UNASSIGNED: It has been recognized that HIV-related stigma hinders efforts in testing, treatment, and prevention. In this systematic review, we aimed to summarize available findings on the association between HIV-related stigma and age, social support, educational status, depression, employment status, wealth index, gender, residence, knowledge about HIV, marital status, duration since diagnosis, and disclosure status using a large number of studies.
UNASSIGNED: Electronic databases including Scopus, Medline/PubMed, Web of Sciences (WOS), Cochrane Library, Google Scholar, and Open Research Dataset Challenge were systematically searched until 15 April 2023. We included all kinds of HIV-stigma studies, regardless of language, publishing date, or geographic location. The inclusion criteria were met by 40 studies, with a total of 171,627 patients. A mixed-effect model was used to pool estimates and evaluate publication bias, as well as to conduct sensitivity analysis.
UNASSIGNED: Factors such as older age, social support, greater education, higher socioeconomic status, good knowledge of HIV, and longer years of living with HIV significantly lowered the likelihood of HIV-related stigma. Contrarily, factors such as depression, residing in rural areas, female respondents, and non-disclosure of HIV status were significantly associated with a high risk of HIV-related stigma.
UNASSIGNED: To combat systemic HIV-associated stigma, it is crucial to develop wholesome and comprehensive social methods by raising community-level HIV awareness. In addition to activism, local economic development is also crucial for creating thriving communities with a strong social fabric.

OBJECTIVE: We aimed to demonstrate the use of quantitative bias analysis (QBA), which reveals the effects of systematic error, including confounding, misclassification and selection bias, on study results in epidemiological studies published in the period from 2010 to mid-23.
METHODS: The articles identified through a keyword search using Pubmed and Scopus were included in the study. The articles obtained from this search were eliminated according to the exclusion criteria, and the articles in which QBA analysis was applied were included in the detailed evaluation.
RESULTS: It can be said that the application of QBA analysis has gradually increased over the 13-year period. Accordingly, the number of articles in which simple is used as a method in QBA analysis is 9 (9.89%), the number of articles in which the multidimensional approach is used is 10 (10.99%), the number of articles in which the probabilistic approach is used is 60 (65.93%) and the number of articles in which the method is not specified is 12 (13.19%). The number of articles with misclassification bias model is 44 (48.35%), the number of articles with uncontrolled confounder(s) bias model is 32 (35.16%), the number of articles with selection bias model is 7 (7.69%) and the number of articles using more than one bias model is 8 (8.79%). Of the 49 (53.85%) articles in which the bias parameter source was specified, 19 (38.78%) used internal validation, 26 (53.06%) used external validation and 4 (8.16%) used educated guess, data constraints and hypothetical data. Probabilistic approach was used as a bias method in 60 (65.93%) of the articles, and mostly beta (8 [13.33%)], normal (9 [15.00%]) and uniform (8 [13.33%]) distributions were selected.
CONCLUSIONS: The application of QBA is rare in the literature but is increasing over time. Future researchers should include detailed analyzes such as QBA analysis to obtain inferences with higher evidence value, taking into account systematic errors.

Alzheimer\'s disease is a complex, multi-factorial, and multi-parametric neurodegenerative etiology. Mathematical models can help understand such a complex problem by providing a way to explore and conceptualize principles, merging biological knowledge with experimental data into a model amenable to simulation and external validation, all without the need for extensive clinical trials. We performed a scoping review of mathematical models describing the onset and evolution of Alzheimer\'s disease as a result of biophysical factors following the PRISMA standard. Our search strategy applied to the PubMed database yielded 846 entries. After using our exclusion criteria, only 17 studies remained from which we extracted data, which focused on three aspects of mathematical modeling: how authors addressed continuous time (since even when the measurements are punctual, the biological processes underlying Alzheimer\'s disease evolve continuously), how models were solved, and how the high dimensionality and non-linearity of models were managed. Most articles modeled Alzheimer\'s disease at the cellular level, operating on a short time scale (e.g., minutes or hours), i.e., the micro view (12/17); the rest considered regional or brain-level processes with longer timescales (e.g., years or decades) (the macro view). Most papers were concerned primarily with amyloid beta (n = 8), few described both amyloid beta and tau proteins (n = 3), while some considered more than these two factors (n = 6). Models used partial differential equations (n = 3), ordinary differential equations (n = 7), and both partial differential equations and ordinary differential equations (n = 3). Some did not specify their mathematical formalism (n = 4). Sensitivity analyses were performed in only a small number of papers (4/17). Overall, we found that only two studies could be considered valid in terms of parameters and conclusions, and two more were partially valid. This puts the majority (n = 13) as being either invalid or with insufficient information to ascertain their status. This was the main finding of our paper, in that serious shortcomings make their results invalid or non-reproducible. These shortcomings come from insufficient methodological description, poor calibration, or the impossibility of experimentally validating or calibrating the model. Those shortcomings should be addressed by future authors to unlock the usefulness of mathematical models in Alzheimer\'s disease.

