An empirical evaluation of approximate and exact regression-based causal mediation approaches for a binary outcome and a continuous or a binary mediator for case-control study designs.

BACKGROUND: In the causal mediation analysis framework, several parametric regression-based approaches have been introduced in past years for decomposing the total effect of an exposure on a binary outcome into a direct effect and an indirect effect through a target mediator. In this context, a well-known strategy involves specifying a logistic model for the outcome and invoking the rare outcome assumption (ROA) to simplify estimation. Recently, exact estimators for natural direct and indirect effects have been introduced to circumvent the challenges prompted by the ROA. As for the approximate approaches relying on the ROA, these exact approaches cannot be used as is on case-control data where the sampling mechanism depends on the outcome. METHODS: Considering a continuous or a binary mediator, we empirically compare the approximate and exact approaches using simulated data under various case-control scenarios. An illustration of these approaches on case-control data is provided, where the natural mediation effects of long-term use of oral contraceptives on ovarian cancer, with lifetime number of ovulatory cycles as the mediator, are estimated. RESULTS: In the simulations, we found few differences between the performances of the approximate and exact approaches when the outcome was rare, both marginally and conditionally on variables. However, the performance of the approximate approaches degraded as the prevalence of the outcome increased in at least one stratum of variables. Differences in behavior were also observed among the approximate approaches. In the data analysis, all studied approaches were in agreement with respect to the natural direct and indirect effects estimates. CONCLUSIONS: In the case where a violation of the ROA applies or is expected, approximate mediation approaches should be avoided or used with caution, and exact estimators favored.

Predictors of low and very low bone mineral density in long-term childhood acute lymphoblastic leukemia survivors: Toward personalized risk prediction.

BACKGROUND: Cohorts of childhood acute lymphoblastic leukemia (cALL) survivors reaching adulthood are increasing. Approximately 30% of survivors meet criteria for low bone mineral density (BMD) 10 years after diagnosis. We investigated risk factors for low BMD in long-term cALL survivors. METHODS: We recruited 245 cALL survivors from the PETALE (Prévenir les effets tardifs des traitements de la leucémie aiguë lymphoblastique chez l'enfant) cohort, who were treated with the Dana Farber Cancer Institute protocols, did not experience disease relapse or hematopoietic stem cell transplants, and presented with more than 5 years of event-free survival. Median time since diagnosis was 15.1 years. RESULTS: Prevalence of low DXA-derived BMD (Z-score ≤-1) ranged between 21.9% and 25.3%, depending on site (lumbar spine (LS-BMD), femoral neck (FN-BMD), and total body (TB-BMD), and between 3.7% and 5.8% for very low BMD (Z-score ≤-2). Males had a higher prevalence of low BMD than females for all three outcomes (26%-32% vs. 18%-21%), and male sex acted as a significant risk factor for low BMD in all models. Treatment-related factors such as cumulative glucocorticoid (GC) doses and cranial radiation therapy (CRT) were associated with lower BMDs in the full cohort and in females at the FN-BMD site. CONCLUSION: Low and very low BMD is more prevalent in male cALL survivors. Male sex, high cumulative GC doses, CRT, risk group, and low body mass index (BMI) were identified as risk factors for low BMD. A longer follow-up of BMD through time in these survivors is needed to establish if low BMD will translate into a higher risk for fragility fractures through adulthood.

Reader reaction to "Outcome-adaptive lasso: Variable selection for causal inference" by Shortreed and Ertefaie (2017).

