Frequentist Approach Research Articles

Confidence distributions and hypothesis testing

The traditional frequentist approach to hypothesis testing has recently come under extensive debate, raising several critical concerns. Additionally, practical applications often blend the decision-theoretical framework pioneered by Neyman and Pearson with the inductive inferential process relied on the p-value, as advocated by Fisher. The combination of the two methods has led to interpreting the p-value as both an observed error rate and a measure of empirical evidence for the hypothesis. Unfortunately, both interpretations pose difficulties. In this context, we propose that resorting to confidence distributions can offer a valuable solution to address many of these critical issues. Rather than suggesting an automatic procedure, we present a natural approach to tackle the problem within a broader inferential context. Through the use of confidence distributions, we show the possibility of defining two statistical measures of evidence that align with different types of hypotheses under examination. These measures, unlike the p-value, exhibit coherence, simplicity of interpretation, and ease of computation, as exemplified by various illustrative examples spanning diverse fields. Furthermore, we provide theoretical results that establish connections between our proposal, other measures of evidence given in the literature, and standard testing concepts such as size, optimality, and the p-value.

Physical activity improves the visual-spatial working memory of individuals with mild cognitive impairment or Alzheimer's disease: a systematic review and network meta-analysis.

Our network meta-analysis aimed to ascertain the effect of physical activity on the visual-spatial working memory of individuals with mild cognitive impairment and Alzheimer's disease as well as to propose tailored exercise interventions for each group. Employing a frequentist approach, we performed a network meta-analysis to compare the effectiveness of different exercise interventions in improving the visual-spatial working memory of individuals with mild cognitive impairment and Alzheimer's disease. Subsequently, we explored the moderating variables influencing the effectiveness of the exercise interventions through a subgroup analysis. We included 34 articles involving 3,074 participants in the meta-analysis, comprised of 1,537 participants from studies on mild cognitive impairment and 1,537 participants from studies on Alzheimer's disease. The articles included exhibited an average quality score of 6.6 (score studies) and 6.75 (reaction time [RT] studies), all passing the inconsistency test (p > 0.05). In the mild cognitive impairment literature, mind-body exercise emerged as the most effective exercise intervention (SMD = 0.61, 95% CI: 0.07-1.14). In Alzheimer's disease research, aerobic exercise was identified as the optimal exercise intervention (SMD = 0.39, 95% CI: 0.06-0.71). The results of the subgroup analysis suggest that the most effective approach to enhancing the visual-spatial working memory of individuals with mild cognitive impairment entails exercising at a frequency of three or more times per week for over 60 min each time and at a moderate intensity for more than 3 months. Suitable exercise options include mind-body exercise, multicomponent exercise, resistance exercise, and aerobic exercise. For individuals with Alzheimer's disease, we recommend moderately intense exercise twice per week for over 90 min per session and for a duration of 3 months or longer, with exercise options encompassing aerobic exercise and resistance exercise.

Complementary and alternative therapies for managing postoperative pain after lower third molar surgery: a systematic review and network meta-analysis.

The objective of this study was to evaluate the impact of complementary and alternative treatments on postoperative pain following lower third molar surgeries. A comprehensive search of Electronic databases (Embase, MEDLINE via PubMed, and Cochrane Library) and grey literature was conducted up until May 2022. Randomized clinical trials investigating the effect of acupuncture, ozone therapy, laser (LLLT), drainage tube, kinesio-taping, ice therapy, and compressions on pain after LTM surgeries were included. The estimated mean differences (MD) for alternative therapies were pooled using the frequentist approach to random-model network meta-analysis NMA. Eighty-two papers were included in the qualitative analysis; 33 of them were included in the quantitative analyzes. NMA revealed that drainage tube and kinesio-taping were superior in controlling pain 24-hours postoperatively than no-treatment. At 48-hours follow-up, kinesio-taping and LLLT more effective than placebo and drainage tube; and kinesio-taping and LLLT were superior to no treatment. At 72h postoperatively, ozone therapy was superior to placebo; and drainage tube, kinesio-taping, and LLLT were better than no treatment. At 7-days follow-up, ozone and LLLT were superior to placebo; and LLLT and kinesio-taping were superior to no treatment. The SUCRA-ranking placed drainage tube as top-ranking intervention at 48-hours (98.2%) and 72-hours (96%) follow-ups, and ozone (83.5%) at 7-days follow-up. The study findings suggest that these alternative and complementary therapies may be useful in reducing postoperative pain after LTM surgeries, and may offer advantages when combined to traditional pain management methods. Non-pharmacological therapies are gaining popularity among healthcare professionals and patients. This study found that some of these therapies, specifically kinesio-taping and drainage tube were effective in controlling postoperative pain after third molar surgeries. These findings have important implications for clinical practice, as they highlight the potential benefits of incorporating these therapies into postoperative pain management plans.

