Probability Of Bias Research Articles

A behavioural test of depression-related probability bias

ABSTRACT Individuals high in depressive symptom severity show probability bias: they believe negative events are relatively probable, and positive events relatively improbable, compared to those with less severe symptoms. However, this has only ever been demonstrated using self-report measures, in which participants explicitly estimate events’ probabilities: this leaves open the risk that “probability bias” is merely an artefact of response bias. We tested the veracity of probability bias using an indirect behavioural measure, based on a sentence-reading task. Study 1 tested 112 Turkish students; Study 2 tested 117 international users of online groups for people with depressive and anxiety disorders. As predicted, participants with higher depressive symptom scores responded relatively quickly to sentences stating negative events might occur, and relatively slowly to sentences stating positive events might occur, compared to those with lower scores. This effect was only marginal in Study 1, but reached significance in Study 2. However, contrary to predictions, this effect was not moderated by the probability level stated in the sentence. This makes our findings difficult to interpret, and we must present these studies as a failure to convincingly demonstrate depression-related probability bias. We hope this stimulates more work on the nature and veracity of probability bias.

An investigation to improve a nonlinear mixed-effects approach for EC50 estimation based on multi-donor dose–response data

ABSTRACT Dose–response relationships are important in assessing the efficacy and potency of compounds, which can usually be characterized by a 4-parameter logistic (4-PL) model estimating EC50, slope factor, lower asymptote, and upper asymptote. EC50, the concentration of a compound that induces a response halfway between the baseline and maximum, is a key quantity to evaluate compound potency. For multi-donor dose–response data, it is often of interest to estimate the overall EC50 (i.e. the average EC50 of the population of donors) and its 95% confidence interval (CI). A few multi-donor EC50 estimation methods have been proposed in the literature. Jiang and Kopp-Schneider (2014) systematically compared the meta-analysis approach and the nonlinear mixed-effects approach and concluded that the meta-analysis approach is simple and robust to summarize EC50 estimates from multiple experiments, especially suited in the case of a small number of experiments, while the nonlinear mixed-effects approach has the issue of convergence failures probably due to overparameterization. In this article, we propose a modification of the nonlinear mixed-effects approach by using the stochastic approximation expectation-maximization (SAEM) algorithm to estimate model parameters and using multiple starting points to search for globally optimal values, which can substantially alleviate the issue of convergence failures even for small number of donors (e.g. n = 3), and achieve a smaller absolute median bias and better coverage probability of 95% confidence interval than the meta-analysis approach when the number of donors is not too small (e.g. n ≥ 7).

Effectiveness of brain magnetic resonance imaging in the early diagnosis and characterization of dementias: A systematic review

IntroductionMagnetic resonance imaging (MRI) is a frequently used test in the diagnosis of dementia. The objective was to evaluate its effectiveness for the early diagnosis of dementia in patients with mild cognitive impairment (MCI). Material and methodsOriginal studies were selected from systematic reviews between 2011 and 2021, according to PRISMA 2020 criteria. QUADAS-2 and GRADE tools were used, and a meta-analysis was performed. ResultsFinal selection of 23 articles. Patient selection and index test had a high probability of bias. The certainty of the evidence was very low. In the hippocampus, sensitivity was 0.62 (95%CI 0.48–0.79) and specificity 0.70 (95%CI 0.55–0.80). In the temporal lobe, sensitivity was 0.65 (range 0.45) and specificity 0.69 (range 0.32). ConclusionsThere is insufficient evidence to recommend routine brain MRI for the early diagnosis of dementia in patients with MCI.

