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.
Many meta-analysis studies evaluate rates as parameter to assess the overall estimate of effects. However, none of these studies address systematic approaches for the meta-analysis of rates. This paper outlines the conditions, analysis and software operation procedures for the meta-analysis of rates. It also compares different operation procedures of three types of commonly-used R software (Comprehensive Meta-Analysis, Stata and MetaAnalyst) through real application examples. The biggest challenge for the meta-analysis of rates is to determine whether rates can be pooled, and how to evaluate heterogeneity between studies' outcomes needs further discussion.
This paper is to discuss the research of heterogeneity in Meta-analysis, including the definition of the heterogeneity in Meta-analysis and classification it into clinical heterogeneity, methodological heterogeneity and statistical heterogeneity, the strategies for diminishing clinical heterogeneity and methodological heterogeneity, the five testing methods in statistical heterogeneity (Q statistic, I2 statistic, H statistic, Galbraith plot and L’Abbe plot) and the examples and applying conditions of the five testing methods, classification of meta-analysis into exploratory meta-analysis and analytic meta-analysis according if the meta-analysis has heterogeneity, and the strategies and the flowchart when existing the heterogeneity in meta-analysis.
The assumption of fixed-effects model is based on that the true effect of the each trial is same. However, the assumption of random-effects model is based on that the true effect of included trials is normal distributed. The total variance is equal to the sum of within-trial variance and between-trial variance under the random-effects model. There are many estimators of the between-trial variance. The aim of this paper is to give a brief introduction of the estimators of between-trial variance in trial sequential analysis for random-effects model.
Some risks affecting the quality of published systematic reviews and in our teaching practice were listed and compared with the correct concept. The current problems include misunderstanding of the relationship of meta-analysis and systematic review, applying meta-analysis and assessing heterogeneity, randomization, allocate concealment, and how to make inclusion and exclusion criteria, etc. This paper aims to help Chinese reviewers improve the quality of their systematic reviews.
Objective To summarize the research progress of distributional heterogeneity of the molecular pathology characteristics in breast cancer. Methods The related literatures about the distribution of the molecular pathology characteristics in breast cancer were reviewed. Results The breast cancer had the same heterogeneity as other cancers. At the same time, the molecular pathology characteristics, such as estrogen receptor (ER), progesterone receptor (PR), Ki-67, and human epidermal growth factor receptor-2 (HER-2), had the distributional heterogeneity. The distributional heterogeneity of molecular pathology characteristics in breast cancer could effect the pathologic diagnosis, the treatment, and the prognosis. Conclusion Although there are some new techniques which were used to investigate the heterogeneity of breast cancer, but each way has some problems. The more attention should be paid to the research about the distributional heterogeneity of the molecular pathology characteristics in breast cancer.
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.
Neutrophils are the most abundant myeloid-derived eukaryotic cells in human blood with increasingly recognized as important regulators of cancer progression. However, the functional importance of tumor-associated neutrophils (TANs) is often overlooked due to their short-lived, terminally differentiated, non-proliferative properties. In recent years, a wealth of evidences obtained from experimental tumor models and cancer patients had indicated that TANs had obvious heterogeneity in morphology and function, and TANs had dual functions of pro- and anti-tumor in cancer patients. This review provides an adequate overview of the heterogeneity and distinct roles of neutrophils.
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.
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.