Shortreed and Ertefaie introduced a clever propensity score variable selection approach for estimating average causal effects, namely, the outcome adaptive lasso (OAL). OAL aims to select desirable covariates, confounders, and predictors of outcome, to build an unbiased and statistically efficient propensity score estimator. Due to its design, a potential limitation of OAL is how it handles the collinearity problem, which is often encountered in high-dimensional data. As seen in Shortreed and Ertefaie, OAL's performance degraded with increased correlation between covariates. In this note, we propose the generalized OAL (GOAL) that combines the strengths of the adaptively weighted L1 penalty and the elastic net to better handle the selection of correlated covariates. Two different versions of GOAL, which differ in their procedure (algorithm), are proposed. We compared OAL and GOAL in simulation scenarios that mimic those examined by Shortreed and Ertefaie. Although all approaches performed equivalently with independent covariates, we found that both GOAL versions were more performant than OAL in low and high dimensions with correlated covariates.

An exact regression-based approach for the estimation of natural direct and indirect effects with a binary outcome and a continuous mediator.

In the causal mediation framework, a number of parametric regression-based approaches have been introduced in recent years for estimating natural direct and indirect effects for a binary outcome in an exact manner, without invoking simplifying assumptions based on the rareness or commonness of the outcome. However, most of these works have focused on a binary mediator. In this article, we aim at a continuous mediator and introduce an exact approach for the estimation of natural effects on the odds ratio, risk ratio, and risk difference scales. Our approach relies on logistic and linear models for the outcome and mediator, respectively, and uses numerical integration to calculate the nested counterfactual probabilities underlying the definition of natural effects. Formulas for the delta method standard errors for all effects estimators are provided. The performance of our proposed exact estimators was evaluated in simulation studies that featured scenarios with different levels of outcome rareness/commonness, including a marginally but not conditionally rare outcome scenario. Furthermore, we evaluated the merit of Firth's penalization to mitigate the bias in the logistic regression coefficients estimators for the smallest outcome prevalences and sample sizes investigated. Using a SAS macro provided, we implemented our approach to assess the effect of placental abruption on low birth weight mediated by gestational age. We found that our exact natural effects estimators worked properly in both simulated and real data applications.

Parametric-Regression-Based Causal Mediation Analysis of Binary Outcomes and Binary Mediators: Moving Beyond the Rareness or Commonness of the Outcome.

In the causal mediation framework, several parametric-regression-based approaches have been introduced in the last decade for estimating natural direct and indirect effects. For a binary outcome, a number of proposed estimators use a logistic model and rely on specific assumptions or approximations that may be delicate or not easy to verify in practice. To circumvent the challenges prompted by the rare outcome assumption in this context, an exact closed-form natural-effects estimator on the odds ratio scale was recently introduced for a binary mediator. In this work, we further push this exact approach and extend it for the estimation of natural effects on the risk ratio and risk difference scales. Explicit formulas for the delta method standard errors are provided. The performance of our proposed exact estimators is demonstrated in simulation scenarios featuring various levels of outcome rareness/commonness. The total effect decomposition property on the multiplicative scales is also examined. Using a SAS macro (SAS Institute, Inc., Cary, North Carolina) we developed, our approach is illustrated to assess the separate effects of exposure to inhaled corticosteroids and placental abruption on low birth weight mediated by prematurity. Our exact natural-effects estimators are found to work properly in both simulations and the real data example.

The impact of adjusting for pure predictors of exposure, mediator, and outcome on the variance of natural direct and indirect effect estimators.

It is now well established that adjusting for pure predictors of the outcome, in addition to confounders, allows unbiased estimation of the total exposure effect on an outcome with generally reduced standard errors (SEs). However, no analogous results have been derived for mediation analysis. Considering the simplest linear regression setting and the ordinary least square estimator, we obtained theoretical results showing that adjusting for pure predictors of the outcome, in addition to confounders, allows unbiased estimation of the natural indirect effect (NIE) and the natural direct effect (NDE) on the difference scale with reduced SEs. Adjusting for pure predictors of the mediator increases the SE of the NDE's estimator, but may increase or decrease the variance of the NIE's estimator. Adjusting for pure predictors of the exposure increases the variance of estimators of the NIE and NDE. Simulation studies were used to confirm and extend these results to the case where the mediator or the outcome is binary. Additional simulations were conducted to explore scenarios featuring an exposure-mediator interaction as well as the relative risk and odds ratio scales for the case of binary mediator and outcome. Both a regression approach and an inverse probability weighting approach were considered in the simulation study. A real-data illustration employing data from the Canadian Study of Health and Aging is provided. This analysis is concerned with the mediating effect of vitamin D in the effect of physical activity on dementia and its results are overall consistent with the theoretical and empirical findings.