Prior probability biases perceptual choices by modulating the accumulation rate, rather than the baseline, of decision evidence

Abstract The prior probability of an upcoming stimulus has been shown to influence the formation of perceptual decisions. Computationally these effects have typically been attributed to changes in the starting point (i.e. baseline) of evidence accumulation in sequential sampling models. More recently, it has also been proposed that prior probability might additionally lead to changes in the rate of evidence accumulation. Here, we introduce a neurally-informed behavioural modelling approach to understand whether prior probability influences the starting point, the rate of evidence accumulation or both. To this end, we employ a well-established visual object categorization task for which two neural components underpinning participants’ choices have been characterised using single-trial analysis of the electroencephalogram. These components are reliable measures of trial-by-trial variability in the quality of the relevant decision evidence, which we use to constrain the estimation of a hierarchical drift diffusion model of perceptual choice. We find that, unlike previous computational accounts, constraining the model with the endogenous variability in the relevant decision evidence, results in prior probability effects being explained primarily by changes in the rate of evidence accumulation rather than changes in the starting point or a combination of both. Ultimately, our neurally-informed modelling approach helps disambiguate the mechanistic effect of prior probability on perceptual decision formation, suggesting that prior probability biases primarily the interpretation of sensory evidence towards the most likely stimulus.

Pairwise Accelerated Failure Time Regression Models for Infectious Disease Transmission in Close-Contact Groups With External Sources of Infection.

Many important questions in infectious disease epidemiology involve associations between covariates (e.g., age or vaccination status) and infectiousness or susceptibility. Because disease transmission produces dependent outcomes, these questions are difficult or impossible to address using standard regression models from biostatistics. Pairwise survival analysis handles dependent outcomes by calculating likelihoods in terms of contact interval distributions in ordered pairs of individuals. The contact interval in the ordered pair is the time from the onset of infectiousness in to infectious contact from to , where an infectious contact is sufficient to infect if they are susceptible. Here, we introduce a pairwise accelerated failure time regression model for infectious disease transmission that allows the rate parameter of the contact interval distribution to depend on individual-level infectiousness covariates for , individual-level susceptibility covariates for , and pair-level covariates (e.g., type of relationship). This model can simultaneously handle internal infections (caused by transmission between individuals under observation) and external infections (caused by environmental or community sources of infection). We show that this model produces consistent and asymptotically normal parameter estimates. In a simulation study, we evaluate bias and confidence interval coverage probabilities, explore the role of epidemiologic study design, and investigate the effects of model misspecification. We use this regression model to analyze household data from Los Angeles County during the 2009 influenza A (H1N1) pandemic, where we find that the ability to account for external sources of infection increases the statistical power to estimate the effect of antiviral prophylaxis.

P068 IDENTIFYING THRESHOLD BIAS IN RECORDED BLOOD PRESSURE

Background and Objective: Even when blood pressure (BP) is measured accurately, BP may be misrecorded. “Threshold bias” occurs when BP measurements are intentionally or unintentionally recorded at values just below BP control goals and may occur due to incentives to improve BP control or to avoid medication titration. Our objectives were to 1) examine the potential for threshold bias in datasets with and without a BP control goal from two countries and 2) propose a method for detecting threshold bias. Methods: We examined the distribution of recorded BP measurements among individuals taking antihypertensive medication in clinical practice or trials with a BP goal <140/90 mmHg and population-based surveillance studies without a BP goal from Bangladesh and the US. Then, we explored whether surveillance data could be used as a reference to evaluate the likelihood of threshold bias in clinical data. We calculated the ratio of BP measurements recorded 10 mmHg below versus above thresholds of interest (i.e., the “threshold ratio,” or number of systolic BP [SBP] measurements 130-139 mmHg over 140-149 mmHg) and used bootstrapping to determine the probability of observing ratios that may indicate threshold bias. We compared the “threshold ratio” from clinical data to the probabilities determined from surveillance data. Results: Visually, we observed a cluster of measurements below SBP 140 mmHg in the clinical but not the surveillance datasets from Bangladesh and the US (Figure). Based on 10,000 random draws of 100 observations in the Bangladesh surveillance dataset, the probability of observing a “threshold ratio” of 3.56 in the clinical dataset was <2%, indicating a high probability of threshold bias. Using the same procedure in the US surveillance dataset, the probability of observing a “threshold ratio” of 2.26 in the clinical dataset was <10%, indicating potential concern. Conclusions: Threshold bias may be an underrecognized but common problem in clinical settings with a BP goal. Our proposed approach to detecting threshold bias should be tested in other settings.

