Evidence Credibility Research Articles

The Testimony or the Eyewitness? Factors That Influence Eyewitness Credibility Assessment Conducted by (Future) Fact-Finders and Laypeople

ABSTRACT Purpose This study examined how the (in)consistency of eyewitness testimony and their relationship with the defendant affect assessments of eyewitness credibility and defendant culpability, along with decision confidence. Methods Judicial apprentices (N = 125) and students (N = 115) rated the guilt and credibility of testimony after reading a case summary. Results Eyewitness-defendant relationships influenced credibility judgements but not guilt assessments. Reminiscent details reduced credibility ratings and increased culpability judgements. Future judges exhibited greater confidence than laypeople. Conclusions The findings emphasize the need for ecologically valid research involving legal professionals to better understand evidence credibility and verdict decision-making.

Comorbid health outcomes in patients with schizophrenia: an umbrella review of systematic reviews and meta-analyses.

There is no comprehensive umbrella review exploring the connection between schizophrenia and various health outcomes. Therefore, we aimed to systematically review existing meta-analyses about schizophrenia-associated comorbid health outcomes and validate the evidence levels. We performed an umbrella review of meta-analyses of observational studies to explore comorbid health outcomes in individuals with schizophrenia. Searches were conducted across PubMed/MEDLINE, EMBASE, ClinicalKey, and Google Scholar up to September 5, 2023, targeting meta-analyses of observational studies related to comorbid health outcomes in individuals with schizophrenia. We applied AMSTAR2 for data extraction and quality assessment, adhering to PRISMA guidelines. Evidence credibility was evaluated and categorized by evidence quality. Our protocol was registered with PROSPERO (CRD42024498833). Risk and protective factors were analyzed and presented through equivalent odds ratios (eRR). In this umbrella review, we analyzed 9 meta-analyses, including 88 original articles, covering 21 comorbid health outcomes with over 66 million participants across 19 countries. Patients with schizophrenia showed significant associations with multiple health outcomes, including asthma (eRR, 1.71 [95% CI, 1.05-2.78], class and quality of evidence [CE] = non-significant), chronic obstructive pulmonary disease (1.73 [1.25-2.37], CE = weak), pneumonia (2.63 [1.11-6.23], CE = weak), breast cancer of female patients (1.31 [1.04-1.65], CE = weak), cardiovascular disease (1.53 [1.12-2.11], CE = weak), stroke (1.71 [1.30-2.25], CE = weak), congestive heart failure (1.81 [1.21-2.69], CE = weak), sexual dysfunction (2.30 [1.75-3.04], CE = weak), fracture (1.63 [1.10-2.40], CE = weak), dementia (2.29 [1.19-4.39], CE = weak), and psoriasis (1.83 [1.18-2.83] CE = weak). Our study underscores the imperative for an integrated treatment approach to schizophrenia, highlighting its broad impact across respiratory, cardiovascular, sexual, neurological, and dermatological health domains. Given the predominantly non-significant to weak evidence levels, further studies are needed to reinforce our understanding.

Who is at risk of bias? Examining dispositional differences in motivated science reception.

The motivated reception of science in line with one's preexisting convictions is a well-documented, pervasive phenomenon. In two studies (N = 743), we investigated whether this bias might be stronger in some people than others due to dispositional differences. Building on the assumptions that motivated science reception is driven by perceived threat and suspicion and higher under perceived ambiguity and uncertainty, we focused on traits associated with such perceptions. In particular, we tested the impact of conspiracy mentality and victim sensitivity on motivated science reception (as indicated by ascriptions of researchers' trustworthiness and evidence credibility). In addition, we explored the role of broader personality traits (generalized mistrust and ambiguity intolerance) in this context. None of the investigated dispositions modulated the motivated science reception effect. This demonstrates once again, that motivated science reception is a ubiquitous challenge for the effective dissemination of science and everyone seems to be at risk of it.

