A Potential Outcomes Approach to Developmental Toxicity Analyses

Estimating the effects of a toxin on fetal development in animal models such as mice can be problematic, because the number of pups that develop and survive until birth may simultaneously affect developmental outcomes such as birth weight and be affected by the introduction of a toxin into the fetal environment. Also, comparing pups that survived until birth at a high dose of the toxin with pups that survived at low doses may underestimate the effect of the toxin, because the lower dose means include the less healthy pups that would not survive if exposed to a higher level of toxin. We consider this problem in a potential outcomes framework that defines the effect of the dose on the outcome as the difference between what the outcome would have been for a pup had the dam in which the pup develops been exposed to dose level Z = z * rather than dose level Z = z . To disentangle the direct effect of dose from the effect of litter size, we focus on effects defined within principal strata that are a function of the survival status of the pups at each of the possible dose levels. A unique contribution to the potential outcomes literature is that we allow the outcome for a subject to be dependent on the principal stratum to which other subjects within a cluster belong.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/65819/1/j.1541-0420.2005.00506.x.pd

Nested Markov Compliance Class Model in the Presence of Time-Varying Noncompliance

We consider a Markov structure for partially unobserved time-varying compliance classes in the Imbens–Rubin (1997, The Annals of Statistics 25, 305–327) compliance model framework. The context is a longitudinal randomized intervention study where subjects are randomized once at baseline, outcomes and patient adherence are measured at multiple follow-ups, and patient adherence to their randomized treatment could vary over time. We propose a nested latent compliance class model where we use time-invariant subject-specific compliance principal strata to summarize longitudinal trends of subject-specific time-varying compliance patterns. The principal strata are formed using Markov models that relate current compliance behavior to compliance history. Treatment effects are estimated as intent-to-treat effects within the compliance principal strata.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/65981/1/j.1541-0420.2008.01113.x.pd

Baseline patient characteristics and mortality associated with longitudinal intervention compliance

Lin et al. ( http://www.biostatsresearch.com/upennbiostat/papers/ , 2006) proposed a nested Markov compliance class model in the Imbens and Rubin compliance class model framework to account for time-varying subject noncompliance in longitudinal randomized intervention studies. We use superclasses, or latent compliance class principal strata, to describe longitudinal compliance patterns, and time-varying compliance classes are assumed to depend on the history of compliance. In this paper, we search for good subject-level baseline predictors of these superclasses and also examine the relationship between these superclasses and all-cause mortality. Since the superclasses are completely latent in all subjects, we utilize multiple imputation techniques to draw inferences. We apply this approach to a randomized intervention study for elderly primary care patients with depression. Copyright © 2007 John Wiley & Sons, Ltd.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/57358/1/2909_ftp.pd

A Bayesian Approach to Surrogacy Assessment Using Principal Stratification in Clinical Trials

A surrogate marker ( S ) is a variable that can be measured earlier and often more easily than the true endpoint ( T ) in a clinical trial. Most previous research has been devoted to developing surrogacy measures to quantify how well  S  can replace  T  or examining the use of  S  in predicting the effect of a treatment ( Z ). However, the research often requires one to fit models for the distribution of  T  given  S  and  Z . It is well known that such models do not have causal interpretations because the models condition on a postrandomization variable  S . In this article, we directly model the relationship among  T ,  S , and  Z  using a potential outcomes framework introduced by Frangakis and Rubin (2002,  Biometrics   58 , 21–29). We propose a Bayesian estimation method to evaluate the causal probabilities associated with the cross-classification of the potential outcomes of  S  and  T  when  S  and  T  are both binary. We use a log-linear model to directly model the association between the potential outcomes of  S  and  T  through the odds ratios. The quantities derived from this approach always have causal interpretations. However, this causal model is not identifiable from the data without additional assumptions. To reduce the nonidentifiability problem and increase the precision of statistical inferences, we assume monotonicity and incorporate prior belief that is plausible in the surrogate context by using prior distributions. We also explore the relationship among the surrogacy measures based on traditional models and this counterfactual model. The method is applied to the data from a glaucoma treatment study.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/79372/1/j.1541-0420.2009.01303.x.pd

An Empirical Assessment of the Economic Effects of WTO Accession and Its Commitments

Besides facilitating access to the world market, WTO accession negotiations entail a process of domestic reforms that are expected to improve the supply side of acceding economies. However, measuring the actual impact of accession remains an empirical debate. The present paper contributes to the issue by offering a novel measure of the specific commitments made during the negotiations. These commitments often trigger a series of domestic structural transformations that are expected to impact economic growth. The accession commitment index proposed in the paper reflects the heterogenous distribution of commitments undertaken by Article XII members. This index is used to conduct a thorough statistical exploration of the effect of WTO accession on a series of variables related to economic growth, such as trade and investment. The results show that the impact of WTO membership on the Trade/GDP ratio is significantly higher than previous studies had found for developing countries, both quantitively and qualitatively. The results on investment, be it foreign or domestic, are also encouraging, but are not fully conclusive

The effect of ambient light condition on road traffic collisions involving pedestrians on pedestrian crossings

Previous research suggests darkness increases the risk of a collision involving a pedestrian and the severity of any injury suffered. Pedestrian crossings are intended to make it safer to cross the road, but it is not clear whether they are effective at doing this after-dark, compared with during daylight. Biannual clock changes resulting from transitions to and from daylight saving time were used to compare RTCs in the UK during daylight and darkness but at the same time of day, thus controlling for potential influences on RTC numbers not related to the ambient light condition. Odds ratios and regression discontinuity analysis suggested there was a significantly greater risk of a pedestrian RTC at a crossing after-dark than during daylight. Results also suggested the risk of an RTC after-dark was greater at a pedestrian crossing than at a location at least 50 m away from a crossing. Whilst these results show the increased danger to pedestrians using a designated crossing after-dark, this increased risk is not due to a lack of lighting at these locations as 98% of RTCs at pedestrian crossings after-dark were lit by road lighting. This raises questions about the adequacy and effectiveness of the lighting used at pedestrian crossings