Efectividad de la resonancia magnética cerebral en el diagnóstico precoz y caracterización de las demencias: revisión sistemática

IntroductionMagnetic resonance imaging (MRI) is a frequently used test in the diagnosis of dementia. The objective was to evaluate its effectiveness for the early diagnosis of dementia in patients with mild cognitive impairment (MCI). Material and methodsOriginal studies were selected from systematic reviews between 2011 and 2021, according to PRISMA 2020 criteria. QUADAS-2 and GRADE tools were used, and a meta-analysis was performed. ResultsFinal selection of 23 articles. Patient selection and index test had a high probability of bias. The certainty of the evidence was very low. In the hippocampus, sensitivity was 0.62 (95%CI 0.48-0.79) and specificity 0.70 (95%CI 0.55-0.80). In the temporal lobe, sensitivity was 0.65 (range 0.45) and specificity 0.69 (range 0.32). ConclusionsThere is insufficient evidence to recommend routine brain MRI for the early diagnosis of dementia in patients with MCI.

Can supervised deep learning architecture outperform autoencoders in building propensity score models for matching?

PurposePropensity score matching is vital in epidemiological studies using observational data, yet its estimates relies on correct model-specification. This study assesses supervised deep learning models and unsupervised autoencoders for propensity score estimation, comparing them with traditional methods for bias and variance accuracy in treatment effect estimations.MethodsUtilizing a plasmode simulation based on the Right Heart Catheterization dataset, under a variety of settings, we evaluated (1) a supervised deep learning architecture and (2) an unsupervised autoencoder, alongside two traditional methods: logistic regression and a spline-based method in estimating propensity scores for matching. Performance metrics included bias, standard errors, and coverage probability. The analysis was also extended to real-world data, with estimates compared to those obtained via a double robust approach.ResultsThe analysis revealed that supervised deep learning models outperformed unsupervised autoencoders in variance estimation while maintaining comparable levels of bias. These results were supported by analyses of real-world data, where the supervised model’s estimates closely matched those derived from conventional methods. Additionally, deep learning models performed well compared to traditional methods in settings where exposure was rare.ConclusionSupervised deep learning models hold promise in refining propensity score estimations in epidemiological research, offering nuanced confounder adjustment, especially in complex datasets. We endorse integrating supervised deep learning into epidemiological research and share reproducible codes for widespread use and methodological transparency.

ZIBGLMM: Zero-Inflated Bivariate Generalized Linear Mixed Model for Meta-Analysis with Double-Zero-Event Studies.

Double-zero-event studies (DZS) pose a challenge for accurately estimating the overall treatment effect in meta-analysis. Current approaches, such as continuity correction or omission of DZS, are commonly employed, yet these ad hoc methods can yield biased conclusions. Although the standard bivariate generalized linear mixed model can accommodate DZS, it fails to address the potential systemic differences between DZS and other studies. In this paper, we propose a zero-inflated bivariate generalized linear mixed model (ZIBGLMM) to tackle this issue. This two-component finite mixture model includes zero-inflation for a subpopulation with negligible or extremely low risk. We develop both frequentist and Bayesian versions of ZIBGLMM and examine its performance in estimating risk ratios (RRs) against the bivariate generalized linear mixed model and conventional two-stage meta-analysis that excludes DZS. Through extensive simulation studies and real-world meta-analysis case studies, we demonstrate that ZIBGLMM outperforms the bivariate generalized linear mixed model and conventional two-stage meta-analysis that excludes DZS in estimating the true effect size with substantially less bias and comparable coverage probability.

