Quantile regression and empirical likelihood for the analysis of longitudinal data with monotone missing responses due to dropout,with applications to quality of life measurements from clinical trials

The analysis of quality of life (QoL) data can be challenging due to the skewness of responses and the presence of missing data. In this paper, we propose a new weighted quantile regression method for estimating the conditional quantiles of QoL data with responses missing at random. The proposed method makes use of the correlation information within the same subject from an auxiliary mean regression model to enhance the estimation efficiency and takes into account of missing data mechanism. The asymptotic properties of the proposed estimator have been studied and simulations are also conducted to evaluate the performance of the proposed estimator. The proposed method has also been applied to the analysis of the QoL data from a clinical trial on early breast cancer, which motivated this study.     

Patient-Centered Framework for Rehabilitation Research in Outpatient Settings

Conducting high-quality clinical research is dependent on merging scientific rigor with the clinical environment. This is often a complex endeavor that may include numerous barriers and competing interests. Overcoming these challenges and successfully integrating clinical research programs into clinical practice settings serving rehabilitation outpatients is beneficial from both a logistical perspective (eg, supports efficient and successful research procedures) and the establishment of a truly patient-centered research approach. Leveraging our experience with navigating this research-clinical care relationship, this article (1) proposes the Patient-Centered Framework for Rehabilitation Research, a model for integrating patient-centered research in an outpatient clinical setting that incorporates a collaborative, team-based model encompassing patient-centered values, as well as strategies for recruitment and retention, with a focus on populations living with disabilities or chronic diseases; (2) describes application of this framework in a comprehensive specialty multiple sclerosis center with both general strategies and specific examples to guide adaptation and implementation in other settings; and (3) discusses the effect of the framework as a model in 1 center, as well as the need for additional investigation and adaptation for other populations. The 5 interconnected principles incorporated in the Framework and which prioritize patient-centeredness include identifying shared values, partnering with the clinical setting, engaging with the population, building relationships with individuals, and designing accessible procedures. The Patient-Centered Framework for Rehabilitation Research is a model presented as an adaptable roadmap to guide researchers in hopes of not only improving individual patients’ experiences but also the quality and relevance of rehabilitation research as a whole. Future investigation is needed to test the Framework in other settings.     

The influence of environmental and health indicators on premature mortality: An empirical analysis of the City of Toronto's 140 neighborhoods

The objective of this paper was to assess the link between premature mortality and a combination of neighbourhood contextual (environmental and health) and compositional (socioeconomic and demographic) characteristics. We statistically and spatially examined six environmental variables (ultrafine particles, carcinogenic and non-carcinogenic pollutants, pollution released to air, tree cover, and walkability index), six health service indicators (number health providers, breast, colorectal and cervical cancer screening uptake rates, student nutrition program uptake rates, and healthy food index), and eight socioeconomic indicators (total income, Gini coefficient, two age categories – below and above 40 years, proportion of females to males, visible minorities, Indigenous peoples, education, less than grade 9) among 140 neighbourhoods of the City of Toronto in Ontario (Canada). We applied principal component analysis to identify patterns and to reduce the number of explanatory variables into combined component axes that represent unique variation in these confounded and overlapping factors. We then applied regression analysis to model the relationship between the indices of enviro-health and socioeconomics and their potential relationship with premature mortality. Residual spatial analysis was used to investigate any remaining spatial structure (such as neighbourhoods with higher residual premature mortality rates). Neighbourhood Equity Index was correlated with our enviro-health and socioeconomic indices. Premature mortality within neighbourhoods was predicted by poor cancer screenings, pollution, lack of tree canopy, increased uptake of student nutrition programs and high walkability index. A negative association between premature mortality and pollution was associated low walkability index and presence of visible minorities within neighbourhoods. There was some unexplained residual spatial variation in our model of premature mortality - especially along the shores of Lake Ontario and in neighbourhoods with major highways or road corridors: premature mortality in Toronto neighbourhoods was higher than expected along highway-corridor neighbourhoods and shorelines. Our analysis revealed a significant relationship between neighbourhood contextual features – both environmental and health – and premature mortality, suggesting that these contextual components of neighbourhoods can predict rates of urban premature mortality in Toronto.     

Impact of different types of olive oil on cardiovascular risk factors: A systematic review and network meta-analysis

Background and aimThis network meta-analysis (NMA) compares the effects of different types of olive oil (OO) on cardiovascular risk factors.Methods and resultsLiterature search was conducted on three electronic databases (Medline, Web of Science, and Cochrane Central). Inclusion criteria: Randomized controlled trials (RCTs) (≥3 weeks duration of intervention) comparing at least two of the following types of OO: refined OO (ROO), mixed OO (MOO), low phenolic (extra) virgin OO (LP(E)VOO), and high phenolic (extra) virgin OO (HP(E)VOO). Random-effects NMA was performed for seven outcomes; and surface under the cumulative ranking curve (SUCRA) was estimated, using an analytical approach (P-score). Thirteen RCTs (16 reports) with 611 mainly healthy participants (mean age: 26–70 years) were identified. No differences for total cholesterol, HDL-cholesterol, triacylglycerols, and diastolic blood pressure were observed comparing ROO, MOO, LP(E)VOO and HP(E)VOO. HP(E)VOO slightly reduce LDL-cholesterol (LDL-C) compared to LP(E)VOO (mean difference [MD]: −0.14 mmol/L, 95%–CI: −0.28, −0.01). Both, HP(E)VOO and LP(E)VOO reduces SBP compared to ROO (range of MD: −2.99 to −2.87 mmHg), and HP(E)VOO may improve oxidized LDL-cholesterol (oxLDL-C) compared to ROO (standardized MD: −0.68, 95%–CI: −1.31, −0.04). In secondary analyses, EVOO may reduce oxLDL-C compared to ROO, and a dose-response relationship between higher intakes of phenolic compounds from OO and lower SBP and oxLDL-C values was detected. HP(E)VOO was ranked as best treatment for LDL-C (P-score: 0.83), oxLDL-C (0.88), and SBP (0.75).ConclusionsHP(E)VOO may improve some cardiovascular risk factors, however, public health implications are limited by overall low or moderate certainty of evidence.     

转录调节因子c-Myb通过炎症因子Ccl2抑制乳腺癌肺转移的实验研究

目的:研究乳腺癌细胞中转录调节因子c-Myb的表达对肺转移的影响及其分子机制。方法:构建c-Myb高表达的4T1乳腺癌细胞株，种植小鼠构建乳腺癌动物模型，检测实验动物肺转移情况，通过荷瘤抑制试验检测肺转移抑制效果。提取组织进行荧光PCR检测相关炎症因子的表达。利用Medisapiens数据库的生物信息学资源，对c-Myb和Ccl2的表达与乳腺癌患者的预后进行分析。结果:高表达c-Myb的荷瘤小鼠肺部转移灶明显减少，其中炎症相关因子表达受限。由数据库分析得出，c-Myb高表达患者生存期得以延长。结论:乳腺癌中c-Myb的表达能够通过炎症因子Ccl2抑制肺转移从而延长患者生存期。     

Challenges with Forecasting Budget Impact: A Case Study of Six ICER Reports

Background

Payers frequently rely on budget impact model (BIM) results to help determine drug coverage policy and its effect on their bottom line. It is unclear whether BIMs typically overestimate or underestimate real-world budget impact.