A flexible copula-based approach for the analysis of secondary phenotypes in ascertained samples.

Data collected for a genome-wide association study of a primary phenotype are often used for additional genome-wide association analyses of secondary phenotypes. However, when the primary and secondary traits are dependent, naïve analyses of secondary phenotypes may induce spurious associations in non-randomly ascertained samples. Previously, retrospective likelihood-based methods have been proposed to correct for sampling biases arising in secondary trait association analyses. However, most methods have been introduced to handle studies featuring a case-control design based on a binary primary phenotype. As such, these methods are not directly applicable to more complicated study designs such as multiple-trait studies, where the sampling mechanism also depends on the secondary phenotype, or extreme-trait studies, where individuals with extreme primary phenotype values are selected. To accommodate these more complicated sampling mechanisms, only a few prospective likelihood approaches have been proposed. These approaches assume a normal distribution for the secondary phenotype (or the latent secondary phenotype) and a bivariate normal distribution for the primary-secondary phenotype dependence. In this paper, we propose a unified copula-based approach to appropriately detect genetic variant/secondary phenotype association in the presence of selected samples. Primary phenotype is either binary or continuous and the secondary phenotype is continuous although not necessary normal. We use both prospective and retrospective likelihoods to account for the sampling mechanism and use a copula model to allow for potentially different dependence structures between the primary and secondary phenotypes. We demonstrate the effectiveness of our approach through simulation studies and by analyzing data from the Avon Longitudinal Study of Parents and Children cohort.

Physical Activity and Sedentary Behaviors in Childhood Acute Lymphoblastic Leukemia Survivors.

INTRODUCTION: More than two thirds of survivors have long-term adverse effects, and no study proposes a portrait of physical activity level in childhood acute lymphoblastic leukemia survivors. The aims of this study were to present the cardiorespiratory fitness (CRF) levels of survivors detailed overview sedentary activities portrait. METHODS: A total of 247 childhood acute lymphoblastic leukemia survivors were included in our study. Survivors underwent a cardiopulmonary exercise test on ergocycle and completed physical activity and sedentary questionnaires to assess their leisure physical and sedentary activities and total daily energy expenditure. RESULTS: Up to 67% of survivors (84% below 18 y and 60% 18 y of age or above) did not fulfill the physical activity guidelines. Their CRF was reduced by almost 16% in regard to their predicted maximum oxygen consumption (VO2peak). Almost three quarters of the survivors (70% below 18 y and 76% 18 y of age or above) spent >2 hours/day in leisure sedentary activities. Adult survivors who received high doses of anthracyclines and those who received radiation therapy had decreased odds to spend ≥2 hours/day in sedentary activities. CONCLUSIONS: Our results showed that survivors, especially children, were not active enough and had a reduced CRF. This study highlights the importance of promoting physical activity in survivors, especially because they are exposed to an increased risk of chronic health problems, which could be mitigated by physical activity.

Point: Risk Ratio Equations for Natural Direct and Indirect Effects in Causal Mediation Analysis of a Binary Mediator and a Binary Outcome-A Fresh Look at the Formulas.

In this article, we review the formulas for the natural direct and indirect effects' risk ratios introduced by Ananth and VanderWeele (Am J Epidemiol. 2011;174(1):99-108) for causal mediation analysis of a binary mediator and a binary outcome. In particular, we show that the closed-form equations Ananth and VanderWeele provided do not correspond to the log-binomial model specified by these authors for the mediator variable, but rather to a logistic model. We then provide risk ratio equations for natural direct and indirect effects that truly pertain to a log-binomial model. We conclude with a discussion on the practical implications of the binary mediator model's specification by analysts. The related impact can be negligible or not, depending on the rareness of the mediator.

