Present results of all sensitivity analyses conducted to assess the robustness of the synthesised results
Essential elements
If any sensitivity analyses were conducted:
report the results for each sensitivity analysis.
comment on how robust the main analysis was given the results of all corresponding sensitivity analyses.
Additional elements
If any sensitivity analyses were conducted, consider:
presenting results in tables that indicate: (i) the summary effect estimate, a measure of precision (and potentially other relevant statistics, for example, I2 statistic) and contributing studies for the original meta-analysis; (ii) the same information for the sensitivity analysis; and (iii) details of the original and sensitivity analysis assumptions.
presenting results of sensitivity analyses visually using forest plots.
Explanation
Presenting results of sensitivity analyses conducted allows readers to assess how robust the synthesised results were to decisions made during the review process. Reporting results of all sensitivity analyses is important; presentation of a subset, based on the nature of the results, risks introducing bias due to selective reporting. Forest plots are a useful way to present results of sensitivity analyses; however, these may be best placed in an appendix, with the main forest plots presented in the main report, to not reduce readability. An exception may be when sensitivity analyses reveal the results are not robust to decisions made during the review process.
Example
“Sensitivity analyses that removed studies with potential bias showed consistent results with the primary meta-analyses (risk ratio 1.00 for undetectable HIV-1 RNA, 1.00 for virological failure, 0.98 for severe adverse effects, and 1.02 for AIDS defining events; supplement 3E, 3F, 3H, and 3I, respectively). Such sensitivity analyses were not performed for other outcomes because none of the studies reporting them was at a high risk of bias. Sensitivity analysis that pooled the outcome data reported at 48 weeks, which also showed consistent results, was performed for undetectable HIV-1 RNA and increase in CD4 T cell count only (supplement 3J and 3K) and not for other outcomes owing to lack of relevant data. When the standard deviations for increase in CD4 T cell count were replaced by those estimated by different methods, the results of Figure 1 either remained similar (that is, quadruple and triple arms not statistically different) or favoured triple therapies (supplement 2).”1
Figure 1: The figure displays for each study included in the meta-analysis the summary statistics (mean, standard deviation, and sample size) for the quadruple and triple combination antiretroviral therapies (cART) groups, and the mean difference and its 95% confidence interval for the continuous outcome, CD4 T cell count (cells/μL). Reproduced from Feng et al.1 (n.d.fi)
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References
1.
Feng Q, Zhou A, Zou H, et al. Quadruple versus triple combination antiretroviral therapies for treatment naive people with HIV: Systematic review and meta-analysis of randomised controlled trials. BMJ. Published online July 2019:l4179. doi:10.1136/bmj.l4179
Citation
For attribution, please cite this work as:
Page
MJ, Moher D, Bossuyt PM, et al. PRISMA 2020 explanation and elaboration:
updated guidance and exemplars for reporting systematic reviews.
BMJ. 372:n160. doi:10.1136/bmj.n160
Reporting Guidelines are recommendations to help describe your work clearly
Your research will be used by people from different disciplines and backgrounds for decades to come. Reporting guidelines list the information you should describe so that everyone can understand, replicate, and synthesise your work.
Reporting guidelines do not prescribe how research should be designed or conducted. Rather, they help authors transparently describe what they did, why they did it, and what they found.
Reporting guidelines make writing research easier, and transparent research leads to better patient outcomes.
Easier writing
Following guidance makes writing easier and quicker.
You work will be read by different people, for different reasons, around the world, and for decades to come. Reporting guidelines help you consider all of your potential audiences. For example, your research may be read by researchers from different fields, by clinicians, patients, evidence synthesisers, peer reviewers, or editors. Your readers will need information to understand, to replicate, apply, appraise, synthesise, and use your work.
Cohort studies
A cohort study is an observational study in which a group of people with a particular exposure (e.g. a putative risk factor or protective factor) and a group of people without this exposure are followed over time. The outcomes of the people in the exposed group are compared to the outcomes of the people in the unexposed group to see if the exposure is associated with particular outcomes (e.g. getting cancer or length of life).
A case-control study is a research method used in healthcare to investigate potential risk factors for a specific disease. It involves comparing individuals who have been diagnosed with the disease (cases) to those who have not (controls). By analysing the differences between the two groups, researchers can identify factors that may contribute to the development of the disease.
An example would be when researchers conducted a case-control study examining whether exposure to diesel exhaust particles increases the risk of respiratory disease in underground miners. Cases included miners diagnosed with respiratory disease, while controls were miners without respiratory disease. Participants' past occupational exposures to diesel exhaust particles were evaluated to compare exposure rates between cases and controls.
A cross-sectional study (also sometimes called a "cross-sectional survey") serves as an observational tool, where researchers capture data from a cohort of participants at a singular point. This approach provides a 'snapshot'— a brief glimpse into the characteristics or outcomes prevalent within a designated population at that precise point in time. The primary aim here is not to track changes or developments over an extended period but to assess and quantify the current situation regarding specific variables or conditions. Such a methodology is instrumental in identifying patterns or correlations among various factors within the population, providing a basis for further, more detailed investigation.
A systematic review is a comprehensive approach designed to identify, evaluate, and synthesise all available evidence relevant to a specific research question. In essence, it collects all possible studies related to a given topic and design, and reviews and analyses their results.
The process involves a highly sensitive search strategy to ensure that as much pertinent information as possible is gathered. Once collected, this evidence is often critically appraised to assess its quality and relevance, ensuring that conclusions drawn are based on robust data. Systematic reviews often involve defining inclusion and exclusion criteria, which help to focus the analysis on the most relevant studies, ultimately synthesising the findings into a coherent narrative or statistical synthesis. Some systematic reviews will include a meta-analysis.
