The process of undertaking a systematic review involves a sequence of decisions.Whilst many of these decisions are clearly objective and non-contentious, some will be somewhat arbitrary or unclear. For instance, if inclusion criteria involve a numerical value, the choice of value is usually arbitrary: for example, defining groups of older people may reasonably have lower limits of 60, 65, 70 or 75 years, or any value in between. Other decisions may be unclear because a study report fails to include the required information. Some decisions are unclear because the included studies themselves never obtained the information required: for example, the outcomes of those who unfortunately were lost to follow-up. Further decisions are unclear because there is no consensus on the best statistical method to use for a particular problem.
It is desirable to prove that the findings from a systematic review are not dependent on such arbitrary or unclear decisions. A sensitivity analysis is a repeat of the primary analysis or meta-analysis, substituting alternative decisions or ranges of values for decisions that were arbitrary or unclear. For example, if the eligibility of some studies in the meta-analysis is dubious because they do not contain full details, sensitivity analysis may involve undertaking the meta-analysis twice: first, including all studies and second, only including those that are definitely known to be eligible. A sensitivity analysis asks the question, “Are the findings robust to the decisions made in the process of obtaining them?”.
There are many decision nodes within the systematic review process which can generate a need for a sensitivity analysis.Examples include:
Searching for studies:
Should abstracts whose results cannot be confirmed in subsequent publications be included in the review?
Characteristics of participants: where a majority but not all people in a study meet an age range, should the study be included?
Characteristics of the intervention: what range of doses should be included in the meta-analysis?
Characteristics of the comparator: what criteria are required to define usual care to be used as a comparator group?
Characteristics of the outcome: what time-point or range of time-points are eligible for inclusion?
Study design: should blinded and unblinded outcome assessment be included, or should study inclusion be restricted by other aspects of methodological criteria?
What data should be analysed?
Time-to-event data: what assumptions of the distribution of censored data should be made?
Continuous data: where standard deviations are missing, when and how should they be imputed?Should analyses be based on change scores or on final values?
Ordinal scales: what cut-point should be used to dichotomize short ordinal scales into two groups?
Cluster-randomized trials: what values of the intraclass correlation coefficient should be used when trial analyses have not been adjusted for clustering?
Cross-over trials: what values of the within-subject correlation coefficient should be used when this is not available in primary reports?
All analyses: what assumptions should be made about missing outcomes to facilitate intention-to-treat analyses?Should adjusted or unadjusted estimates of treatment effects used?
Should fixed-effect or random-effects methods be used for the analysis?
For dichotomous outcomes, should odds ratios, risk ratios or risk differences be used?
And for continuous outcomes, where several scales have assessed the same dimension, should results be analysed as a standardized mean difference across all scales or as mean differences individually for each scale?
Some sensitivity analyses can be pre-specified in the study protocol, but many issues suitable for sensitivity analysis are only identified during the review process where the individual peculiarities of the studies under investigation are identified.When sensitivity analyses show that the overall result and conclusions are not affected by the different decisions that could be made during the review process, the results of the review can be regarded with a higher degree of certainty. Where sensitivity analyses identify particular decisions or missing information that greatly influence the findings of the review, greater resources can be deployed to try and resolve uncertainties and obtain extra information, possibly through contacting trial authors and obtained individual patient data. If this cannot be achieved, the results must be interpreted with an appropriate degree of caution. Such findings may generate proposals for further investigations and future research.
Reporting of sensitivity analyses in a systematic review may best be done by producing a summary table.Rarely is it informative to produce individual forest plots for each sensitivity analysis undertaken.
Sensitivity analyses are sometimes confused with subgroup analysis.Although some sensitivity analyses involve restricting the analysis to a subset of the totality of studies, the two methods differ in two ways. First, sensitivity analyses do not attempt to estimate the effect of the intervention in the group of studies removed from the analysis, whereas in subgroup analyses, estimates are produced for each subgroup. Second, in sensitivity analyses, informal comparisons are made between different ways of estimating the same thing, whereas in subgroup analyses, formal statistical comparisons are made across the subgroups.