Nevin Manimala

Nurse Res. 2022 Jul 7. doi: 10.7748/nr.2022.e1835. Online ahead of print.

ABSTRACT

BACKGROUND: Self-administered questionnaires are efficient and low-cost ways of collecting data with wide cohorts. Nonetheless, their use in studies can result in a high occurrence of missing data, which can affect the statistical power, representativeness and generalisability of the findings. Imputation methods have been considered efficient statistical techniques for managing missing data. However, they have also been associated with limits, such as the risk of under-estimation of the effect, lower statistical power and decrease of correlation among variables. Recent studies have highlighted the importance of using prevention strategies to avoid missing data before the data are analysed.

AIM: To identify strategies for preventing the occurrence of missing data and to discuss their effects, as well as their methodological and statistical considerations.

DISCUSSION: The article discusses prevention strategies related to the administration format and follow-up and reminders. Strategies such as the use of electronic tablets, email and telephone reminders are associated with lower rates of missing data in self-administered questionnaires. However, methodological and statistical limits, including the absence of a comparison group and statistical validation of the reported results, limits the capacity to establish robust consensus.

CONCLUSION: Prevention strategies represent relevant and feasible avenues for handling missing data in a wide range of clinical, nursing and epidemiological research. More projects based on robust design are needed to ensure accurate and reliable data are collected from patients, families, communities and clinicians.

IMPLICATIONS FOR PRACTICE: It is important for clinicians and nurses to understand the phenomenon of missing data and the strategies available to prevent missing data, to collect data representing the patients’ and families’ perspectives and experiences.

PMID:35796061 | DOI:10.7748/nr.2022.e1835

Elife. 2022 Jul 7;11:e72602. doi: 10.7554/eLife.72602.

ABSTRACT

Analysis of the content of medical journals enables us to frame the shifting scientific, material, ethical, and epistemic underpinnings of medicine over time, including today. Leveraging a dataset comprised of nearly half-a-million articles published in the Journal of the American Medical Association (JAMA) and the New England Journal of Medicine (NEJM) over the past 200 years, we (a) highlight the evolution of medical language, and its manifestations in shifts of usage and meaning, (b) examine traces of the medical profession’s changing self-identity over time, reflected in its shifting ethical and epistemic underpinnings, (c) analyze medicine’s material underpinnings and how we describe where medicine is practiced, (d) demonstrate how the occurrence of specific disease terms within the journals reflects the changing burden of disease itself over time and the interests and perspectives of authors and editors, and (e) showcase how this dataset can allow us to explore the evolution of modern medical ideas and further our understanding of how modern disease concepts came to be, and of the retained legacies of prior embedded values.

PMID:35796055 | DOI:10.7554/eLife.72602

Clin Exp Dent Res. 2022 Jul 7. doi: 10.1002/cre2.623. Online ahead of print.

ABSTRACT

OBJECTIVES: Dental implants are a known treatment today. It is necessary to have at least 2 mm of bone around the implant, especially in the buccal aspect of the anterior maxilla (esthetic zone). Some systemic conditions, such as menopause, can affect the body’s bone mass as well as the alveolar bone. Considering that few studies have been carried out on the effect of menopause on the thickness and topography of alveolar bone, we decided to investigate the effect of menopause on buccal alveolar bone thickness in the anterior maxillary teeth in menopausal women. MATERIAL AND METHODS: In this descriptive-analytical cross-sectional study, two subgroups of menopausal women and nonmenopausal women were considered. Data were extracted from 30 patients referred to a private radiology center in Mashhad for CBCT imaging. In addition, the buccal bone thickness in the crest and middle areas of the anterior maxillary teeth was measured and the difference between the two groups was investigated. The buccal bone thickness of the aesthetic area was evaluated with CBCT Planmeca ProMax 3D Max (Planmeca) by Planmeca Romexis 5.3.4 software, with 200 μm Voxel size and Fov 90 × 60 mm.

RESULTS: In this study, 30 women with a mean age of 49.75 ± 3.65 years in the nonmenopausal and menopausal groups were examined. It was found that the mean buccal bone thickness of the anterior maxilla in the nonmenopausal group (0.65 ± 0.25 mm) was higher than in the menopausal group (0.56 ± 0.20 mm), but the difference was not statistically significant (p = .2999). Only in the crestal bone of the right canine, the average bone thickness in nonmenopausal group (0.77 ± 0.33 mm) was significantly higher than the menopausal group (0.49 ± 0.22 mm) (p = .011).

CONCLUSIONS: Owing to changes in the volume and thickness of alveolar bone in menopausal women, the thickness of the buccal bone in the aesthetic area decreases, but this is not statistically significant.

PMID:35796053 | DOI:10.1002/cre2.623

Immunotherapy. 2022 Jul 7. doi: 10.2217/imt-2022-0082. Online ahead of print.

