Statistical Methods for Non-Ignorable Missing Data With Applications to Quality-of-Life Data.

Researchers increasingly use more and more survey studies, and design medical studies to better understand the relationships of patients, physicians, their health care system utilization, and their decision making processes in disease prevention and management. Longitudinal data is widely used to capture trends occurring over time. Each subject is observed as time progresses, but a common problem is that repeated measurements are not fully observed due to missing response or loss to follow up. An individual can move in and out of the observed data set during a study, giving rise to a large class of distinct non-monotone missingness patterns. In such medical studies, sample sizes are often limited due to restrictions on disease type, study design and medical information availability. Small sample sizes with large proportions of missing information are problematic for researchers trying to understand the experience of the total population. The information in the data collected may produce biased estimators if, for example, the patients who don\u27t respond have worse outcomes, or the patients who answered unknown are those without access to medical or non-medical information or care. Data modeled without considering this missing information may cause biased results. A first-order Markov dependence structure is a natural data structure to model the tendency of changes. In my first project, we developed a Markov transition model using a full-likelihood based algorithm to provide robust estimation accounting for non-ignorable\u27\u27 missingness information, and applied it to data from the Penn Center of Excellence in Cancer Communication Research. In my second project, we extended the method to a pseudo-likelihood based approach by considering only pairs of adjacent observations to significantly ease the computational complexities of the full-likelihood based method proposed in the first project. In my third project, we proposed a two stage pseudo hidden Markov model to analyze the association between quality of life measurements and cancer treatments from a randomized phase III trial (RTOG 9402) in brain cancer patients. By incorporating selection models and shared parameter models with a hidden Markov model, this approach provides targeted identification of treatment effects

TKwinFormer: Top k Window Attention in Vision Transformers for Feature Matching

Local feature matching remains a challenging task, primarily due to difficulties in matching sparse keypoints and low-texture regions. The key to solving this problem lies in effectively and accurately integrating global and local information. To achieve this goal, we introduce an innovative local feature matching method called TKwinFormer. Our approach employs a multi-stage matching strategy to optimize the efficiency of information interaction. Furthermore, we propose a novel attention mechanism called Top K Window Attention, which facilitates global information interaction through window tokens prior to patch-level matching, resulting in improved matching accuracy. Additionally, we design an attention block to enhance attention between channels. Experimental results demonstrate that TKwinFormer outperforms state-of-the-art methods on various benchmarks. Code is available at: https://github.com/LiaoYun0x0/TKwinFormer.Comment: 11 pages, 7 figure

A transition model for quality-of-life data with non-ignorable non-monotone missing data

In this paper, we consider a full likelihood method to analyze continuous longitudinal responses with nonignorable non-monotone missing data. We consider a transition probability model for the missingness mechanism. A first-order Markov dependence structure is assumed for both the missingness mechanism and observed data. This process fits the natural data structure in the longitudinal framework. Our main interest is in estimating the parameters of the marginal model and evaluating the missing-at-random assumption in the Effects of Public Information Study, a cancer-related study recently conducted at the University of Pennsylvania. We also present a simulation study to assess the performance of the model

Puerarin Alleviates Neuropathic Pain by Inhibiting Neuroinflammation in Spinal Cord

Neuropathic pain responds poorly to drug treatments, and partial relief is achieved in only about half of the patients. Puerarin, the main constituent of Puerariae Lobatae Radix, has been used extensively in China to treat hypertension and tumor. The current study examined the effects of puerarin on neuropathic pain using two most commonly used animal models: chronic constriction injury (CCI) and diabetic neuropathy. We found that consecutive intrathecal administration of puerarin (4–100 nM) for 7 days inhibited the mechanical and thermal nociceptive response induced by CCI and diabetes without interfering with the normal pain response. Meanwhile, in both models puerarin inhibited the activation of microglia and astroglia in the spinal dorsal horn. Puerarin also reduced the upregulated levels of nuclear factor-κB (NF-κB) and other proinflammatory cytokines, such as IL-6, IL-1β, and TNF-α, in the spinal cord. In summary, puerarin alleviated CCI- and diabetes-induced neuropathic pain, and its effectiveness might be due to the inhibition of neuroinflammation in the spinal cord. The anti-inflammation effect of puerarin might be related to the suppression of spinal NF-κB activation and/or cytokines upregulation. We conclude that puerarin has a significant effect on alleviating neuropathic pain and thus may serve as a therapeutic approach for neuropathic pain

Independent effects of the triglyceride-glucose index on all-cause mortality in critically ill patients with coronary heart disease: analysis of the MIMIC-III database