A Bayesian multivariate latent t-regression model for assessing the association between corticosteroid and cranial radiation exposures and cardiometabolic complications in survivors of childhood acute lymphoblastic leukemia: a PETALE study.

BACKGROUND: Childhood acute lymphoblastic leukemia (cALL) is the most frequent pediatric cancer. Over the past decades, treatment of cALL has significantly improved, with cure rates close to 90%. However intensive chemotherapy and cranial radiotherapy (CRT) during a critical period of a child's development have been shown to lead to significant long-term side effects including cardiometabolic complications. Using the PETALE (Prévenir les effets tardifs des traitements de la leucémie aiguë lymphoblastique chez l'enfant) cALL survivor cohort, we investigated the association between combined cumulative corticosteroids (CS) doses and CRT exposures and obesity, insulin resistance, (pre-)hypertension, and dyslipidemia jointly. METHODS: A Bayesian multivariate latent-t model which accounted for our correlated binary outcomes was used for the analyses (n = 241 survivors). CS doses were categorized as low (LD) or high (HD). Combined exposure levels investigated were: 1) LD/no CRT; 2) LD/CRT, and; 3) HD/CRT. We also performed complementary sensitivity analyses for covariate adjustment. RESULTS: Prevalence of cardiometabolic complications ranged from 12.0% for (pre-)hypertension to 40.2% for dyslipidemia. The fully adjusted odds ratio (OR) for dyslipidemia associated with LD/CRT (vs. LD/No CRT) was OR = 1.98 (95% credible interval (CrI): 1.02 to 3.88). LD/CRT level also led to a 0.15 (95% CrI: 0.00 to 0.29) excess risk to develop at least one cardiometabolic complication. Except for obesity, adjusted results for the highest exposure category HD/CRT were generally similar to those for LD/CRT albeit not statistically significant. White blood cell count at diagnosis, a proxy for cALL burden at diagnosis, was found associated with insulin resistance (OR = 1.08 for a 10-unit increase (× 109/L), 95% CrI: 1.02 to 1.14). CONCLUSIONS: Our results indicated that combined LD/CRT exposure is a likely determinant of dyslipidemia among cALL survivors. No evidence was found to suggest that high doses of CS lead to additional risk for obesity, insulin resistance, (pre-)hypertension, and dyslipidemia beyond that induced by CRT. The multivariate model selected for analyses was judged globally useful to assess potential exposure-related concomitance of binary outcomes.

Longitudinal assessment of 1H-MRS (GABA and Glx) and TMS measures of cortical inhibition and facilitation in the sensorimotor cortex.

The purpose of the present study was to investigate the long-term stability of water-referenced GABA and Glx neurometabolite concentrations in the sensorimotor cortex using MRS and to assess the long-term stability of GABA- and glutamate-related intracortical excitability using transcranial magnetic stimulation (TMS). Healthy individuals underwent two sessions of MRS and TMS at a 3-month interval. A MEGA-PRESS sequence was used at 3 T to acquire MRS signals in the sensorimotor cortex. Metabolites were quantified by basis spectra fitting and metabolite concentrations were derived using unsuppressed water reference scans accounting for relaxation and partial volume effects. TMS was performed using published standards. After performing stability and reliability analyses for MRS and TMS, reliable change indexes were computed for all measures with a statistically significant test-retest correlation. No significant effect of time was found for GABA, Glx and TMS measures. There was an excellent ICC and a strong correlation across time for GABA and Glx. Analysis of TMS measure stability revealed an excellent ICC for rMT CSP and %MSO and a fair ICC for 2 ms SICI. There was no significant correlation between MRS and TMS measures at any time point. This study shows that MRS-GABA and MRS-Glx of the sensorimotor cortex have good stability over a 3-month period, with variability across time comparable to that reported in other brain areas. While resting motor threshold, %MSO and CSP were found to be stable and reliable, other TMS measures had greater variability and lesser reliability.

