Practical guidance on modeling choices for the virtual twins method.

Individuals can vary drastically in their response to the same treatment, and this heterogeneity has driven the push for more personalized medicine. Accurate and interpretable methods to identify subgroups that respond to the treatment differently from the population average are necessary to achieving this goal. The Virtual Twins (VT) method is a highly cited and implemented method for subgroup identification because of its intuitive framework. However, since its initial publication, many researchers still rely heavily on the authors' initial modeling suggestions without examining newer and more powerful alternatives. This leaves much of the potential of the method untapped. We comprehensively evaluate the performance of VT with different combinations of methods in each of its component steps, under a collection of linear and nonlinear problem settings. Our simulations show that the method choice for Step 1 of VT, in which dense models with high predictive performance are fit for the potential outcomes, is highly influential in the overall accuracy of the method, and Superlearner is a promising choice. We illustrate our findings by using VT to identify subgroups with heterogeneous treatment effects in a randomized, double-blind trial of very low nicotine content cigarettes.

Borrowing from supplemental sources to estimate causal effects from a primary data source.

The increasing multiplicity of data sources offers exciting possibilities in estimating the effects of a treatment, intervention, or exposure, particularly if observational and experimental sources could be used simultaneously. Borrowing between sources can potentially result in more efficient estimators, but it must be done in a principled manner to mitigate increased bias and Type I error. Furthermore, when the effect of treatment is confounded, as in observational sources or in clinical trials with noncompliance, causal effect estimators are needed to simultaneously adjust for confounding and to estimate effects across data sources. We consider the problem of estimating causal effects from a primary source and borrowing from any number of supplemental sources. We propose using regression-based estimators that borrow based on assuming exchangeability of the regression coefficients and parameters between data sources. Borrowing is accomplished with multisource exchangeability models and Bayesian model averaging. We show via simulation that a Bayesian linear model and Bayesian additive regression trees both have desirable properties and borrow under appropriate circumstances. We apply the estimators to recently completed trials of very low nicotine content cigarettes investigating their impact on smoking behavior.

Modeling the Dependence Structure in Genome Wide Association Studies of Binary Phenotypes in Family Data.

Genome-wide association studies (GWASs) are a popular tool for detecting association between genetic variants or single nucleotide polymorphisms (SNPs) and complex traits. Family data introduce complexity due to the non-independence of the family members. Methods for non-independent data are well established, but when the GWAS contains distinct family types, explicit modeling of between-family-type differences in the dependence structure comes at the cost of significantly increased computational burden. The situation is exacerbated with binary traits. In this paper, we perform several simulation studies to compare multiple candidate methods to perform single SNP association analysis with binary traits. We consider generalized estimating equations (GEE), generalized linear mixed models (GLMMs), or generalized least square (GLS) approaches. We study the influence of different working correlation structures for GEE on the GWAS findings and also the performance of different analysis method(s) to conduct a GWAS with binary trait data in families. We discuss the merits of each approach with attention to their applicability in a GWAS. We also compare the performances of the methods on the alcoholism data from the Minnesota Center for Twin and Family Research (MCTFR) study.

Estimating causal effects from a randomized clinical trial when noncompliance is measured with error.

Noncompliance or non-adherence to randomized treatment is a common challenge when interpreting data from randomized clinical trials. The effect of an intervention if all participants were forced to comply with the assigned treatment (i.e., the causal effect) is often of primary scientific interest. For example, in trials of very low nicotine content (VLNC) cigarettes, policymakers are interested in their effect on smoking behavior if their use were to be compelled by regulation. A variety of statistical methods to estimate the causal effect of an intervention have been proposed, but these methods, including inverse probability of compliance weighted (IPCW) estimators, assume that participants' compliance statuses are reported without error. This is an untenable assumption when compliance is based on self-report. Biomarkers (e.g., nicotine levels in the urine) may provide more reliable indicators of compliance but cannot perfectly discriminate between compliers and non-compliers. However, by modeling the distribution of the biomarker as a mixture distribution and writing the probability of compliance as a function of the mixture components, we show how the probability of compliance can be directly estimated from the data even when compliance status is unknown. To estimate the causal effect, we develop a new approach which re-weights participants by the product of their probability of compliance given the observed data and the inverse probability of compliance given confounders. We show that our proposed estimator is consistent and asymptotically normal and show that in some situations the proposed approach is more efficient than standard IPCW estimators. We demonstrate via simulation that the proposed estimator achieves smaller bias and greater efficiency than ad hoc approaches to estimating the causal effect when compliance is measured with error. We apply our method to data from a recently completed randomized trial of VLNC cigarettes.

The Importance of Estimating Causal Effects for Evaluating a Nicotine Standard for Cigarettes.

