Tag: statistics

PLoS One. 2023 Dec 21;18(12):e0295857. doi: 10.1371/journal.pone.0295857. eCollection 2023.

ABSTRACT

BACKGROUND: Although statins are often discontinued when myalgia arises, a causal relationship may not always exist. How well-tolerated statins are when rechallenge is blinded and controlled is unclear.

METHODS AND FINDINGS: We performed a systematic review and meta-analysis (PROSPERO CRD42023437648) to evaluate the success of statin rechallenge versus matched placebo in those who were previously statin intolerant. Our primary outcome was intolerance; our secondary outcome was the myalgia or global symptom score. Medline, Embase, CINAHL Plus, Scopus, and CENTRAL were searched from inception to May 1, 2023. Eligible trials were randomized controlled trials with parallel or crossover designs examining statin rechallenge in statin-intolerant adults. Two independent reviewers selected studies, extracted data, and assessed risk of bias (Cochrane Collaboration’s risk-of-bias tool 1). Relative risk (RR) and mean difference (MD) were estimated using fixed effect Mantel-Haenszel statistics. Of 1,941 studies screened, 8 met our inclusion criteria (8 to 491 participants from Asia, Europe, North America, and Oceana). Compared to placebo, intolerance was more common in statin users [325/906 (36%) vs 233/911 (26%), RR 1.40, 95% CI, 1.23 to 1.60, I2 = 0%, 7 trials, number needed to harm 10] and there was no statistically significant difference in myalgia or global symptom score on a 100-point scale [MD 1.08, 95% CI, -1.51 to 3.67, I2 = 0%, 5 trials]. Limitations include only 1 trial asking participants about intolerable symptoms (vs inferring intolerance from discontinuation or trial withdrawal); the small number of trials; the possibility of attrition bias; and the potential for carryover effects in crossover/n-of-1 trial designs.

CONCLUSIONS: Of those previously intolerant of statins who were rechallenged with a statin and compared to placebo recipients, medication intolerance was more common amongst statin recipients. However, there was no significant difference in mean myalgia or global symptom score between statin and placebo, and only one-third of those previously believed to be statin intolerant were unable to tolerate a statin on blinded rechallenge; one-quarter were intolerant of placebo.

PMID:38128013 | DOI:10.1371/journal.pone.0295857

PLoS One. 2023 Dec 21;18(12):e0296020. doi: 10.1371/journal.pone.0296020. eCollection 2023.

ABSTRACT

Randomized clinical trials (RCTs) are designed for measuring the effectiveness of the treatments and testing a hypothesis regarding the relative effect between two or more treatments. Trial designers are often interested in maximizing power when the total sample size is fixed or minimizing the required total sample size to reach a pre-specified power. One approach to maximizing power proposed by previous researchers is to leverage prior evidence using meta-analysis (NMA) to inform the sample size determination of a new trial. For example, researchers may be interested in designing a two-arm trial comparing treatments A and B which are already in the existing trial network but do not have any direct comparison. The researchers’ intention is to incorporate the result into an existing network for meta-analysis. Here we develop formulas to address these options and use simulations to validate our formula and evaluate the performance of different analysis methods in terms of power. We also implement our proposed method into the R package OssaNMA and publish an R Shiny app for the convenience of the application. The goal of the package is to enable researchers to readily adopt the proposed approach which can improve the power of an RCT and is therefore resource-saving. In the R Shiny app, We also provide the option to include the cost of each treatment which would enable researchers to compare the total treatment cost associated with each design and analysis approach. Further, we explore the effect of allocation to treatment group on study power when the a priori plan is to incorporate the new trial result into an existing network for meta-analysis.

PMID:38128003 | DOI:10.1371/journal.pone.0296020

PLoS One. 2023 Dec 21;18(12):e0293497. doi: 10.1371/journal.pone.0293497. eCollection 2023.

ABSTRACT

BACKGROUND: To curb the growing impact of drug shortages, Health Canada developed the Tiered Notification and Communication Framework which assigns potential shortages a corresponding tiered status. Tier-3 is assigned to shortages with the greatest potential impact on the healthcare system. This study aims to describe drug purchasing trends in response to Tier-3 shortages using three case-examples.

