Sensitivity Analyses
Sensitivity Analyses - Related Videos
06:53
Cell Population Analyses During Skin Carcinogenesis
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Cited by 6 •
2013
Carcinogenesis is a process involving interactions between cancer cells and the cancer microenvironment. To dissect the molecular events, one needs to isolate different cell populations at different stages during cancer development. Using a mouse model for basal cell carcinoma, we describe a protocol for cell analyses during carcinogenesis.
10:30
Dye-sensitized Solar Cells
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2023
Source: Tamara M. Powers, Department of Chemistry, Texas A&M University Today's modern world requires the use of a large amount of energy. While we harness energy from fossil fuels such as coal and oil, these sources are nonrenewable and thus the supply is limited. To maintain our global lifestyle, we must extract energy from renewable sources. The most promising renewable source, in terms of abundance, is the sun, which provides us with more than enough solar energy to fully fuel our...
02:39
Evisceration of Mouse Vitreous and Retina for Proteomic Analyses
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Cited by 39 •
2011
The dissection technique illustrates evisceration of the vitreous, retina, and lens from the mouse eye, separation by centrifugation, and characterization with protein assays.
11:29
miRNA Expression Analyses in Prostate Cancer Clinical Tissues
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Cited by 16 •
2015
Here we describe a simplified protocol for microRNA (miRNA) expression analyses in archived Formalin-Fixed, Paraffin-Embedded (FFPE) or fresh frozen prostate cancer (PCa) clinical tissues employing quantitative real-time PCR (RT-PCR) and in situ hybridization (ISH).
14:33
Morphometric Analyses of Retinal Sections
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Cited by 6 •
2012
This video demonstrates three types of morphometric analyses of the retina, which include measuring the inner nuclear layer thickness, quantifying the number of retinal ganglion cells (RGCs) and measuring the sizes of RGCs. The technique can offer a simple but scientific platform for morphometric analyses.
FAQs
Changing one assumption while keeping other conditions unchanged helps isolate that factor’s influence on the result. This makes it easier to determine whether a shift in the estimate arises from a treatment effect, cost, probability, or measurement value rather than from several simultaneous changes. The resulting comparison provides a clearer assessment of which inputs drive the study’s findings.
A changing conclusion indicates that the finding is sensitive to uncertainty in one or more inputs. This does not automatically invalidate the main analysis, but it shows that interpretation depends on the assumptions selected. Reporting such changes helps readers distinguish robust conclusions from conclusions that require caution because alternative plausible values produce different clinical, economic, or epidemiological results.
Relevant inputs may include estimated treatment effects, costs, probabilities, and measurement values. Analysts can vary these factors to examine how uncertainty in each one affects the study’s result. The appropriate choice depends on the analysis, but focusing on influential or uncertain inputs can reveal which assumptions deserve closer review and which data gaps matter most.
The analysis shows which uncertainties produce the largest changes in the reported result. If a treatment effect, cost, probability, or measurement value strongly influences the conclusion, obtaining better information about that input may improve future analyses. This connects interpretation with research planning by directing additional data collection toward factors that could most change clinical, economic, or epidemiological findings.
Analysts first identify assumptions, inputs, or model parameters that may affect the study result. They then vary selected values, usually examining each factor while other conditions remain unchanged, and compare every resulting estimate with the main analysis. Finally, they assess whether the conclusion remains consistent and report which uncertainties have the greatest influence.
In clinical research, they can test how treatment-effect assumptions influence conclusions. In economic evaluations, analysts can examine the effects of changing costs or probabilities. Epidemiological studies may assess the impact of uncertain measurements or other model inputs. Across these settings, the purpose is to show whether findings remain stable across plausible scenarios and to clarify their interpretation.
By showing how results respond to alternative assumptions, sensitivity analyses make uncertainty visible rather than leaving it implicit. Decision-makers can see whether a conclusion persists across plausible scenarios and identify circumstances in which it changes. This supports more transparent decisions and helps distinguish evidence that is relatively stable from findings that depend strongly on uncertain inputs.