A statistical method for treating the “negative-age” and “negative-atom-number” conundrums in radioisotope dating

In radioisotope dating, statistical fluctuations of atom counts or decay counts can sometimes lead to unphysical results, such as negative counts after subtracting the background or negative apparent ages near the boundaries of the dating range. How to treat these boundary cases with a unified approach and give proper estimates on age limits and confidence intervals is an important issue in radioisotope dating. In this work, we combine the Feldman-Cousins likelihood-ratio ordering method for interval estimation, a Frequentist approach to make consistent transitions from the choice of one-sided limits to two-sided confidence intervals, and a Bayesian method to treat the background uncertainties. This data treatment method naturally eliminates the unphysical results. We use 81Kr dating and Atom Trap Trace Analysis as an example to illustrate the advantages. This method can be generalized to other radioisotopes and ultra-sensitive analysis techniques.

Effectiveness and safety of biological and target synthetic drugs treatment for psoriatic arthritis: a systematic review with network meta-analysis

BackgroundPsoriatic arthritis (PA) is a chronic inflammatory systemic arthritis that can result in loss of functional capacity and joint deformation. This systematic review assessed the effectiveness and safety of biological and target synthetic drugs for treating PA.MethodsWe searched for randomized clinical trials (RCTs) that evaluated the use of Adalimumab, Etanercept, Infliximab, Golimumab, Secukinumab, Certolizumab Pegol and Tofacitinib in the main general databases and clinical trial registers databases. The primary outcomes were ACR 50, PsARC, and serious adverse events. Two independent reviewers performed study selection and data extraction. Network meta-analyses were conducted using a random effects model and frequentist approach. The CINeMA software was used to assess the certainty of evidence.ResultsWe included 33 RCTs (n = 11,034). The results from the network meta-analysis for the ACR 50 at 6-months follow-up showed that all drugs were superior to placebo, with Secukinumab (high certainty of evidence), Infliximab (very low certainty of evidence) and Adalimumab (high certainty of evidence) ranking the highest. Regarding the PsARC (at 6-months follow-up), all drugs, except for Golimumab (very low certainty of evidence), were superior to placebo, with Etanercept (low certainty of evidence), Infliximab (low certainty of evidence) and Certolizumab Pegol (low certainty of evidence) being the most effective drugs. There were no significant differences in the risk of serious adverse events between the drugs and placebo. Golimumab (very low certainty of evidence), Secukinumab (low certainty of evidence), and Adalimumab (very low certainty of evidence) ranked the highest for safety.ConclusionsIn conclusion, based on the balance between efficacy and safety, Secukinumab and Adalimumab may be the preferred options among the evaluated drugs for treating patients with PsA. However, caution is necessary when interpreting the safety findings, as they are supported by evidence of low to very low certainty. Consequently, the balance between benefits and potential risks may change as new safety evaluation studies become available.Protocol registrationPROSPERO: CRD42022315577

Theoretical Model that Explain the Knowledge of the CSE Program in Special Education Teachers: A Bayesian Approach

This study introduces new evidence of the theoretical model that connects 21 theoretical hypotheses of personal experience with sexual education knowledge. Participants were 82 Special Education students with a comparison group of 55 female students from Psychology and Natural Sciences Education studying special education. This correlational and exploratory study proposes an analysis path analyzed with a frequentist approach. It presents new empirical evidence from a Bayesian approach’s theoretical and methodological assumptions that offer estimates similar to the real values. The information in the model can be used to guide the education of Latinas who are Special Education teachers to identify other aspects that condition their teaching.

Epidemiological insights into paratuberculosis in camels in Saudi Arabia: Bayesian estimation of true prevalence and identification of risk factors.