METHODS: We conducted a time-series analysis of monthly purchasing data for three out of 17 Tier-3 drug shortages (hydralazine, sarilumab, and medroxyprogesterone acetate) with publicly available reports in July 2021 and available IQVIA MIDAS data from January 2016 to December 2021. We assessed percent changes in purchasing at 1-, 3-, and 6-months after the onset of each Tier-3 drug shortage and interventional ARIMA modelling was used to assess the statistical significance.

RESULTS: Medroxyprogesterone acetate experienced a significant shift (p = 0.0370) in purchasing following its shortage, and the 1-, 3-, and 6-month percent changes were +14.9%, +6.8% and -3.1%, respectively. Hydralazine and sarilumab did not show a significant shift. The 1-, 3-, and 6-month percent changes for hydralazine were +15.5%, +10.2%, and +9.6% respectively and +25.2%, +45.1% and +39.2 for sarilumab.

CONCLUSIONS: These results indicate that drugs assigned a Tier-3 status may not show declines in purchasing in the months following status assignment, which may be due to policy responses following the assignment. However, more insight is needed into the mechanisms through which these policy measures impact shortages and whether they are functioning as intended.

PMID:38127996 | DOI:10.1371/journal.pone.0293497

JNCI Cancer Spectr. 2023 Dec 21:pkad106. doi: 10.1093/jncics/pkad106. Online ahead of print.

ABSTRACT

BACKGROUND: Many women diagnosed with cancer as adolescents and young adults (AYAs, ages 15-39 years) want biological children after cancer but lack information on the potential impact of their cancer history on future reproductive outcomes. We investigated the risk of adverse birth outcomes among AYA cancer survivors.

METHODS: We identified insured women diagnosed with AYA breast cancer, thyroid cancer, gynecologic cancers, lymphoma, or melanoma from 2003 to 2016 in the state of North Carolina or the Kaiser Permanente healthcare systems in Northern and Southern California. Post-diagnosis births to cancer survivors were each matched with up to 5 births to women without cancer. Risk ratios for preterm birth (<37 completed weeks,) very preterm birth (<34 completed weeks), low birth weight (<2,500 g), and small for gestational age (SGA, < 10th percentile of weight for gestational age) were estimated using modified Poisson regression.

RESULTS: Analyses included 1,648 births to 1,268 AYA cancer survivors and 7,879 births to 6,066 women without cancer. Overall, risk of preterm birth, very preterm birth, low birth weight, and SGA did not significantly differ between births to women with and without cancer. However, births to women with gynecologic cancers had a significantly increased risk of low birth weight (RR = 1.82; 95% CI: 1.03-3.21) and suggested increased risk of preterm birth (RR = 1.59; 95% CI: 0.99-2.54). Chemotherapy exposure was not associated with increased risk of adverse birth outcomes.

CONCLUSIONS: Women with gynecologic cancers, but not other cancers, had an increased risk of adverse birth outcomes compared to women without cancer.

PMID:38127994 | DOI:10.1093/jncics/pkad106

Proc Natl Acad Sci U S A. 2023 Dec 26;120(52):e2300842120. doi: 10.1073/pnas.2300842120. Epub 2023 Dec 21.

ABSTRACT

Normal and pathologic neurobiological processes influence brain morphology in coordinated ways that give rise to patterns of structural covariance (PSC) across brain regions and individuals during brain aging and diseases. The genetic underpinnings of these patterns remain largely unknown. We apply a stochastic multivariate factorization method to a diverse population of 50,699 individuals (12 studies and 130 sites) and derive data-driven, multi-scale PSCs of regional brain size. PSCs were significantly correlated with 915 genomic loci in the discovery set, 617 of which are newly identified, and 72% were independently replicated. Key pathways influencing PSCs involve reelin signaling, apoptosis, neurogenesis, and appendage development, while pathways of breast cancer indicate potential interplays between brain metastasis and PSCs associated with neurodegeneration and dementia. Using support vector machines, multi-scale PSCs effectively derive imaging signatures of several brain diseases. Our results elucidate genetic and biological underpinnings that influence structural covariance patterns in the human brain.

PMID:38127979 | DOI:10.1073/pnas.2300842120

Med Phys. 2023 Dec 21. doi: 10.1002/mp.16898. Online ahead of print.

ABSTRACT

BACKGROUND: Radiotherapy dose predictions have been trained with data from previously treated patients of similar sites and prescriptions. However, clinical datasets are often inconsistent and do not contain the same number of organ at risk (OAR) structures. The effects of missing contour data in deep learning-based dose prediction models have not been studied.

