Modular organization of functional brain networks in patients with degenerative cervical myelopathy.

Previous studies have indicated that brain functional plasticity and reorganization in patients with degenerative cervical myelopathy (DCM). However, the effects of cervical cord compression on the functional integration and separation between and/or within modules remain unclear. This study aimed to address these questions using graph theory. Functional MRI was conducted on 46 DCM patients and 35 healthy controls (HCs). The intra- and inter-modular connectivity properties of the whole-brain functional network and nodal topological properties were then calculated using theoretical graph analysis. The difference in categorical variables between groups was compared using a chi-squared test, while that between continuous variables was evaluated using a two-sample t-test. Correlation analysis was conducted between modular connectivity properties and clinical parameters. Modules interaction analyses showed that the DCM group had significantly greater inter-module connections than the HCs group (DMN-FPN: t = 2.38, p = 0.02); inversely, the DCM group had significantly lower intra-module connections than the HCs group (SMN: t = - 2.13, p = 0.036). Compared to HCs, DCM patients exhibited higher nodal topological properties in the default-mode network and frontal-parietal network. In contrast, DCM patients exhibited lower nodal topological properties in the sensorimotor network. The Japanese Orthopedic Association (JOA) score was positively correlated with inter-module connections (r = 0.330, FDR p = 0.029) but not correlated with intra-module connections. This study reported alterations in modular connections and nodal centralities in DCM patients. Decreased nodal topological properties and intra-modular connection in the sensory-motor regions may indicate sensory-motor dysfunction. Additionally, increased nodal topological properties and inter-modular connection in the default mode network and frontal-parietal network may serve as a compensatory mechanism for sensory-motor dysfunction in DCM patients. This could provide an implicative neural basis to better understand alterations in brain networks and the patterns of changes in brain plasticity in DCM patients.

Dietary Inflammatory Index and Dietary Diversity Score Associated with Sarcopenia and Its Components: Findings from a Nationwide Cross-Sectional Study.

Little is known about the independent and joint effects of the energy-adjusted dietary inflammatory index (E-DII) and dietary diversity score (DDS) on sarcopenia and its components (low muscle mass, low muscle strength, and low physical performance). A total of 155,669 UK Biobank participants with ≥1 (maximum 5) 24 h dietary assessments were included in this cross-sectional analysis. We used logistic regression models to investigate the associations of E-DII and DDS with sarcopenia and its three components. We further examined the joint effects of E-DII and DDS on sarcopenia and its components using additive and multiplicative interaction analyses. We observed that lower E-DII and higher DDS were associated with lower odds of sarcopenia and its components. There were significant joint associations of E-DII and DDS with sarcopenia and low physical performance (p-interaction < 0.05) on the multiplicative interactive scale. Our study suggests that lower dietary inflammatory potential and higher dietary diversity might be important protective factors against sarcopenia and its components. More cases of sarcopenia and low physical performance might be preventable by adherence to a more anti-inflammatory diet combined with a higher dietary diversity.

SEAOP: a statistical ensemble approach for outlier detection in quantitative proteomics data.

Quality control in quantitative proteomics is a persistent challenge, particularly in identifying and managing outliers. Unsupervised learning models, which rely on data structure rather than predefined labels, offer potential solutions. However, without clear labels, their effectiveness might be compromised. Single models are susceptible to the randomness of parameters and initialization, which can result in a high rate of false positives. Ensemble models, on the other hand, have shown capabilities in effectively mitigating the impacts of such randomness and assisting in accurately detecting true outliers. Therefore, we introduced SEAOP, a Python toolbox that utilizes an ensemble mechanism by integrating multi-round data management and a statistics-based decision pipeline with multiple models. Specifically, SEAOP uses multi-round resampling to create diverse sub-data spaces and employs outlier detection methods to identify candidate outliers in each space. Candidates are then aggregated as confirmed outliers via a chi-square test, adhering to a 95% confidence level, to ensure the precision of the unsupervised approaches. Additionally, SEAOP introduces a visualization strategy, specifically designed to intuitively and effectively display the distribution of both outlier and non-outlier samples. Optimal hyperparameter models of SEAOP for outlier detection were identified by using a gradient-simulated standard dataset and Mann-Kendall trend test. The performance of the SEAOP toolbox was evaluated using three experimental datasets, confirming its reliability and accuracy in handling quantitative proteomics.