There\'s an increasing body of evidence on vitamin D deficiency and the risk of neuromyelitis optica spectrum disorder (NMOSD). The aim of this meta-analysis was to assess serum vitamin D levels in patients with NMOSD versus healthy controls.
We searched PubMed, EMBASE, Cochrane Library, Web of Science and CNKI for publications up to November 2022 and explored the relationship between NMOSD and serum vitamin D levels. The standardized mean differences (SMD) and 95% confidence intervals (CI) were calculated using a random-effects model. Subgroup analysis and sensitivity analysis were applied to explore the sources of heterogeneity. Begg\'s test, Egger\'s test, and Egger\'s funnel plot were adopted to evaluate publication bias.
6 studies (including 319 patients and 595 healthy controls) met the inclusion criteria and all compared vitamin D levels in patients with NMOSD versus healthy controls. Levels of serum vitamin D detected in NMOSD patients were significantly lower than those in healthy controls (SMD=-1.57, 95% CI=-2.27 ∼ -0.87, P<0.001, I2 = 94.6%). The results of the different sensitivity analysis remained statistically significant, which demonstrated the robustness of the meta-analysis. There was no significant publication bias in our meta-analysis (P>0.05).
Patients with NMOSD showed significantly reduced vitamin D levels compared with healthy controls. Our findings highlighted the importance of measuring vitamin D levels in patients with NMOSD. Multi-center randomized controlled trials with large samples will further confirm whether the association is casual and modifiable.

This review addresses the common problem of missing patient-reported outcome (PRO) data in clinical trials by assessing the current practice of their statistical handling as reported in publications of randomized controlled trials (RCTs) in patients with breast cancer.
We searched PubMed to identify RCTs evaluating biomedical treatments in breast cancer patients with at least one PRO endpoint published between January 2019 and February 2022. Two reviewers independently assessed the eligibility of the publications for this scoping review and extracted prespecified information on missing PRO data and related statistical practices.
Of 1,598 publications identified, 118 trials met the inclusion criteria. Eighty-eight (74.6%) trials reported the extent of missing data, with 11 (9.3%) not containing any missing PRO data. Twenty-one (19.6%) trials explicitly stated the statistical approach for handling missing data, with a preference for single imputation over multiple imputation approaches (57.2%/19.0%). Only six (5.6%) trials reported a sensitivity analysis to examine the extent to the results being affected by changes in assumptions made about missing PRO data.
International efforts to raise awareness of the importance of accurately reporting state-of-the-art handling of missing PRO data are not yet fully reflected in the current literature of breast cancer RCTs.

OBJECTIVE: This study aimed to review, assess, and report the characteristics and strategies of sensitivity analyses (SAs) that were performed in the context of published economic evaluations of immuno-oncology drugs.
METHODS: The systematic literature search was conducted in Scopus and MEDLINE for articles published from 2005 to 2021. Study selection, based on a predefined set of criteria, was performed by 2 reviewers independently. We included economic evaluations of Food and Drug Administration-approved immuno-oncology drugs that were published in English and assessed the accompanying SAs on a set of items, including the range justification of the baseline parameters within the deterministic SA, the provisions for the correlation/overlay between parameters, and the justification of the chosen parameter distribution for the probabilistic SA, among others.
RESULTS: A total of 98 of 295 publications met the inclusion criteria. A total of 90 studies included a one-way and probabilistic SA and 16 of 98 studies had one-way and scenario analysis, alone or together with probabilistic analysis. Most studies provide explicit references as to the choice of parameters and values; nevertheless, there is a lack of a reference of correlation/overlay between parameters in most of the evaluations. In 26 of 98 studies, the most influential parameter for the incremental cost-effectiveness ratio was the under-evaluation drug cost.
CONCLUSIONS: Most of included articles contained an SA that was implemented according to commonly accepted published guidance. The under-evaluation drug cost, the estimates of progression-free survival, the hazard ratio for overall survival, and the time horizon of the analysis seem to play an important part in the robustness of the outcomes.