Credibility of the evidence on green space and human health: an overview of meta-analyses using evidence grading approaches

Green space is an important part of the human living environment, with many epidemiological studies estimating its impact on human health. However, no study has quantitatively assessed the credibility of the existing evidence, impeding their translations into policy decisions and hindering researchers from identifying new research gaps. This overview aims to evaluate and rank such evidence credibility. Following the PRISMA guideline, we systematically searched PubMed, Web of Science, and Embase databases for systematic reviews with meta-analyses concerning green spaces and health outcomes published up to January 15, 2024. We categorized the credibility of meta-analytical evidence from interventional studies into four levels (i.e., high, moderate, low, and very low) using the Grading of Recommendation, Assessment, Development and Evaluations framework, based on five domains including risk of bias, inconsistency, indirectness, imprecision, and publication bias. Further, we recalculated all the meta-analyses from observational studies and classified evidence into five levels (i.e., convincing, highly suggestive, suggestive, weak, and non-significant) by considering stringent thresholds for P-values, sample size, robustness, heterogeneity, and testing for biases. In total, 154 meta-analysed associations (interventional=44, observational=110) between green spaces and health outcomes were graded. Among meta-analyses from interventional studies, zero, four (wellbeing, systolic blood pressure, negative affect, and positive affect), 20, and 20 associations between green spaces and health outcomes were graded as high, moderate, low, and very low credibility evidence, respectively. Among meta-analyses from observational studies, one (cardiovascular disease mortality), four (prevalence/incidence of diabetes mellitus, preterm birth, and small for gestational age infant, and all-cause mortality), 12, 22, and 71 associations were categorized as convincing, highly suggestive, suggestive, weak, and non-significant evidence, respectively. The current evidence largely confirms beneficial associations between green spaces and human health. However, only a small subset of these associations can be deemed to have a high or convincing credibility. Hence, future better designed primary studies and meta-analyses are still needed to provide higher quality evidence for informing health promotion strategies. The National Natural Science Foundation of China of China; the Guangzhou Science and Technology Program; the Guangdong Medical Science and Technology Research Fund; the Research Grant Council of the Hong Kong SAR; and Sino-German mobility program.

Substantial discrepancies exist between registered protocol and published manuscript in trials on exercise interventions for chronic low back pain: a metaresearch study

ObjectivesReporting bias, prevalent in biomedical fields, can undermine evidence credibility. Our objective was to evaluate the proportion of discrepancies between registered protocols and published manuscripts in randomized controlled trials (RCTs) on exercise interventions for patients with chronic low back pain (CLBP). Study Design and SettingWe conducted a cross-sectional meta-research study, starting from the 2021 “Exercise therapy for CLBP” Cochrane Review. We selected all RCTs reporting a protocol registration on a primary register of the World Health Organization (WHO) International Clinical Trials Registry Platform (ICTRP) or in ClinicalTrials.gov. We extracted data from both registered protocol and published manuscript of RCTs, collecting recruitment and administrative information (eg, record dates) and details of trial characteristics (eg, outcomes, arms, statistical analysis plan details [SAPs]). Independent pairs of reviewers assessed discrepancies between registered protocol and published manuscript for the reporting of primary and secondary outcomes domains, measurement instruments, time-points, number of arms and SAPs(if attached). Outcome discrepancies were characterized as addition, omission, upgrade or downgrade. ResultsWe included 116 RCTs reporting an available protocol registration. Overall, 100 RCTs (86.2%) distinguished between primary and secondary outcomes. Of these, 39 RCTs (39.0%) reported one or more discrepancies in primary outcomes, and 78 RCTs (78.0%) reported one or more discrepancies in secondary outcomes. Focusing on discrepancies for the primary outcome, 64.5% of added, upgraded or downgraded outcomes favored statistically significant effects. Few RCTs (n = 6) reported discrepancies in the number of arms. SAPs were poorly reported in the registered protocols (n = 3) for being compared to the publications. ConclusionWe found substantial outcome discrepancies comparing registered protocols and published manuscripts in RCTs assessing exercise interventions for patients with CLBP, with some impacting the statistical significance of the effects. Readers are encouraged to approach RCTs results in this field with caution.

Effects of task structure and confirmation bias in alternative hypotheses evaluation

We empirically examined the effectiveness of how the Analysis of Competing Hypotheses (ACH) technique structures task information to help reduce confirmation bias (Study 1) and the portrayal of intelligence analysts as suffering from such bias (Study 2). Study 1 (N = 161) showed that individuals presented with hypotheses in rows and evidence items in columns were significantly less likely to demonstrate confirmation bias, whereas those presented with the ACH-style matrix (with hypotheses in columns and evidence items in rows) or a paragraph of text (listing the evidence for each hypothesis) were not less likely to demonstrate bias. The ACH-style matrix also did not confer any benefits regarding increasing sensitivity to evidence credibility. Study 2 showed that the majority of 62 Dutch military analysts did not suffer from confirmation bias and were sensitive to evidence credibility. Finally, neither judgmental coherence nor cognitive reflection differentiated between better or worse performers in the hypotheses evaluation tasks.