Objectively Structured Practical Examination (OSPE) in traditional examination system versus integrated modular system: Perception of teaching faculty at a Public Sector Medical College of Punjab.

Objective: To access the perception of teaching faculty regarding traditional OSPE and integrated modular OSPE. Study Design: Cross-sectional study. Setting: Sahiwal Medical College, Sahiwal. Period: February 2024 to March 2024. Methods: To collect data on the perceptions of the teaching faculty (n=29) (who were performing duty as examiners in first year professional examination), towards the newly introduced mode of assessment; integrated modular OSPE (which consists of OSPE, OSVE, PERLs and OSCE stations). Convenient sampling technique was used and a validated questionnaire was distributed through WHATSAPP in as Google form. Data was analyzed through SPSS version 20. Results: According to the teaching faculty, out of which 11(36.7%) were males and 19(63.3%) were females, integrated modular OSPE is relatively lengthy and stressful for the students 25(83.3%), while traditional OSPE is comparatively more transparent, fair, objective 17(56.7%) and in line with the curriculum 19(63.3%). Furthermore, traditional OSPE is comparatively easier to pass 23(76.7%), easier to conduct 21(70%) and also easier for the students 24(80%). According to teaching faculty, traditional OSPE has low probability of bias comparatively 20(66.6%). Conclusion: As per the perception of the selected teaching faculty. Traditional OSPE is comparatively a better mode of assessment.

Estigma de peso y su asociación con el estado nutricional en estudiantes de carreras de salud en una universidad privada en la región de Ñuble, Chile

Introduction: Obesity and overweight constitute global public health challenges, negatively impacting more than one billion people. They are accompanied by significant psychological and social consequences, including the stigma associated with weight, with severe repercussions on mental and physical well-being. Health professionals are directly involved with this problem; however, few studies address weight stigma in Chile. Objective: To relate nutritional status (BMI) and the level of weight stigma in university students of health careers in the region of Ñuble, Chile. Methodology: Observational design of correlational and cross-sectional scope. The GAMS-27 scale was applied to assess weight stigma and BMI to evaluate the nutritional status of 156 students of health careers. Chi2 tests of independence and binary logistic regression were applied to evaluate the association between the variables, with the R statistical package version 4.3.0. Results: A statistically significant association was found between weight stigma and nutritional status (p=0.0145). Presenting normal BMI (p=0.0163) and being female (p=0.0156) were associated with a higher probability of presenting weight bias. Conclusions: The present study identified a statistically significant association between weight stigma and nutritional status in college students. It is vital to promote education regarding weight stigma in college students to avoid bias and improve respect for future users.

Stochastic characterization of navigation strategies in an automated variant of the Barnes maze.

Animals can use a repertoire of strategies to navigate in an environment, and it remains an intriguing question how these strategies are selected based on the nature and familiarity of environments. To investigate this question, we developed a fully automated variant of the Barnes maze, characterized by 24 vestibules distributed along the periphery of a circular arena, and monitored the trajectories of mice over 15 days as they learned to navigate towards a goal vestibule from a random start vestibule. We show that the patterns of vestibule visits can be reproduced by the combination of three stochastic processes reminiscent of random, serial, and spatial strategies. The processes randomly selected vestibules based on either uniform (random) or biased (serial and spatial) probability distributions. They closely matched experimental data across a range of statistical distributions characterizing the length, distribution, step size, direction, and stereotypy of vestibule sequences, revealing a shift from random to spatial and serial strategies over time, with a strategy switch occurring approximately every six vestibule visits. Our study provides a novel apparatus and analysis toolset for tracking the repertoire of navigation strategies and demonstrates that a set of stochastic processes can largely account for exploration patterns in the Barnes maze.

