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1.
Soc Sci Res ; 120: 103013, 2024 May.
Artigo em Inglês | MEDLINE | ID: mdl-38763532

RESUMO

Subjective well-being (SWB) describes an individual's life evaluation. Direct elicitation methods for SWB via rating scales do not force individuals to trade-off among life domains, whilst best-worst scaling (BWS) approaches only provide relative measures. This paper instead offers a dual-response BWS task, where respondents nominate areas of most and least importance and satisfaction with respect to 11 SWB domains, whilst also eliciting anchoring points to obtain an absolute measure of domain satisfaction. Combining domain satisfaction and importance produces a robust measure of individual SWB, but statistically unique relative to other life satisfaction measures utilizing single- and multi-item ratings, including global satisfaction and those aggregated over SWB domains, as well as eudemonia. Surveying 2500 Australians reveals anchored-BWS improves discrimination amongst domains in terms of importance and satisfaction, illustrating its value as a diagnostic tool for SWB measurement to focus services, policy, and initiatives in areas to most impact wellbeing. This includes highlighting a major discrepancy between health satisfaction and importance, whilst also reporting that SWB is significantly lower for Indigenous, unemployed, middle-aged, males and lower income groups.

2.
Clin Nurs Res ; : 10547738241253652, 2024 May 20.
Artigo em Inglês | MEDLINE | ID: mdl-38767246

RESUMO

This study aimed to explore whether differences exist in anesthesia care providers' use of intraoperative medication between African American and non-Hispanic White patients in adult surgical patients who underwent noncardiothoracic nonobstetric surgeries with general anesthesia. A retrospective observational cohort study used electronic health records between January 1, 2018 and August 31, 2019 at a large academic health system in the southeastern United States. To evaluate the isolated impact of race on intraoperative medication use, inverse probability of treatment weighting using the propensity scores was used to balance the covariates between African American and non-Hispanic White patients. Regression analyses were then performed to evaluate the impact of race on the total dose of opioid analgesia administered, and the use of midazolam, sugammadex, antihypotensive drugs, and antihypertensive drugs. Of the 31,790 patients included in the sample, 58.9% were non-Hispanic Whites and 13.6% were African American patients. After adjusting for significant covariates, African American patients were more likely to receive midazolam premedication (p < .0001; adjusted odds ratio [aOR] = 1.17, 99.9% CI [1.06, 1.30]), and antihypertensive drugs (p = .0002; aOR = 1.15, 99.9% CI [1.02, 1.30]), and less likely to receive antihypotensive drugs (p < .0001; aOR = 0.85, 99.9% CI [0.76, 0.95]) than non-Hispanic White patients. However, we did not find significant differences in the total dose of opioid analgesia administered, or sugammadex. This study identified differences in intraoperative anesthesia care delivery between African American and non-Hispanic White patients; however, future research is needed to understand mechanisms that contribute to these differences and whether these differences are associated with patient outcomes.

3.
J Crit Care ; 83: 154833, 2024 May 21.
Artigo em Inglês | MEDLINE | ID: mdl-38776846

RESUMO

PURPOSE: Few studies have measured the association between pre-existing comorbidities and post-sepsis physical impairment. The study aimed to estimate the risk of physical impairment at hospital discharge among sepsis patients, adjusting for pre-existing physical impairment prior to ICU admission and in-hospital mortality. MATERIALS AND METHODS: We analyzed all consecutive adult patients admitted to an ICU in a tertiary community hospital, Kameda Medical Center, with sepsis diagnosis from September 2014 to October 2020. Inverse probability attrition weighting using machine learning was employed to estimate the risk of physical impairment at hospital discharge for sepsis patients with and without pre-existing comorbidities at ICU admission. This estimation was adjusted for baseline covariates, pre-ICU physical impairment, and in-hospital mortality. RESULTS: Of 889 sepsis patients analyzed, 668 [75.1%] had at least one comorbidity and 221 [24.9%] had no comorbidities at ICU admission. Upon adjusting for baseline covariates, pre-ICU physical impairment, and in-hospital mortality, pre-existing comorbidities were not associated with an elevated risk of physical impairment at hospital discharge (RR: 1.02, 95% CI: 0.92, 1.14). CONCLUSIONS: Pre-existing comorbidities prior to ICU admission were not associated with an increased risk of physical impairment at hospital discharge among sepsis patients after adjusting for baseline covariates and in-hospital mortality.