Abstract Background The triglyceride-glucose (TyG) index is a reliable alternative biomarker of insulin resistance (IR). However, whether the TyG index has prognostic value in critically ill patients with coronary heart disease (CHD) remains unclear. Methods Participants from the Medical Information Mart for Intensive Care III (MIMIC-III) were grouped into quartiles according to the TyG index. The primary outcome was in-hospital all-cause mortality. Cox proportional hazards models were constructed to examine the association between TyG index and all-cause mortality in critically ill patients with CHD. A restricted cubic splines model was used to examine the associations between the TyG index and outcomes. Results A total of 1,618 patients (65.14% men) were included. The hospital mortality and intensive care unit (ICU) mortality rate were 9.64% and 7.60%, respectively. Multivariable Cox proportional hazards analyses indicated that the TyG index was independently associated with an elevated risk of hospital mortality (HR, 1.71 [95% CI 1.25–2.33] P = 0.001) and ICU mortality (HR, 1.50 [95% CI 1.07–2.10] P = 0.019). The restricted cubic splines regression model revealed that the risk of hospital mortality and ICU mortality increased linearly with increasing TyG index (P for non-linearity = 0.467 and P for non-linearity = 0.764). Conclusions The TyG index was a strong independent predictor of greater mortality in critically ill patients with CHD. Larger prospective studies are required to confirm these findings

Puerarin Alleviates Neuropathic Pain by Inhibiting Neuroinflammation in Spinal Cord

National Natural Science Foundation [30973912]; Suhuan Liu [81270901]; Xuejun Li [81073113]; Key Project of Fujian Provincial Science and Technology Planning programs [2012D60]; Xiamen Innovation Programfor Outstanding Youth Scientist [2011S0446]; Xiamen Science and Technology Bureau (Xiamen Research Platform for Systems Biology of Metabolic Disease) [3502Z20100001]Neuropathic pain responds poorly to drug treatments, and partial relief is achieved in only about half of the patients. Puerarin, the main constituent of Puerariae Lobatae Radix, has been used extensively in China to treat hypertension and tumor. The current study examined the effects of puerarin on neuropathic pain using two most commonly used animal models: chronic constriction injury (CCI) and diabetic neuropathy. We found that consecutive intrathecal administration of puerarin (4-100 nM) for 7 days inhibited the mechanical and thermal nociceptive response induced by CCI and diabetes without interfering with the normal pain response. Meanwhile, in both models puerarin inhibited the activation of microglia and astroglia in the spinal dorsal horn. Puerarin also reduced the upregulated levels of nuclear factor-kappa B (NF-kappa B) and other proinflammatory cytokines, such as IL-6, IL-1 beta, and TNF-alpha, in the spinal cord. In summary, puerarin alleviated CCI- and diabetes-induced neuropathic pain, and its effectiveness might be due to the inhibition of neuroinflammation in the spinal cord. The anti-inflammation effect of puerarin might be related to the suppression of spinal NF-kappa B activation and/or cytokines upregulation. We conclude that puerarin has a significant effect on alleviating neuropathic pain and thus may serve as a therapeutic approach for neuropathic pain

Robotic versus Open Gastrectomy for Gastric Cancer: A Meta-Analysis

<div><p>Aim</p><p>To evaluate the safety and efficacy of robotic gastrectomy versus open gastrectomy for gastric cancer.</p> <p>Methods</p><p>A comprehensive search of PubMed, EMBASE, Cochrane Library, and Web of Knowledge was performed. Systematic review was carried out to identify studies comparing robotic gastrectomy and open gastrectomy in gastric cancer. Intraoperative and postoperative outcomes were also analyzed to evaluate the safety and efficacy of the surgery. A fixed effects model or a random effects model was utilized according to the heterogeneity.</p> <p>Results</p><p>Four studies involving 5780 patients with 520 (9.00%) cases of robotic gastrectomy and 5260 (91.00%) cases of open gastrectomy were included in this meta-analysis. Compared to open gastrectomy, robotic gastrectomy has a significantly longer operation time (weighted mean differences (WMD) =92.37, 95% confidence interval (CI): 55.63 to 129.12, P<0.00001), lower blood loss (WMD: -126.08, 95% CI: -189.02 to -63.13, P<0.0001), and shorter hospital stay (WMD = -2.87; 95% CI: -4.17 to -1.56; P<0.0001). No statistical difference was noted based on the rate of overall postoperative complication, wound infection, bleeding, number of harvested lymph nodes, anastomotic leakage and postoperative mortality rate.</p> <p>Conclusions</p><p>The results of this meta-analysis suggest that robotic gastrectomy is a better alternative technique to open gastrectomy for gastric cancer. However, more prospective, well-designed, multicenter, randomized controlled trials are necessary to further evaluate the safety and efficacy as well as the long-term outcome.</p> </div

Funnel plot of overall postoperative complication rate in patients between RG and OG.

<p>OR, odds ratio; SE, standard error.</p