Category: Statistics

World Neurosurg. 2023 Jul 17:S1878-8750(23)00973-7. doi: 10.1016/j.wneu.2023.07.045. Online ahead of print.

ABSTRACT

The incidence of ischemic stroke in young adults (18-45 years old) is increasing gradually. However, performing nutritional assessment in stroke patients is often challenging due to the lack of an accepted standard for nutritional assessment. 260 young stroke patients were recruited in this study and 144 cases in the good prognosis group and 116 cases in the poor prognosis group were scored according to the modified Rankin scale (mRS) 90 days after treatment. The National Institutes of Health Stroke Scale (NIHSS) was performed on admission and discharge of patients. Serum interleukin 6 (IL-6) and high-sensitivity C-reactive protein (hs-CRP) were detected at patient presentation. The Prognostic Nutritional Index (PNI) was assessed on admission. Calculation formula of PNI score: serum albumin (g/L) + 5× total lymphocyte count (10⁹/L). The PNI at admission of young stroke patients with poor prognosis was higher than that of those with good prognosis. PNI at admission was significantly negatively correlated with NIHSS at discharge and mRS score after 90 days in young stroke patients. PNI at admission was also significantly negatively correlated with serum levels of high-sensitivity C-reactive protein and IL-6 at admission. PNI has a statistically predictive value for the 90-day prognosis of young stroke patients.

PMID:37467954 | DOI:10.1016/j.wneu.2023.07.045

Contemp Clin Trials. 2023 Jul 17:107301. doi: 10.1016/j.cct.2023.107301. Online ahead of print.

ABSTRACT

In recent decades, there has been growing interest in leveraging external data information for clinical development as it improves the efficiency of the design and inference of clinical trials when utilized properly and more importantly, alleviates potential ethical and recruitment challenges. When it is of interest to augment the concurrent study’s control arm using external control data, the potential outcome heterogeneity across data sources, also known as prior-data conflict, should be accounted for. In addition, in the outcome modeling, inclusion of prognostic covariates that may have impact on the outcome can avoid efficiency loss or potential bias. In this paper, we propose a Bayesian hierarchical modeling strategy incorporating covariate-adjusted meta-analytic predictive approach (cMAP) and also introduce a propensity score (PS) based sequential procedure that integrates the cMAP. In the simulation study, the proposed methods are found to have advantages in the estimation, power, and type I error control over the standard methods such as PS matching alone and hierarchical modeling that ignores the covariates. An illustrative example is used to illustrate the procedure.

PMID:37467950 | DOI:10.1016/j.cct.2023.107301

Contemp Clin Trials. 2023 Jul 17:107300. doi: 10.1016/j.cct.2023.107300. Online ahead of print.

ABSTRACT

To accelerate clinical development, seamless 2/3 adaptive design is an attractive strategy to combine phase 2 dose selection with phase 3 confirmatory objectives. As the regulatory requirement for dose optimization in oncology drugs shifted from maximum tolerated dose to maximum effective dose, it’s important to gather more data on multiple candidate doses to inform dose selection. A phase 3 dose may be selected based on phase 2 results and carried forward in phase 3 study. Data obtained from both phases will be combined in the final analysis. In many disease settings biomarker endpoints are utilized for dose selection as they are correlated with the clinical efficacy endpoints. As discussed in Li et al. (2015), the combined analysis may cause type I error inflation due to the correlation and dose selection. Sidák adjustment has been proposed to control the overall type I error by adjusting p-values in phase 2 when performing the combined p-value test. However, this adjustment could be overly conservative as it does not consider the underlying correlations among doses/endpoints. We propose an alternative approach utilizing biomarker rank-based ordered test statistics which takes the rank order of the selected dose and the correlation into consideration. If the correlation is unknown, we propose a rank-based Dunnett adjustment, which includes the traditional Dunnett adjustment as a special case. We show that the proposed method controls the overall type I error, and leads to a uniformly higher power than Sidák adjustment and the traditional Dunnett adjustment under all potential correlation scenarios discussed.

PMID:37467949 | DOI:10.1016/j.cct.2023.107300

Environ Res. 2023 Jul 17:116686. doi: 10.1016/j.envres.2023.116686. Online ahead of print.

