Search

Abstract: In vaccine trials, the vaccination of one person might prevent the infection of another. This dependency makes it difficult to estimate the effect of a vaccine on infection. To deal with this issue, causal inference along with a principal stratification framework has been discussed. Unfortunately, however, no standard method has been established for estimating the causal infectiousness effect (CIE). Recently, in a setting of two persons per household, it has been reported that the crude estimator becomes the upper bound of the CIE under two plausible assumptions. Here, we present the lower bound for the CIE by strengthening one of these two assumptions. [Copyright &y& Elsevier]

Abstract: Noncompliance is a common concern in drawing causal inference in randomized trials. There is a large body of literature on methods for dealing with this issue. Specifically, it is well known that the average causal effect within the principal strata of compliance behavior (complier average causal effect, CACE) is equal to the instrumental variable estimator under the monotonicity assumption. However, there is only a small amount of published literature that discusses CACE without this assumption. Here, we present bounds on the CACE without the monotonicity assumption. To derive the bounds, we utilize the idea considered to derive bounds on the principal strata effects in the context of causal mediation analysis and those on the average causal effects among the whole population. The bounds presented here are illustrated using a classic randomized trial. [Copyright &y& Elsevier]

Abstract: Many randomized experiments suffer from the “truncation-by-death” problem where potential outcomes are not defined for some subpopulations. For example, in medical trials, quality-of-life measures are only defined for surviving patients. In this article, I derive the sharp bounds on causal effects under various assumptions. My identification analysis is based on the idea that the “truncation-by-death” problem can be formulated as the contaminated data problem. The proposed analytical techniques can be applied to other settings in causal inference including the estimation of direct and indirect effects and the analysis of three-arm randomized experiments with noncompliance. [Copyright &y& Elsevier]