Paratuberculosis, caused by Mycobacterium avium subsp. paratuberculosis (MAP), is a significant concern in the camel population of Saudi Arabia. This study aimed to provide epidemiological insights into the disease by estimating the true prevalence in camels in the Eastern Province and Riyadh, using a Bayesian estimation framework, and exploring the associated risk factors through a frequentist approach. A total of 1200 camel blood samples were collected and analyzed using an indirect ELISA method. The true herd-level prevalence was estimated at 0.7 (95% probability interval: 0.57 to 0.81), and the mean expected true animal-level prevalence was 0.17 (0.14 to 0.20). Risk factors associated with Map seropositivity were identified, including sex, breed, raising system, and production type. Females, single breed camels, and nomadic raising systems were found to have lower odds of seropositivity, while camels used for racing and show had significantly higher odds. The study's Bayesian approach, adjusting for the imperfect accuracy of MAP tests, provides a nuanced understanding of the disease's prevalence in the region. The integration of true prevalence estimates with risk factor analysis offers a comprehensive framework that can guide future policies and strategies in the fight against paratuberculosis in Saudi Arabia. The findings emphasize the importance of targeted control measures, underscoring the urgent need for interventions in Saudi Arabia's camel population. By understanding the true disease prevalence and its associated risk factors, we can enhance disease management strategies, offering valuable insights for future control and eradication efforts in the region.

A fecal score approximation model for analysis of real-time quantitative PCR fecal source identification measurements

Numerous qPCR-based methods are available to estimate the concentration of fecal pollution sources in surface waters. However, qPCR fecal source identification data sets often include a high proportion of non-detections (reactions failing to attain a prespecified minimal signal intensity for detection) and measurements below the assay lower limit of quantification (minimal signal intensity required to estimate target concentration), making it challenging to interpret results in a quantitative manner while accounting for error. In response, a Bayesian statistic based Fecal Score (FS) approach was developed that estimates the weighted average concentration of a fecal source identification genetic marker across a defined group of samples, mathematically incorporating qPCR measurements from all samples. Yet, implementation is technically demanding and computationally intensive requiring specialized training, the use of expert software, and access to high performance computing. To address these limitations, this study reports a novel approximation model for FS determination based on a frequentist approach. The performance of the Bayesian and Frequentist models are compared using fecal source identification qPCR data representative of different ‘censored’ data scenarios from a recently published study focusing on the impact of stormwater discharge in urban streams. In addition, data set eligibility recommendations for the responsible use of these models are presented. Findings indicate that the Frequentist model can generate similar average concentrations and uncertainty estimates for FS, compared to the original Bayesian approach. The Frequentist model should make calculations less computationally and technically intensive, allowing for the development of easier to use data analysis tools for fecal source identification applications.

Bayesian and Frequentist Approaches to Rescuing Disrupted Trials: A Report from the NISS Ingram Olkin Forum Series on Unplanned Clinical Trial Disruptions

The COVID-19 pandemic impacted clinical trials in ways never expected. However, similar challenges should now be expected going forward. These challenges made us aware of statistical problems arising from other types of disruptions that had not previously captured the attention of the statistical community. This article describes some frequentist and Bayesian statistical tools that can be used with future disruptions and illuminates issues that could benefit from more statistical research. Disruptions may threaten a clinical trial’s validity. Here, we address two resultant challenges: (a) performing an unplanned analysis with options to stop and/or change the sample size; and (b) changes in the study population that are observable or unobservable at the patient level. Different paradigms lead to different ways of doing things, but many statisticians work exclusively within a Bayesian or frequentist paradigm. We propose and provide side-by-side descriptions of Bayesian and frequentist approaches to dealing with these challenges. An illustrative phase III trial aims to compare second-line therapies for type 2 diabetes. We compare and contrast Bayesian and frequentist coping strategies assuming the trial was interrupted due to COVID-19, focusing on Type I error control and the expected loss from a specific utility function.

A comparison of Bayesian and frequentist approaches to incorporating clinical and biological information for the prediction of response to standardized pediatric colitis therapy.