Recoverability and estimation of causal effects under typical multivariable missingness mechanisms.

In the context of missing data, the identifiability or "recoverability" of the average causal effect (ACE) depends not only on the usual causal assumptions but also on missingness assumptions that can be depicted by adding variable-specific missingness indicators to causal diagrams, creating missingness directed acyclic graphs (m-DAGs). Previous research described canonical m-DAGs, representing typical multivariable missingness mechanisms in epidemiological studies, and examined mathematically the recoverability of the ACE in each case. However, this work assumed no effect modification and did not investigate methods for estimation across such scenarios. Here, we extend this research by determining the recoverability of the ACE in settings with effect modification and conducting a simulation study to evaluate the performance of widely used missing data methods when estimating the ACE using correctly specified g-computation. Methods assessed were complete case analysis (CCA) and various implementations of multiple imputation (MI) with varying degrees of compatibility with the outcome model used in g-computation. Simulations were based on an example from the Victorian Adolescent Health Cohort Study (VAHCS), where interest was in estimating the ACE of adolescent cannabis use on mental health in young adulthood. We found that the ACE is recoverable when no incomplete variable (exposure, outcome, or confounder) causes its own missingness, and nonrecoverable otherwise, in simplified versions of 10 canonical m-DAGs that excluded unmeasured common causes of missingness indicators. Despite this nonrecoverability, simulations showed that MI approaches that are compatible with the outcome model in g-computation may enable approximately unbiased estimation across all canonical m-DAGs considered, except when the outcome causes its own missingness or causes the missingness of a variable that causes its own missingness. In the latter settings, researchers may need to consider sensitivity analysis methods incorporating external information (e.g., delta-adjustment methods). The VAHCS case study illustrates the practical implications of these findings.

Predicting absolute risk for a person with missing risk factors.

We compared methods to project absolute risk, the probability of experiencing the outcome of interest in a given projection interval accommodating competing risks, for a person from the target population with missing predictors. Without missing data, a perfectly calibrated model gives unbiased absolute risk estimates in a new target population, even if the predictor distribution differs from the training data. However, if predictors are missing in target population members, a reference dataset with complete data is needed to impute them and to estimate absolute risk, conditional only on the observed predictors. If the predictor distributions of the reference data and the target population differ, this approach yields biased estimates. We compared the bias and mean squared error of absolute risk predictions for seven methods that assume predictors are missing at random (MAR). Some methods imputed individual missing predictors, others imputed linear predictor combinations (risk scores). Simulations were based on real breast cancer predictor distributions and outcome data. We also analyzed a real breast cancer dataset. The largest bias for all methods resulted from different predictor distributions of the reference and target populations. No method was unbiased in this situation. Surprisingly, violating the MAR assumption did not induce severe biases. Most multiple imputation methods performed similarly and were less biased (but more variable) than a method that used a single expected risk score. Our work shows the importance of selecting predictor reference datasets similar to the target population to reduce bias of absolute risk predictions with missing risk factors.

Estimands for clinical endpoints in tuberculosis treatment randomized controlled trials: a retrospective application in a completed trial.