Recent evidence from randomized clinical trials (RCTs) of very low nicotine content (VLNC) cigarettes indicates that smokers randomized to VLNC cigarettes had significantly lower cigarette use, dependence, and biomarkers of exposure than smokers randomized to normal nicotine content control cigarettes. In these trials, a substantial number of participants did not adhere to their randomized treatment assignment, i.e., they used commercial cigarettes not provided by the trial in place of or in addition to the VLNC cigarettes provided by the trial. As with most RCTs, the analysis of these trials followed the intention-to-treat principle, where participants are analyzed according to their randomized treatment assignment regardless of adherence. Alternately, the analysis of an RCT could focus on the estimation and testing of the causal effect of the intervention, which is the treatment effect if all subjects were to adhere to their randomized treatment assignment. In this commentary, we compare these two approaches, highlighting the important role of causal estimation and inference for evaluating the regulatory effect of a nicotine standard for cigarettes. Additionally, we review the results of the secondary analyses of randomized trials of VLNC cigarettes using causal inference methodology to account for non-adherence to the assigned treatment and discuss the implications for a nicotine standard for cigarettes.

Estimating the causal effect of treatment regimes for organ transplantation.

Patients awaiting cadaveric organ transplantation face a difficult decision if offered a low-quality organ: accept the organ or remain on the waiting list and hope a better organ is offered in the future. A dynamic treatment regime (DTR) for transplantation is a rule that determines whether a patient should decline an offered organ. Existing methods can estimate the effect of DTRs on survival outcomes, but these were developed for applications where treatment is abundantly available. For transplantation, organ availability is limited, and existing methods can only estimate the effect of a DTR assuming a single patient follows the DTR. We show for transplantation that the effect of a DTR depends on whether other patients follow the DTR. To estimate the anticipated survival if the entire population awaiting transplantation were to adopt a DTR, we develop a novel inverse probability weighted estimator (IPCW) which re-weights patients based on the probability of following their transplant history in the counterfactual world in which all patients follow the DTR of interest. We estimate this counterfactual probability using hot deck imputation to fill in data that is not observed for patients who are artificially censored by IPCW once they no longer follow the DTR of interest. We show via simulation that our proposed method has good finite-sample properties, and we apply our method to a lung transplantation observational registry.

Efficiency and robustness of causal effect estimators when noncompliance is measured with error.

Estimating causal effects from randomized controlled trials is often complicated due to participant noncompliance to randomized treatment. Although there are a variety of methods to estimate causal effects in the presence of noncompliance, they generally make the assumption that noncompliance is measured without error. This is frequently an untenable assumption, particularly when noncompliance is based on participant self-report. To overcome this issue, we treat compliance as an unobserved variable and show how to estimate the probability of compliance given a biomarker of treatment and the other observed data. We present inverse probability weighted estimators, regression-based estimators, and a doubly-robust augmented estimator that rely on the estimated probability of compliance rather than an indicator of compliance. We investigate the finite-sample properties of the estimators and their efficiency and robustness under correctly specified or misspecified models, and we apply the estimators to a recently completed trial of very low nicotine content cigarettes.

A sequential multiple assignment randomized trial (SMART) protocol for empirically developing an adaptive preventive intervention for college student drinking reduction.

College student alcohol use and associated negative consequences are clear public health problems with consequences including damage to self, others, and institutions. This paper describes the protocol of a research study designed to answer a number of important questions in the development of an adaptive preventive intervention (API) to reduce high-risk drinking among first-year college students. The API is designed to educate students and to motivate heavy-drinking college students to engage in existing resources to support reducing high-risk alcohol use, by leveraging technology-based intervention modalities. The primary outcome is a reduction in binge drinking, with secondary outcomes of reducing negative alcohol-related consequences and increasing health services utilization. Adaptive preventive interventions have the potential to reduce the acute and long-term negative health consequences of young adult alcohol use.

Risk Factors for Low CD4+ Count Recovery Despite Viral Suppression Among Participants Initiating Antiretroviral Treatment With CD4+ Counts > 500 Cells/mm3: Findings From the Strategic Timing of AntiRetroviral Therapy (START) Trial.

BACKGROUND: Low CD4 recovery among HIV-positive individuals who achieve virologic suppression is common but has not been studied among individuals initiating treatment at CD4 counts of >500 cells/mm. SETTING: United States, Africa, Asia, Europe and Israel, Australia, Latin America. METHODS: Among participants randomized to immediate antiretroviral therapy (ART) in the Strategic Timing of AntiRetroviral Therapy trial, low CD4 recovery was defined as a CD4 increase of <50 cells/mm from baseline after 8 months despite viral load of ≤200 copies/mL. Risk factors for low recovery were investigated with logistic regression. RESULTS: Low CD4 recovery was observed in 39.7% of participants. Male sex [odds ratio (OR), 1.53; P = 0.007], lower screening CD4 cell counts (OR, 1.09 per 100 fewer cells/mm; P = 0.004), higher baseline CD8 cell counts (OR, 1.05 per 100 more cells/mm; P < 0.001), and lower HIV RNA levels (OR, 1.93 per log10 decrease; P < 0.001) were associated with low CD4 recovery. D-dimer had a quadratic association with low CD4 recovery, with lowest odds occurring at 0.32 µg/mL. At lower HIV RNA levels, the odds of low CD4 recovery were elevated across the levels of screening CD4 count; but at higher HIV RNA levels, the odds of low CD4 recovery were higher among those with lower vs. higher screening CD4. CONCLUSIONS: Low CD4 recovery is frequent among participants starting ART at high CD4 counts. Risk factors include male sex, lower screening CD4 cell counts, higher CD8 cell counts, and lower HIV RNA levels. More follow-up is required to determine the impact of low CD4 recovery on clinical outcomes.