Comparing external and internal validation methods in correcting outcome misclassification bias in logistic regression: A simulation study and application to the case of postsurgical venous thromboembolism following total hip and knee arthroplasty.

PURPOSE: We assessed the validity of postsurgery venous thromboembolism (VTE) diagnoses identified from administrative databases and compared Bayesian and multiple imputation (MI) approaches in correcting for outcome misclassification in logistic regression models. METHODS: Sensitivity and specificity of postsurgery VTE among patients undergoing total hip or knee replacement (THR/TKR) were assessed against chart review in six Montreal hospitals in 2009 to 2010. Administrative data on all THR/TKR Quebec patients in 2009 to 2010 were obtained. The performance of Bayesian external, Bayesian internal, and MI approaches to correct the odds ratio (OR) of postsurgery VTE in tertiary versus community hospitals was assessed using simulations. Bayesian external approach used prior information from external sources, while Bayesian internal and MI approaches used chart review. RESULTS: In total, 17 319 patients were included, 2136 in participating hospitals, among whom 75 had VTE in administrative data versus 81 in chart review. VTE sensitivity was 0.59 (95% confidence interval, 0.48-0.69) and specificity was 0.99 (0.98-0.99), overall. The adjusted OR of VTE in tertiary versus community hospitals was 1.35 (1.12-1.64) using administrative data, 1.45 (0.97-2.19) when MI was used for misclassification correction, and 1.53 (0.83-2.87) and 1.57 (0.39-5.24) when Bayesian internal and external approaches were used, respectively. In simulations, all three approaches reduced the OR bias and had appropriate coverage for both nondifferential and differential misclassification. CONCLUSION: VTE identified from administrative data had low sensitivity and high specificity. The Bayesian external approach was useful to reduce outcome misclassification bias in logistic regression; however, it required accurate specification of the misclassification properties and should be used with caution.

Prevention of Long-term Adverse Health Outcomes With Cardiorespiratory Fitness and Physical Activity in Childhood Acute Lymphoblastic Leukemia Survivors.

BACKGROUND: Most childhood acute lymphoblastic leukemia (ALL) survivors develop chronic treatment-related adverse effects several years after the end of therapy. A regular practice of physical activity and a good cardiorespiratory fitness have the potential to reduce the risk of chronic disease and improve quality of life. The aim of this study was to evaluate in a cohort of ALL survivors, the association between a good cardiorespiratory fitness or the respect of physical activity guidelines and major long-term health outcomes. METHODS: In total, 247 ALL survivors underwent a cardiopulmonary exercise test, completed a physical activity questionnaire and a battery of clinical examinations. We calculated the odds ratio to obtain the preventive fraction (PF) to evaluate the effects of the cardiorespiratory fitness and physical activity levels on health outcomes (ie, obesity, metabolic health, cardiac health, cognitive health and mood, bone health). RESULTS: Despite their young age, 88% of the participants presented at least one adverse health outcome, and 46% presented ≥3. Their cardiorespiratory fitness was also lower than expected with a median VO2 peak reaching 84% of the predicted value. In the analyses using cardiorespiratory fitness, statistically significant PFs were observed for obesity (0.30), low-high-density lipoprotein-cholesterol (0.21) and depression (0.26). In the physical activity level analyses, statistically significant PFs were observed for obesity, depression, and low bone mineral density, with a PF of 0.55, 0.81, and 0.60, respectively. CONCLUSIONS: Our results indicate that a good cardiorespiratory fitness and physical activity level induced a preventive action for most health outcomes studied and was associated with a lower late adverse effects prevalence in ALL survivors.

Using a Mixture-of-Bivariate-Regressions Model to Explore Heterogeneity of Effects of the Use of Inhaled Corticosteroids on Gestational Age and Birth Weight Among Pregnant Women With Asthma.