BACKGROUND: Randomized trials for the treatment of tuberculosis (TB) rely on a composite primary outcome to capture unfavorable treatment responses. However, variability between trials in the outcome definition and estimation methods complicates across-trial comparisons and hinders the advancement of treatment guidelines. The International Council for Harmonization (ICH) provides international regulatory standards for clinical trials. The estimand framework outlined in the recent ICH E9(R1) addendum offers a timely opportunity for randomized trials of TB treatment to adopt broadly standardized outcome definitions and analytic approaches. We previously proposed and defined four estimands for use in this context. Our objective was to evaluate how the use of these estimands and choice of estimation method impacts results and interpretation of a large phase III TB trial. METHODS: We reanalyzed participant-level data from the REMoxTB trial. We applied four estimands and various methods of estimation to assess non-inferiority of both novel 4-month treatment regimens against standard of care. RESULTS: With each of the four estimands, we reached the same conclusion as the original trial analysis that the novel regimens were not non-inferior to standard of care. Each estimand and method of estimation gave similar estimates of the treatment effect with fluctuations in variance and differences driven by the methods applied for handling intercurrent events. CONCLUSIONS: Our application of estimands defined by the ICH E9 (R1) addendum offers a formalized framework for addressing the primary TB treatment trial objective and can promote uniformity in future trials by limiting heterogeneity in trial outcome definitions. We demonstrated the utility of our proposal using data from the REMoxTB randomized trial. We outlined methods for estimating each estimand and found consistent conclusions across estimands. We recommend future late-phase TB treatment trials to implement some or all of our estimands to promote rigorous outcome definitions and reduce variability between trials. TRIAL REGISTRATION: ClinicalTrials.gov NCT00864383. Registered on March 2009.

Multiply robust generalized estimating equations for cluster randomized trials with missing outcomes.

Generalized estimating equations (GEEs) provide a useful framework for estimating marginal regression parameters based on data from cluster randomized trials (CRTs), but they can result in inaccurate parameter estimates when some outcomes are informatively missing. Existing techniques to handle missing outcomes in CRTs rely on correct specification of a propensity score model, a covariate-conditional mean outcome model, or require at least one of these two models to be correct, which can be challenging in practice. In this article, we develop new weighted GEEs to simultaneously estimate the marginal mean, scale, and correlation parameters in CRTs with missing outcomes, allowing for multiple propensity score models and multiple covariate-conditional mean models to be specified. The resulting estimators are consistent provided that any one of these models is correct. An iterative algorithm is provided for implementing this more robust estimator and practical considerations for specifying multiple models are discussed. We evaluate the performance of the proposed method through Monte Carlo simulations and apply the proposed multiply robust estimator to analyze the Botswana Combination Prevention Project, a large HIV prevention CRT designed to evaluate whether a combination of HIV-prevention measures can reduce HIV incidence.

Statistics for the Non-Statistician: How to Read the Respiratory Care Literature.

Statistical analysis is an important part of the research process. Researchers are advised to include a statistician from the moment that the study is being planned. The statistical plan informs the research process, including sample size requirements and the most robust data collection. Once the data are collected, descriptive and inferential statistical analyses are performed. The results of this analysis determine whether the findings are significant, which leads to an interpretation of the findings. The importance of the statistical plan and analysis for the researcher is self-evident. However, it is also important for the reader of published papers to have some knowledge of statistical analysis. This allows critical review of all aspects of the published manuscript. The intent of this paper is to review some basic statistical concepts and thus allow the reader to become a better consumer of the literature.

Does national forest city improve residents' health? Evidence from China.

Objective: National health is essential for economic and social development. The aim of this article is to examine the relationship, heterogeneity effects and influential mechanisms between National Forest Cities and the residents' health. Methods: The article matches the China Family Panel Studies data in 2018 (CFPS2018) with the 2016-2018 National Forest Cities Construction List, resulting in a final sample of 20,041. Oprobit, Ologit, Instrumental Variable technique (2SLS) and interaction term analysis were used as the main research methods in this article. Results: The findings indicate that: (1) The construction of National Forest Cities significantly improves the residents' health in terms of both physical and mental health, and this conclusion is still valid after a series of robustness tests. (2) On the one hand, National Forest Cities promote residents' health by reducing air pollutants such as SO2 and soot to reduce residents' health risk exposure; On the other hand, it promotes residents' health by positively guiding them to engage in healthy behaviors. (3) National Forest Cities have a greater effect on the health of urban residents, older adult and lower-income group, suggesting that National Forest Cities are a public benefit. Conclusions: The construction of National Forest Cities is a public welfare that promotes residents' health, and it is an important revelation for accelerating the realization of the Healthy China Strategy. The article provides new empirical evidence for understanding the welfare effects of forest cities and offers new practical paths for improving residents' health.

