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Non\u2010monotone nonresponse, however, can often make such sensitivity analysis infeasible because the likelihood functions for alternative models involve high\u2010dimensional and difficult\u2010to\u2010evaluate integrals with respect to missing outcomes. We develop an extension of the local sensitivity method that overcomes computational difficulty and completely avoids fitting alternative models and evaluating these high\u2010dimensional integrals. The proposed method is applicable to a wide range of panel outcomes. We apply the method to a Smoking Trend dataset where we relax the standard ignorability assumption and evaluate how smoking\u2010trend estimates in different groups of US young adults are affected by alternative assumptions about the missing\u2010data mechanism. The main finding is that the standard estimate in the black male group is sensitive to nonignorable missingness but those in other groups are reasonably robust. 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