Improved Evidence Fusion Theory for the Safety Assessment of Prestressed Concrete Bridges

The safety condition assessment of prestressed concrete bridges is currently subject to great uncertainty due to the subjectivity of data collection and data types. This study proposes an improved evidence fusion method, improving the conventional Dempster–Shafer fusion method to reduce assessment inaccuracies caused by data uncertainty. Firstly, the uncertain analytic hierarchy process was applied to construct a three-level safety assessment model for 15 different indicators with their initial weights. Secondly, the fuzzy matter element theory was proposed to obtain basic probability assignments required for the evidence fusion. Finally, an improved evidence fusion method was proposed based on the evidence credibility and preprocessing corrections for highly conflicting evidence. In this study, a prestressed concrete bridge in eastern China was used as a case study to perform a comprehensive safety assessment and verify the effectiveness and practicality of the proposed method. The assessment results demonstrate that the improved fusion method in this study can deal with conflicting evidence better than existing fusion methods. Compared with conventional fuzzy AHP method, it has greater sensitivity to certain indicators with severe damages, which prevents those indicators from being overshadowed by other well-performing ones in the overall assessment.

Ultra-processed foods and human health: An umbrella review and updated meta-analyses of observational evidence

Background & aimsUltra-processed food (UPF) intake has increased sharply over the last few decades and has been consistently asserted to be implicated in the development of non-communicable diseases. We aimed to evaluate and update the existing observational evidence for associations between ultra-processed food (UPF) consumption and human health. MethodsWe searched Medline and Embase from inception to March 2023 to identify and update meta-analyses of observational studies examining the associations between UPF consumption, as defined by the NOVA classification, and a wide spectrum of health outcomes. For each health outcome, we estimated the summary effect size, 95% confidence interval (CI), between-study heterogeneity, evidence of small-study effects, and evidence of excess-significance bias. These metrics were used to evaluate evidence credibility of the identified associations. ResultsThis umbrella review identified 39 meta-analyses on the associations between UPF consumption and health outcomes. We updated all meta-analyses by including 122 individual articles on 49 unique health outcomes. The majority of the included studies divided UPF consumption into quartiles, with the lowest quartile being the reference group. We identified 25 health outcomes associated with UPF consumption. For observational studies, 2 health outcomes, including renal function decline (OR: 1.25; 95% CI: 1.18, 1.33) and wheezing in children and adolescents (OR: 1.42; 95% CI: 1.34, 1.49), showed convincing evidence (Class I); and five outcomes were reported with highly suggestive evidence (Class II), including diabetes mellitus, overweight, obesity, depression, and common mental disorders. ConclusionsHigh UPF consumption is associated with an increased risk of a variety of chronic diseases and mental health disorders. At present, not a single study reported an association between UPF intake and a beneficial health outcome. These findings suggest that dietary patterns with low consumption of UPFs may render broad public health benefits.

A GMDA clustering algorithm based on evidential reasoning architecture

The traditional clustering algorithm is difficult to deal with the identification and division of uncertain objects distributed in the overlapping region, and aimed at solving this problem, the Evidential Clustering based on General Mixture Decomposition Algorithm (GMDA-EC) is proposed. First, the belief classification of target cluster is carried out, and the sample category of target distribution overlapping region is extended. Then, on the basis of General Mixture Decomposition Algorithm (GMDA) clustering, the fusion model of evidence credibility and evidence relative entropy is constructed to generate the basic probability assignment of the target and achieve the belief division of the target. Finally, the performance of the algorithm is verified by the synthetic dataset and the measured dataset. The experimental results show that the algorithm can reflect the uncertainty of target clustering results more comprehensively than the traditional probabilistic partition clustering algorithm.

Efficacy and safety of antidepressants for smoking cessation: A systematic review and network meta-analysis.

