Estimating generalized propensity scores with survey and attrition weighted data.

Prior work in causal inference has shown that using survey sampling weights in the propensity score estimation stage and the outcome model stage for binary treatments can result in a more robust estimator of the effect of the binary treatment being analyzed. However, to date, extending this work to continuous treatments and exposures has not been explored nor has consideration been given for how to handle attrition weights in the propensity score model. Nonetheless, generalized propensity score (GPS) analyses are being used for estimating continuous treatment effects on outcomes when researchers have observational data, and those data sets often have survey or attrition weights that need to be accounted for in the analysis. Here, we extend prior work and show with analytic results that using survey sampling or attrition weights in the GPS estimation stage and the outcome model stage for continuous treatments can result in a more robust estimator than one that does not. Simulation study results show that, although using weights in both estimation stages is sufficient for robust estimation, it is not necessary and unbiased estimation is possible in some cases under various approaches to using weights in estimation. Analysts do not know if the conditions of our simulation studies hold, so use of weights in both estimation stages might provide insurance for reducing potential bias. We discuss the implications of our results in the context of an empirical example.

Sampling weighting strategies in causal mediation analysis.

BACKGROUND: Causal mediation analysis plays a crucial role in examining causal effects and causal mechanisms. Yet, limited work has taken into consideration the use of sampling weights in causal mediation analysis. In this study, we compared different strategies of incorporating sampling weights into causal mediation analysis. METHODS: We conducted a simulation study to assess 4 different sampling weighting strategies-1) not using sampling weights, 2) incorporating sampling weights into mediation "cross-world" weights, 3) using sampling weights when estimating the outcome model, and 4) using sampling weights in both stages. We generated 8 simulated population scenarios comprising an exposure (A), an outcome (Y), a mediator (M), and six covariates (C), all of which were binary. The data were generated so that the true model of A given C and the true model of A given M and C were both logit models. We crossed these 8 population scenarios with 4 different sampling methods to obtain 32 total simulation conditions. For each simulation condition, we assessed the performance of 4 sampling weighting strategies when calculating sample-based estimates of the total, direct, and indirect effects. We also applied the four sampling weighting strategies to a case study using data from the National Survey on Drug Use and Health (NSDUH). RESULTS: Using sampling weights in both stages (mediation weight estimation and outcome models) had the lowest bias under most simulation conditions examined. Using sampling weights in only one stage led to greater bias for multiple simulation conditions. DISCUSSION: Using sampling weights in both stages is an effective approach to reduce bias in causal mediation analyses under a variety of conditions regarding the structure of the population data and sampling methods.

A Tutorial for Propensity Score Weighting for Moderation Analysis With Categorical Variables: An Application Examining Smoking Disparities Among Sexual Minority Adults.

OBJECTIVE: To provide step-by-step guidance and STATA and R code for using propensity score (PS) weighting to estimate moderation effects with categorical variables. RESEARCH DESIGN: Tutorial illustrating the key steps for estimating and testing moderation using observational data. Steps include: (1) examining covariate overlap across treatment groups within levels of the moderator; (2) estimating the PS weights; (3) evaluating whether PS weights improved covariate balance; (4) estimating moderated treatment effects; and (5) assessing the sensitivity of findings to unobserved confounding. Our illustrative case study uses data from 41,832 adults from the 2019 National Survey on Drug Use and Health to examine if gender moderates the association between sexual minority status (eg, lesbian, gay, or bisexual [LGB] identity) and adult smoking prevalence. RESULTS: For our case study, there were no noted concerns about covariate overlap, and we were able to successfully estimate the PS weights within each level of the moderator. Moreover, balance criteria indicated that PS weights successfully achieved covariate balance for both moderator groups. PS-weighted results indicated there was significant evidence of moderation for the case study, and sensitivity analyses demonstrated that results were highly robust for one level of the moderator but not the other. CONCLUSIONS: When conducting moderation analyses, covariate imbalances across levels of the moderator can cause biased estimates. As demonstrated in this tutorial, PS weighting within each level of the moderator can improve the estimated moderation effects by minimizing bias from imbalance within the moderator subgroups.