4.
Soc Sci Med ; 351: 116958, 2024 May 10.
Artigo em Inglês | MEDLINE | ID: mdl-38759384

RESUMO

While empirical studies have observed that homeownership is associated with improved mental health conditions, research indicates that this relationship might vary by race. Moreover, such a White-Black disparity in the impacts of homeownership on mental health could be complexed by poverty status, as maintaining one's homeownership could be a financial burden for people living in poverty status, defined by the US official poverty threshold. We add to the existing literature by analyzing the impacts of homeownership on psychological distress, simultaneously disaggregating by race and poverty status using survey data from the Panel Study on Income Dynamics from the 2017 and 2019 waves (N = 7059). Propensity score weighting and doubly robust estimation are applied to estimate causal inference for the impact of 2017 homeownership on 2019 psychological distress using negative binomial models. First, we found the impacts of homeownership on reducing psychological distress are significant for White Americans, not for Black Americans. Second, we found such a White-Black disparity is only observable for populations not living in poverty. On the other hand, for populations living in poverty, homeownership no longer lowers psychological distress for either race. Findings suggest that financial support and mental health support are needy to address inequality in the impacts of homeownership on mental health, which could simultaneously vary by poverty status and race. Implications are discussed.

5.
Stat Med ; 43(11): 2062-2082, 2024 May 20.
Artigo em Inglês | MEDLINE | ID: mdl-38757695

RESUMO

This paper discusses regression analysis of interval-censored failure time data arising from semiparametric transformation models in the presence of missing covariates. Although some methods have been developed for the problem, they either apply only to limited situations or may have some computational issues. Corresponding to these, we propose a new and unified two-step inference procedure that can be easily implemented using the existing or standard software. The proposed method makes use of a set of working models to extract partial information from incomplete observations and yields a consistent estimator of regression parameters assuming missing at random. An extensive simulation study is conducted and indicates that it performs well in practical situations. Finally, we apply the proposed approach to an Alzheimer's Disease study that motivated this study.


Assuntos
Doença de Alzheimer , Simulação por Computador , Modelos Estatísticos , Humanos , Análise de Regressão , Interpretação Estatística de Dados
6.
Sensors (Basel) ; 24(9)2024 Apr 27.
Artigo em Inglês | MEDLINE | ID: mdl-38732892

RESUMO

Future air quality monitoring networks will integrate fleets of low-cost gas and particulate matter sensors that are calibrated using machine learning techniques. Unfortunately, it is well known that concept drift is one of the primary causes of data quality loss in machine learning application operational scenarios. The present study focuses on addressing the calibration model update of low-cost NO2 sensors once they are triggered by a concept drift detector. It also defines which data are the most appropriate to use in the model updating process to gain compliance with the relative expanded uncertainty (REU) limits established by the European Directive. As the examined methodologies, the general/global and the importance weighting calibration models were applied for concept drift effects mitigation. Overall, for all the devices under test, the experimental results show the inadequacy of both models when performed independently. On the other hand, the results from the application of both models through a stacking ensemble strategy were able to extend the temporal validity of the used calibration model by three weeks at least for all the sensor devices under test. Thus, the usefulness of the whole information content gathered throughout the original co-location process was maximized.

7.
Stat Med ; 2024 May 27.
Artigo em Inglês | MEDLINE | ID: mdl-38801062

RESUMO

Weighting methods are widely used for causal effect estimation in non-randomised studies. In general, these methods use the propensity score (PS), the probability of receiving the treatment given the covariates, to arrive at the respective weights. All of these "modelling" methods actually optimize prediction of the respective outcome, which is, in the PS model, treatment assignment. However, this does not match with the actual aim of weighting, which is eliminating the association between covariates and treatment assignment. In the "balancing" approach, covariates are thus balanced directly by solving systems of numerical equations, explicitly without fitting a PS model. To compare modelling, balancing and hybrid approaches to weighting we performed a large simulation study for a binary treatment and a survival outcome. For maximal practical relevance all simulation parameters were selected after a systematic review of medical studies that used PS methods for analysis. We also introduce a new hybrid method that uses the idea of the covariate balancing propensity score and matching weights, thus avoiding extreme weights. In addition, we present a corrected robust variance estimator for some of the methods. Overall, our simulations results indicate that balancing approach methods work worse than expected. However, among the considered balancing methods, entropy balancing consistently outperforms the variance balancing approach. All methods estimating the average treatment effect in the overlap population perform well with very little bias and small standard errors even in settings with misspecified propensity score models. Finally, the coverage using the standard robust variance estimator was too high for all methods, with the proposed corrected robust variance estimator improving coverage in a variety of settings.

