Working Paper: NBER ID: w30461
Authors: Sheyu Li; Valentyn Litvin; Charles F. Manski
Abstract: Medical journals have adhered to a reporting practice that seriously limits the usefulness of published trial findings. Medical decision makers commonly observe many patient covariates and seek to use this information to personalize treatment choices. Yet standard summaries of trial findings only partition subjects into broad subgroups, typically into binary categories. Given this reporting practice, we study the problem of inference on long mean treatment outcomes E[y(t)|x], where t is a treatment, y(t) is a treatment outcome, and the covariate vector x has length K, each component being a binary variable. The available data are estimates of {E[y(t)|xk = 0], E[y(t)|xk = 1], P(xk)}, k = 1, . . . , K reported in journal articles. We show that reported trial findings partially identify {E[y(t)|x], P(x)}. Illustrative computations demonstrate that the summaries of trial findings in journal articles may imply only wide bounds on long mean outcomes. One can realistically tighten inferences if one can combine reported trial findings with credible assumptions having identifying power, such as bounded-variation assumptions.
Keywords: No keywords provided
JEL Codes: C13; I10
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| eytxk (short mean outcomes) (C41) | eytx (long mean outcomes) (C41) |
| pxk (marginal probabilities of covariates) (C29) | eytx (long mean outcomes) (C41) |
| eytxk (short mean outcomes) and pxk (marginal probabilities of covariates) (C51) | eytx (long mean outcomes) (C41) |
| eytx (long mean outcomes) (C41) | eytx (long mean outcomes) (C41) |