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Measure content performance. Develop and improve products. List of Partners vendors. Sensitivity analysis determines how different values of an independent variable affect a particular dependent variable under a given set of assumptions.
In other words, sensitivity analyses study how various sources of uncertainty in a mathematical model contribute to the model's overall uncertainty. This technique is used within specific boundaries that depend on one or more input variables. Sensitivity analysis is used in the business world and in the field of economics. It is commonly used by financial analysts and economists and is also known as a what-if analysis.
Sensitivity analysis is a financial model that determines how target variables are affected based on changes in other variables known as input variables.
This model is also referred to as what-if or simulation analysis. It is a way to predict the outcome of a decision given a certain range of variables. By creating a given set of variables, an analyst can determine how changes in one variable affect the outcome. Both the target and input—or independent and dependent—variables are fully analyzed when sensitivity analysis is conducted. The person doing the analysis looks at how the variables move as well as how the target is affected by the input variable.
Sensitivity analysis can be used to help make predictions about the share prices of public companies. The analysis can be refined about future stock prices by making different assumptions or adding different variables. This model can also be used to determine the effect that changes in interest rates have on bond prices. In this case, the interest rates are the independent variable, while bond prices are the dependent variable.
Investors can also use sensitivity analysis to determine the effects different variables have on their investment returns. Sensitivity analysis allows for forecasting using historical, true data.
By studying all the variables and the possible outcomes, important decisions can be made about businesses, the economy, and making investments. Assume Sue is a sales manager who wants to understand the impact of customer traffic on total sales. She determines that sales are a function of price and transaction volume. This allows her to build a financial model and sensitivity analysis around this equation based on what-if statements.
The sensitivity analysis demonstrates that sales are highly sensitive to changes in customer traffic. Typically, it is advisable to limit sensitivity analyses to the primary outcome. Conducting multiple sensitivity analysis on all outcomes is often neither practical, nor necessary. A: Ideally, one can study the impact of all key elements using a factorial design—which would allow the assessment of the impact of individual and joint factors.
Alternatively, one can vary one factor at a time to be able to assess whether the factor is responsible for the resulting impact if any. For example, in a sensitivity analysis to assess the impact of the Normality assumption analysis assuming Normality e. T-test vs. Based on a sign test and outlier analysis with and without outlier , this can be achieved through 2x2 factorial design.
A: Secondary analyses are typically analyses of secondary outcomes. Like primary analyses which deal with primary outcome s , such analyses need to be documented in the protocol or SAP. In most studies such analyses are exploratory—because most studies are not powered for secondary outcomes.
They serve to provide support that the effects reported in the primary outcome are consistent with underlying biology. They are different from sensitivity analyses as described above. A: Subgroup analyses are intended to assess whether the effect is similar across specified groups of patients or modified by certain patient characteristics [ 60 ].
If the primary results are statistically significant, subgroup analyses are intended to assess whether the observed effect is consistent across the underlying patient subgroups—which may be viewed as some form of sensitivity analysis. Typically subgroup analyses require specification of the subgroup hypothesis and rationale, and performed through inclusion of an interaction term i.
They may also require adjustment for alpha—the overall level of significance. Furthermore, most studies are not usually powered for subgroup analyses. There has been considerable attention paid to enhancing the transparency of reporting of clinical trials.
Not one of these guidelines specifically addresses how sensitivity analyses need to be reported. On the other hand, there is some guidance on how sensitivity analyses need to be reported in economic analyses [ 62 ]—which may partly explain the differential rates of reporting of sensitivity analyses shown in Table 1.
We strongly encourage some modifications of all reporting guidelines to include items on sensitivity analyses—as a way to enhance their use and reporting. The proposed reporting changes can be as follows:. In Methods Section: Report the planned or posthoc sensitivity analyses and rationale for each. In Results Section: Report whether or not the results of the sensitivity analyses or conclusions are similar to those based on primary analysis.
If similar, just state that the results or conclusions remain robust. If different, report the results of the sensitivity analyses along with the primary results.
In Discussion Section: Discuss the key limitations and implications of the results of the sensitivity analyses on the conclusions or findings. This can be done by describing what changes the sensitivity analyses bring to the interpretation of the data, and whether the sensitivity analyses are more stringent or more relaxed than the primary analysis.
Sensitivity analyses play an important role is checking the robustness of the conclusions from clinical trials. They are important in interpreting or establishing the credibility of the findings. If the results remain robust under different assumptions, methods or scenarios, this can strengthen their credibility.
The results of our brief survey of January editions of major medical and health economics journals that show that their use is very low. We recommend that some sensitivity analysis should be the default plan in statistical or economic analyses of any clinical trial. Investigators need to identify any key assumptions, variations, or methods that may impact or influence the findings, and plan to conduct some sensitivity analyses as part of their analytic strategy.
The final report must include the documentation of the planned or posthoc sensitivity analyses, rationale, corresponding results and a discussion of their consequences or repercussions on the overall findings. Schneeweiss S: Sensitivity analysis and external adjustment for unmeasured confounders in epidemiologic database studies of therapeutics. Pharmacoepidemiol Drug Saf. Article PubMed Google Scholar. Stat Med.
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Regression modeling and sample size. J Am Soc Nephrol. Clin J Am Soc Nephrol. PLoS One. Value Health. Download references. Michael G. You can also search for this author in PubMed Google Scholar. Correspondence to Lehana Thabane. LT conceived the idea and drafted the outline and paper. LM and SZ performed literature search and data abstraction. All authors reviewed several draft versions of the manuscript and approved the final manuscript.
This article is published under license to BioMed Central Ltd. Reprints and Permissions. Thabane, L. A tutorial on sensitivity analyses in clinical trials: the what, why, when and how. Download citation. Received : 11 December Accepted : 10 July Published : 16 July Anyone you share the following link with will be able to read this content:.
Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative. Skip to main content. Search all BMC articles Search. Download PDF. Abstract Background Sensitivity analyses play a crucial role in assessing the robustness of the findings or conclusions based on primary analyses of data in clinical trials.
Discussion In this paper we will provide a detailed exploration of the key aspects of sensitivity analyses including: 1 what sensitivity analyses are, why they are needed, and how often they are used in practice; 2 the different types of sensitivity analyses that one can do, with examples from the literature; 3 some frequently asked questions about sensitivity analyses; and 4 some suggestions on how to report the results of sensitivity analyses in clinical trials.
Summary When reporting on a clinical trial, we recommend including planned or posthoc sensitivity analyses, the corresponding rationale and results along with the discussion of the consequences of these analyses on the overall findings of the study.
Background The credibility or interpretation of the results of clinical trials relies on the validity of the methods of analysis or models used and their corresponding assumptions. For a primary analysis of data from a prospective randomized controlled trial RCT , the key questions for investigators and for readers include: How confident can I be about the results?
Will the results change if I change the method of analysis? Financial Analysis. Your Privacy Rights. To change or withdraw your consent choices for Investopedia.
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Sensitivity analysis can identify the best data to be collected for analyses to evaluate a project's return on investment ROI. Sensitivity analysis helps engineers create more reliable, robust designs by assessing points of uncertainty in the design's structure. Compare Accounts. The offers that appear in this table are from partnerships from which Investopedia receives compensation. This compensation may impact how and where listings appear. Investopedia does not include all offers available in the marketplace.
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