Causal Effects, Migration and Legacy Studies

Social scientists have long been interested in the persistent effects of history on contemporary behavior and attitudes. To estimate legacy effects, studies typically compare people living in places that were historically exposed to some event and those that were not. Using principal stratification, we provide a formal framework to analyze how migration limits our ability to learn about the persistent effects of history from observed differences between historically exposed and unexposed places. We state the necessary assumptions about movement behavior to causally identify legacy effects. We highlight that these assumptions are strong; therefore, we recommend that legacy studies circumvent bias by collecting data on people's place of residence at the exposure time. Reexamining a study on the persistent effects of US civil-rights protests, we show that observed attitudinal differences between residents and non-residents of historic protest sites are more likely due to migration rather than attitudinal change.

Principal surrogates in context of high vaccine efficacy

Objectives The use of correlates of protection (CoPs) in vaccination trials offers significant advantages as useful clinical endpoint substitutes. Vaccines with very high vaccine efficacy (VE) are documented in the literature (95% or above). Callegaro, A., and F. Tibaldi. 2019. “Assessing Correlates of Protection in Vaccine Trials: Statistical Solutions in the Context of High Vaccine Efficacy.” BMC Medical Research Methodology 19: 47 showed that the rare infections observed in the vaccinated groups of these trials poses challenges when applying conventionally-used statistical methods for CoP assessment such as the Prentice criteria and meta-analysis. The objective of this work is to investigate the impact of this problem on another statistical method for the assessment of CoPs called Principal stratification. Methods We perform simulation experiments to investigate the effect of high vaccine efficacy on the performance of the Principal Stratification approach. Results Similarly to the Prentice framework, simulation results show that the power of the Principal Stratification approach decreases when the VE grows. Conclusions It can be challenging to validate principal surrogates (and statistical surrogates) for vaccines with very high vaccine efficacy.

Post-randomization Biomarker Effect Modification Analysis in an HIV Vaccine Clinical Trial

While the HVTN 505 trial showed no overall efficacy of the tested vaccine to prevent HIV infection over placebo, markers measuring immune response to vaccination were strongly correlated with infection. This finding generated the hypothesis that some marker-defined vaccinated subgroups were partially protected whereas others had their risk increased. This hypothesis can be assessed using the principal stratification framework (Frangakis and Rubin, 2002) for studying treatment effect modification by an intermediate response variable, using methods in the sub-field of principal surrogate (PS) analysis that studies multiple principal strata. Unfortunately, available methods for PS analysis require an augmented study design not available in HVTN 505, and make untestable structural risk assumptions, motivating a need for more robust PS methods. Fortunately, another sub-field of principal stratification, survivor average causal effect (SACE) analysis (Rubin, 2006) – which studies effects in a single principal stratum – provides many methods not requiring an augmented design and making fewer assumptions. We show how, for a binary intermediate response variable, methods developed for SACE analysis can be adapted to PS analysis, providing new and more robust PS methods. Application to HVTN 505 supports that the vaccine partially protected individuals with vaccine-induced T-cells expressing certain combinations of functions.

Administrative Records Mask Racially Biased Policing

Researchers often lack the necessary data to credibly estimate racial discrimination in policing. In particular, police administrative records lack information on civilians police observe but do not investigate. In this article, we show that if police racially discriminate when choosing whom to investigate, analyses using administrative records to estimate racial discrimination in police behavior are statistically biased, and many quantities of interest are unidentified—even among investigated individuals—absent strong and untestable assumptions. Using principal stratification in a causal mediation framework, we derive the exact form of the statistical bias that results from traditional estimation. We develop a bias-correction procedure and nonparametric sharp bounds for race effects, replicate published findings, and show the traditional estimator can severely underestimate levels of racially biased policing or mask discrimination entirely. We conclude by outlining a general and feasible design for future studies that is robust to this inferential snare.

A Bayesian nonparametric approach for evaluating the causal effect of treatment in randomized trials with semi-competing risks

Summary We develop a Bayesian nonparametric (BNP) approach to evaluate the causal effect of treatment in a randomized trial where a nonterminal event may be censored by a terminal event, but not vice versa (i.e., semi-competing risks). Based on the idea of principal stratification, we define a novel estimand for the causal effect of treatment on the nonterminal event. We introduce identification assumptions, indexed by a sensitivity parameter, and show how to draw inference using our BNP approach. We conduct simulation studies and illustrate our methodology using data from a brain cancer trial. The R code implementing our model and algorithm is available for download at https://github.com/YanxunXu/BaySemiCompeting.

Principal Surrogates in Context of High Vaccine Efficacy

Background: The use of correlates of protection (CoPs) in vaccination trials offers significant advantages as useful clinical endpoint substitutes. Vaccines with very high vaccine efficacy (VE) are documented in the literature (95% or above). Callegaro and Tibaldi, (2019) showed that the rare infections observed in thevaccinated groups of these trials poses challenges when applying conventionally-used statistical methods for CoP assessment such as the Prentice criteria and meta-analysis.Methods: In this paper, we extended Callegaro and Tibaldi, (2019) simulation study by evaluating the impact of high VE on the Principal stratification approach.Results: Similarly to the Prentice framework, we showed that the power decreases when the VE grows. It follows that it can be challenging to validate a principal surrogate (and a statistical surrogate) when rare infections are observed in the vaccinated groups.