Objective To investigate confidence interval estimation for the amount of heterogeneity in meta-analysis. Methods On the basis of BT’s method, the approximate Q-statistic distribution following linear transformation of Chi-square was applied to improve the accuracy of Q-statistic distribution, and to obtain the confidence interval for the amount of heterogeneity in meta-analysis. Results In case, the Q1 distribution obtained 95%CI 0.07 to 2.20, while the Q2 distribution obtained 95%CI 0.00 to 1.41; The proposed method Q2 narrowed down the range of confidence interval. Conclusion On account of improving the accuracy of Q-statistic distribution, the proposed method effectively strengthens the coverage probabilities of the confidence interval for the amount of heterogeneity. And the proposed method can also improve the precision of the confidence interval estimation for the amount of heterogeneity.
Randomized controlled trials are the gold standard for evaluating the effects of medical interventions, primarily providing estimates of the average effect of an intervention in the overall study population. However, there may be significant differences in the effect of the same intervention across sub-populations with different characteristics, that is, treatment heterogeneity. Traditional subgroup analysis and interaction analysis tend to have low power to examine treatment heterogeneity or identify the sources of heterogeneity. With the recent development of machine learning techniques, causal forest has been proposed as a novel method to evaluate treatment heterogeneity, which can help overcome the limitations of the traditional methods. However, the application of causal forest in the evaluation of treatment heterogeneity in medicine is still in the beginning stage. In order to promote proper use of causal forest, this paper introduces its purposes, principles and implementation, interprets the examples and R codes, and highlights some attentions needed for practice.
Objective To summarize and review the heterogeneity of bone marrow derived stem cells (BMDSCs) and its formation mechanism and significance, and to analyze the possible roles and mechanisms in intestinal epithel ial reconstruction. Methods The related l iterature about BMDSCs heterogeneity and its role in intestinal epithel ial repair was reviewed and analyzed. Results The heterogeneity of BMDSCs provided better explanations for its multi-potency. The probable mechanisms of BMDSCs to repair intestinal epithel ium included direct implantation into intestinal epithel ium, fusion between BMDSCs and intestinal stem cells, and promotion of injury microcirculation reconstruction. Conclusion BMDSCs have a bright future in gastrointestinal injury caused by inflammatory bowl disease and regeneration.
ObjectiveStudy how to quantify the bias of each study and how to estimate them. MethodIn the random-effect model, it is commonly assumed that the effect size of each study in meta-analysis follows a skew normal distribution which has different shape parameter. Through introducing a shape parameter to quantify the bias and making use of Markov estimation as well as maximum likelihood estimation to estimate the overall effect size, bias of each study, heterogeneity variance. ResultIn simulation study, the result was closer to the real value when the effect size followed a skew normal distribution with different shape parameter and the impact of heterogeneity of random effects meta-analysis model based on the skew normal distribution with different shape parameter was smaller than it in a random effects metaanalysis model. Moreover, in this specific example, the length of the 95%CI of the overall effect size was shorter compared with the model based on the normal distribution. ConclusionIncorporate the bias of each study into the random effects meta-analysis model and by quantifying the bias of each study we can eliminate the influence of heterogeneity caused by bias on the pooled estimate, which further make the pooled estimate closer to its true value.
【Abstract】 Objective To introduce the cl inical appl ication of heterogeneity (cattle) acellular dermal matrix(ADM)in the repair of mucosa defect otolaryngology. Methods From October 2006 to March 2007, 12 cases of mucosa defect was repaired with heterogeneity ADM after the surgery. There were 10 males and 2 females, aged 18-76 years. Defect was caused by deflection of nasal septum in 1 case, melanoma of front and midst basal is (capillary hemangioma) in 1 case, nasal vestibule angioma (T2N2M0)in 1 case, cancer of hypopharynx (T2N1M0) in 1 case, cancer of amygdale in 3 cases (2 of T2N0M0 and 1 of T3N1M0),cervical segments esophageal carcinoma in 1 case, and cancer of larynx in 4 cases (3 of T2N0M0 and 1 of T3N1M0). Results All these 12 cases were followed up for 6 months. The results of endoscope showed that heterogeneity ADM mingled with mucosa within 3 months after operation and the function was recovered. Pharynx fistula occurred in 1 case of hypopharynx cancer afterthe operation. After treatment of dressing change and antibiotics for 10 days, the wound healed, but after 2 months tumor recurred. All the patients were treated by radiation treatment. One case of amygdala cancer recurred and transferred to the neck after 2 months of radiation treatment. But 1 case of hypopharynx cancer died of massive haemorrhage after radiation treatment for 3 months. Conclusion Heterogeneity ADM can be easily obtained and it is a new method to repair mucosa defect. Theoperative procedure is easy to perform and worthwhile to be appl ied to cl inical operation.
Through collecting and synthesizing the paper concerning the method of dealing with heterogeneity in the meta analysis, to introduce the multi-levels statistical models, such as meta regression and baseline risk effect model based on random effects, and random effects model based on hierarchical bayes, and to introduce their application of controlling the meta analysis heterogeneity. The multi-levels statistical model will decompose the single random error in the traditional model to data structure hierarchical. Its fitting effect can not only make the meta-analysis result more robust and reasonable, but also guide clinical issues through the interpretation of association variable.