The prospective cohort study PROTECT is the largest study in pediatric ulcerative colitis (UC) with standardized treatments, providing valuable data for predicting clinical outcomes. PROTECT and previous studies have identified characteristics associated with clinical outcomes. In this study, we aimed to compare predictive modeling between Bayesian analysis including machine learning and frequentist analysis. The key outcomes for this analysis were week 4, 12 and 52 corticosteroid (CS)-free remission following standardized treatment from diagnosis. We developed predictive modeling with multivariable Bayesian logistic regression (BLR), Bayesian additive regression trees (BART) and frequentist logistic regression (FLR). The effect estimate of each risk factor was estimated and compared between the BLR and FLR models. The predictive performance of the models was assessed including area under curve (AUC) of the receiver operating characteristic (ROC) curve. Ten-fold cross-validation was performed for internal validation of the models. The estimation contained 95% credible (or confidence) interval (CI). The statistically significant associations between the risk factors and early or late outcomes were consistent between all BLR and FLR models. The model performance was similar while BLR and BART models had narrower credible intervals of AUCs. To predict week 4 CS-free remission, the BLR model had AUC of 0.69 (95% CI 0.67-0.70), the BART model had AUC of 0.70 (0.67-0.72), and the FLR had AUC of 0.70 (0.65-0.76). To predict week 12 CS-free remission, the BLR model had AUC of 0.78 (0.77-0.79), the BART model had AUC of 0.78 (0.77-0.79), and the FLR model had AUC of 0.79 (0.74-0.83). To predict week 52 CS-free remission, the BLR model had AUC of 0.69 (0.68-0.70), the BART model had AUC of 0.69 (0.67-0.70), and the FLR model had AUC of 0.69 (0.64-0.74). The BART model identified nonlinear associations. BLR and BART models had intuitive interpretation on interval estimation, better precision in estimating the AUC and can be alternatives for predicting clinical outcomes in pediatric patients with UC. BART model can estimate nonlinear nonparametric association.

Evaluation of Alternative Treatment Strategies for Bile Acid Malabsorption in Inflammatory Bowel Disease Patients: A Network Meta-Analysis.

Bile acid malabsorption (BAM) is characterized by chronic watery diarrhea resulting from excessive bile acids in the feces. BAM is often an overlooked cause of chronic diarrhea, with its prevalence not being sufficiently researched. This review aimed to assess existing literature that explores diverse treatment strategies, to review the published studies that examine the various therapies for BAM patients, emphasizing their influence on clinical results. We conducted a comprehensive review of various databases, including PubMed, Scopus, Web of Science, Cochrane Database, and EMBASE. Our criteria for inclusion focused on randomized controlled studies (RCTs) that evaluated the effectiveness of different treatment options for patients with BAM. To rank the treatments, we adopted the frequentist approach through the "netrank" function of the network meta-analysis (NMA). Moreover, we utilized the "netsplit" function in the NMA to separate direct and indirect evidence. Our analysis was carried out using RStudio version 1.4.1717 (2009 - 2021 RStudio, Inc.), and we used the "netmeta" and "meta" packages for NMA. We found seven relevant articles involving 213 participants, the average age being approximately 50 years, including 53 males and 92 females. Of the drugs examined, tropifexor was proved to be the most effective in raising the fibroblast growth factor 19 (FGF19) levels and reducing the 7 alpha-hydroxy-4-cholesten-3-one (C4) levels, compared to the placebo (mean difference (MD) = 335.30, 95% confidence interval (CI) (334.86, 335.74), MD = -24.60, 95% CI (-25.37, -23.83); respectively). Compared to colesevelam and the placebo, liraglutide was more efficient in decreasing fecal bile acid concentration (liraglutide; MD = -19, 95% CI (-37.61, -0.39)). Tropifexor has been identified as the most successful medication in mitigating BAM symptoms. To ensure more accurate results, there is a need for randomized controlled clinical trials that involve a larger participant pool.

Efficacy and Safety of Thermal Ablation after Endoscopic Mucosal Resection: A Systematic Review and Network Meta-Analysis.

(1) Background: Large colonic polyps during colonoscopy can be managed by Endoscopic mucosal resection (EMR). To decrease the polyp recurrence rate, thermal ablation methods like argon plasma coagulation (APC) and snare tip soft coagulation (STSC) have been introduced. We performed this network meta-analysis to assess the efficacy and safety of these modalities. (2) Methods: We performed a comprehensive literature review, through 5 January 2024, of databases including Embase, PubMed, SciELO, KCI, Cochrane Central, and Web of Science. Using a random effects model, we conducted a frequentist approach network meta-analysis. The risk ratio (RR) with 95% confidence interval (CI) was calculated. Safety and efficacy endpoints including rates of recurrence, bleeding, perforation, and post polypectomy syndrome were compared. (3) Results: Our search yielded a total of 13 studies with 2686 patients. Compared to placebo, both APC (RR: 0.33 CI: 0.20-0.54, p < 0.01) and STSC (RR: 0.27, CI: 0.21-0.34, p < 0.01) showed decreased recurrence rates. On ranking, STSC showed the lowest recurrence rate, followed by APC and placebo. Regarding individual adverse events, there was no statistically significant difference between either of the thermal ablation methods and placebo. (4) Conclusions: We demonstrated the efficacy and safety of thermal ablation after EMR for decreasing recurrence of adenoma.

