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| EDITORIAL |
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| Year : 2016 | Volume
: 5
| Issue : 2 | Page : 61-62 |
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Meta-analysis: Introduction and steps
Jayant N Palaskar
Editor-in-Chief, Journal of Dental and Allied Sciences, Department of Prosthodontics, Sinhgad Dental College and Hospital, Pune, Maharashtra, India
| Date of Web Publication | 25-Oct-2016 |
Correspondence Address:
Jayant N Palaskar
Department of Prosthodontics, Sinhgad Dental College and Hospital, Pune - 411 041, Maharashtra
India
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/2277-4696.192980
How to cite this article:
Palaskar JN. Meta-analysis: Introduction and steps. J Dent Allied Sci 2016;5:61-2 |
A meta-analysis is a statistical analysis that combines the results of multiple scientific studies. The term “meta-analysis” was coined by Glass,[1] who was the first modern statistician to formalize the use of the term meta-analysis. He states “my major interest currently is in what we have come to call… the meta-analysis of research. The term is a bit grand, but it is precise and apt… Meta-analysis refers to the analysis of analyses.”
Conceptually, a meta-analysis uses a statistical approach to combine the results from multiple studies in an effort to increase power (over individual studies), to improve estimates of the size of the effect, and/or to resolve uncertainty when reports disagree. A meta-analysis is a statistical overview of the results from one or more systematic reviews. Meta-analyses are often, but not always, important components of a systematic review procedure. Basically, it produces a weighted average of the included study results, and this approach has several advantages as follows:
- Results can be generalized to a larger population
- The precision and accuracy of estimates can be improved as more data are used
- Inconsistency of results across studies can be quantified and analyzed
- Hypothesis testing can be applied on summary estimates
- Moderators can be included to explain variation between studies
- The presence of publication bias can be investigated.
| Problems Associated With Meta-Analysis | |  |
Publication bias: the file drawer problem
Some journals have a tendency for not publishing articles with nonsignificant or negative results which may create exaggerated outcomes due to publication bias. Second, the investigators do not include such studies in their meta-analysis which is termed file drawer problem. This issue should be seriously considered when interpreting the outcomes of a meta-analysis.[2],[3]
The positive studies problem
Searches of databases such as PubMed or Embase can yield long lists of studies. However, these databases include only studies that have been published. Such searches are unlikely to yield a representative sample because studies that show a “positive” result (usually in favor of a new treatment or against a well-established one) are more likely to be published than those that do not. This selective publication of studies is called publication bias.[4]
Search bias: Identifying relevant studies
Even in the ideal case that all relevant studies were available (i.e., no publication bias), a faulty search can miss some of them. In searching databases, much care should be taken to assure that the set of key words used for searching is as complete as possible. This step is so critical that most recent meta-analyses include the list of key words used. The search engine (e.g., PubMed, Google) is also critical, affecting the type and number of studies that are found.[5] Small differences in search strategies can produce large differences in the set of studies found.[6]
Selection bias: Choosing the studies to be included
The identification phase usually yields a long list of potential studies, many of which are not directly relevant to the topic of the meta-analysis. This list is then subject to additional criteria to select the studies to be included. This critical step is also designed to reduce differences among studies, eliminate replication of data or studies, and improve data quality, and thus enhance the validity of the results.[7]
Problems related to the statistical approach
Other weaknesses are that it has not been determined whether the statistically most accurate method for combining results is the fixed, random, or quality effect model, though the criticism against the random effects model is mounting because of the perception that the new random effects (used in meta-analysis) are essentially formal devices to facilitate smoothing or shrinkage and prediction may be impossible or ill advised.[5]
Problems arising from agenda-driven bias
The most severe fault in meta-analysis often occurs when the person or persons doing the meta-analysis have an economic, social, or political agendum such as the passage or defeat of legislation. People with these types of agendas may be more likely to abuse meta-analysis due to personal bias.
Steps in a meta-analysis
- Formulation of the problem
- Search of literature
- Selection of studies (“incorporation criteria”)
- Decide which dependent variables or summary measures are allowed
- Selection of a meta-regression statistical model, for example, simple regression, fixed-effect meta-regression, or random-effect meta-regression. Meta-regression is a tool used in meta-analysis to examine the impact of moderator variables on study effect size using regression-based techniques. Meta-regression is more effective at this task than are standard regression techniques.
For reporting guidelines, see the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) statement.[8]
| References | | |
| 1. | Glass GV. Primary, secondary, and meta-analysis of research. Educ Res 1976;5:3-8.  |
| 2. | Rosenthal R. The “file drawer problem” and the tolerance for null results. Psychol Bull 1979;86:638-41. |
| 3. | Hunter, Schmidt, Jackson, John E. Meta-analysis: Cumulating Research Findings Across Studies. Beverly Hills, California: Sage; 1982. |
| 4. | Turner EH, Matthews AM, Linardatos E, Tell RA, Rosenthal R. Selective publication of antidepressant trials and its influence on apparent efficacy. N Engl J Med 2008;358:252-60. |
| 5. | |
| 6. | Dickersin K, Scherer R, Lefebvre C. Identifying relevant studies for systematic reviews. BMJ 1994;309:1286-91. |
| 7. | Ng TT, McGory ML, Ko CY, Maggard MA. Meta-analysis in surgery: methods and limitations. Arch Surg 2006;141:1125-30. |
| 8. | Moher D, Liberati A, Tetzlaff J, Altman DG; PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med 2009;6:e1000097. |
| Authors | | |
Jayant N. Palaskar
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