Estudio de teleconexión entre variables hidrológicas y climatológicas en una cuenca de cabecera del Rio Maipo para la aplicación de modelos de pronóstico

This study conducts a teleconnection analysis of the seasonal streamflow during the dry season (winter and summer) at the Olivares River basin, a headwater of the Maipo River basin, with traditional climate indices (Antarctic Oscillation, Niño1+2, and Niño3.4), new indices obtained from sea surface temperature (SST) anomaly spatial fields, and in situ hydrometeorological variables from the previous season to identify potential predictors for implementing seasonal streamflow forecast models in the study area. To illustrate the potential of the predictors identified, we fit multiple linear regression models (MLRM) for seasonal streamflow forecast for 0- and 3-month lead times. The forecasts are validated using the leave-1-year-out cross-validation (LOOCV) approach and performance metrics such as the Pearson correlation coefficient (R), BIAS, Nash-Sutcliffe efficiency (NSE), and continuous rank probability skill score (CRPSS). Results show a good performance of the forecast model for cross-validation with R and NSE values ranging from 0.55 to 0.95 and from 0.28 to 0.88 for 0- and 3-month lead times during the dry season. This early implementation provides good perspectives for implementing probabilistic seasonal streamflow forecasting models, which can provide a powerful output to develop robust water management strategies to tackle water scarcity in the study area.

Laparoscopic versus open surgery in treating patients with gallbladder cancer: a systematic review and meta-analysis.

Concerns over the security of laparoscopic radical operation for gallbladder cancer (GBC) persist. This systematic review and meta-analysis attempted to compare the safety and efficacy of laparoscopic surgery (LS) versus open surgery (OS) in the treatment of GBC. The PubMed, EMBASE, and Web of Science were searched from inception to July 18, 2022. Literature search, quality assessment, and data extraction were completed independently and in duplicate. Effect-size estimates expressed as weighted mean difference (WMD) or odds ratio (OR) with 95% confidence interval (CI) were derived under the random-effects model. A total of 27 independent studies including 2,868 participants were meta-analyzed. Significance was noted for intraoperative blood loss (WMD: -117.194, 95% CI: -170.188 to 64.201, P<0.001), harvested lymph nodes (WMD: -1.023, 95% CI: -1.776 to -0.269, P=0.008), postoperative hospital stay (WMD: -3.555, 95% CI: -4.509 to -2.601, P<0.001), postoperative morbidity (OR: 0.596, 95% CI: 0.407 to 0.871, P=0.008), overall survival rate at 2-year (OR: 1.524, 95% CI: 1.143 to 2.031, P=0.004), T2 survival at 1-year (OR: 1.799, 95% CI: 1.777 to 2.749, P<0.01) and 2-year (OR: 2.026, 95% CI: 1.392 to 2.949, P<0.001), as well as T3 survival at 1-year (OR: 2.669, 95% CI: 1.564 to 4.555, P<0.001) and 2-year (OR: 2.300, 95% CI: 1.308 to 4.046, P=0.004). Subgroup analyses revealed that ethnicity, incidental GBC, sample size, and follow-up period were possible sources of heterogeneity. There was a low probability of publication bias for all outcomes except postoperative morbidity. Our findings indicated that LS statistically had better 2-year survival rates, less intraoperative bleeding, shorter hospitalization times, and lower rates of complications than OS. However, the superiority and even the safety of LS still remain an open question due to the impact of incidental GBC, unaccounted heterogeneity, publication bias, lymph node dissection, and port-site metastasis.

Optimized rejection sampling for estimating facies probabilities from seismic data