Statistical inference for diagnostic test accuracy studies with multiple comparisons.

Diagnostic accuracy studies assess the sensitivity and specificity of a new index test in relation to an established comparator or the reference standard. The development and selection of the index test are usually assumed to be conducted prior to the accuracy study. In practice, this is often violated, for instance, if the choice of the (apparently) best biomarker, model or cutpoint is based on the same data that is used later for validation purposes. In this work, we investigate several multiple comparison procedures which provide family-wise error rate control for the emerging multiple testing problem. Due to the nature of the co-primary hypothesis problem, conventional approaches for multiplicity adjustment are too conservative for the specific problem and thus need to be adapted. In an extensive simulation study, five multiple comparison procedures are compared with regard to statistical error rates in least-favourable and realistic scenarios. This covers parametric and non-parametric methods and one Bayesian approach. All methods have been implemented in the new open-source R package cases which allows us to reproduce all simulation results. Based on our numerical results, we conclude that the parametric approaches (maxT and Bonferroni) are easy to apply but can have inflated type I error rates for small sample sizes. The two investigated Bootstrap procedures, in particular the so-called pairs Bootstrap, allow for a family-wise error rate control in finite samples and in addition have a competitive statistical power.

Simple but powerful interactive data analysis in R with R/LinekdCharts.

In research involving data-rich assays, exploratory data analysis is a crucial step. Typically, this involves jumping back and forth between visualizations that provide overview of the whole data and others that dive into details. For example, it might be helpful to have one chart showing a summary statistic for all samples, while a second chart provides details for points selected in the first chart. We present R/LinkedCharts, a framework that renders this task radically simple, requiring very few lines of code to obtain complex and general visualization, which later can be polished to provide interactive data access of publication quality.

Association between systemic immune inflammation index, systemic inflammation response index and adult psoriasis: evidence from NHANES.

Background: The systemic immune-inflammation index (SII) and systemic inflammation response index (SIRI) are both novel biomarkers and predictors of inflammation. Psoriasis is a skin disease characterized by chronic inflammation. This study aimed to investigate the potential association between SII, SIRI, and adult psoriasis. Methods: Data of adults aged 20 to 80 years from the National Health and Nutrition Examination Survey (NHANES) (2003-2006, 2009-2014) were utilized. The K-means method was used to group SII and SIRI into low, medium, and high-level clusters. Additionally, SII or SIRI levels were categorized into three groups: low (1st-3rd quintiles), medium (4th quintile), and high (5th quintile). The association between SII-SIRI pattern, SII or SIRI individually, and psoriasis was assessed using multivariate logistic regression models. The results were presented as odds ratios (ORs) and confidence intervals (CIs). Restricted cubic spline (RCS) regression, subgroup, and interaction analyses were also conducted to explore the potential non-linear and independent relationships between natural log-transformed SII (lnSII) levels or SIRI levels and psoriasis, respectively. Results: Of the 18208 adults included in the study, 511 (2.81%) were diagnosed with psoriasis. Compared to the low-level group of the SII-SIRI pattern, participants in the medium-level group had a significantly higher risk for psoriasis (OR = 1.40, 95% CI: 1.09, 1.81, p-trend = 0.0031). In the analysis of SII or SIRI individually, both SII and SIRI were found to be positively associated with the risk of psoriasis (high vs. low group OR = 1.52, 95% CI: 1.18, 1.95, p-trend = 0.0014; OR = 1.48, 95% CI: 1.12, 1.95, p-trend = 0.007, respectively). Non-linear relationships were observed between lnSII/SIRI and psoriasis (both p-values for overall < 0.05, p-values for nonlinearity < 0.05). The association between SII levels and psoriasis was stronger in females, obese individuals, people with type 2 diabetes, and those without hypercholesterolemia. Conclusion: We observed positive associations between SII-SIRI pattern, SII, SIRI, and psoriasis among U.S. adults. Further well-designed studies are needed to gain a better understanding of these findings.

