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A tipping-point analysis can examine how far selected assumptions must change before a conclusion changes. The resulting boundary does not itself establish robustness; the clinical plausibility of the altered assumptions also needs careful assessment.
Define the question and assumptions
Define which missing observations change, on which scale and how changes apply to each arm. A change in conclusions need not concern only a significance threshold: effect magnitude and clinical interpretation can also matter. Explain whether the exercise assesses the same target effect or addresses a different scientific question.
Hypothetical study example
In a hypothetical trial, the main result reverses only under substantially different assumptions for missing observations. The team considers whether those differences correspond to plausible participant experiences. A distant boundary on a graph is not presented as unquestionable robustness without examining the reasons data became missing.
Design review checks
- State the scale of altered values and its clinical interpretation explicitly.
- Show how assumptions are applied separately to both treatment groups.
- Check that scenario generation does not produce impossible or out-of-scale observations.
Record the decision and its limits
Accepted reporting displays the tipping boundary alongside the main assumption, scenario range and clinical assessment. Preserving a result in one region does not exclude every bias. Interpret the numerical findings with missingness patterns and design limitations, while identifying uncertainties that the sensitivity exercise does not investigate.
Frequently asked questions
- Does no tipping point prove robustness?
- No. It informs only the scenarios and model examined.
- Should statisticians choose scenarios alone?
- Statistical expertise and assessment of clinical plausibility should work together.