8.
Int J Cardiol ; : 132182, 2024 May 14.
Artigo em Inglês | MEDLINE | ID: mdl-38754583

RESUMO

BACKGROUND: This study aimed to assess the early- and mid-term outcomes of aortic root repair and replacement, and to provide evidence to improve root management in acute type A aortic dissection (AAAD). METHODS: This study enrolled 455 patients who underwent AAAD root repair (n = 307) or replacement (n = 148) between January 2016 and December 2017. Inverse probability of treatment weighting (IPTW) method was used to control for treatment selection bias. The primary outcomes were in-hospital mortality, mid-term survival, and proximal aortic reintervention. RESULTS: The success rate of root repair was 99.7%. The in-hospital mortality in the conservative root repair (CRR) and aggressive root replacement (ARR) were 8.1% and 10.8%. The median follow-up time was 67.76 months (IQR, 67-72 months). After adjusting for baseline factors, there was no significant differences in mid-term survival (p = .750) or the proximal aortic reintervention rate (p = .550) between the two groups. According to Cox analysis, age, hypertension, severe aortic regurgitation, CPB time, and concomitant CABG were all factors associated with mid-term mortality. Regarding reintervention, multivariate analysis identified renal insufficiency, bicuspid aortic valve, root diameter ≥ 45 mm, and severe aortic regurgitation as risk factors, while CRR did not increase the risk of reintervention. The subgroup analysis revealed heterogeneity in the effects of surgical treatment across diverse populations based on a variety of risk factors. CONCLUSIONS: For patients with AAAD, both CRR and ARR are appropriate operations with promising early and mid-term outcomes. The effects of treatment show heterogeneity across diverse populations based on various risk factors.

9.
Entropy (Basel) ; 26(5)2024 May 01.
Artigo em Inglês | MEDLINE | ID: mdl-38785649

RESUMO

Medical image diagnosis using deep learning has shown significant promise in clinical medicine. However, it often encounters two major difficulties in real-world applications: (1) domain shift, which invalidates the trained model on new datasets, and (2) class imbalance problems leading to model biases towards majority classes. To address these challenges, this paper proposes a transfer learning solution, named Dynamic Weighting Translation Transfer Learning (DTTL), for imbalanced medical image classification. The approach is grounded in information and entropy theory and comprises three modules: Cross-domain Discriminability Adaptation (CDA), Dynamic Domain Translation (DDT), and Balanced Target Learning (BTL). CDA connects discriminative feature learning between source and target domains using a synthetic discriminability loss and a domain-invariant feature learning loss. The DDT unit develops a dynamic translation process for imbalanced classes between two domains, utilizing a confidence-based selection approach to select the most useful synthesized images to create a pseudo-labeled balanced target domain. Finally, the BTL unit performs supervised learning on the reassembled target set to obtain the final diagnostic model. This paper delves into maximizing the entropy of class distributions, while simultaneously minimizing the cross-entropy between the source and target domains to reduce domain discrepancies. By incorporating entropy concepts into our framework, our method not only significantly enhances medical image classification in practical settings but also innovates the application of entropy and information theory within deep learning and medical image processing realms. Extensive experiments demonstrate that DTTL achieves the best performance compared to existing state-of-the-art methods for imbalanced medical image classification tasks.

10.
Stat Med ; 2024 May 23.
Artigo em Inglês | MEDLINE | ID: mdl-38780593

RESUMO

In evaluating the performance of different facilities or centers on survival outcomes, the standardized mortality ratio (SMR), which compares the observed to expected mortality has been widely used, particularly in the evaluation of kidney transplant centers. Despite its utility, the SMR may exaggerate center effects in settings where survival probability is relatively high. An example is one-year graft survival among U.S. kidney transplant recipients. We propose a novel approach to estimate center effects in terms of differences in survival probability (ie, each center versus a reference population). An essential component of the method is a prognostic score weighting technique, which permits accurately evaluating centers without necessarily specifying a correct survival model. Advantages of our approach over existing facility-profiling methods include a metric based on survival probability (greater clinical relevance than ratios of counts/rates); direct standardization (valid to compare between centers, unlike indirect standardization based methods, such as the SMR); and less reliance on correct model specification (since the assumed model is used to generate risk classes as opposed to fitted-value based 'expected' counts). We establish the asymptotic properties of the proposed weighted estimator and evaluate its finite-sample performance under a diverse set of simulation settings. The method is then applied to evaluate U.S. kidney transplant centers with respect to graft survival probability.