PURPOSE: The purpose of this study was to investigate the impacts of incomplete contour sets in the context of deep learning-based radiotherapy dose prediction models trained with clinical datasets and to introduce a novel data substitution method that utilizes automated contours for undefined structures.

METHODS: We trained Standard U-Nets and Cascade U-Nets to predict the volumetric dose distributions of patients with head and neck cancers (HNC) using three input variations to evaluate the effects of missing contours, as well as a novel data substitution method. Each architecture was trained with the original contour (OC) inputs, which included missing information, hybrid contour (HC) inputs, where automated OAR contours generated in software were substituted for missing contour data, and automated contour (AC) inputs containing only automated OAR contours. 120 HNC treatments were used for model training, 30 were used for validation and tuning, and 44 were used for evaluation and testing. Model performance and accuracy were evaluated with global whole body dose agreement, PTV coverage accuracy, and OAR dose agreement. The differences in these values between dataset variations were used to determine the effects of missing data and automated contour substitutions.

RESULTS: Automated contours used as substitutions for missing data were found to improve dose prediction accuracy in the Standard U-Net and Cascade U-Net, with a statistically significant difference in some global metrics and/or OAR metrics. For both models, PTV coverage between input variations was unaffected by the substitution technique. Automated contours in HC and AC datasets improved mean dose accuracy for some OAR contours, including the mandible and brainstem, with a greater improvement seen with HC datasets. Global dose metrics, including mean absolute error, mean error, and percent error were different for the Standard U-Net but not for the Cascade U-Net.

CONCLUSION: Automated contours used as a substitution for contour data improved prediction accuracy for some but not all dose prediction metrics. Compared to the Standard U-Net models, the Cascade U-Net achieved greater precision.

PMID:38127972 | DOI:10.1002/mp.16898

PLoS One. 2023 Dec 21;18(12):e0291686. doi: 10.1371/journal.pone.0291686. eCollection 2023.

ABSTRACT

BACKGROUND: Heart failure is an important global health problem which is associated with high mortality. Uncontrolled heart failure leads to hospitalization and reduction in quality of life. Therefore, the study aimed to assess the treatment outcome such as improved, death, hospitalization, and self-discharges without improvement and associated factors in heart failure patients admitted to south western Ethiopian hospitals.

METHODS: We will use databases such as PubMed, Science Direct, HINARI, Scopus and Google Scholar. The final systematic review and meta-analysis will contain papers that fulfill the eligible criteria. A systematic data extraction check list will be used to extract the data, and STATA version 14 will be used for the analysis. Heterogeneity is evaluated using the I2 tests and the Cochrane Q test statistic. To examine publication bias, a funnel plot, Egger’s weighted regression, and Begg’s test are utilized. The sensitivity analysis and subgroup analysis will be done for studies having heterogeneity. The Joanna Briggs institute meta-analysis of statistics assessment and review instrument (JBI- MAStARI) will be used for quality assessment.

DISCUSSION: This protocol is expected to provide adequate evidence on the burden of poor heart failure treatment outcome that includes self-discharge, developing complication and finally leads to death in acute and chronic heart failure patients in Ethiopia. Furthermore, to enrich our estimation, we also intended to assess the associated factors of poor treatment outcome. Therefore, our review will call for government and non-government interventions in reducing the mortality associated with heart failure.

PMID:38127971 | DOI:10.1371/journal.pone.0291686

PLoS One. 2023 Dec 21;18(12):e0295573. doi: 10.1371/journal.pone.0295573. eCollection 2023.