Seismic inversion for facies has nonunique solutions. There are invariably many vertical facies arrays that are consistent with both a data trace and the prior information. Stochastic sampling algorithms set within a Bayesian framework can provide an estimate of the posterior probability distribution of facies arrays by finding the arrays with relatively high posterior probabilities for each data trace. Sample-by-sample facies probabilities can be estimated by measuring the proportions of each facies type at each sample location from the set of posterior facies arrays. To enable the estimation of probabilities of facies mixtures and to obtain high-quality images of facies probability curves, facies must be modeled at high resolution. The facies arrays, or vectors, on which the sampling algorithm operates, must also be long enough to allow for vertical coupling caused by the wavelet. This results in very large sample spaces. The posterior probability distribution is highly nonconvex, which, combined with the large sample space, severely challenges conventional stochastic sampling methods in obtaining convergence of the estimated posterior distribution. The posterior sets of vectors from conventional methods tend to be either correlated or have low predictabilities, resulting in biased or noisy facies probability estimates, respectively. However, accurate estimates of facies probabilities can be obtained from a relatively small number of posterior facies vectors (about 100), provided that they are uncorrelated and have high predictabilities. Full convergence of the posterior distribution is not required. A hybrid algorithm optimized rejection sampling can be designed specifically for the seismic facies probability inversion problem by combining independent sampling of the prior, which ensures posterior vectors are uncorrelated, with an optimization step to obtain high predictabilities. Tests on both real and synthetic data demonstrate better results than conventional rejection sampling and Markov chain Monte Carlo methods.

Specificity of cost and probability biases in social anxiety: Comparing status and belongingness threats.

Social anxiety (SA) is characterized by concerns about the expected occurrence (probability) and anticipated distress (cost) of social threats. Unclear is whether SA correlates specifically with biased expectations of belongingness or status threats. We aimed to discern if SA is uniquely tied to biased expectancies of either belongingness or status threats. We assessed 757 participants' perceptions of exclusion and put-down scenarios, analysing associations between SA and threat perceptions. Our findings support the status-sensitivity hypothesis, suggesting individuals with high SA are particularly attuned to the perceived cost of status threats, potentially informing treatment approaches. Understanding SA's link to status concerns enhances therapeutic strategies, emphasizing the need to address status-related situations, cognitions, and emotions in interventions.

Probability, cost, and interpretation biases’ relationships with depressive and anxious symptom severity: differential mediation by worry and repetitive negative thinking

ABSTRACT People high in depressive or anxious symptom severity show repetitive negative thinking, including worry and rumination. They also show various cognitive phenomena, including probability, cost, and interpretation biases. Since there is conceptual overlap between these cognitive biases and repetitive negative thinking – all involve thinking about potential threats and misfortunes – we wondered whether repetitive negative thinking could account for (mediate) these cognitive biases’ associations with depressive and anxious symptom severity. In three studies, conducted in two languages and cultures, cost bias and (in two studies) interpretation bias only predicted symptom severity via worry and repetitive negative thinking; this suggests these biases are actually associated with repetitive negative thinking, rather than with symptoms. In contrast, probability bias showed direct relationships with depressive (all studies) and anxious (two studies) symptom severity, suggesting its relationships with symptoms are partly independent of repetitive negative thinking. These results show the value of studying relationships among the various cognitive features of psychopathology. Furthermore, new interventions which target cognitive biases in depression or anxiety must show that they can improve upon cognitive behavioural therapy, which is already widely available, targets both repetitive negative thinking and probability bias, and is highly effective.

Higher-order functional connectivity analysis of resting-state functional magnetic resonance imaging data using multivariate cumulants.

Blood-level oxygenation-dependent (BOLD) functional magnetic resonance imaging (fMRI) is the most common modality to study functional connectivity in the human brain. Most research to date has focused on connectivity between pairs of brain regions. However, attention has recently turned towards connectivity involving more than two regions, that is, higher-order connectivity. It is not yet clear how higher-order connectivity can best be quantified. The measures that are currently in use cannot distinguish between pairwise (i.e., second-order) and higher-order connectivity. We show that genuine higher-order connectivity can be quantified by using multivariate cumulants. We explore the use of multivariate cumulants for quantifying higher-order connectivity and the performance of block bootstrapping for statistical inference. In particular, we formulate a generative model for fMRI signals exhibiting higher-order connectivity and use it to assess bias, standard errors, and detection probabilities. Application to resting-state fMRI data from the Human Connectome Project demonstrates that spontaneous fMRI signals are organized into higher-order networks that are distinct from second-order resting-state networks. Application to a clinical cohort of patients with multiple sclerosis further demonstrates that cumulants can be used to classify disease groups and explain behavioral variability. Hence, we present a novel framework to reliably estimate genuine higher-order connectivity in fMRI data which can be used for constructing hyperedges, and finally, which can readily be applied to fMRI data from populations with neuropsychiatric disease or cognitive neuroscientific experiments.

