  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.
