Introduction
Meta analysis has grown popular in medical field for the synthesis of evidence from various random controlled trials. There are numerous points to be implemented for a successful statistical methodology for Meta analysis of epidemiological studies. The crucial link to success of Meta analysis is disciplined adherence to the key points stated below.
Body
The age we live in is supported by Meta analyst exhortation in finding medical evidence. This is done by use of Meta analysis. The best tools in attaining a satisfactory quality result used for Meta analysis emphasizes on efforts of collecting relevant studies, and the description of these efforts in the document (Oxman, 1270-80). The first point is the use of funnel plots. Funnel plots will assure the Meta analyst will ensure lack of bias in their research. The Meta analyst will also be able to calculate the effect of inclusion of the missing study on the conclusion they have achieved. This will assist them gain a resourceful conclusion.
The second point is constant counting of studies. The Meta analyst should avoid double counting of studies. This can be easily done by considering the reference material of each literal material used in the study. Double counting is very evident in previous Meta-analysis. This is just similar to the wellbeing of anticholinergics in (COPD). In this case, the studies were counted twice giving an errand result and consecutive conclusion.
This leads to the third point in which numerous counting of certain aspects of the studies used should be avoided. This is a more subtle error and can easily be achieved. Arms of the similar study can be use in multiple cases affecting the results of the Meta analysis. Kozyrskyj et al. see an example of this error in JAMA document of Meta analysis in comparison long and short course antibiotic otitis media treatment; they counted multiple arms of the same study (Senn, pp. 1415-35)
The Meta analyst, on my fourth point, should try and accept implausible claims to bring out the key aspects of individual studies. This is a precise way to evaluate all claims. Hackshaw et al in BMJ see an example in a Meta analysis, where he used standard error in his reports. According to lee, Hackshaw used a study that had impossibly low errors. When he compiled the documents, his conclusion had a clear-cut guideline.
The Meta analyst must have and use proper methods of imputing data. Proper imputing of standard deviation is vital to a proper Meta analysis. Excluding some studies in the Meta analysis will bring out a cohesive report. Not all document studies in the field are correct and alignment to a common leveling field is necessary to avoid errors in imputing data. This aspect is my fifth point.
The sixth point provides that the Meta analyst should use individual trials with spurious precision. This means that the variable should be aligned according to significance rather that priority. It is necessary when compiling observational data and experimental data. The Meta analyst should avoid overstating of data.
The Meta analyst should also be able to pool treatments appropriately. Inappropriate pooling might cause clash in discussion. The comparators should have significant differences to avoid null hypothesis. If they are similar, the stakeholders of the Meta analysis should be informed.
Many Meta analysts are victims of my eighth point. From time to time, the Meta analysts incur numerical slips. Numerical slips should be avoided at all fronts as it distorts results, discussions and recommendations. Mistakes are likely to be perpetuated but should be avoided, especially numerical ones as they have immense consequences.
Finally, the Meta analyst must produce complete reporting of their work. In circumstances, where the statistical analysis modes are not specified or the original data of the study is not available, faith is the only supporting notion and should be stated. With this, the part will be taken as hearsay and might not be use legally against the writer. Incomplete reporting brings in legal and ethical responsibility on the reliable party; hence, clarity is necessary when uncertainty is experienced.
Conclusion
The points on approaches described take into consideration the underlying assumptions and theories. Meta analysts should follow these approaches and provide some guidelines for future research. It is believed that the approaches will produce excellent work.
References
Lee N. (1999). Simple methods for checking for possible errors in reported odds ratios, relative risks and confidence intervals. Statistics in Medicine, pp. 1970-1985.
Oxman, D., & Guyatt, H. (1991). Validation of an index of the quality of review articles. Journal of Clinical Epidemiology, pp. 1270-1280.
Senn, J. (2007). Trying to be precise about vagueness. Statistics in Medicine, pp. 1415-1435.