Best practices in statistical computing.

The world is becoming increasingly complex, both in terms of the rich sources of data we have access to and the statistical and computational methods we can use on data. These factors create an ever-increasing risk for errors in code and the sensitivity of findings to data preparation and the execution of complex statistical and computing methods. The consequences of coding and data mistakes can be substantial. In this paper, we describe the key steps for implementing a code quality assurance (QA) process that researchers can follow to improve their coding practices throughout a project to assure the quality of the final data, code, analyses, and results. These steps include: (i) adherence to principles for code writing and style that follow best practices; (ii) clear written documentation that describes code, workflow, and key analytic decisions; (iii) careful version control; (iv) good data management; and (v) regular testing and review. Following these steps will greatly improve the ability of a study to assure results are accurate and reproducible. The responsibility for code QA falls not only on individual researchers but institutions, journals, and funding agencies as well.

Heightened amygdala responsiveness in s-carriers of 5-HTTLPR genetic polymorphism reflects enhanced cortical rather than subcortical inputs: An MEG study.

Short allele carriers (S-carriers) of the serotonin transporter gene (5-HTTLPR) show an elevated amygdala response to emotional stimuli relative to long allele carriers (LL-homozygous). However, whether this reflects increased responsiveness of the amygdala generally or interactions between the amygdala and the specific input systems remains unknown. It is argued that the amygdala receives input via a quick subcortical and a slower cortical pathway. If the elevated amygdala response in S-carriers reflects generally increased amygdala responding, then group differences in amygdala should be seen across the amygdala response time course. However, if the difference is a secondary consequence of enhanced amygdala-cortical interactions, then group differences might only be present later in the amygdala response. Using magnetoencephalography (MEG), we found an enhanced amygdala response to fearful expressions starting 40-50 ms poststimulus. However, group differences in the amygdala were only seen 190-200 ms poststimulus, preceded by increased superior temporal sulcus (STS) responses in S-carriers from 130 to 140 ms poststimulus. An enhanced amygdala response to angry expressions started 260-270 ms poststimulus with group differences in the amygdala starting at 160-170 ms poststimulus onset, preceded by increased STS responses in S-carriers from 150 to 160 ms poststimulus. These suggest that enhanced amygdala responses in S-carriers might reflect enhanced STS-amygdala connectivity in S-carriers. Hum Brain Mapp 38:4313-4321, 2017. © 2017 Wiley Periodicals, Inc.

The Right Tool for the Job: Choosing Between Covariate-balancing and Generalized Boosted Model Propensity Scores.

Estimating the causal effect of an exposure (vs. some control) on an outcome using observational data often requires addressing the fact that exposed and control groups differ on pre-exposure characteristics that may be related to the outcome (confounders). Propensity score methods have long been used as a tool for adjusting for observed confounders in order to produce more valid causal effect estimates under the strong ignorability assumption. In this article, we compare two promising propensity score estimation methods (for time-invariant binary exposures) when assessing the average treatment effect on the treated: the generalized boosted models and covariate-balancing propensity scores, with the main objective to provide analysts with some rules-of-thumb when choosing between these two methods. We compare the methods across different dimensions including the presence of extraneous variables, the complexity of the relationship between exposure or outcome and covariates, and the residual variance in outcome and exposure. We found that when noncomplex relationships exist between outcome or exposure and covariates, the covariate-balancing method outperformed the boosted method, but under complex relationships, the boosted method performed better. We lay out criteria for when one method should be expected to outperform the other with no blanket statement on whether one method is always better than the other.

Time-varying effect moderation using the structural nested mean model: estimation using inverse-weighted regression with residuals.