ABSTRACT

During the mining of rare earth minerals, the application of neodymium-containing manures, and the treatment of spent neodymium iron boron magnet, the generation of ammonia wastewater containing neodymium is increasing. Thus, the effects of neodymium (Nd(III)) on anaerobic ammonium oxidation (Anammox) were investigated from the aspects of performance, kinetics, statistics, microbial community and sludge morphology, and the recovery strategy of EDTA-2Na wash was discussed. The nitrogen removal efficiency of the Anammox reactor decreased significantly and eventually collapsed at the Nd(III) dosing levels of 20 and 40 mg L^-1, respectively. And the toxicity of Nd(III) to AnAOB was determined by the amount internalized into the cells. The EDTA-2Na wash successfully increased the total nitrogen removal rate (TNRR) of Nd(III)-inhibited Anammox to 41.60% of its initial value within 30 days, and the modified Boltzmann model accurately simulated this recovery process. The transient and extended effects of Nd(III), self-recovery, and EDTA-2Na wash on Anammox were effectively assessed using a one-sample t-test. 16S rRNA gene sequencing indicated that Nd(III) remarkably decreased the relative abundance of Planctomycetes and Candidatus Brocadia. The scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS) revealed crystal-like neodymium particles on the surface of Anammox sludge. The above-mentioned results demonstrate that the concentration of Nd(III) should be below the toxicity threshold (20 mg L^-1) when treating ammonia wastewater containing neodymium by Anammox, and also emphasize the importance of an appropriate recovery strategy.

PMID:37467943 | DOI:10.1016/j.envres.2023.116686

Environ Pollut. 2023 Jul 17:122217. doi: 10.1016/j.envpol.2023.122217. Online ahead of print.

ABSTRACT

Air pollution exposure may affect child weight gain, but observational studies provide inconsistent evidence. Residential relocation can be leveraged as a natural experiment by studying changes in health outcomes after a sudden change in exposure within an individual. We aimed to evaluate whether changes in air pollution exposure due to residential relocation are associated with changes in body mass index (BMI) in children and adolescents in a natural experiment study. This population-based study included children and adolescents, between 2 and 17 years, who moved during 2011-2018 and were registered in the primary healthcare in Catalonia, Spain (N = 46,644). Outdoor air pollutants (nitrogen dioxides (NO₂), particulate matter <10 μm (PM₁₀) and <2.5 μm (PM_2.5)) were estimated at residential census tract level before and after relocation; tertile cut-offs were used to define changes in exposure. Routinely measured weight and height were used to calculate age-sex-specific BMI z-scores. A minimum of 180 days after moving was considered to observe zBMI changes according to changes in exposure using linear fixed effects regression. The majority of participants (60-67% depending on the pollutant) moved to areas with similar levels of air pollution, 15-49% to less polluted, and 14-31% to more polluted areas. Moving to areas with more air pollution was associated with zBMI increases for all air pollutants (β NO₂ = 0.10(95%CI 0.09; 0.12), β PM_2.5 0.06(0.04; 0.07), β PM₁₀ 0.08(0.06; 0.10)). Moving to similar air pollution areas was associated with decreases in zBMI for all pollutants. No associations were found for those moving to less polluted areas. Associations with moving to more polluted areas were stronger in preschool- and primary school-ages. Associations did not differ by area deprivation strata. This large, natural experiment study suggests that increases in outdoor air pollution may be associated with child weight gain, supporting ongoing efforts to lower air pollution levels.

PMID:37467916 | DOI:10.1016/j.envpol.2023.122217

Cognition. 2023 Jul 17;239:105564. doi: 10.1016/j.cognition.2023.105564. Online ahead of print.

ABSTRACT

Our brains are capable of discriminating similar inputs (pattern separation) and rapidly generalizing across inputs (statistical learning). Are these two processes dissociable in behavior? Here, we asked whether cognitive aging affects them in a differential or parallel manner. Older and younger adults were tested on their ability to discriminate between similar trisyllabic words and to extract trisyllabic words embedded in a continuous speech stream. Older adults demonstrated intact statistical learning on an implicit, reaction time-based measure and an explicit, familiarity-based measure of learning. However, they performed poorly in discriminating similar items presented in isolation, both for episodically-encoded items and for statistically-learned regularities. These results indicate that pattern separation and statistical learning are dissociable and differentially affected by aging. The acquisition of implicit representations of statistical regularities operates robustly into old age, whereas pattern separation influences the expression of statistical learning with high representational fidelity and is subject to age-related decline.

PMID:37467624 | DOI:10.1016/j.cognition.2023.105564

Clin Neurol Neurosurg. 2023 Jun 23;232:107846. doi: 10.1016/j.clineuro.2023.107846. Online ahead of print.