Bayesian regression models in gretl: the BayTool package

AbstractThis article presents the package which integrates the software functionalities, mostly concerned with frequentist approaches, with Bayesian estimation methods of commonly used econometric models. Computational efficiency is achieved by pairing an extensive use of Gibbs sampling for posterior simulation with the possibility of splitting single-threaded experiments into multiple cores or machines by means of parallelization. From the user’s perspective, the package requires only basic knowledge of scripting to fully access its functionality, while providing a point-and-click solution in the form of a graphical interface for a less experienced audience. These features, in particular, make stand out as an excellent teaching device without sacrificing more advanced or complex applications.

Evaluating the performance of Bayesian geostatistical prediction with physical barriers in the Chesapeake Bay.

The Chesapeake Bay is one of the most widely studied bodies of water in the United States and around the world. Routine monitoring of water quality indicators (e.g., salinity) relies on fixed sampling stations throughout the Bay. Utilizing this rich monitoring data, various methods produce surface predictions of water quality indicators to further characterize the health of the Bay as well as to support wildlife and human health research studies. Bayesian approaches for geostatistical modelling are becoming increasingly popular and can be preferred over frequentist approaches because full and exact inference can be computed, along with more accurate characterization of uncertainty. Traditional geostatistical prediction methods assume a Euclidean distance between two points when characterizing spatial dependence as a function of distance. However, Euclidean approaches may not be appropriate in estuarine environments when water-land boundaries are crossed during the modelling process. In this study, we compare stationary and barrier INLA geostatistical models with a classic kriging geostatistical model to predict salinity in the Chesapeake Bay during 4 months in 2019. Cross-validation is conducted for each approach to evaluate model performance based on prediction accuracy and precision. The results provide evidence that the two Bayesian-based models outperformed ordinary kriging, especially when examining prediction accuracy (most notably in the tributaries). We also suggest that the non-Euclidean model accounts for the appropriate water-based distances between sampling locations and is likely better at characterizing the uncertainty. However, more complex bodies of water may better showcase the capabilities and efficacy of the physical barrier INLA model.

Location Analytics of Routine Occurrences (LARO) to Identify Locations with Regularly Occurring Events with a Case Study on Traffic Accidents

Conventional spatiotemporal methods take frequentist or density-based approaches to map event clusters over time. While these methods discern hotspots of varying continuity in space and time, their findings overlook locations of routine occurrences where the geographic context may contribute to the regularity of event occurrences. Hence, this research aims to recognize the routine occurrences of point events and relate site characteristics and situation dynamics around these locations to explain the regular occurrences. We developed an algorithm, Location Analytics of Routine Occurrences (LARO), to determine an appropriate temporal unit based on event periodicity, seek locations of routine occurrences, and geographically contextualize these locations through spatial association mining. We demonstrated LARO in a case study with over 250,000 reported traffic accidents from 2010 to 2018 in Dallas, Texas, United States. LARO identified three distinctive locations, each exhibiting varying frequencies of traffic accidents at each weekly hour. The findings indicated that locations with routine traffic accidents are surrounded by high densities of stores, restaurants, entertainment, and businesses. The timing of traffic accidents showed a strong relationship with human activities around these points of interest. Besides the LARO algorithm, this study contributes to the understanding of previously overlooked periodicity in traffic accidents, emphasizing the association between periodic human activities and the occurrence of street-level traffic accidents. The proposed LARO algorithm is applicable to occurrences of point-based events, such as crime incidents or animal sightings.