11.
J Gastrointest Oncol ; 15(2): 689-709, 2024 Apr 30.
Artigo em Inglês | MEDLINE | ID: mdl-38756630

RESUMO

Background: While surufatinib, sunitinib, and everolimus have shown efficacy for advanced neuroendocrine neoplasms (NENs) in randomized controlled trials (RCTs), direct comparisons in a real-world setting remain unexplored. This gap highlights the clinical need to understand their comparative effectiveness and safety within the diverse Chinese population. Addressing this, our study provides insights into the real-world performance of these therapies, aiming to inform treatment selection and improve patient outcomes. Methods: A retrospective, observational study was conducted at Fudan University Shanghai Cancer Center, including patients with advanced NENs treated with surufatinib, sunitinib, or everolimus between July 2020 and April 2023. Eligibility criteria focused on histologically confirmed, locally advanced, unresectable, or metastatic NENs, with patients having received at least one month of targeted therapy. We employed inverse probability weighting (IPW) with the propensity score (PS) matching to adjust for the bias of baseline characteristics. The assessment of covariates included age, sex, performance status, primary tumor site, functional status, genetic mutations, tumor differentiation, Ki67 index, tumor grade, metastasis site, and previous therapies. The primary outcome was progression-free survival (PFS), and secondary outcomes included objective response rate (ORR), disease control rate (DCR), and adverse events (AEs). Results: The study enrolled 123, 56, and 68 locally advanced or metastatic NEN patients treated with surufatinib, sunitinib, and everolimus, respectively. Before adjusting for confounding factors, surufatinib was used less frequently as a first-line treatment compared to sunitinib and everolimus in pancreatic NENs (pNENs) (11.1% vs. 22.1%, P=0.057). Significant differences were noted in prior treatments and tumor characteristics between surufatinib and everolimus groups in extrapancreatic NENs (epNENs) (P<0.05). Post-IPW, these disparities were resolved (P>0.05). Surufatinib demonstrated superior median PFS (mPFS) in both pancreatic [8.30 vs. 6.33 months, hazard ratio (HR) 0.592, P<0.001] and epNENs (8.73 vs. 3.70 months, HR 0.608, P<0.001) compared to everolimus or sunitinib. Notably, male gender (HR 1.75, P=0.001), functional status (HR 2.09, P=0.01), Ki67 index >20% (HR 12.7, P=0.004), previous somatostatin analogue (SSA) treatment (HR 1.73, P=0.001), germline mutation (HR 5.62, P<0.001), poor differentiation (HR 7.45, P<0.001), liver metastasis (HR 1.72, P=0.001) and multiple treatment lines (HR 1.62 for 2nd line, P=0.04; HR 1.88 for ≥3rd line, P=0.01) were identified as negative prognostic factors for PFS. Conversely, dose adjustment (HR 0.63, P=0.009) and treatment with surufatinib (HR 0.58 for pNEN, P<0.001; HR 0.62 for epNEN, P=0.002) were correlated with longer PFS. Conclusions: In a real-world Chinese cohort, surufatinib significantly outperformed sunitinib and everolimus in prolonging PFS among advanced NEN patients, with identifiable clinical features impacting survival, and conclusions regarding superiority should be interpreted with caution due to the retrospective design. Our findings underscore the need for prospective studies to further validate these results and explore additional predictive biomarkers for personalized treatment strategies.

12.
Stat Med ; 2024 May 21.
Artigo em Inglês | MEDLINE | ID: mdl-38772875

RESUMO

Recurrent events, including cardiovascular events, are commonly observed in biomedical studies. Understanding the effects of various treatments on recurrent events and investigating the underlying mediation mechanisms by which treatments may reduce the frequency of recurrent events are crucial tasks for researchers. Although causal inference methods for recurrent event data have been proposed, they cannot be used to assess mediation. This study proposed a novel methodology of causal mediation analysis that accommodates recurrent outcomes of interest in a given individual. A formal definition of causal estimands (direct and indirect effects) within a counterfactual framework is given, and empirical expressions for these effects are identified. To estimate these effects, a semiparametric estimator with triple robustness against model misspecification was developed. The proposed methodology was demonstrated in a real-world application. The method was applied to measure the effects of two diabetes drugs on the recurrence of cardiovascular disease and to examine the mediating role of kidney function in this process.