ABSTRACT

Objective: This study evaluated the cost-effectiveness of pembrolizumab/chemotherapy combinations for previously untreated metastatic triple-negative breast cancer patients in the USA with PD-L1 combined positive score ≥10. Methods: A partitioned-survival model was developed to project health outcomes and direct medical costs over a 20-year time horizon. Efficacy and safety data were from randomized clinical trials. Comparative effectiveness of indirect comparators was assessed using network meta-analyses. A series of sensitivity analyses were performed to test the robustness of the results. Results: Pembrolizumab/chemotherapy resulted in total quality-adjusted life-year (QALY) gains of 0.70 years and incremental cost-effectiveness ratio of US$182,732/QALY compared with chemotherapy alone. The incremental cost-effectiveness ratio for pembrolizumab/nab-paclitaxel versus atezolizumab/nab-paclitaxel was US$44,157/QALY. Sensitivity analyses showed the results were robust over plausible values of model inputs. Conclusion: Pembrolizumab/chemotherapy is cost effective compared with chemotherapy as well as atezolizumab/nab-paclitaxel as first-line treatment for PD-L1-positive metastatic triple-negative breast cancer from a US payer perspective.

PMID:35796042 | DOI:10.2217/imt-2022-0082

Psychol Med. 2022 Jul 7:1-13. doi: 10.1017/S003329172200160X. Online ahead of print.

ABSTRACT

BACKGROUND: While adolescent-onset schizophrenia (ADO-SCZ) and adolescent-onset bipolar disorder with psychosis (psychotic ADO-BPD) present a more severe clinical course than their adult forms, their pathophysiology is poorly understood. Here, we study potentially state- and trait-related white matter diffusion-weighted magnetic resonance imaging (dMRI) abnormalities along the adolescent-onset psychosis continuum to address this need.

METHODS: Forty-eight individuals with ADO-SCZ (20 female/28 male), 15 individuals with psychotic ADO-BPD (7 female/8 male), and 35 healthy controls (HCs, 18 female/17 male) underwent dMRI and clinical assessments. Maps of extracellular free-water (FW) and fractional anisotropy of cellular tissue (FA_T) were compared between individuals with psychosis and HCs using tract-based spatial statistics and FSL’s Randomise. FA_T and FW values were extracted, averaged across all voxels that demonstrated group differences, and then utilized to test for the influence of age, medication, age of onset, duration of illness, symptom severity, and intelligence.

RESULTS: Individuals with adolescent-onset psychosis exhibited pronounced FW and FA_T abnormalities compared to HCs. FA_T reductions were spatially more widespread in ADO-SCZ. FW increases, however, were only present in psychotic ADO-BPD. In HCs, but not in individuals with adolescent-onset psychosis, FA_T was positively related to age.

CONCLUSIONS: We observe evidence for cellular (FA_T) and extracellular (FW) white matter abnormalities in adolescent-onset psychosis. Although cellular white matter abnormalities were more prominent in ADO-SCZ, such alterations may reflect a shared trait, i.e. neurodevelopmental pathology, present across the psychosis spectrum. Extracellular abnormalities were evident in psychotic ADO-BPD, potentially indicating a more dynamic, state-dependent brain reaction to psychosis.

PMID:35796024 | DOI:10.1017/S003329172200160X

Brief Bioinform. 2022 Jul 6:bbac259. doi: 10.1093/bib/bbac259. Online ahead of print.

ABSTRACT

In 2014, the Immune Epitope Database automated benchmark was created to compare the performance of the MHC class I binding predictors. However, this is not a straightforward process due to the different and non-standardized outputs of the methods. Additionally, some methods are more restrictive regarding the HLA alleles and epitope sizes for which they predict binding affinities, while others are more comprehensive. To address how these problems impacted the ranking of the predictors, we developed an approach to assess the reliability of different metrics. We found that using percentile-ranked results improved the stability of the ranks and allowed the predictors to be reliably ranked despite not being evaluated on the same data. We also found that given the rate new data are incorporated into the benchmark, a new method must wait for at least 4 years to be ranked against the pre-existing methods. The best-performing tools with statistically indistinguishable scores in this benchmark were NetMHCcons, NetMHCpan4.0, ANN3.4, NetMHCpan3.0 and NetMHCpan2.8. The results of this study will be used to improve the evaluation and display of benchmark performance. We highly encourage anyone working on MHC binding predictions to participate in this benchmark to get an unbiased evaluation of their predictors.

PMID:35794711 | DOI:10.1093/bib/bbac259

Genomics Inform. 2022 Jun;20(2):e23. doi: 10.5808/gi.22036. Epub 2022 Jun 30.