This article considers the problem of examining time-varying causal effect moderation using observational, longitudinal data in which treatment, candidate moderators, and possible confounders are time varying. The structural nested mean model (SNMM) is used to specify the moderated time-varying causal effects of interest in a conditional mean model for a continuous response given time-varying treatments and moderators. We present an easy-to-use estimator of the SNMM that combines an existing regression-with-residuals (RR) approach with an inverse-probability-of-treatment weighting (IPTW) strategy. The RR approach has been shown to identify the moderated time-varying causal effects if the time-varying moderators are also the sole time-varying confounders. The proposed IPTW+RR approach provides estimators of the moderated time-varying causal effects in the SNMM in the presence of an additional, auxiliary set of known and measured time-varying confounders. We use a small simulation experiment to compare IPTW+RR versus the traditional regression approach and to compare small and large sample properties of asymptotic versus bootstrap estimators of the standard errors for the IPTW+RR approach. This article clarifies the distinction between time-varying moderators and time-varying confounders. We illustrate the methodology in a case study to assess if time-varying substance use moderates treatment effects on future substance use.

A tutorial on propensity score estimation for multiple treatments using generalized boosted models.

The use of propensity scores to control for pretreatment imbalances on observed variables in non-randomized or observational studies examining the causal effects of treatments or interventions has become widespread over the past decade. For settings with two conditions of interest such as a treatment and a control, inverse probability of treatment weighted estimation with propensity scores estimated via boosted models has been shown in simulation studies to yield causal effect estimates with desirable properties. There are tools (e.g., the twang package in R) and guidance for implementing this method with two treatments. However, there is not such guidance for analyses of three or more treatments. The goals of this paper are twofold: (1) to provide step-by-step guidance for researchers who want to implement propensity score weighting for multiple treatments and (2) to propose the use of generalized boosted models (GBM) for estimation of the necessary propensity score weights. We define the causal quantities that may be of interest to studies of multiple treatments and derive weighted estimators of those quantities. We present a detailed plan for using GBM to estimate propensity scores and using those scores to estimate weights and causal effects. We also provide tools for assessing balance and overlap of pretreatment variables among treatment groups in the context of multiple treatments. A case study examining the effects of three treatment programs for adolescent substance abuse demonstrates the methods.

Subgroups analysis when treatment and moderators are time-varying.

Prevention scientists are often interested in understanding characteristics of participants that are predictive of treatment effects because these characteristics can be used to inform the types of individuals who benefit more or less from treatment or prevention programs. Often, effect moderation questions are examined using subgroups analysis or, equivalently, using covariate × treatment interactions in the context of regression analysis. This article focuses on conceptualizing and examining causal effect moderation in longitudinal settings in which both treatment and the putative moderators are time-varying. Studying effect moderation in the time-varying setting helps identify which individuals will benefit more or less from additional treatment services on the basis of both individual characteristics and their evolving outcomes, symptoms, severity, and need. Examining effect moderation in these longitudinal settings, however, is difficult because moderators of future treatment may themselves be affected by prior treatment (for example, future moderators may be mediators of prior treatment). This article introduces moderated intermediate causal effects in the time-varying setting, describes how they are part of Robins' Structural Nested Mean Model, discusses two problems with using a traditional regression approach to estimate these effects, and describes a new approach (a two-stage regression estimator) to estimate these effects. The methodology is illustrated using longitudinal data to examine the time-varying effects of receiving community-based substance abuse treatment as a function of time-varying severity (or need).

Comparative effectiveness of individual versus family-based substance use treatment on adolescent self-injurious thoughts and behaviors.

INTRODUCTION: Self-injurious thoughts and behaviors (SITB) are of increasing concern among adolescents, especially those who use substances. Some evidence suggests that existing evidence-based substance use treatments (EBTs) could impact not only their intended substance use targets but also SITB. However, which types of substance use treatments may have the greatest impact on youth SITB is not yet clear. Based on prior literature showing that family support and connection may buffer youth from SITB, we initially hypothesized that family-based EBTs would show greater improvement in SITB compared to those receiving individually focused EBTs and that the size of the effects would be small given the comparison between two active, evidence-based interventions, and base rates of SITB. METHODS: In a sample of 2893 youth in substance use treatment, we compared the effectiveness of individually and family-based EBTs in reducing SITBs. The study used entropy balancing and regression modeling to balance the groups on pre-treatment characteristics and examine change in outcomes over a one-year follow-up period. RESULTS: Both groups improved in self-injury and suicide attempts over the one-year study period, but only youth in individual treatment improved in suicidal ideation. However, the study found no significant difference between the changes over time in the two groups for any outcome. As expected, effect sizes were small and power was constrained in this study given the rarity of the outcomes, but effect sizes are similar to those observed with substance use outcomes. CONCLUSIONS: The results provide important exploratory evidence on the potential relative effectiveness of these two treatments for SITBs. This study supports prior findings that EBTs for youth substance use may help to improve SITB and suggests that different treatment formats (individual or family-based) could result in different benefits for SITB outcomes.