13.
Artigo em Inglês | MEDLINE | ID: mdl-38775345

RESUMO

Electrochromic devices, capable of modulating light transmittance under the influence of an electric field, have garnered significant interest in the field of smart windows and car rearview mirrors. However, the development of high-performance electrochromic devices via large-scale explorations under miscellaneous experimental settings remains challenging and is still an urgent problem to be solved. In this study, we employed a two-step machine learning approach, combining machine learning algorithms such as KNN and XGBoost with the reality of electrochromic devices, to construct a comprehensive evaluation system for electrochromic materials. Utilizing our predictive evaluation system, we successfully screened the preparation conditions for the best-performing device, which was experimentally verified to have a high transmittance modulation amplitude (62.6%) and fast response time (5.7 s/7.1 s) at 70 A/m2. To test its stability, experiments over a long cycle time (1000 cycles) are performed. In this study, we develop an innovative framework for assessing the performance of electrochromic material devices. Our approach effectively filters experimental samples based on their distinct properties, substantially minimizing the expenditure of human and material resources in electrochromic research. Our approach to a mathematical machine learning evaluation framework for device performance has effectively propelled and informed research in electrochromic devices.

14.
Bioinformatics ; 2024 May 22.
Artigo em Inglês | MEDLINE | ID: mdl-38775719

RESUMO

MOTIVATION: In predicting HIV therapy outcomes, a critical clinical question is whether using historical information can enhance predictive capabilities compared with current or latest available data analysis. This study analyses whether historical knowledge, which includes viral mutations detected in all genotypic tests before therapy, their temporal occurrence, and concomitant viral load measurements, can bring improvements. We introduce a method to weigh mutations, considering the previously enumerated factors and the reference mutation-drug Stanford resistance tables. We compare a model encompassing history (H) with one not using this information (NH). RESULTS: The H-model demonstrates superior discriminative ability, with a higher ROC-AUC score (76.34%) than the NH-model (74.98%). Wilcoxon test results confirm significant improvement of predictive accuracy for treatment outcomes through incorporating historical information. The increased performance of the H-model might be attributed to its consideration of latent HIV reservoirs, probably obtained when leveraging historical information. The findings emphasize the importance of temporal dynamics in acquiring mutations. However, our result also shows that prediction accuracy remains relatively high even when no historical information is available. SUPPLEMENTARY INFORMATION: Supplementary material is available.

15.
Med Biol Eng Comput ; 2024 May 10.
Artigo em Inglês | MEDLINE | ID: mdl-38727760

RESUMO

Medical image classification plays a pivotal role within the field of medicine. Existing models predominantly rely on supervised learning methods, which necessitate large volumes of labeled data for effective training. However, acquiring and annotating medical image data is both an expensive and time-consuming endeavor. In contrast, semi-supervised learning methods offer a promising approach by harnessing limited labeled data alongside abundant unlabeled data to enhance the performance of medical image classification. Nonetheless, current methods often encounter confirmation bias due to noise inherent in self-generated pseudo-labels and the presence of boundary samples from different classes. To overcome these challenges, this study introduces a novel framework known as boundary sample-based class-weighted semi-supervised learning (BSCSSL) for medical image classification. Our method aims to alleviate the impact of intra- and inter-class boundary samples derived from unlabeled data. Specifically, we address reliable confidential data and inter-class boundary samples separately through the utilization of an inter-class boundary sample mining module. Additionally, we implement an intra-class boundary sample weighting mechanism to extract class-aware features specific to intra-class boundary samples. Rather than discarding such intra-class boundary samples outright, our approach acknowledges their intrinsic value despite the difficulty associated with accurate classification, as they contribute significantly to model prediction. Experimental results on widely recognized medical image datasets demonstrate the superiority of our proposed BSCSSL method over existing semi-supervised learning approaches. By enhancing the accuracy and robustness of medical image classification, our BSCSSL approach yields considerable implications for advancing medical diagnosis and future research endeavors.