ABSTRACT

A survival prediction model has recently been developed to evaluate the prognosis of resected nonmetastatic pancreatic ductal adenocarcinoma based on a Cox model using two nationwide databases: Surveillance, Epidemiology and End Results (SEER) and Korea Tumor Registry System-Biliary Pancreas (KOTUS-BP). In this study, we applied two machine learning methods-random survival forests (RSF) and support vector machines (SVM)-for survival analysis and compared their prediction performance using the SEER and KOTUS-BP datasets. Three schemes were used for model development and evaluation. First, we utilized data from SEER for model development and used data from KOTUS-BP for external evaluation. Second, these two datasets were swapped by taking data from KOTUS-BP for model development and data from SEER for external evaluation. Finally, we mixed these two datasets half and half and utilized the mixed datasets for model development and validation. We used 9,624 patients from SEER and 3,281 patients from KOTUS-BP to construct a prediction model with seven covariates: age, sex, histologic differentiation, adjuvant treatment, resection margin status, and the American Joint Committee on Cancer 8th edition T-stage and N-stage. Comparing the three schemes, the performance of the Cox model, RSF, and SVM was better when using the mixed datasets than when using the unmixed datasets. When using the mixed datasets, the C-index, 1-year, 2-year, and 3-year time-dependent areas under the curve for the Cox model were 0.644, 0.698, 0.680, and 0.687, respectively. The Cox model performed slightly better than RSF and SVM.

PMID:35794703 | DOI:10.5808/gi.22036

Genomics Inform. 2022 Jun;20(2):e22. doi: 10.5808/gi.22025. Epub 2022 Jun 22.

ABSTRACT

The rise of newer coronavirus disease 2019 (COVID-19) variants has brought a challenge to ending the spread of COVID-19. The variants have a different fatality, morbidity, and transmission rates and affect vaccine efficacy differently. Therefore, the impact of each new variant on the spread of COVID-19 is of interest to governments and scientists. Here, we proposed mathematical SEIQRDVP and SEIQRDV3P models to predict the impact of the Omicron variant on the spread of the COVID-19 situation in South Korea. SEIQEDVP considers one vaccine level at a time while SEIQRDV3P considers three vaccination levels (only one dose received, full doses received, and full doses + booster shots received) simultaneously. The omicron variant’s effect was contemplated as a weighted sum of the delta and omicron variants’ transmission rate and tuned using a hyperparameter k. Our models’ performances were compared with common models like SEIR, SEIQR, and SEIQRDVUP using the root mean square error (RMSE). SEIQRDV3P performed better than the SEIQRDVP model. Without consideration of the variant effect, we don’t see a rapid rise in COVID-19 cases and high RMSE values. But, with consideration of the omicron variant, we predicted a continuous rapid rise in COVID-19 cases until maybe herd immunity is developed in the population. Also, the RMSE value for the SEIQRDV3P model decreased by 27.4%. Therefore, modeling the impact of any new risen variant is crucial in determining the trajectory of the spread of COVID-19 and determining policies to be implemented.

PMID:35794702 | DOI:10.5808/gi.22025

Genomics Inform. 2022 Jun;20(2):e17. doi: 10.5808/gi.22033. Epub 2022 Jun 30.

ABSTRACT

Genetic associations have been quantified using a number of statistical measures. Entropy-based mutual information may be one of the more direct ways of estimating the association, in the sense that it does not depend on the parametrization. For this purpose, both the entropy and conditional entropy of the phenotype distribution should be obtained. Quantitative traits, however, do not usually allow an exact evaluation of entropy. The estimation of entropy needs a probability density function, which can be approximated by kernel density estimation. We have investigated the proper sequence of procedures for combining the kernel density estimation and entropy estimation with a probability density function in order to calculate mutual information. Genotypes and their interactions were constructed to set the conditions for conditional entropy. Extensive simulation data created using three types of generating functions were analyzed using two different kernels as well as two types of multifactor dimensionality reduction and another probability density approximation method called m-spacing. The statistical power in terms of correct detection rates was compared. Using kernels was found to be most useful when the trait distributions were more complex than simple normal or gamma distributions. A full-scale genomic dataset was explored to identify associations using the 2-h oral glucose tolerance test results and γ-glutamyl transpeptidase levels as phenotypes. Clearly distinguishable single-nucleotide polymorphisms (SNPs) and interacting SNP pairs associated with these phenotypes were found and listed with empirical p-values.

PMID:35794697 | DOI:10.5808/gi.22033

Genomics Inform. 2022 Jun;20(2):e16. doi: 10.5808/gi.22022. Epub 2022 Jun 30.

ABSTRACT

Various methodologies for the genetic analysis of longitudinal data have been proposed and applied to data from large-scale genome-wide association studies (GWAS) to identify single nucleotide polymorphisms (SNPs) associated with traits of interest and to detect SNP-time interactions. We recently proposed a grid-based Bayesian mixed model for longitudinal genetic data and showed that our Bayesian method increased the statistical power compared to the corresponding univariate method and well detected SNP-time interactions. In this paper, we further analyze longitudinal obesity-related traits such as body mass index, hip circumference, waist circumference, and waist-hip ratio from Korea Association Resource data to evaluate the proposed Bayesian method. We first conducted GWAS analyses of cross-sectional traits and combined the results of GWAS analyses through a meta-analysis based on a trajectory model and a random-effects model. We then applied our Bayesian method to a subset of SNPs selected by meta-analysis to further discover SNPs associated with traits of interest and SNP-time interactions. The proposed Bayesian method identified several novel SNPs associated with longitudinal obesity-related traits, and almost 25% of the identified SNPs had significant p-values for SNP-time interactions.

PMID:35794696 | DOI:10.5808/gi.22022