To evaluate the effectiveness, safety and tolerability of antidepressants in helping smokers quit tobacco dependence, five databases were searched for randomized controlled trials (RCTS ) on different antidepressant interventions involving smoking cessation in populations (September 2022). The STATA 15.1 software was used to perform network meta-analysis. The Cochrane bias risk tool was used to assess the risk of bias, and CINeMA was used to evaluate the evidence credibility for the effect of different interventions on smoking cessation. In all, 107 RCTs involving 42 744 patients were included. Seven studies were rated as having a low risk of bias. All trials reported 18 interventions and 153 pairwise comparisons were generated. The network meta-analysis showed that compared with placebo, varenicline + bupropion (OR = 3.53, 95% CI [2.34, 5.34]), selegiline + nicotine replacement therapy (NRT) (OR = 3.78, 95% CI [1.20, 11.92]), nortriptyline + NRT (OR = 2.33, 95% CI [1.21, 4.47), nortriptyline (OR = 1.58, 95% CI [1.11,2.26]), naltrexone + bupropion (OR = 3.84, 95% CI [1.39, 10.61]), bupropion + NRT (OR = 2.29, 95% CI [1.87, 2.81]) and bupropion (OR = 1.70, 95% CI [1.53, 1.89]) showed benefits with respect to smoking cessation. In addition, bupropion + NRT showed better effects than bupropion (OR = 1.35, 95% CI [1.12, 1.64]) and NRT (OR = 1.38, 95% CI [1.13, 1.69]) alone. The final cumulative ranking curve showed that varenicline + bupropion was the most likely to be the best intervention. There was moderate- to very-low-certainty evidence that most interventions showed benefits for smoking cessation compared with placebo, including monotherapy and combination therapies. Varenicline + bupropion had a higher probability of being the best intervention for smoking cessation.

Neonatal risk factors associated with attention-deficit/hyperactivity disorder: an umbrella review.

Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder that is being encountered more frequently. In this study, by compiling the evidence from available meta-analyses, an umbrella systematic review was performed of the neonatal risk factors associated with ADHD. The PubMed, Scopus, and Web of Science databases were searched for eligible studies. Only systematic reviews were included. Using a random-effects model, 95% prediction intervals were reported for each risk factor. Three studies were ultimately included in the review. Congenital heart disease, short-duration or incomplete breastfeeding, low birth weight, and 5-minute Apgar scores <7 were significant risk factors for ADHD. However, the quality of the included systematic reviews was low to moderate and the evidence credibility level was suggestive to weak. The results of this umbrella review proposed that congenital anomalies, short-duration or incomplete breastfeeding, low birth weight, and low Apgar scores were important factors for the manifestation of ADHD symptoms. However, the inclusion of more high-quality studies is needed to validate our results.

Ultra-Processed Food Intake Is Associated with Non-Alcoholic Fatty Liver Disease in Adults: A Systematic Review and Meta-Analysis.

Non-alcoholic fatty liver disease (NAFLD) is associated with overweight/obesity, metabolic syndrome and type 2 diabetes (T2D) due to chronic caloric excess and physical inactivity. Previous meta-analyses have confirmed associations between ultra-processed food (UPF) intake and obesity and T2D. We aim to ascertain the contribution of UPF consumption to the risk of developing NAFLD. We performed a systematic review and meta-analysis (PROSPERO (CRD42022368763)). All records registered on Ovid Medline and Web of Science were searched from inception until December 2022. Studies that assessed UPF consumption in adults, determined according to the NOVA food classification system, and that reported NAFLD determined by surrogate (steatosis) scores, imaging or liver biopsy were included. The association between UPF consumption and NAFLD was assessed using random-effects meta-analysis methods. Study quality was assessed, and evidence credibility evaluated, using the Newcastle Ottawa Scale and NutriGrade systems, respectively. A total of 5454 records were screened, and 112 records underwent full text review. From these, 9 studies (3 cross-sectional, 3 case-control and 3 cohort), analysing 60,961 individuals, were included in the current review. Both moderate (vs. low) (pooled relative risk 1.03 (1.00-1.07) (p = 0.04) (I2 = 0%)) and high (vs. low) (1.42 (1.16-1.75) (<0.01) (I2 = 89%)) intake of UPF significantly increased the risk of NAFLD. Funnel plots demonstrate low risk of publication bias. Consumption of UPF is associated with NAFLD with a dose-response effect. Public health measures to reduce overconsumption of UPF are imperative to reduce the burden of NAFLD, and the related conditions, obesity and T2D.