Asthma is a heterogeneous disease, and responses to asthma medications vary noticeably among patients. A substantively oriented objective of this study was to explore the potentially heterogeneous effects of exposure to maternal inhaled corticosteroids (ICS) on gestational age (GA) at delivery and birth weight (BW) using a cohort of 6,197 pregnancies among women with asthma (Quebec, Canada, 1998-2008). A methodologically oriented objective was to comprehensively describe the application of a Bayesian 2-component mixture-of-bivariate-regressions model to address this issue and estimate the effects of ICS on GA and BW jointly. Based on the proposed model, no association between ICS and GA/BW was found for a large proportion of asthmatic pregnancies. However, a positive association between ICS exposure and GA/BW was revealed in a small subset of pregnancies comprising mainly preterm and low-birth-weight infants. A novel application of this model was also subsequently performed using BW z score instead of BW as the outcome variable. In conclusion, the studied mixture-of-bivariate-regressions model was useful for detecting heterogeneity in the effect of ICS on GA and BW in our population of women with asthma. These analyses pave the way for analogous uses of this model for general assessment of exposure effect heterogeneity for these perinatal outcomes.

A Bayesian finite mixture of bivariate regression model for causal mediation analyses.

Building on the work of Schwartz et al, Joint Bayesian analysis of birthweight and censored gestational age using finite mixture models in Statistics in Medicine, we propose a Bayesian finite mixture of bivariate regression model for causal mediation analyses. Using an identifiability condition within each component of the mixture, we express the natural direct and indirect effects of the exposure on the outcome as functions of the component-specific regression coefficients. On the basis of simulated data, we examine the behavior of the model for estimating these effects in situations where the associations between exposure, mediator, and outcome are confounded or not. Additionally, we demonstrate that this mixture model can be used to account for heterogeneity arising through unmeasured binary or categorical mediator-outcome confounders. Considering gestational age as a potential mediator, we then illustrate our mediation mixture model to estimate the natural direct and indirect effects of exposure to inhaled corticosteroids during pregnancy on birthweight using a cohort of asthmatic women from the province of Quebec (Canada).

Correction to: On the use of the outcome variable "small for gestational age" when gestational age is a potential mediator: a maternal asthma perspective.

CORRECTION: Following publication of the original article [1], the authors reported that the following four references in Table 2 are incorrect.

On the use of the outcome variable "small for gestational age" when gestational age is a potential mediator: a maternal asthma perspective.

BACKGROUND: The variable "small for gestational age," frequently defined as birth weight below the 10th percentile in a gestational age and sex-normalized population, is nowadays generally perceived as a more adequate measure than birth weight or low birth weight (birth weight < 2500 g) to capture fetal growth. However, the use of small for gestational age rather than birth weight or low birth weight as an outcome (dependent) variable may have important impacts on the interpretation of analyses aimed at estimating the causal effect of an exposure of interest on infants. We hypothesized potential differences in both types of effects estimated (direct or total) and in ability to control for confounding bias. METHODS: We first examined the use of outcome variables birth weight and small for gestational age to get insights on modeling practices within the field of maternal asthma. Using directed acyclic graph simulations where gestational age was a potential mediator, we then compared estimated exposure effects in regression models for birth weight, low birth weight, and small for gestational age. Graphs with and without confounding were considered. RESULTS: Our simulations showed that the variable small for gestational age captures the direct effect of exposure on birth weight, but not the indirect effect of exposure on birth weight through gestational age. Interestingly, exposure effect estimates from small for gestational age models were found unbiased whenever exposure effect estimates from birth weight models were affected by collider bias due to conditioning on gestational age in the models. CONCLUSIONS: The sole consideration of the outcome small for gestational age in a study may lead to suboptimal understanding and quantification of the underlying effect of an exposure on birth weight-related measures. Instead, our results suggest that both outcome variables (low) birth weight and small for gestational age should minimally be considered in studies investigating perinatal outcomes.

