Relatedly, this may be a welcome opportunity to re-examine whether our data definition and collection paradigms are up to the task of informing such models and estimating ambitious counterfactuals (34). For example, much of health-equity research relies on cross-sectional panel studies that are useful for assessing disease burden, health care use, and carrying out policy evaluations but lack the linkages needed to model trajectories of marginalized groups. Cohort studies overcome this but often have limited survey value and do not always contain detailed assessments of relevant psychosocial stressors and life experiences.
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As may be noted in the presentation of the two methods, Rosenbaum developed his analysis on the basis of criteria that are strictly statistical, while Greenland concentrated more on the standpoint of the epidemiologist in the proposed analysis. Rosenbaum did not discuss whether the unmeasured variable causes confounding or interaction in the findings, because he was interested in determining whether the value obtained is statistically significant or not. On the other hand, Greenland was concerned about the epidemiological construct of the findings, and hence, proposed an analysis that takes the existence of confounding into consideration, and does not apply the hypothesis of interaction. As the considerations of both models are important when assessing the potential effect of an unmeasured covariate in an observational study, it has been proposed to merge them to perform a sensitivity analysis that integrates these two approaches. 2ff7e9595c
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