Robust fault diagnosis of a high-voltage circuit breaker via an ensemble echo state network with evidence fusion

Reliable mechanical fault diagnosis of high-voltage circuit breakers is important to ensure the safety of electric power systems. Recent fault diagnosis approaches are mostly based on a single classifier whose performance relies heavily on expert prior knowledge. In this study, we propose an improved Dempster–Shafer evidence theory fused echo state neural network, an ensemble classifier for fault diagnosis. Evidence credibility is calculated through the evidence deviation matrix and the segmented circle function and employed as credibility weights to rectify the raw evidence. Then, an improved Dempster–Shafer evidence fusion algorithm is proposed to fuse evidence from different echo state network modules and sensors. Unlike conventional classifiers, the proposed methodology consists of multiple echo state neural network modules. It has better flexibility and stronger robustness, and its model performance is not sensitive to network parameters. Comparative analysis indicates that it can handle the paradox evidence fusion analysis and thus can achieve better diagnostic performance. The superiority of the reported fault diagnosis approaches is verified with the experimental data of a ZN12 high-voltage circuit breaker.

A new D numbers’ integration rule based on pessimistic criterion

For D numbers theory, there are some drawbacks in the D numbers’ integration rule. For example, the missing information is ignored in the final decision judgment for multi-attribute decision (MADM). For this problem, some researchers have improved the D numbers’ integration rules based on optimistic criterion for overcoming the shortcoming of D numbers’ integration rule. However, optimistic and pessimistic criterion are two sides of the coin for fuzzy environment. Therefore, in this article, a new D numbers’ integration rules based on pessimistic criterion is proposed. We improve the D numbers’ integration rules to redefine the missing information distribution rules based on pessimistic criterion. The missing information is distributed in inverse proportion to each D number according to the size of the original evidence credibility. Two examples of MADM is applied by the proposed method, the results show that the proposed method can be applied to MADM.

A Fusion Recognition Method Based on Temporal Evidence Reasoning

In order to improve the effectiveness of system decision‐making, the use of the evidence theory to identify target intentions has always been a research hotspot. In information fusion using the evidence theory, there are relatively few research studies on temporal domain evidence information fusion. Due to the obvious dynamic, sequential, and real‐time characteristics of temporal domain information fusion, traditional spatial domain information fusion methods are not suitable. Therefore, it is very necessary to study new methods for the temporal evidence fusion problem. In this article, a temporal evidence fusion method under the framework of the evidence reasoning rule (the ER rule) is proposed. The method uses complementary reliability integration rules and the time‐series evidence distance function to obtain the reliability of evidence at adjacent moments. According to the temporal domain evidence credibility decay model, the evidence weight of the temporal domain evidence is determined. Then, through the integration of the ER rule, the temporal domain evidence reliability and evidence weight are used to combine the evidence. The capability of this method is verified by numerical experiments and compared with other methods. The results show that the proposed method can effectively deal with the temporal domain evidence combination problem, has strong anti‐interference ability, and can support target intent recognition.

Gut microbiota–derived metabolite trimethylamine-N-oxide and multiple health outcomes: an umbrella review and updated meta-analysis

ABSTRACTBackgroundTrimethylamine-N-oxide (TMAO) is a gut microbiota–derived metabolite produced from dietary nutrients. Many studies have discovered that circulating TMAO concentrations are linked to a wide range of health outcomes.ObjectivesThis study aimed to summarize health outcomes related to circulating TMAO concentrations.MethodsWe searched the Embase, Medline, Web of Science, and Scopus databases from inception to 15 February, 2022 to identify and update meta-analyses examining the associations between TMAO and multiple health outcomes. For each health outcome, we estimated the summary effect size, 95% prediction CI, between-study heterogeneity, evidence of small-study effects, and evidence of excess-significance bias. These metrics were used to evaluate the evidence credibility of the identified associations.ResultsThis umbrella review identified 24 meta-analyses that investigated the association between circulating TMAO concentrations and health outcomes including all-cause mortality, cardiovascular diseases (CVDs), diabetes mellitus (DM), cancer, and renal function. We updated these meta-analyses by including a total of 82 individual studies on 18 unique health outcomes. Among them, 14 associations were nominally significant. After evidence credibility assessment, we found 6 (33%) associations (i.e., all-cause mortality, CVD mortality, major adverse cardiovascular events, hypertension, DM, and glomerular filtration rate) to present highly suggestive evidence.ConclusionsTMAO might be a novel biomarker related to human health conditions including all-cause mortality, hypertension, CVD, DM, cancer, and kidney function. Further studies are needed to investigate whether circulating TMAO concentrations could be an intervention target for chronic disease.This review was registered at www.crd.york.ac.uk/prospero/ as CRD42021284730.

