is that the adaptive elements are completely prespecified. This allows for explicit descriptions of the calculations involved, which makes it possible to evaluate different methods in a fast and accurate manner. We will explain how to do so, and present an extensive comparison of the performance characteristics of various estimators between an optimal adaptive design and its group-sequential counterpart.

A Software Tool for Estimating Uncertainty of Bayesian Posterior Probability for Disease.

The role of medical diagnosis is essential in patient care and healthcare. Established diagnostic practices typically rely on predetermined clinical criteria and numerical thresholds. In contrast, Bayesian inference provides an advanced framework that supports diagnosis via in-depth probabilistic analysis. This study's aim is to introduce a software tool dedicated to the quantification of uncertainty in Bayesian diagnosis, a field that has seen minimal exploration to date. The presented tool, a freely available specialized software program, utilizes uncertainty propagation techniques to estimate the sampling, measurement, and combined uncertainty of the posterior probability for disease. It features two primary modules and fifteen submodules, all designed to facilitate the estimation and graphical representation of the standard uncertainty of the posterior probability estimates for diseased and non-diseased population samples, incorporating parameters such as the mean and standard deviation of the test measurand, the size of the samples, and the standard measurement uncertainty inherent in screening and diagnostic tests. Our study showcases the practical application of the program by examining the fasting plasma glucose data sourced from the National Health and Nutrition Examination Survey. Parametric distribution models are explored to assess the uncertainty of Bayesian posterior probability for diabetes mellitus, using the oral glucose tolerance test as the reference diagnostic method.

Multiple Confidence Intervals and Surprisal Intervals to Avoid Significance Fallacy.

Overconfidence in statistical results in medicine is fueled by improper practices and historical biases afflicting the concept of statistical significance. In particular, the dichotomization of significance (i.e., significant vs. not significant), blending of Fisherian and Neyman-Pearson approaches, magnitude and nullification fallacies, and other fundamental misunderstandings distort the purpose of statistical investigations entirely, impacting their ability to inform public health decisions or other fields of science in general. For these reasons, the international statistical community has attempted to propose various alternatives or different interpretative modes. However, as of today, such misuses still prevail. In this regard, the present paper discusses the use of multiple confidence (or, more aptly, compatibility) intervals to address these issues at their core. Additionally, an extension of the concept of confidence interval, called surprisal interval (S-interval), is proposed in the realm of statistical surprisal. The aforementioned is based on comparing the statistical surprise to an easily interpretable phenomenon, such as obtaining S consecutive heads when flipping a fair coin. This allows for a complete departure from the notions of statistical significance and confidence, which carry with them longstanding misconceptions.

How the Post-Data Severity Converts Testing Results into Evidence for or against Pertinent Inferential Claims.

The paper makes a case that the current discussions on replicability and the abuse of significance testing have overlooked a more general contributor to the untrustworthiness of published empirical evidence, which is the uninformed and recipe-like implementation of statistical modeling and inference. It is argued that this contributes to the untrustworthiness problem in several different ways, including [a] statistical misspecification, [b] unwarranted evidential interpretations of frequentist inference results, and [c] questionable modeling strategies that rely on curve-fitting. What is more, the alternative proposals to replace or modify frequentist testing, including [i] replacing p-values with observed confidence intervals and effects sizes, and [ii] redefining statistical significance, will not address the untrustworthiness of evidence problem since they are equally vulnerable to [a]-[c]. The paper calls for distinguishing between unduly data-dependant 'statistical results', such as a point estimate, a p-value, and accept/reject H0, from 'evidence for or against inferential claims'. The post-data severity (SEV) evaluation of the accept/reject H0 results, converts them into evidence for or against germane inferential claims. These claims can be used to address/elucidate several foundational issues, including (i) statistical vs. substantive significance, (ii) the large n problem, and (iii) the replicability of evidence. Also, the SEV perspective sheds light on the impertinence of the proposed alternatives [i]-[iii], and oppugns [iii] the alleged arbitrariness of framing H0 and H1 which is often exploited to undermine the credibility of frequentist testing.

Association of Cystatin C Level with All-cause Mortality in Patients with Liver Cirrhosis: A Meta-analysis.

BACKGROUND AND OBJECTIVE: Blood cystatin C level has been introduced as a promising biomarker to detect early kidney injury in cirrhotic patients. The purpose of this meta-analysis was to investigate the association of blood cystatin C level with allcause mortality in patients with liver cirrhosis. METHODS: PubMed, ScienceDirect, and Embase databases were searched from the inception to November 15, 2022. Observational studies evaluating the value of blood cystatin C level in predicting all-cause mortality in patients with ACS were selected. The pooled hazard risk (HR) with 95% confidence intervals (CI) was calculated using a random effect model meta-analysis. RESULTS: Twelve studies with 1983 cirrhotic patients were identified. The pooled adjusted HR of all-cause mortality was 3.59 (95% CI 2.39-5.39) for the high versus low group of cystatin C level. Stratified analysis by study design, characteristics of patients, geographical region, sample size, and length of follow-up further supported the predictive value elevated cystatin C level. CONCLUSION: Elevated cystatin C level was an independent predictor of poor survival in patients with liver cirrhosis. Detection of blood cystatin C level may provide important prognostic information in cirrhotic patients.