Regional areas and widths of the midsagittal corpus callosum among HIV-infected patients on stable antiretroviral therapies.

Recent reports suggest that a growing number of human immunodeficiency virus (HIV)-infected persons show signs of persistent cognitive impairment even in the context of combination antiretroviral therapies (cART). The basis for this finding remains poorly understood as there are only a limited number of studies examining the relationship between CNS injury, measures of disease severity, and cognitive function in the setting of stable disease. This study examined the effects of HIV infection on cerebral white matter using quantitative morphometry of the midsagittal corpus callosum (CC) in 216 chronically infected participants from the multisite HIV Neuroimaging Consortium study currently receiving cART and 139 controls. All participants underwent MRI assessment, and HIV-infected subjects also underwent measures of cognitive function and disease severity. The midsagittal slice of the CC was quantified using two semi-automated procedures. Group comparisons were accomplished using ANOVA, and the relationship between CC morphometry and clinical covariates (current CD4, nadir CD4, plasma and CSF HIV RNA, duration of HIV infection, age, and ADC stage) was assessed using linear regression models. HIV-infected patients showed significant reductions in both the area and linear widths for several regions of the CC. Significant relationships were found with ADC stage and nadir CD4 cell count, but no other clinical variables. Despite effective treatment, significant and possibly irreversible structural loss of the white matter persists in the setting of chronic HIV disease. A history of advanced immune suppression is a strong predictor of this complication and suggests that antiretroviral intervention at earlier stages of infection may be warranted.

Smoothing across time in repeated cross-sectional data.

Repeated cross-sectional samples are common in national surveys of health like the National Health Interview Survey (NHIS). Because population health outcomes generally evolve slowly, pooling data across years can improve the precision of current-year annual estimates of disease prevalence and other health outcomes. Pooling over time is particularly valuable in health disparities research, where outcomes for small groups are often of interest and pooling data across groups would bias disparity estimates. State-space modeling and Kalman filtering are appealing choices for smoothing data across time. However, filtering can be problematic when few time points are available, as is common with annual cross-sectional data. Problems arise because filtering relies on estimated variance components, which can be biased and imprecise when estimated with small samples, especially when estimated in tandem with linear trends. We conduct a simulation study showing that even when trends and variance components are estimated poorly, smoothing with these estimates can improve the mean squared error (MSE) of estimated health states for multiple racial/ethnic groups when the variance components are estimated with the pooled sample. We consider frequentist estimators with no trends, one common trend across groups, and separate trends for every group, as well as shrinkage estimators of trends through a Bayesian model. We show that the Bayesian model offers the greatest improvement in MSE, and that Bayesian Information Criterion (BIC)-based model averaging of the frequentist estimators with different trend assumptions performs nearly as well. We present empirical examples using the NHIS data.

Nonparametric Estimation of Population Average Dose-Response Curves using Entropy Balancing Weights for Continuous Exposures.

Weighted estimators are commonly used for estimating exposure effects in observational settings to establish causal relations. These estimators have a long history of development when the exposure of interest is binary and where the weights are typically functions of an estimated propensity score. Recent developments in optimization-based estimators for constructing weights in binary exposure settings, such as those based on entropy balancing, have shown more promise in estimating treatment effects than those methods that focus on the direct estimation of the propensity score using likelihood-based methods. This paper explores recent developments of entropy balancing methods to continuous exposure settings and the estimation of population dose-response curves using nonparametric estimation combined with entropy balancing weights, focusing on factors that would be important to applied researchers in medical or health services research. The methods developed here are applied to data from a study assessing the effect of non-randomized components of an evidence-based substance use treatment program on emotional and substance use clinical outcomes.