A Novel Method for Conflict Data Fusion Using an Improved Belief Divergence Measure in Dempster–Shafer Evidence Theory

Dempster–Shafer (D-S) evidence theory plays an important role in multisource data fusion. Due to the nature of the Dempster combination rule, there can be counterintuitive results when fusing highly conflicting evidence data. To date, conflict management in D-S evidence theory is still an open issue. Inspired by evidence modification considering internal indeterminacy and external support, a novel method for conflict data fusion is proposed based on an improved belief divergence, evidence distance, and belief entropy. First, an improved belief divergence measure is defined to characterize the discrepancy and conflict between bodies of evidence (BOEs). Second, evidence credibility is generated to describe the external support based on the complementary advantages of the improved belief divergence and evidence distance. Third, belief entropy is utilized to quantify the internal indeterminacy and further determine evidence weight. Lastly, the classical Dempster combination rule is applied to fuse the BOEs modified by their credibility degrees and weights. As the results of numerical examples and an application show, the proposed divergence measure can overcome the invalidity of the existing measures in some special cases. Additionally, the proposed fusion method recognizes the correct target with the highest belief value of 98.96%, which outperforms other related methods in conflict management. The proposed fusion method also displays better convergence, validity, and robustness.

Research on improved evidence theory based on multi-sensor information fusion

In view of the lack of effective information fusion model for heterogeneous multi-sensor, an improved Dempster/Shafer (DS) evidence theory algorithm is designed to fuse heterogeneous multi-sensor information. The algorithm first introduces the compatibility coefficient to characterize the compatibility between the evidence, obtains the weight matrix of each proposition, and then redistributes the basic probability distribution of each focal element to obtain a new evidence source. Then the concept of credibility is introduced, and the average support of evidence credibility and evidence focal element is used to improve the synthesis rule, so as to obtain the fusion result. Compared with other algorithms, the proposed algorithm can solve the problems existing in DS evidence theory when dealing with highly conflicting evidence to a certain extent, and the fusion results are more reasonable and the convergence speed is faster.

An Improved Algorithm of D-S Evidence Fusion

In the theory of evidence fusion, it is a very important task to deal with the conflicts between evidences correctly. Based on the credibility degree of evidence proposed in [10], in order to increase the credibility degree of each group of evidence, the concept of falsity is introduced, which is another concept to represent the conflict between two groups of evidence, this treatment makes up for the deficiency of normalized evidence weight coefficient when one group of evidence credibility degree is zero. Because we consider not only the credibility degree of evidence, but also the falsity of evidence, the weight coefficient of unreliable information is effectively reduced. The numerical simulation results show that the algorithm has the advantages of simple idea, fast convergence speed and strong focusing.

An Evidence Combination Rule Based on Transferable Belief Model and Application in Reliability Assessment With Multi-Source Data

Increasingly complex system environments have put forward higher requirements for information fusion. Under this background, the application prospects of evidence theory which can function well in uncertain information representation and information fusion is promising. The effect of evidence theory on information fusion is closely related to evidence distance measurement method and evidence combination rules. Considering the difference of evidence information in belief level and that in decision level, this paper redefines the evidence distance and puts forward the measurement method of evidence credibility, thus realizing the correction of the original basic probability assignment function (BPA). Based on the evidence credibility, the evidence weight is generated, which leads to an efficient distribution of evidence conflict in the process of evidence combination. So, a new evidence combination rule is generated. According to a series of examples, the evidence distance proposed in this paper can effectively measure the evidence differences, and the new combination rules can reasonably distribute evidence conflicts thus avoiding evidence paradox. At the end of this paper, the new evidence distance and combination rule, combing with the Transferable Belief Model (TBM), are used to fuse the multi-source reliability test information, then the life cycle evaluation of a product is obtained. In addition, through the comparative analysis of the results, the feasibility and validity of this method being applied to practice are verified.