A Bayesian spatially explicit estimation of daily egg production: application to anchovy in the Bay of Biscay

Biomass estimates of fish resources by the daily egg production method (DEPM) are sensitive to the high variability of the daily egg production ( P0) and egg mortality (Z) in space. This work presents a Bayesian approach to estimate these parameters. A prior distribution of Z based on literature serves to overcome the biologically implausible Z estimates that can result from frequentist approaches. In addition to the classical estimation of a single P0 over the spawning area, the Bayesian framework allows also the modelling of egg densities in space, by including either spatial random effects, smoothing functions, or kriging like models, providing insights into the spatial variability of P0. The Bayesian approach was applied to the Bay of Biscay anchovy DEPM surveys. Results showed that this Bayesian approximation solved the implausible Z problem resulting in tighter credible intervals of both P0 and Z. Overall, spatial models outperformed the non-spatial model in terms of goodness of fit and resulted in slightly different total production estimates across models for each year, with a moderate decrease on uncertainty estimates.

A Bayesian workflow for the analysis and reporting of international large-scale assessments: a case study using the OECD teaching and learning international survey

This paper aims to showcase the value of implementing a Bayesian framework to analyze and report results from international large-scale assessments and provide guidance to users who want to analyse ILSA data using this approach. The motivation for this paper stems from the recognition that Bayesian statistical inference is fast becoming a popular methodological framework for the analysis of educational data generally, and large-scale assessments more specifically. The paper argues that Bayesian statistical methods can provide a more nuanced analysis of results of policy relevance compared to standard frequentist approaches commonly found in large-scale assessment reports. The data utilized for this paper comes from the Teaching and Learning International Survey (TALIS). The paper provides steps in implementing a Bayesian analysis and proposes a workflow that can be applied not only to TALIS but to large-scale assessments in general. The paper closes with a discussion of other Bayesian approaches to international large-scale assessment data, in particularly for predictive modeling.

Comparison of Multiple Surgical Treatments for Massive Irreparable Rotator Cuff Tears in Patients Younger Than 70 Years of Age: A Systematic Review and Network Meta-analysis

Background: Massive irreparable rotator cuff tears (MIRCTs) remain a challenging treatment paradigm, particularly for nonelderly patients without pseudoparalysis or arthritis. Purpose: To use a network meta-analysis to analyze comparative studies of surgical treatment options for MIRCTs in patients <70 years of age for several patient-reported outcomes, range of motion (ROM), and acromiohumeral distance (AHD). Study Design: Network meta-analysis of comparative studies; Level of evidence, 3. Methods: A systematic review of the literature, using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, of the MEDLINE, Embase, and Cochrane Library databases was conducted from 2017 to 2022. Inclusion criteria were (1) clinical comparative studies of MIRCTs (with several study-specific criteria); (2) ≥1 outcome of interest reported on, with standard deviations; (3) minimum 1-year follow-up; and (4) mean age of <70 years for both cohorts, without arthritis or pseudoparalysis. There were 8 treatment arms compared. Outcomes of interest were the American Shoulder and Elbow Surgeons (ASES) score, Constant-Murley score, visual analog scale for pain, AHD, and forward flexion and external rotation ROM. A frequentist approach to network meta-analysis with a random-effects model was performed using the netmeta package Version 0.9-6 in R. Results: A total of 23 studies met the inclusion criteria, with 1178 patients included in the network meta-analysis. There was a mean weighted age of 62.8 years, 568 (48.2%) men, with a mean follow-up of 28.9 months. There were no significant differences between groups in regard to sex (P = .732) or age (P = .469). For the ASES score, InSpace balloon arthroplasty (mean difference [MD], 12.34; 95% CI, 2.18 to 22.50; P = .017), arthroscopic bridging graft (aBG) (MD, 7.07; 95% CI, 0.28 to 13.85; P = .041), and long head of biceps augmented superior capsular reconstruction (BSCR) (MD, 5.16; 95% CI, 1.10 to 9.22; P = .013) resulted in the highest P-scores. For the Constant-Murley score, debridement (MD, 21.03; 95% CI, 8.98 to 33.08; P < .001) and aBG (MD, 6.97; 95% CI, 1.88 to 12.05; P = .007) resulted in the highest P-scores. For AHD, BSCR resulted in the highest P-score (MD, 1.46; 95% CI, 0.45 to 2.48; P = .005). For forward flexion ROM, debridement (MD, 45.77; 95% CI, 25.41 to 66.13; P < .001) resulted in the highest P-score, while RSA resulted in the lowest P-score (MD, –16.70; 95% CI, −31.20 to −2.20; P = .024). Conclusion: For patients <70 years with MIRCT without significant arthritis or pseudoparalysis, it appears that graft interposition repair techniques, superior capsular reconstruction using the long head of the biceps tendon, arthroscopic debridement, and balloon arthroplasty provide superiority in various outcome domains, while RSA provides the least benefit in forward flexion.

