Bias and Sensitivity Analysis When Estimating Treatment Effects from the Cox Model with Omitted Covariates

Omission of relevant covariates can lead to bias when estimating treatment or exposure effects from survival data in both randomized controlled trials and observational studies. This paper presents a general approach to assessing bias when covariates are omitted from the Cox model. The proposed method is applicable to both randomized and non‐randomized studies. We distinguish between the effects of three possible sources of bias: omission of a balanced covariate, data censoring and unmeasured confounding. Asymptotic formulae for determining the bias are derived from the large sample properties of the maximum likelihood estimator. A simulation study is used to demonstrate the validity of the bias formulae and to characterize the influence of the different sources of bias. It is shown that the bias converges to fixed limits as the effect of the omitted covariate increases, irrespective of the degree of confounding. The bias formulae are used as the basis for developing a new method of sensitivity analysis to assess the impact of omitted covariates on estimates of treatment or exposure effects. In simulation studies, the proposed method gave unbiased treatment estimates and confidence intervals with good coverage when the true sensitivity parameters were known. We describe application of the method to a randomized controlled trial and a non‐randomized study

The Economics of Testing for Biotech Grain: Application to StarLink Corn

StarLink corn, a biotech variety not approved for human food use, disrupted the marketing system in 2000 because of inadvertent commingling. Testing protocols have since been established for detection of StarLink in corn shipments to Japan. Domestic food manufacturers, anxious to avoid risks of contamination and product recalls, also test for StarLink kernels. This paper provides an overview of the economics of testing. What are the risks facing buyers and sellers, and how are these influenced by different testing protocols? How do market premiums and discounts, testing costs, and prior beliefs affect the incentives to test? A conceptual model is developed in which sellers can choose whether to pre-test grain prior to shipment. Simulation analysis is used to illustrate the impact of market premiums and other variables on testing incentives and buyer risk.Research and Development/Tech Change/Emerging Technologies,