ABSTRACT

The aim of this study was to investigate the effect of zinc sulphate on the activities of different enzymes and metabolites of Pholiota adiposa. In the experiment, we used the conventional enzyme activity assay to determine the changes of six indicators, including protein content, laccase activity, cellulase activity, amylase activity and polyphenol oxidase activity, under different concentrations of zinc sulphate treatment. The results showed that the activities of amylase, laccase, cellulase and peroxidase were Zn2+(200)>Zn2+(0)>Zn2+(400)>Zn2+(800).The activities of catalase and superoxide dismutase were Zn2+(200)>Zn2+(400)>Zn2+(800), and zinc sulfate could significantly affect the activity of polylipic squamase in a dose-dependent manner. Further correlation analysis showed that all six enzyme activities were significantly correlated with each other (P<001); the results of the statistical model test showed that the regression model constructed was statistically significant; overall the residuals met the conditions of normal distribution, and the corresponding points of different enzyme activities Q-Q’ were more evenly distributed around y = x, and all fell in the 90% acceptance interval, thus the series was considered to obey normal distribution; the results of the principal The results of the principal component analysis showed that principal component 1 was positively correlated with amylase, laccase and cellulase. Principal component 2 was positively correlated with superoxide dismutase and catalase, and negatively correlated with peroxidase. The analysis of Metabonomic data revealed that zinc sulfate had a significant impact on the expression of metabolites in the mycelium. Moreover, varying concentrations of zinc sulfate exerted significant effects on the levels of amino acids, organic acids, and gluconic acid. This conclusion was confirmed by other experimental data. The results of the study provide a scientific reference for better research, development and utilization of Pholiota adiposa.

PMID:38127967 | DOI:10.1371/journal.pone.0295573

PLoS One. 2023 Dec 21;18(12):e0295763. doi: 10.1371/journal.pone.0295763. eCollection 2023.

ABSTRACT

The mortality impact of COVID-19 has mainly been studied at the national level. However, looking at the aggregate impact of the pandemic at the country level masks heterogeneity at the subnational level. Subnational assessments are essential for the formulation of public health policies. This is especially important for federal countries with decentralised healthcare systems, such as Germany. Therefore, we assess geographical variation in the mortality impact of COVID-19 for the 16 German federal states in 2020 and 2021 and the sex differences therein. For this purpose, we adopted an ecological study design, using population-level mortality data by federal state, age, and sex, for 2005-2021 obtained from the German Federal Statistical Office. We quantified the impact of the pandemic using the excess mortality approach. We estimated period life expectancy losses (LE losses), excess premature mortality, and excess deaths by comparing their observed with their expected values. The expected mortality was based on projected age-specific mortality rates using the Lee-Carter methodology. Saxony was the most affected region in 2020 (LE loss 0.77 years, 95% CI 0.74;0.79) while Saarland was the least affected (-0.04, -0.09;0.003). In 2021, the regions with the highest losses were Thuringia (1.58, 1.54;1.62) and Saxony (1.57, 1.53;1.6) and the lowest in Schleswig-Holstein (0.13, 0.07;0.18). Furthermore, in 2021, eastern regions experienced higher LE losses (mean: 1.13, range: 0.85 years) than western territories (mean: 0.5, range: 0.72 years). The regional variation increased between 2020 and 2021, and was higher among males than among females, particularly in 2021. We observed an unequal distribution of the mortality impact of COVID-19 at the subnational level in Germany, particularly in 2021 among the male population. The observed differences between federal states might be partially explained by the heterogeneous spread of the virus in 2020 and by differences in the population’s propensity to follow preventive guidelines.

PMID:38127957 | DOI:10.1371/journal.pone.0295763

PLoS One. 2023 Dec 21;18(12):e0295676. doi: 10.1371/journal.pone.0295676. eCollection 2023.

ABSTRACT

Ambient air quality is one of the most critical threats to human health. In this study, the health and economic benefits of reducing PM2.5 were estimated in the city of Arak during the period of 2017-2019. The concentration data were obtained from the Environmental Protection Organization of Central Province, while the demographic data were obtained from the website of the Iran Statistics Center. The number of premature deaths from all causes, ischemic heart disease, chronic obstructive pulmonary disease, and lung cancer, attributable to PM2.5 pollution was estimated using the Environmental Benefits Mapping and Analysis Program-Comprehensive Version (BenMAP_CE) to limit the guidelines of the World Health Organization. The results showed that improving air quality in 2017, 2018, and 2019 in Arak could prevent the deaths of 729, 654, and 460 people, respectively. The number of years of life lost (YLL) in 2017, 2018, and 2019 was 11383, 10362, and 7260 years, respectively. The total annual economic benefits of reducing the PM2.5 concentration in Arak under the proposed scenarios in 2017, 2018, and 2019 were estimated to be 309,225,507, 262,868,727, and 182,224,053 USD, respectively, using the statistical life method (VSL). Based on the results of this study, there are significant health and economic benefits to reducing PM2.5 concentrations in Arak City. Therefore, planning and adopting control policies to reduce air pollution in this city are necessary.

PMID:38127954 | DOI:10.1371/journal.pone.0295676