16.
Int J Epidemiol ; 53(3)2024 Apr 11.
Artigo em Inglês | MEDLINE | ID: mdl-38715336

RESUMO

BACKGROUND: Biobanks typically rely on volunteer-based sampling. This results in large samples (power) at the cost of representativeness (bias). The problem of volunteer bias is debated. Here, we (i) show that volunteering biases associations in UK Biobank (UKB) and (ii) estimate inverse probability (IP) weights that correct for volunteer bias in UKB. METHODS: Drawing on UK Census data, we constructed a subsample representative of UKB's target population, which consists of all individuals invited to participate. Based on demographic variables shared between the UK Census and UKB, we estimated IP weights (IPWs) for each UKB participant. We compared 21 weighted and unweighted bivariate associations between these demographic variables to assess volunteer bias. RESULTS: Volunteer bias in all associations, as naively estimated in UKB, was substantial-in some cases so severe that unweighted estimates had the opposite sign of the association in the target population. For example, older individuals in UKB reported being in better health, in contrast to evidence from the UK Census. Using IPWs in weighted regressions reduced 87% of volunteer bias on average. Volunteer-based sampling reduced the effective sample size of UKB substantially, to 32% of its original size. CONCLUSIONS: Estimates from large-scale biobanks may be misleading due to volunteer bias. We recommend IP weighting to correct for such bias. To aid in the construction of the next generation of biobanks, we provide suggestions on how to best ensure representativeness in a volunteer-based design. For UKB, IPWs have been made available.


Assuntos
Bancos de Espécimes Biológicos , Voluntários , Humanos , Viés de Seleção , Reino Unido , Masculino , Feminino , Pessoa de Meia-Idade , Idoso , Adulto , Censos , Biobanco do Reino Unido
17.
Front Pharmacol ; 15: 1245825, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38720775

RESUMO

Objectives: Multi-Criteria Decision Analysis (MCDA) has gained increasing attention in supporting drug risk-benefit assessment, pricing and reimbursement, as well as optimization of clinical interventions. The objective of this study was to systematically collect and categorize evaluation criteria and techniques of weighting and scoring of MCDA for drug value assessment. Methods: A systematic review of the literature was conducted across seven databases to identify articles utilizing the MCDA frameworks for the evaluation of drug value. Evaluation criteria mentioned in the included studies were extracted and assigned to 5 dimensions including clinical, economic, innovative, societal and humanistic value. A descriptive statistical analysis was performed on the identified drug value evaluation criteria, as well as the weighting and scoring techniques employed. The more a criterion or technique were mentioned in articles, the more important we consider it. Results: Out of the 82 articles included, 111 unique criteria were identified to evaluate the value of drug. Among the 56 unique criteria (448 times) used to measure clinical value, the most frequently mentioned were "comparative safety/tolerability" (58 times), "comparative effectiveness/efficacy" (56 times), "comparative patient-perceived health/patient reported outcomes" (37 times), "disease severity" (34 times), and "unmet needs" (25 times). Regarding economic value measurement, out of the 20 unique criteria (124 times), the most frequently utilized criteria were "cost of intervention" (17 times), "comparative other medical costs" (16 times), and "comparative non-medical costs" (18 times). Out of the 10 criteria (18 times) for assessing innovative value, "a novel pharmacological mechanism" was the most frequently mentioned criterion (5 times). Among the 22 criteria (73 times) used to measure societal value, "system capacity and appropriate use of intervention" was the most frequently cited criterion (14 times). Out of the 3 criteria (15 times) utilized to measure humanistic value, "political/historical/cultural context" was the most frequently mentioned criterion (9 times). Furthermore, 11 scoring and 11 weighting techniques were found from various MCDA frameworks. "Swing weighting" and "a direct rating scale" were the most frequently used techniques in included articles. Conclusion: This study comprehensively presented the current evaluation dimensions, criteria, and techniques for scoring and weighting in drug-oriented MCDA articles. By highlighting the frequently cited evaluation criteria and techniques for scoring and weighting, this analysis will provide a foundation to reasonably select appropriate evaluation criteria and technique in constructing the MCDA framework that aligns with research objectives.

