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there is also a simulation-based sensitivity analysis for matching estimators, which is described in Nannici (2007) and Ichino et al. (2008). In Stata, the program sensatt can perform this kind of analysis. Moreover, you could imagine a binary unmeasured confounder and stratify on it as proposed by Greenland (1996) and discussed in the context of matching ...

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A risk model is a model that models risk. The models you show as $\pi = \beta_1 x_1 + \ldots$ are additive risk models. Which is interesting because they perform so badly with even modest numbers of covariates. In both cases, the models are presumably the same in terms of the number of covariates and the expression of the written form. The difference ...

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The table displayed in this question shows the choices of parameter values for a simulation study, not experimentally determined or calculated odds ratios. The simulation involved 12 binary covariates (the $\mathsf {x_i}$) each with a prevalence of 20% having the indicated associations with outcome without treatment ($\mathsf {OR_C}$). In one set of ...

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