  Omitted variable bias can affect treatment effect estimates obtained from
observational data due to the lack of random assignment to treatment groups.
Sensitivity analyses adjust these estimates to quantify the impact of potential
omitted variables. This paper presents methods of sensitivity analysis to
adjust interval estimates of treatment effect---both the point estimate and
standard error---obtained using multiple linear regression. Central to our
approach is what we term benchmarking, the use of data to establish reference
points for speculation about omitted confounders. The method adapts to
treatment effects that may differ by subgroup, to scenarios involving omission
of multiple variables, and to combinations of covariance adjustment with
propensity score stratification. We illustrate it using data from an
influential study of health outcomes of patients admitted to critical care.
