No significant association was found between geographical area (except Asia) and type of sample with bladder cancer. Unlike previous studies, despite the relatively high prevalence of the virus no significant association was found between HPV and bladder cancer (OR 2.077, 95% CI 0.940–4.587).
Also, the possible associations between the prevalence of the virus and bladder cancer and the possible impact of variables in the geographical area and the type of sample were measured by comprehensive meta-analysis software (V2.2, BIOSTAT). In the present study overall prevalence of the virus in bladder cancer patients was estimated along with the prevalence of subgroups. MethodĪ search of major databases was conducted to retrieve published English language studies between January 2011 and March 2021. The aim of this study was to evaluate the data from the last ten years to estimate the prevalence of the virus in bladder cancer patients and to assess the association between the virus and cancer. Older findings suggest a significant association between the virus and bladder cancer. 2010 Nov 63(Pt 3):665–94.The possible association of human papillomavirus (HPV) and bladder cancer has been controversial.
Evidence favoring the use of anticoagulants in the hospital phase of acute myocardial infarction. Chalmers TC, Matta RJ, Smith H Jr, Kunzler AM. There are a range of software for this purpose, such as Review Manager and Comprehensive Meta-Analysis Software.ġ. You ought to extract data for your outcomes of interest to be pooled (combined) in the final analysis set. Specifically, we may overstate the benefit of a treatment (for example), because studies which fail to find a significant effect are less likely to be published than those which do not find a significant effect. Ideally, we would also include unpublished studies in order to avoid publication bias. (If we fail to include all of the relevant studies, our conclusions may be erroneous. For this reason, we tend mostly to include randomized controlled trials (and may exclude observational studies). You should use inclusion and exclusion criteria that will ensure that high-quality evidence, of direct relevance to your research question, is included. This will probably involve searching multiple databases that index reliable peer-reviewed articles, such as PubMed, Scopus, Web of science, Embase, etc.ģ- Deciding on selection/inclusion criteria: to determine the effectiveness of exercise for depression compared with no treatment and comparator treatments). **for further reading, please visit: ( ) How is a meta-analysis performed?īelow are the basic steps involved in a meta-analysis (3):ġ- Identifying/formulating a problem (i.e. Additionally, if the individual studies were underpowered, combining them in a meta-analysis can increase the overall statistical power to detect an effect. This is because results can vary from one study to another for various reasons, including confounding factors, and the different study samples used.īy combining individual studies, and thus using more data, the precision and accuracy of the estimates in the individual studies can be improved upon. To make a valid decision about using an intervention, ideally we should not rely on the results obtained from single studies.
Why should we carry out & use meta-analyses? Since then, they have become a common way for synthesizing evidence and summarizing the results of individual studies (2).
Meta-analyses began to appear as a leading part of research in the late 70s. Therefore, meta-analyses can be seen as the pinnacle of healthcare evidence (1). As we go up the pyramid, each level of evidence is less subject to bias than the level below it. This is a pyramid which enables us to weigh up the different levels of evidence available to us. Compared to other study designs (such as randomized controlled trials or cohort studies), the meta-analysis comes in at the top of the ‘ levels of evidence’ pyramid in evidence-based healthcare. Meta-analyses play a fundamental role in evidence-based healthcare. Meta-analysis is a statistical technique for combining data from multiple studies on a particular topic.