Latin American Forensic Odontology: A Scoping Review of its Current Research and the Objective/Subjective Nature of its Studies

SUMMARY: The new paradigm in Forensic Sciences initiated by the entry of genetics (the current standard of legal evidence) and accentuated by recognized wrongful convictions derived from experts today in the eye of criticism, has highlighted the potential for bias and error in forensic disciplines when they depend on human interpretation and subjectivity, which has not been avoided by Forensic Odontology (FO). However, a subjective judgment is not necessarily wrong, so the refinement of processes, the development of standards, and robust research can contribute to the validity of interpretation to increase objectivity. Latin America (LATAM) has its own realities and needs, which have conditioned the priorities and objectives of FO research. A scoping review is presented to systematically map the investigation of LATAM researchers and identify the objective or subjective nature of their assessments. LATAM shows interesting productivity and intentions to adhere to international standards, with Brazil leading this research significantly, followed by Chile and Colombia, among others. However, there is a disproportionate approach in certain lines of research (dental age estimation), and needs to address other quantitative studies, and to improve the visibility of the LATAM FO research.

El nuevo paradigma en ciencias forenses iniciado por la entrada de la genética (el actual estándar de la evidencia jurídica), y acentuado por reconocidas condenas injustas derivadas de pericias hoy en el ojo de la crítica, ha destacado el potencial de sesgo y error que poseen algunas disciplinas forenses cuando dependen de la interpretación humana y la subjetividad, lo cual no ha sido ajeno a la odontología forense (OF). Sin embargo, un juicio subjetivo no necesariamente es erróneo, con lo que el refinamiento de procesos, el desarrollo de estándares y la investigación robusta pueden contribuir a validar esa interpretación para aumentar su objetividad. Latinoamérica (LATAM) posee realidades y necesidades propias que han condicionado las prioridades y objetivos de la investigación en OF. Se presenta una revisión con búsqueda sistemática para mapear sistemáticamente la investigación en OF realizada por investigadores latinoamericanos, así como identificar la naturaleza objetiva o subjetiva de sus evaluaciones. LATAM demuestra una productividad interesante e intenciones de adherirse a estándares internacionales, con Brasil liderando significativamente esta investigación, seguido por Chile y Colombia entre otros. Sin embargo, se observa un enfoque desproporcionado en ciertas líneas de investigación (estimación de edad dental particularmente), y necesidad tanto de abordar otros estudios cuantitativos como de mejorar la visibilidad de la investigación latinoamericana en OF.

Efficient designs and analysis of two-phase studies with longitudinal binary data.

Researchers interested in understanding the relationship between a readily available longitudinal binary outcome and a novel biomarker exposure can be confronted with ascertainment costs that limit sample size. In such settings, two-phase studies can be cost-effective solutions that allow researchers to target informative individuals for exposure ascertainment and increase estimation precision for time-varying and/or time-fixed exposure coefficients. In this paper, we introduce a novel class of residual-dependent sampling (RDS) designs that select informative individuals using data available on the longitudinal outcome and inexpensive covariates. Together with the RDS designs, we propose a semiparametric analysis approach that efficiently uses all data to estimate the parameters. We describe a numerically stable and computationally efficient EM algorithm to maximize the semiparametric likelihood. We examine the finite sample operating characteristics of the proposed approaches through extensive simulation studies, and compare the efficiency of our designs and analysis approach with existing ones. We illustrate the usefulness of the proposed RDS designs and analysis method in practice by studying the association between a genetic marker and poor lung function among patients enrolled in the Lung Health Study (Connett et al, 1993).