18.
Magn Reson Med ; 2024 May 10.
Artigo em Inglês | MEDLINE | ID: mdl-38725389

RESUMO

PURPOSE: Demonstrate the feasibility and evaluate the performance of single-shot diffusion trace-weighted radial echo planar spectroscopic imaging (Trace DW-REPSI) for quantifying the trace ADC in phantom and in vivo using a 3T clinical scanner. THEORY AND METHODS: Trace DW-REPSI datasets were acquired in 10 phantom and 10 healthy volunteers, with a maximum b-value of 1601 s/mm2 and diffusion time of 10.75 ms. The self-navigation properties of radial acquisitions were used for corrections of shot-to-shot phase and frequency shift fluctuations of the raw data. In vivo trace ADCs of total NAA (tNAA), total creatine (tCr), and total choline (tCho) extrapolated to pure gray and white matter fractions were compared, as well as trace ADCs estimated in voxels within white or gray matter-dominant regions. RESULTS: Trace ADCs in phantom show excellent agreement with reported values, and in vivo ADCs agree well with the expected differences between gray and white matter. For tNAA, tCr, and tCho, the trace ADCs extrapolated to pure gray and white matter ranged from 0.18-0.27 and 0.26-0.38 µm2/ms, respectively. In sets of gray and white matter-dominant voxels, the values ranged from 0.21 to 0.27 and 0.24 to 0.31 µm2/ms, respectively. The overestimated trace ADCs from this sequence can be attributed to the short diffusion time. CONCLUSION: This study presents the first demonstration of the single-shot diffusion trace-weighted spectroscopic imaging sequence using radial echo planar trajectories. The Trace DW-REPSI sequence could provide an estimate of the trace ADC in a much shorter scan time compared to conventional approaches that require three separate measurements.

19.
Value Health Reg Issues ; 43: 100992, 2024 May 06.
Artigo em Inglês | MEDLINE | ID: mdl-38714097

RESUMO

OBJECTIVES: To estimate the incremental medical cost of diabetes mellitus using information from administrative databases in Colombia. METHODS: We carried out a retrospective cohort study with administrative health databases from Colombian population affiliated in the contributory health insurance scheme. We used an operative definition to select the cohort with diabetes. Incremental cost and cost ratio of diabetes were estimated using an inverse probability weighting of treatment approach to find the causal effect of having the disease. Weights were calculated by a propensity score method using a Random Forest model. The flexibility of this machine learning algorithm allows to have a better specification and bias reduction. Additionally, we reported incremental costs and cost ratios with confidence intervals using bootstrapping and analyzed costs by age groups and complications associated with diabetes. RESULTS: The estimated prevalence of diabetes was 2834 per 100 000 cases, in 2018. The group with diabetes was comprised 634 015 people and the control group 1 524 808. The calculated annual direct medical cost was $860, for which the incremental cost was $493 and the cost ratio 2.34. The incremental annual cost for some type of complication ranges from $1239 to $2043, renal complication being the most expensive. Incremental cost by age groups ranges from $347 to $878, being higher in younger people. CONCLUSIONS: Although the cost of diabetes in Colombia ranges among the global averages and is similar to other Latin-American countries, a greater incremental cost was found in patients with renal, circulatory, and neurologic complications.

20.
Stat Med ; 43(13): 2575-2591, 2024 Jun 15.
Artigo em Inglês | MEDLINE | ID: mdl-38659326

RESUMO

Complex diseases are often analyzed using disease subtypes classified by multiple biomarkers to study pathogenic heterogeneity. In such molecular pathological epidemiology research, we consider a weighted Cox proportional hazard model to evaluate the effect of exposures on various disease subtypes under competing-risk settings in the presence of partially or completely missing biomarkers. The asymptotic properties of the inverse and augmented inverse probability-weighted estimating equation methods are studied with a general pattern of missing data. Simulation studies have been conducted to demonstrate the double robustness of the estimators. For illustration, we applied this method to examine the association between pack-years of smoking before the age of 30 and the incidence of colorectal cancer subtypes defined by a combination of four tumor molecular biomarkers (statuses of microsatellite instability, CpG island methylator phenotype, BRAF mutation, and KRAS mutation) in the Nurses' Health Study cohort.


Assuntos
Neoplasias Colorretais , Simulação por Computador , Modelos de Riscos Proporcionais , Humanos , Neoplasias Colorretais/genética , Feminino , Fumar/efeitos adversos , Ilhas de CpG , Metilação de DNA , Proteínas Proto-Oncogênicas B-raf/genética , Mutação , Instabilidade de Microssatélites , Biomarcadores Tumorais/genética , Proteínas Proto-Oncogênicas p21(ras)/genética , Adulto , Pessoa de Meia-Idade
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