Rapid urbanization and consuming lifestyles have intensified air pollution in urban areas. Air pollution in megacities has imposed severe environmental damages to human health. Proper management of the issue necessitates identification of the share of emission sources. Therefore, numerous research works have studied the apportionment of the total emissions and observed concentrations among different emissions sources. In this research, a comprehensive review is conducted to compare the source apportioning results for ambient air PM2.5 in the megacity of Tehran, the capital of Iran. One hundred seventy-seven pieces of scientific literatures, published between 2005 and 2021, were reviewed. The reviewed research are categorized according to the source apportioning methods: emission inventory (EI), source apportionment (SA), and sensitivity analysis of the concentration to the emission sources (SNA). The possible reasons for inconsistency among the results are discussed according to the scope of the studies and the implemented methods. Although 85% of the reviewed original estimates identify that mobile sources contribute to more thant 60% of Tehran air pollution, the distribution of vehicle types and modes are clearly inconsistent among the EI studies. Our review suggests that consistent results in the SA studies in different locations in central Tehran may indicate the reliability of this method for the identification of the type and share of the emission sources. In contrast, differences among the geographical and sectoral coverage of the EI studies and the disparities among the emission factors and activity data have caused significant deviations among the reviewed EI studies. Also, it is shown that the results of the SNA studies are highly dependent on the categorization type, model capabilities and EI presumptions and data input to the pollutant dispersion modelings. As a result, integrated source apportioning in which the three methods complement each other\'s results is necessary for consistent air pollution management in megacities.
UNASSIGNED: The online version contains supplementary material available at 10.1007/s40201-023-00855-0.

Failure to appropriately account for unmeasured confounding may lead to erroneous conclusions. Quantitative bias analysis (QBA) can be used to quantify the potential impact of unmeasured confounding or how much unmeasured confounding would be needed to change a study\'s conclusions. Currently, QBA methods are not routinely implemented, partly due to a lack of knowledge about accessible software. Also, comparisons of QBA methods have focused on analyses with a binary outcome.
We conducted a systematic review of the latest developments in QBA software published between 2011 and 2021. Our inclusion criteria were software that did not require adaption (i.e., code changes) before application, was still available in 2022, and accompanied by documentation. Key properties of each software tool were identified. We provide a detailed description of programs applicable for a linear regression analysis, illustrate their application using two data examples and provide code to assist researchers in future use of these programs.
Our review identified 21 programs with [Formula: see text] created post 2016. All are implementations of a deterministic QBA with [Formula: see text] available in the free software R. There are programs applicable when the analysis of interest is a regression of binary, continuous or survival outcomes, and for matched and mediation analyses. We identified five programs implementing differing QBAs for a continuous outcome: treatSens, causalsens, sensemakr, EValue, and konfound. When applied to one of our illustrative examples, causalsens incorrectly indicated sensitivity to unmeasured confounding whereas the other four programs indicated robustness. sensemakr performs the most detailed QBA and includes a benchmarking feature for multiple unmeasured confounders.
Software is now available to implement a QBA for a range of different analyses. However, the diversity of methods, even for the same analysis of interest, presents challenges to their widespread uptake. Provision of detailed QBA guidelines would be highly beneficial.

Tree-based machine learning methods have gained traction in the statistical and data science fields. They have been shown to provide better solutions to various research questions than traditional analysis approaches. To encourage the uptake of tree-based methods in health research, we review the methodological fundamentals of three key tree-based machine learning methods: random forests, extreme gradient boosting and Bayesian additive regression trees. We further conduct a series of case studies to illustrate how these methods can be properly used to solve important health research problems in four domains: variable selection, estimation of causal effects, propensity score weighting and missing data. We exposit that the central idea of using ensemble tree methods for these research questions is accurate prediction via flexible modeling. We applied ensemble trees methods to select important predictors for the presence of postoperative respiratory complication among early stage lung cancer patients with resectable tumors. We then demonstrated how to use these methods to estimate the causal effects of popular surgical approaches on postoperative respiratory complications among lung cancer patients. Using the same data, we further implemented the methods to accurately estimate the inverse probability weights for a propensity score analysis of the comparative effectiveness of the surgical approaches. Finally, we demonstrated how random forests can be used to impute missing data using the Study of Women\'s Health Across the Nation data set. To conclude, the tree-based methods are a flexible tool and should be properly used for health investigations.

UNASSIGNED: The role of early economic evaluation (EEE) in the development of medical technology has been increasingly recognized; however, data on the use of EEE in surgical technology are sparse. The objective of this review was to explore the use of EEE in the development of surgical technologies, with emphasis on how uncertainty has been addressed.
UNASSIGNED: A systematic review was conducted, and original articles employing any form of EEE of surgical technology were selected for review, with 10 studies included in the analysis. These studies demonstrated significant variation in the approach to managing parameter uncertainty, specifically regarding the type of analysis used and the inclusion of effectiveness parameters in sensitivity analysis. The conclusions drawn did not appear to factor in uncertainty in the models.
UNASSIGNED: Approaches to handling parameter uncertainty in previous EEEs of surgical technology have been limited, with some studies failing to address parameter uncertainty. In addition, EEEs do not appear to follow established guidelines with respect to the use of sensitivity analyses. It is important that EEEs of surgical technology address parameter uncertainty in order to draw more robust conclusions from the analysis and allow investors to consider this uncertainty when making investment decisions.