Low-intensity mindfulness and cognitive–behavioral therapy for social anxiety: a pilot randomized controlled trial

BackgroundCognitive–behavioral therapy (CBT) effectively improves the clinical symptoms of social anxiety disorder. However, there are non-responders who cannot decrease their cost/probability bias significantly; hence, their social anxiety symptoms remain unaddressed. Mindfulness training and cognitive–behavioral approaches promote a reduction in cost/probability bias and social anxiety symptoms. This study examines the effectiveness of a four-session program of mindfulness and CBT (M-CBT) in a non-clinical sample of individuals with high social anxiety.MethodsParticipants were 50 Japanese undergraduate students (37 women and 13 men) randomly allocated to an intervention group (n = 27) and a control group (n = 23). The intervention group underwent a four-session M-CBT program, while the control group did not receive any treatment.ResultsA group × time analysis of covariances showed significant interactions in the negative cognition generated when paying attention to others in probability bias, fear of negative evaluation by others, dispositional mindfulness, depressive symptoms, and subjective happiness. M-CBT also produced significant pre-post improvements in the above outcomes with moderate to high effect sizes (ds = .51–1.55). Conversely, there were no interactions in social anxiety symptoms and self-focused attention.ConclusionsThese results indicate that M-CBT was effective for the negative cognition generated when paying attention to others in probability bias, fear of negative evaluation by others, dispositional mindfulness, depressive symptoms, and subjective happiness. The combination of mindfulness training with cognitive restructuring is proposed as potentially helpful for individuals with probability bias, leading to negative cognition from paying attention to others.Trial registrationUniversity Hospital Medical Information Network (UMIN CTR) UMIN000036763. Registered May 16, 2019.

Trapping Sets Search Using the Method of Mixed Integer Linear Programming with a Priori List of Variable Nodes

Purpose of research is to develop a new high-speed method for searching trappin sets in graph codes, ensuring the completeness of the search.Methods. There are two approaches to finding trappin sets. The first, based on the Monte Carlo method with a biased probability estimation using Importance Sampling, involves the use of a decoder. The advantage of this approach is its high performance. The disadvantages are the dependence on decoder parameters and channel characteristics and the finite probability of missing trappin sets. The second approach is based on the use of linear programming methods. The advantage of this approach is the completeness of the resulting list of trappin sets, due to its independence from the decoder parameters and channel characteristics. The disadvantage of this approach is its high computational complexity. In the article, within the framework of the second approach, a new method for searching trappin sets with less computational complexity is proposed. The method involves solving a mixed integer linear programming problem using an a priori list of code vertices participating in the shortest cycles in the code graph. Results. Using the proposed method, a search for trappin sets was performed for several low-density codes. For this purpose, the mathematical linear programming package IBM CPLEX version 12.8 was used, which was run on 32 threads of a 16-core AMD Ryzen 3950X processor with 32GB of RAM (DDR4). In the Margulis code (2640, 1320), using the proposed method, the trappin set TS(6,6) was found in a time of 0.53 s. The speedup provided by the method proposed in the paper compared to the Velazquez-Subramani method is 8252.415 times. Thanks to the high speed and completeness of the search, trappin sets were found for the first time TS(62,16) and TS(52,14) in the Margulis code (4896, 2474 ).Conclusion. The paper proposes a new method for searching trapping sets by solving a mixed integer linear programming problem with an a priori list of code. The method is fast and provides completeness of the search.