SpeechSpiro: Lung Function Assessment from Speech Pattern as an Alternative to Spirometry for Mobile Health Tracking.

Respiratory illnesses are common in the United States and globally; people deal with these illnesses in various forms, such as asthma, chronic obstructive pulmonary diseases, or infectious respiratory diseases (e.g., coronavirus). The lung function of subjects affected by these illnesses degrades due to infection or inflammation in their respiratory airways. Typically, lung function is assessed using in-clinic medical equipment, and quite recently, via portable spirometry devices. Research has shown that the obstruction and restriction in the respiratory airways affect individuals' voice characteristics. Hence, audio features could play a role in predicting the lung function and severity of the obstruction. In this paper, we go beyond well-known voice audio features and create a hybrid deep learning model using CNN-LSTM to discover spatiotemporal patterns in speech and predict the lung function parameters with accuracy comparable to conventional devices. We validate the performance and generalizability of our method using the data collected from 201 subjects enrolled in two studies internally and in collaboration with a pulmonary hospital. SpeechSpiro measures lung function parameters (e.g., forced vital capacity) with a mean normalized RMSE of 12% and R2 score of up to 76% using 60-second phone audio recordings of individuals reading a passage.Clinical relevance - Speech-based spirometry has the potential to eliminate the need for an additional device to carry out the lung function assessment outside clinical settings; hence, it can enable continuous and mobile track of the individual's condition, healthy or with a respiratory illness, using a smartphone.

Reliability and validity of MRI-based automated volumetry software relative to auto-assisted manual measurement of subcortical structures in HIV-infected patients from a multisite study.

The automated volumetric output of FreeSurfer and Individual Brain Atlases using Statistical Parametric Mapping (IBASPM), two widely used and well published software packages, was examined for accuracy and consistency relative to auto-assisted manual (AAM) tracings (i.e., manual correction of automated output) when measuring the caudate, putamen, amygdala, and hippocampus in the baseline scans of 120 HIV-infected patients (86.7% male, 47.3+/-6.3y.o., mean HIV duration 12.0+/-6.3years) from the NIH-funded HIV Neuroimaging Consortium (HIVNC) cohort. The data was examined for accuracy and consistency relative to auto-assisted manual tracing, and construct validity was assessed by correlating automated and AAM volumetric measures with relevant clinical measures of HIV progression. When results were averaged across all patients in the eight structures examined, FreeSurfer achieved lower absolute volume difference in five, higher sensitivity in seven, and higher spatial overlap in all eight structures. Additionally, FreeSurfer results exhibited less variability in all measures. Output from both methods identified discrepant correlations with clinical measures of HIV progression relative to AAM segmented data. Overall, FreeSurfer proved more effective in the context of subcortical volumetry in HIV-patients, particularly in a multisite cohort study such as this. These findings emphasize that regardless of the automated method used, visual inspection of segmentation output, along with manual correction if necessary, remains critical to ensuring the validity of reported results.

Marijuana use and high school dropout: the influence of unobservables.

In this study, we reconsider the relationship between heavy and persistent marijuana use and high school dropout status. Using a unique prospective panel study of over 4500 7th grade students from South Dakota who are followed through high school, we developed propensity score weights to adjust for baseline differences found to exist before marijuana initiation occurs for most students (7th grade). We then used weighted logistic regression that incorporates these propensity score weights to examine the extent to which time-varying factors, including substance use, also influence the likelihood of dropping out of school. We found a positive association between marijuana use and dropping out (OR=5.6, RR=3.8), over half of which was explained by prior differences in observational characteristics and behaviors. The remaining association (OR=2.4, RR=1.7) became statistically insignificant when measures of cigarette smoking were included in the analysis. Because cigarette smoking is unlikely to seriously impair cognition, we interpret this result as evidence that the association between marijuana use and high school dropout is unlikely to be due to its adverse effects on cognition. We then explored which constructs drive this result, determining that they are time-varying parental and peer influences.

Visual awareness, emotion, and gamma band synchronization.

