Conditional estimation and inference to address observed covariate imbalance in randomized clinical trials

Background Baseline covariate imbalance (between treatment groups) is a common problem in randomized clinical trials which often raises questions about the validity of trial results. Answering these questions requires careful consideration of the statistical implications of covariate imbalance. The possibil ity of having covariate imbalance contributes to the marginal variance of an unadjusted treatment difference estimator, which can be reduced by making appropriate adjustments. Actual observed imbalance introduces a conditional bias into an unadjusted estimator, which may increase the conditional size of an unadjusted test. Methods This article provides conditional estimation and inference procedures to address the conditional bias due to observed imbalance. Interestingly, it is possible to use the same adjusted treatment difference estimator to address the marginal variance issue and the conditional bias issue associated with covariate imbalance. Its marginal variance estimator tends to be conservative for conditional inference, and we propose a conditionally appropriate variance estimator. We also provide an estimator of the conditional bias in an unadjusted treatment difference estimator, together with a conditional variance estimator. Results The proposed methodology is illustrated with real data from a stroke trial and evaluated in simulation experiments based on the same trial. The simulation results show that covariate imbalance can result in a substantial conditional bias and that the proposed methods deal with the conditional bias quite effectively. Discussion We recommend that the proposed methodology be used routinely to address the observed covariate imbalance in randomized clinical trials.

Online Adversarial Learning of Reactor State

This paper is in support of our recent efforts to designing intelligent defenses against false data injection attacks, where false data are injected in the raw data used to control the reactor. Adopting a game-model between the attacker and the defender, we focus here on how the attacker may estimate reactor state in order to inject an attack that can bypass normal reactor anomaly and outlier detection checks. This approach is essential to designing defensive strategies that can anticipate the attackers moves. More importantly, it is to alert the community that defensive methods based on approximate physics models could be bypassed by the attacker who can approximate the models in an online mode during a lie-in-wait period. For illustration, we employ a simplified point kinetics model and show how an attacker, once gaining access to the reactor raw data, i.e., instrumentation readings, can inject small perturbations to learn the reactor dynamic behavior. In our context, this equates to estimating the reactivity feedback coefficients, e.g., Doppler, Xenon poisoning, etc. We employ a non-parametric learning approach that employs alternating conditional estimation in conjunction with discrete Fourier transform and curve fitting techniques to estimate reactivity coefficients. An Iranian model of the Bushehr reactor is employed for demonstration. Results indicate that very accurate estimation of reactor state could be achieved using the proposed learning method.