ObjectiveTo systematically review the correlation between Survivin expression and prostate cancer, as well as its clinicopathologic features in Chinese population. MethodsSuch databases as PubMed, EMbase, CBM, CNKI, VIP and WanFang Data were electronically searched from inception to November, 2015 to collect case-control studies about the correlation between Survivin expression and prostate cancer, as well as its clinically pathologic characteristics in Chinese population. Two reviewers independently screened literature, extracted data and assessed the methodological quality of included studies. Then, meta-analysis was performed using RevMan 5.3 software. ResultsA total of 32 case-control studies were included, involving 1613 prostate cancer cases, 708 benign prostatic hyperplasia cases, and 93 controls. The results of meta-analysis showed that the prostate cancer group had a higher Survivin expression level when compared with the benign prostatic hyperplasia group (OR=32.95, 95% CI 19.88 to 54.63, P<0.00001) or the control group (OR=75.78, 95% CI 26.97 to 212.98, P<0.00001). Moreover, the expression level of Survivin was higher in the low and medium differentiation group than in the high differentiation group (OR=4.45, 95% CI 3.13 to 6.32, P<0.00001), higher in the stage of C+D than in the stage of A+B (OR 5.42, 95% CI 2.91 to10.10, P<0.00001), and higher in the prostate cancer with lymph node metastasis than in the prostate cancer without lymph node metastasis (OR 4.07, 95% CI 2.91 to 10.10, P<0.00001). ConclusionCurrent evidence indicates that the expression level of Survivin is significantly correlated with prostate cancer and its clinicopathologic features in Chinese population. Due to the limited quantity and quality of included studies, above conclusions need to be verified by conducting more high quality studies.
The robustness of results of statistical analysis would be altered on the condition of repeated update of traditional meta-analysis and cumulative meta-analysis. In addition, the cumulative meta-analysis lacks estimation of the sample size. While trail sequential analysis (TSA), which introduces group sequential analysis in meta-analysis, can adjust the random error and ultimately estimate the required sample size of the systematic review or meta-analysis. TSA is performed in TSA software. In the present study, we aimed to introduce how to use the TSA software for performing meta-analysis.
One important problem in meta-analysis is heterogeneity, the result of bias. When inconsistency occurs, traditional work in meta-analysis is employing a random effect model based on inverse variance method to combine the results. Such a method used the moment-based estimator τ2 measuring the deviation from true value across studies to obtain a conservative result. It however failed to estimate the influence on each study due to bias and this method may at risk of underestimate the standard error which then may leads to biased summarized estimator. Accordingly, Doi proposed a new weighting procedure, QE method, hopefully be a good solution. In this article, we will introduce the QE method with details on the methodology and software, and then make a comparison between QE and random effect model of the results.