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Figure 6 | BMC Medical Research Methodology

Figure 6

From: Detecting and correcting the bias of unmeasured factors using perturbation analysis: a data-mining approach

Figure 6

Perturbation diagnostics for a hypothetical data ( n  = 200) taken from Table  2 (A: perturbation adjustment when the unmeasured is associated with neither exposure nor disease; B: perturbation adjustment when the unmeasured is not associated with exposure but is associated with disease; C: perturbation adjustment when the unmeasured is not associated with disease but is associated with exposure). The perturbation variables have an f PV of 0.025 and are dependent of one another through a first-order Markov chain with an odds ratio of 10.0 between successive perturbation variables. Bootstrap was done for a total of 10000 times.

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