What makes us become aware? A popular hypothesis is that if cortical neurons fire in synchrony at a certain frequency band (gamma), we become aware of what they are representing. We tested this hypothesis adopting brain-imaging techniques with good spatiotemporal resolution and frequency-specific information. Specifically, we examined the degree to which increases in event-related synchronization (ERS) in the gamma band were associated with awareness of a stimulus (its detectability) and/or the emotional content of the stimulus. We observed increases in gamma band ERS within prefrontal-anterior cingulate, visual, parietal, posterior cingulate, and superior temporal cortices to stimuli available to conscious awareness. However, we also observed increases in gamma band ERS within the amygdala, visual, prefrontal, parietal, and posterior cingulate cortices to emotional relative to neutral stimuli, irrespective of their availability to conscious access. This suggests that increased gamma band ERS is related to, but not sufficient for, consciousness.

Switching cluster membership in cluster randomized control trials: Implications for design and analysis.

Randomized control trials (RCTs) often use clustered designs, where intact clusters (such as classroom, schools, or treatment centers) are randomly assigned to treatment and control conditions. Hierarchical linear models (HLMs) are used almost universally to estimate the effects in such experiments. While study designs that utilize intact clusters have many potential advantages, there is little guidance in the literature on how to respond when cluster switching induces noncompliance with the randomization protocol. In the presence of noncompliance the intent-to-treat (ITT) effect becomes the estimand of interest. When fitting the HLM, these individuals who switch clusters can be assigned to either their as-assigned cluster (the cluster they belonged to at the time of randomization) or their as-treated cluster (the cluster they belonged to at the time the outcome was collected). We show analytically and via simulation, that using the as-treated cluster in HLM will bias the estimate of the ITT effect and using the as-assigned cluster will bias the standard error estimates when heterogeneity among clusters is because of heterogeneity in treatment effects. We show that using linear regression with two-way cluster adjusted standard errors can yield unbiased ITT estimates and consistent standard errors regardless of the source of the random effects. We recommend this method replace HLM as the method of choice for testing intervention effects with cluster-randomized trials with noncompliance and cluster switching. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Expanding outcomes when considering the relative effectiveness of two evidence-based outpatient treatment programs for adolescents.

The current study seeks to advance understanding about how to address substance use and co-occurring mental health problems in adolescents. Specifically, we compared the effectiveness of two evidence-based treatment programs (Motivational Enhancement Treatment/Cognitive Behavior Therapy, 5 Sessions [MET/CBT5] and Adolescent Community Reinforcement Approach [A-CRA]) for both substance use and mental health outcomes (i.e., crossover effects). We used statistical methods designed to approximate randomized controlled trials when comparing nonequivalent groups using observational study data. Our methods also included an assessment of the potential impact of omitted variables. We found that after applying balancing weighting to ensure similarity of the baseline samples (given the nonrandomized study design), both groups significantly improved on the two substance use outcomes (days abstinent and percent of youth in recovery) and on the two mental health outcomes (post-traumatic stress disorder (PTSD) symptoms and general emotional problems). Youth in A-CRA were significantly more likely to be in recovery at the 3-month follow-up compared to youth in MET/CBT5, but the size of this effect was very small. Youth receiving MET/CBT5 appeared to show significantly more improvement in the two mental health measures compared to youth in A-CRA, though these effect sizes were also very small. The findings indicate that adolescents with co-occurring substance use and mental health problems improve on both substance use and mental health outcomes with both treatments even though they are not specifically targeting mental health problems.

Creating a developmentally sensitive measure of adolescent alcohol misuse: an application of item response theory.

This study, funded by the US National Institute of Drug Abuse, evaluates the usefulness of item response theory (IRT) to create a developmental alcohol misuse scale. Data were collected during 1997-2006 from 5,828 Midwestern US students who completed annual surveys at grades 7 through 11 and 2 and 4 years after high school. Seventeen alcohol misuse items were calibrated with IRT and examined for differential item functioning (DIF) across 5 study waves. Eight items displayed DIF; in most cases, properties for items assessed 2 years after high school were different from those assessed in grades 8-11. Implications and suggestions for future research are discussed.