:package: :game_die: R/txshift: Efficient Estimation of the Causal Effects of Stochastic Interventions, with Corrections for Outcome-Dependent Sampling
The summarization procedure of projecting individual risk estimates onto a working linear MSM has been very useful, but it could potentially be improved somewhat by incorporating an option for montonicity. As is, we can fit either linear or piecewise linear working MSM summaries (no inference for the latter unless the knot point is pre-specified), but the individual risk estimates need not follow a monotonic pattern. In certain use-cases (e.g., VE curves), it may be useful to enforce monotonicity when it can be justified appropriately by background knowledge (e.g., observed biological responses).
The summarization procedure of projecting individual risk estimates onto a working linear MSM has been very useful, but it could potentially be improved somewhat by incorporating an option for montonicity. As is, we can fit either linear or piecewise linear working MSM summaries (no inference for the latter unless the knot point is pre-specified), but the individual risk estimates need not follow a monotonic pattern. In certain use-cases (e.g., VE curves), it may be useful to enforce monotonicity when it can be justified appropriately by background knowledge (e.g., observed biological responses).