Frequentist, Bayesian Analysis and Complementary Statistical Tools for Geriatric and Rehabilitation Fields: Are Traditional Null-Hypothesis Significance Testing Methods Sufficient?

Null hypothesis significant testing (NHST) is the dominant statistical approach in the geriatric and rehabilitation fields. However, NHST is routinely misunderstood or misused. In this case, the findings from clinical trials would be taken as evidence of no effect, when in fact, a clinically relevant question may have a "non-significant" p-value. Conversely, findings are considered clinically relevant when significant differences are observed between groups. To assume that p-value is not an exclusive indicator of an association or the existence of an effect, researchers should be encouraged to report other statistical analysis approaches as Bayesian analysis and complementary statistical tools alongside the p-value (eg, effect size, confidence intervals, minimal clinically important difference, and magnitude-based inference) to improve interpretation of the findings of clinical trials by presenting a more efficient and comprehensive analysis. However, the focus on Bayesian analysis and secondary statistical analyses does not mean that NHST is less important. Only that, to observe a real intervention effect, researchers should use a combination of secondary statistical analyses in conjunction with NHST or Bayesian statistical analysis to reveal what p-values cannot show in the geriatric and rehabilitation studies (eg, the clinical importance of 1kg increase in handgrip strength in the intervention group of long-lived older adults compared to a control group). This paper provides potential insights for improving the interpretation of scientific data in rehabilitation and geriatric fields by utilizing Bayesian and secondary statistical analyses to better scrutinize the results of clinical trials where a p-value alone may not be appropriate to determine the efficacy of an intervention.

Dashing Growth Curves: a web application for rapid and interactive analysis of microbial growth curves.

BACKGROUND: Recording and analyzing microbial growth is a routine task in the life sciences. Microplate readers that record dozens to hundreds of growth curves simultaneously are increasingly used for this task raising the demand for their rapid and reliable analysis. RESULTS: Here, we present Dashing Growth Curves, an interactive web application ( http://dashing-growth-curves.ethz.ch/ ) that enables researchers to quickly visualize and analyze growth curves without the requirement for coding knowledge and independent of operating system. Growth curves can be fitted with parametric and non-parametric models or manually. The application extracts maximum growth rates as well as other features such as lag time, length of exponential growth phase and maximum population size among others. Furthermore, Dashing Growth Curves automatically groups replicate samples and generates downloadable summary plots for of all growth parameters. CONCLUSIONS: Dashing Growth Curves is an open-source web application that reduces the time required to analyze microbial growth curves from hours to minutes.

Use of win time for ordered composite endpoints in clinical trials.

Consider the choice of outcome for overall treatment benefit in a clinical trial which measures the first time to each of several clinical events. We describe several new variants of the win ratio that incorporate the time spent in each clinical state over the common follow-up, where clinical state means the worst clinical event that has occurred by that time. One version allows restriction so that death during follow-up is most important, while time spent in other clinical states is still accounted for. Three other variants are described; one is based on the average pairwise win time, one creates a continuous outcome for each participant based on expected win times against a reference distribution and another that uses the estimated distributions of clinical state to compare the treatment arms. Finally, a combination testing approach is described to give robust power for detecting treatment benefit across a broad range of alternatives. These new methods are designed to be closer to the overall treatment benefit/harm from a patient's perspective, compared to the ordinary win ratio. The new methods are compared to the composite event approach and the ordinary win ratio. Simulations show that when overall treatment benefit on death is substantial, the variants based on either the participants' expected win times (EWTs) against a reference distribution or estimated clinical state distributions have substantially higher power than either the pairwise comparison or composite event methods. The methods are illustrated by re-analysis of the trial heart failure: a controlled trial investigating outcomes of exercise training.