Diabetic retinopathy (DR) is one of the main causes of vision loss and irreversible blindness in the working-age population, closely regarded as the destruction of the retinal neurovascular unit (NVU). As an important component of the NVU, retinal microglia (RMG) plays a vital role in the progression of DR. In recent years, single-cell RNA sequencing (scRNA-seq) technology has emerged as an important tool in transcriptomic analysis. This latest method reveals the heterogeneity and complexity of RNA transcriptional profiles within individual cells, as well as the composition of different cell types and functions. Utilizing scRNA-seq technology, researchers have further revealed the role of RMG in the occurrence and development of DR, discovering phenotypic heterogeneity, regional heterogeneity, and cell-to-cell communication in RMG. It is anticipated that in the future, more omics technologies and multi-omics correlation analysis methods will be applied to DR and even other ophthalmic diseases, exploring potential diagnostic and therapeutic targets, providing different perspectives for the clinical diagnosis, treatment, and scientific research of DR, and truly promoting clinical translation through technological innovation, thereby benefiting patients with DR diseases.
Compared with traditional head to head meta-analysis, network meta-analysis has more confounding factors and difficulties to handle. Due to the mutual transitivity of evidence in network meta-analysis, heterogeneity may be brought into indirect meta-analysis. Hence, effective differentiation and correct handling of heterogeneity are being current focus. In order to ensure the reliability of the results of network meta-analysis, the concept of homogeneity is proposed and a series of methods are developed for differentiation and handling of homogeneity. Based on the extension of Bucher methods, current methods for differentiation and handling of homogeneity has extended to ten quantitative measures (eg., node analysis method, hypothesis tests, and two-step method). However, because of the differences and the focus of fundamental methodological theories as well as the limitation of statistics power, no highly-effective method has been worked out. Therefore, the exploration of highly-effective, simple and high-resolved methods are still needed.
Objective To analyze the heterogeneity of systematic reviews (SRs)/Meta-analysis on traditional Chinese medicine (TCM), and explore strategies for addressing heterogeneity correctly during the process of conducting TCM related to systematic reviews (SRs). Methods Both electronic and hand searches were used to identify TCM SRs in CBM, CNKI, VIP database, and Chinese Journal of Evidence-Based Medicine. Two researchers performed data extracting and heterogeneity evaluation independently. Results A total of 115 TCM SRs were included, involving 17 types of diseases, among which the cardiovascular and cerebrovascular diseases were the most addressed (n=36, 31.30%). There were 35.65% (n=41) of SRs which integrated two or more types of studies; interventions of the included studies were inconsistent in 53.91% (n=62) of TCM SRs; control groups of the included studies were completely different in 60 (52.17%) SRs; and 8.7% (n=10) of SRs failed to investigate heterogeneity in the process of synthesis analysis. Conclusion The heterogeneity is common in TCM related to SRs, and the most addressed is clinical heterogeneity. Addressing heterogeneity incorrectly would downgrade the quality of TCM related to SRs.
ObjectiveWhen using multi-center data to construct clinical prediction models, the independence assumption of data will be violated, and there is an obvious clustering effect among research objects. In order to fully consider the clustering effect, this study intends to compare the model performance of the random intercept logistic regression model (RI) and the fixed effects model (FEM) considering the clustering effect with the standard logistic regression model (SLR) and the random forest algorithm (RF) without considering the clustering effect under different scenarios. MethodsIn the process of forecasting model establishment, the prediction performance of different models at the center level was simulated when there were different degrees of clustering effects, including the difference of discrimination and calibration in different scenarios, and the change trend of this difference at different event rates was compared. ResultsAt the center level, different models, except RF, showed little difference in the discrimination of different scenarios under the clustering effect, and the mean of their C-index changed very little. When using multi-center highly clustered data for forecasting, the marginal forecasts (M.RI, SLR and RF) had calibrated intercepts slightly less than 0 compared with the conditional forecasts, which overestimated the average probability of prediction. RF performed well in intercept calibration under the condition of multi-center and large samples, which also reflected the advantage of machine learning algorithm for processing large sample data. When there were few multiple patients in the center, the FEM made conditional predictions, the calibrated intercept was greater than 0, and the predicted mean probability was underestimated. In addition, when the multi-center large sample data were used to develop the prediction model, the slopes of the three conditional forecasts (FEM, A.RI, C.RI) were well calibrated, while the calibrated slopes of the marginal forecasts (M.RI and SLR) were greater than 1, which led to the problem of underfitting, and the underfitting problem became more prominent with the increase in the central aggregation effect. In particular, when there were few centers and few patients, overfitting of the data could mask the difference in calibration performance between marginal and conditional forecasts. Finally, the lower the event rate the central clustering effect at the central level had a more pronounced impact on the forecasting performance of the different models. ConclusionThe highly clustered multi-center data are used to construct the model and apply it to the prediction in a specific environment. RI and FEM can be selected for conditional prediction when the number of centers is small or the difference between centers is large due to different incidence rates. When the number of hearts is large and the sample size is large, RI can be selected for conditional prediction or RF for edge prediction.