A randomised controlled trial (RCT) is a trial in which participants are randomly assigned to one of two or more groups: the experimental group or groups receive the intervention or interventions being tested; the comparison group (control group) receive usual care or no treatment or a placebo. The groups are then followed up to see if there are any differences between the results. This helps in assessing the effectiveness of the intervention.
Research that aims to gather and analyse non-numerical (descriptive) data in order to gain an understanding of individuals' social reality, including understanding their attitudes, beliefs, and motivation. This type of research typically involves in-depth interviews, focus groups, or field observations in order to collect data that is rich in detail and context. Qualitative research is often used to explore complex phenomena or to gain insight into people's experiences and perspectives on a particular topic. It is particularly useful when researchers want to understand the meaning that people attach to their experiences or when they want to uncover the underlying reasons for people's behavior. Qualitative methods include ethnography, grounded theory, discourse analysis, and interpretative phenomenological analysis.
Diagnostic accuracy studies focus on estimating the ability of the test(s) to correctly identify subjects with a predefined target condition, or the condition of interest (sensitivity) as well as to clearly identify those without the condition (specificity).
Prediction Models
Prediction model research is used to test the accurarcy of a model or test in estimating an outcome value or risk. Most models estimate the probability of the presence of a particular health condition (diagnostic) or whether a particular outcome will occur in the future (prognostic). Prediction models are used to support clinical decision making, such as whether to refer patients for further testing, monitor disease deterioration or treatment effects, or initiate treatment or lifestyle changes. Examples of well known prediction models include EuroSCORE II for cardiac surgery, the Gail model for breast cancer, the Framingham risk score for cardiovascular disease, IMPACT for traumatic brain injury, and FRAX for osteoporotic and hip fractures.
Quality improvement research is about finding out how to improve and make changes in the most effective way. It is about systematically and rigourously exploring "what works" to improve quality in healthcare and the best ways to measure and disseminate this to ensure positive change. Most quality improvement effectiveness research is conducted in hospital settings, is focused on multiple quality improvement interventions, and uses process measures as outcomes. There is a great deal of variation in the research designs used to examine quality improvement effectiveness.
A meta-analysis is a statistical technique that amalgamates data from multiple studies to yield a single estimate of the effect size. This approach enhances precision and offers a more comprehensive understanding by integrating quantitative findings. Central to a meta-analysis is the evaluation of heterogeneity, which examines variations in study outcomes to ensure that differences in populations, interventions, or methodologies do not skew results. Techniques such as meta-regression or subgroup analysis are frequently employed to explore how various factors might influence the outcomes. This method is particularly effective when aiming to quantify the effect size, odds ratio, or risk ratio, providing a clearer numerical estimate that can significantly inform clinical or policy decisions.
How Meta-analyses and Systematic Reviews Work Together
Systematic reviews and meta-analyses function together, each complementing the other to provide a more robust understanding of research evidence. A systematic review meticulously gathers and evaluates all pertinent studies, establishing a solid foundation of qualitative and quantitative data. Within this framework, if the collected data exhibit sufficient homogeneity, a meta-analysis can be performed. This statistical synthesis allows for the integration of quantitative results from individual studies, producing a unified estimate of effect size. Techniques such as meta-regression or subgroup analysis may further refine these findings, elucidating how different variables impact the overall outcome. By combining these methodologies, researchers can achieve both a comprehensive narrative synthesis and a precise quantitative measure, enhancing the reliability and applicability of their conclusions. This integrated approach ensures that the findings are not only well-rounded but also statistically robust, providing greater confidence in the evidence base.
Why Don't All Systematic Reviews Use a Meta-Analysis?
Systematic reviews do not always have meta-analyses, due to variations in the data. For a meta-analysis to be viable, the data from different studies must be sufficiently similar, or homogeneous, in terms of design, population, and interventions. When the data shows significant heterogeneity, meaning there are considerable differences among the studies, combining them could lead to skewed or misleading conclusions. Furthermore, the quality of the included studies is critical; if the studies are of low methodological quality, merging their results could obscure true effects rather than explain them.
Protocol
A plan or set of steps that defines how something will be done. Before carrying out a research study, for example, the research protocol sets out what question is to be answered and how information will be collected and analysed.
The combination of quantitative results of two or more studies. This encompasses meta-analysis of effect estimates (described below) and other methods, such as combining P values, calculating the range and distribution of observed effects, and vote counting based on the direction of effect (see McKenzie and Brennan for a description of each method)
Meta-analysis of effect estimates
A statistical technique used to synthesize results when study effect estimates and their variances are available, yielding a quantitative summary of results.
An event or measurement collected for participants in a study (such as quality of life, mortality).
Result
The combination of a point estimate (such as a mean difference, risk ratio or proportion) and a measure of its precision (such as a confidence/credible interval) for a particular outcome.
Reports
Documents (paper or electronic) supplying information about a particular study. A report could be a journal article, preprint, conference abstract, study register entry, clinical study report, dissertation, unpublished manuscript, government report, or any other document providing relevant information.
Record
The title or abstract (or both) of a report indexed in a database or website (such as a title or abstract for an article indexed in Medline). Records that refer to the same report (such as the same journal article) are “duplicates”; however, records that refer to reports that are merely similar (such as a similar abstract submitted to two different conferences) should be considered unique.
Study
An investigation, such as a clinical trial, that includes a defined group of participants and one or more interventions and outcomes. A “study” might have multiple reports. For example, reports could include the protocol, statistical analysis plan, baseline characteristics, results for the primary outcome, results for harms, results for secondary outcomes, and results for additional mediator and moderator analyses.