The PETALE study: Late adverse effects and biomarkers in childhood acute lymphoblastic leukemia survivors.

BACKGROUND: Childhood cancer survivorship issues represent an established public health challenge. Most late adverse effects (LAEs) have been demonstrated to be time and treatment dependent. The PETALE study is a multidisciplinary research project aiming to comprehensively characterize LAEs and identify associated predictive biomarkers in childhood acute lymphoblastic leukemia (cALL) survivors. METHODS: cALL survivors treated at Sainte-Justine University Health Center with Dana-Farber Cancer Institution-ALL protocols 87-01 through 2005-01 were eligible. During Phase I of the study, the participants underwent comprehensive clinical, biologic, and psychosocial investigation targeting metabolic syndrome, cardiotoxicity, bone morbidity, neurocognitive problems, and quality of life issues. Whole-exome sequencing was performed for all participants. Subjects identified with an extreme phenotype during Phase I were recalled for additional testing (Phase II). RESULTS: Phase I included 246 survivors (recall rate 71.9%). Of those, 85 participants completed Phase II (recall rate 88.5%). Survivors agreeing to participate in Phase I (n = 251) were similar to those who refused (n = 31) in terms of relapse risk profile, radiotherapy exposure, and age at the time of study. Participants, however, tended to be slightly older at diagnosis (6.1 vs. 4.7 years old, P = 0.08), with a higher proportion of female agreeing to participate compared with males (93.2 vs. 86.5%, P = 0.07). CONCLUSION: The PETALE study will contribute to comprehensively characterize clinical, psychosocial, biologic, and genomic features of cALL survivors using an integrated approach. Expected outcomes include LAE early detection biomarkers, long-term follow-up guidelines, and recommendations for physicians and health professionals.

A cautionary note concerning the use of stabilized weights in marginal structural models.

Marginal structural models are commonly used to estimate the causal effect of a time-varying treatment in presence of time-dependent confounding. When fitting an MSM to data, the analyst must specify both the structural model for the outcome and the treatment models for the inverse-probability-of-treatment weights. The use of stabilized weights is recommended because they are generally less variable than the standard weights. In this paper, we are concerned with the use of the common stabilized weights when the structural model is specified to only consider partial treatment history, such as the current or most recent treatments. We present various examples of settings where these stabilized weights yield biased inferences while the standard weights do not. These issues are first investigated on the basis of simulated data and subsequently exemplified using data from the Honolulu Heart Program. Unlike common stabilized weights, we find that basic stabilized weights offer some protection against bias in structural models designed to estimate current or most recent treatment effects.

Probing the effects of mild traumatic brain injury with transcranial magnetic stimulation of the primary motor cortex.

PRIMARY OBJECTIVE: The present paper systematically reviews studies using transcranial magnetic stimulation (TMS) over the primary motor cortex (M1) to assess cortical excitability, intra-cortical inhibition/facilitation and synaptic plasticity following mild traumatic brain injury (mTBI). METHODS: Articles using TMS over M1 in patients with mTBI or sport-related concussion indexed in PubMed and published between 1998 and September 2014 were included in the present review. MAIN OUTCOMES AND RESULTS: From the 17 articles that matched search criteria, results from various TMS paradigms were summarized and divided in three main areas of interest: motor cortical excitability/facilitation, motor cortical inhibition and cortical plasticity. Although studies suggest a trend of abnormal intra-cortical inhibition following mTBI, no clear and specific pattern emerges from the surveyed data. CONCLUSIONS: At this time and with the possible exception of intra-cortical inhibitory measures, TMS cannot reliably detect changes in M1 excitability in individuals with mTBI or a concussion at both the acute and chronic stages of injury. This may be explained by the small number of studies and large variety of stimulation parameters. Additional longitudinal and multimodal studies are needed to better understand the nature of the excitability changes that may occur within M1 following mTBI.