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Abstract:
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To analyze incomplete longitudinal clinical trial data, there has been a shift from simple ad hoc methods to more principled ignorable analyses, which are valid under the assumption of random missingness. While the occurrence of nonrandom dropout cannot be ruled out, models for data under nonrandom missingness have gained some popularity. Meanwhile, cautionary remarks have been issued regarding their sensitivity to often unverifiable modeling assumptions. Consequently, there is evidence for a shift towards using ignorable methodology, supplemented with sensitivity analyses to explore the impact of potential deviations of this assumption in the direction of missingness at random. One such tool is local influence, which tends to pick up a lot of different anomalies, not just deviations in the MNAR mechanism. This sensitivity analysis tool is applied to the depression data.
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