ABSTRACT

BACKGROUND: Several studies demonstrated the association between tobacco smoking and higher risk and increased progression of multiple sclerosis (MS). Data about the effect of smoking during the recovery from MS attacks is limited. Furthermore, different types of tobacco exposures such as water pipe and passive smoking are not well assessed separately. So this study evaluated the effect of different types of smokes, cigarette and water pipe as well as passive smoking on the function recovery of relapsing-remitting MS (RRMS) attacks METHODS: This cohort study evaluated the adult patients with RRMS and Expanded Disability Status Scale (EDSS) < 5 in the attack phase. Patients were divided into two groups: smokers and non-smokers. The smokers included those who use cigarette, water pipe as well as passive smokers as subgroups for more analyses later. EDSS was monitored after relapse and two months after relapse. Change of EDSS considered as the criteria for functional recovery. The correlation between the amount of consumption and disability level was assessed among smokers by Pearson’s correlation test. While, the difference of EDSS between smoker and non-smoker were assessed by Independent samples T-test.

RESULTS: 142 patients were evaluated. 79 (55.6%) were smokers (43% male) while 63 (44.4%) were non-smokers (36.5% male). There was a statistically significant difference in change of EDSS between smoker and non-smoker groups, which change of EDSS was higher in non-smoker (-2.62 ± 0.90 non-smoker vs. -1.75 ± 0.76 smoker, P < 0.001). Also, only there was a significantly lesser decline in EDSS after two months in the cigarette smokers in subgroups analyses (P < 0.001). A correlation analysis revealed a significant positive correlation between the number per day of cigarette smoking and EDSS after relapse (r = 0.3, P = 0.03) and a significant positive correlation between minutes per month of smoking of water pipe and EDSS two months after relapse (r = ‎0.6‎‎, P > 0.001).

CONCLUSION: Tobacco smoking especially cigarette smoking is associated with a negative effect on recovery from the attack in patients with RRMS.

PMID:37467576 | DOI:10.1016/j.clineuro.2023.107846

Leuk Res. 2023 Jul 12;132:107354. doi: 10.1016/j.leukres.2023.107354. Online ahead of print.

ABSTRACT

The treatment patterns for patients with newly diagnosed acute myeloid leukemia (AML) were compared between 2013 and 16 and 2021-22 in a real-world setting. A significantly higher proportion of patients age 70 and over received non-intensive therapy (NIT) in 2021-22 as compared with 2013-16 (65 % vs 44 %, p = 0.014), with a corresponding reduction in the proportion receiving either intensive therapy or no antileukemic treatment. Treatment patterns among patients < age 70 were unchanged. The complete response rate in the NIT group was 69 % in 2021-22 vs. 24 % in 2013-16 (p < 0.001); the overall survival (OS) of NIT patients was 11.5 months in 2021-22 vs. 7.8 months in 2013-16. Older patients from rural areas were more likely to decline therapy than those from urban regions. The increase in the proportion of patients opting for NIT may be related to the availability of more effective treatment options. Although outcomes are improving, the OS with NIT remains suboptimal.

PMID:37467567 | DOI:10.1016/j.leukres.2023.107354

Ecotoxicol Environ Saf. 2023 Jul 17;263:115257. doi: 10.1016/j.ecoenv.2023.115257. Online ahead of print.

NO ABSTRACT

PMID:37467562 | DOI:10.1016/j.ecoenv.2023.115257

Comput Biol Med. 2023 Jul 10;164:107237. doi: 10.1016/j.compbiomed.2023.107237. Online ahead of print.

ABSTRACT

Medical datasets are primarily made up of numerous pointless and redundant elements in a collection of patient records. None of these characteristics are necessary for a medical decision-making process. Conversely, a large amount of data leads to increased dimensionality and decreased classifier performance in terms of machine learning. Numerous approaches have recently been put out to address this issue, and the results indicate that feature selection can be a successful remedy. To meet the various needs of input patterns, medical diagnostic tasks typically involve learning a suitable categorization model. The k-Nearest Neighbors algorithm (kNN) classifier’s classification performance is typically decreased by the input variables’ abundance of irrelevant features. To simplify the kNN classifier, essential attributes of the input variables have been searched using the feature selection approach. This paper presents the Coati Optimization Algorithm (DCOA) in a dynamic form as a feature selection technique where each iteration of the optimization process involves the introduction of a different feature. We enhance the exploration and exploitation capability of DCOA by employing dynamic opposing candidate solutions. The most impressive feature of DCOA is that it does not require any preparatory parameter fine-tuning to the most popular metaheuristic algorithms. The CEC’22 test suite and nine medical datasets with various dimension sizes were used to evaluate the performance of the original COA and the proposed dynamic version. The statistical results were validated using the Bonferroni-Dunn test and Kendall’s W test and showed the superiority of DCOA over seven well-known metaheuristic algorithms with an overall accuracy of 89.7%, a feature selection of 24%, a sensitivity of 93.35% a specificity of 96.81%, and a precision of 93.90%.

PMID:37467535 | DOI:10.1016/j.compbiomed.2023.107237