Classification Accuracy of Biomarkers of Compliance.

Objectives: Evaluate the classification accuracy of biomarkers of nicotine exposure for identifying noncompliance in randomized controlled trials (RCTs) of very low nicotine content (VLNC) cigarettes. Methods: We combined data from 2 studies to evaluate the classification accuracy of biomarkers of nicotine exposure for identifying noncompliance in RCTs of VLNC cigarettes. Using a novel approach that did not require knowledge of each participant's compliance status, we modeled the distributions of total nicotine equivalents (TNE), total cotinine, and anatabine in compliant and noncompliant participants using a mixture model. Estimates of the classification accuracy were derived from the estimated densities. Results: TNE and total cotinine had near-perfect classification accuracy, but TNE had better classification accuracy than total cotinine (p = 0.03) and anatabine (p = 0.014). The classification accuracy of TNE and total cotinine decreased as self-reported study cigarettes increased; anatabine was similar, but the results were more statistically variable. Conclusions: TNE and total cotinine are better classifiers of compliance than anatabine. These results will be useful in determining biomarker thresholds for identifying noncompliance and will aid in the interpretation of future RCTs of VLNC cigarettes.

GWAS of 126,559 individuals identifies genetic variants associated with educational attainment.

A genome-wide association study (GWAS) of educational attainment was conducted in a discovery sample of 101,069 individuals and a replication sample of 25,490. Three independent single-nucleotide polymorphisms (SNPs) are genome-wide significant (rs9320913, rs11584700, rs4851266), and all three replicate. Estimated effects sizes are small (coefficient of determination R(2) ≈ 0.02%), approximately 1 month of schooling per allele. A linear polygenic score from all measured SNPs accounts for ≈2% of the variance in both educational attainment and cognitive function. Genes in the region of the loci have previously been associated with health, cognitive, and central nervous system phenotypes, and bioinformatics analyses suggest the involvement of the anterior caudate nucleus. These findings provide promising candidate SNPs for follow-up work, and our effect size estimates can anchor power analyses in social-science genetics.

Auditory Cortex is Important in the Extinction of Two Different Tone-Based Conditioned Fear Memories in Rats.

Extensive fear extinction research is guided by the view that there are structures in the brain that develop inhibitory control over the expression of conditioned fear memories. While the medial prefrontal cortex has recently captured attention as the locus of plasticity essential for extinction of conditioned fear, the auditory cortex is another plausible cortical area involved in extinction learning since it is considered a sufficient conditioned stimulus (CS) pathway in tone fear conditioning. We examined the role of auditory cortex in extinction of auditory-based fear memories with a standard tone-on conditioning, wherein a tone CS predicted a footshock unconditioned stimulus (US), or a novel tone-off conditioning, in which the tone was continually present and the offset of the tone was the CS predicting the US. Rats with bilateral auditory cortex lesions were trained in either paradigm and subsequently trained in extinction to the CS. Auditory cortex lesions had no effect on acquisition but impaired extinction to both CSs. These findings indicate that the auditory cortex contributes to extinction of wide-ranging auditory fear memories, as evidenced by deficits in both tone-on CS and tone-off CS extinction training.

A thalamo-cortico-amygdala pathway mediates auditory fear conditioning in the intact brain.

The neural substrates of fear conditioning in rats have been well characterized, with converging lines of evidence indicating that conditioned stimulus (CS) and unconditioned stimulus (US) information form a CS-US association in the amygdala. Auditory CS information can reach the amygdala via two routes: a direct thalamo-amygdala pathway, and an indirect thalamo-cortico-amygdala pathway. Although either pathway can fully support learning when the alternate pathway is disrupted, many studies to date have argued that the thalamo-amygdala pathway is the principal auditory CS pathway in intact brains. To test this hypothesis, we trained rats in auditory fear conditioning, and 24 h later lesioned either pathway, leaving the alternate pathway intact. Later, animals were tested for conditioned freezing to the auditory CS. We report that lesions of the thalamo-amygdala pathway produced severe but incomplete deficits in freezing during the tone retention test, while lesions of the thalamo-cortico-amygdala pathway completely abolished freezing during tone presentation. These results suggest that the thalamo-cortico-amygdala pathway is the principal auditory CS pathway when the brain is intact.