Frequentist and Bayesian Approaches in Modeling and Prediction of Extreme Rainfall Series: A Case Study from Southern Highlands Region of Tanzania

This study focuses on modeling and predicting extreme rainfall based on data from the Southern Highlands region, the critical for rain-fed agriculture in Tanzania. Analyzing 31 years of annual maximum rainfall data spanning from 1990 to 2020, the Generalized Extreme Value (GEV) model proved to be the best for modeling extreme rainfall in all stations. Three estimation methods–L-moments, maximum likelihood estimation (MLE), and Bayesian Markov chain Monte Carlo (MCMC)–were employed to estimate GEV parameters and future return levels. The Bayesian MCMC approach demonstrated superior performance by incorporating noninformative priors to ensure that the prior information had minimal influence on the analysis, allowing the observed data to play a dominant role in shaping the posterior distribution. Furthermore, return levels for various future periods were estimated, providing guidance for flood protection measures and infrastructure design. Trend analysis using p value, Kendall’s tau, and Sen’s slope indicated no statistically significant trends in rainfall patterns, although a weak positive trend in extreme rainfall events was observed, suggesting a gradual and modest increase over time. Overall, the study contributes valuable insights into extreme rainfall patterns and underscores the importance of L-moments in identifying the best fit distribution and Bayesian MCMC methodology for accurate parameter estimation and prediction, enabling effective measures and infrastructure planning in the region.

P756 Efficacy and safety of medical therapies for maintenance of surgically induced remission in Crohn’s disease: A systematic review and network meta-analysis

Abstract Background Approximately half of patients with Crohn’s disease (CD) undergo surgical resection in first decade after diagnosis. Majority experience disease recurrence following surgery requiring further intervention. There is limited evidence on efficacy of medical therapies in this setting. We performed a network meta-analysis to evaluate comparative efficacies of medical therapies for the prevention of clinical and endoscopic relapse after surgical resection. Methods We searched MEDLINE, Embase, and Cochrane Central Register of Controlled Trials between inception and October 2023 for randomised controlled trials (RCTs). Outcomes assessed were clinical relapse, endoscopic relapse, and safety outcomes including adverse events (AE), withdrawal due to AE, and serious AE. We performed random-effects network meta- analysis using a frequentist approach, and estimated relative risk (RRs), 95% CI values and used GRADE to ascertain certainty of evidence. Results A total of 37 RCTs comprising 4411 patients were included. On network meta-analysis of clinical relapse outcome, there was moderate certainty of evidence favouring adalimumab (RR 0.14, 95% CI [0.03-0.7]) over placebo with number needed to treat (NNT) of 3. There was low certainty of evidence supporting 5-amainosalycilates (RR 0.81, 95% CI [0.71-0.92]; NNT of 13) and purine analogues (RR 0.82, 95% CI [0.72-0.93]; NNT of 14) but the effect size was trivial (Figure 1). Whereas for endoscopic relapse outcome, vedolizumab (RR 0.23, 95% CI [0.07-0.8]) and adalimumab (RR 0.32, 95% CI [0.17-0.62], low certainty) and infliximab (RR 0.63, 95% CI [0.48-0.82], low certainty) were the only agents superior to placebo but not purine analogues (RR 0.87, 95% CI [0.7-1.08], very low certainty), 5-ASA (RR 1.04, 95% CI [0.85-1.26], very low certainty), antibiotics (RR 1.13, 95% CI [0.83-1.53], very low certainty), probiotics (RR 1.15, 95% CI [0.94-1.39], very low certainty), vitamin-D (RR 0.9, 95% CI [0.62-1.31], very low certainty) and curcumin (RR 1.17, 95% CI [0.73-1.87], low certainty). On analysis of total adverse events outcomes, all therapies were safe. Conclusion On network meta-analysis, adalimumab had a large magnitude of effect size for preventing clinical and endoscopic relapse (low certainty), vedolizumab a large magnitude for preventing endoscopic relapse (low certainty) and 5-ASA a small magnitude result for preventing clinical relapse (low certainty). Overall certainty of evidence is very low to low suggesting need for further research in this field.