The Impact of Bullying and Sexual Harassment on Health Outcomes of Middle School and High School Girls

The impact of bullying and sexual harassment on six health outcomes among middle school girls were compared to these outcomes among high school girls. High school girls experienced more bullying and sexual harassment and poorer health outcomes than their middle school counterparts, but the impact of these experiences was less among high school students. Differences in outcomes may be the result of better support systems and coping mechanisms among high school girls and/or challenging developmental changes during middle school. Sexual orientation, race, and disability had some notable relationships to bullying and sexual harassment experiences as well as health outcomes

Youth at work: Adolescent Employment and Sexual Harassment

An examination of the frequency and impact of workplace sexual harassment on work, health, and school outcomes on high school girls is presented in two parts. The first compares the frequency of harassment in this sample (52%) to published research on adult women that used the same measure of sexual harassment. The second part compares outcomes for girls who experienced harassment versus those who did not

Collaborative Targeted Maximum Likelihood Estimation

Collaborative double robust targeted maximum likelihood estimators represent a fundamental further advance over standard targeted maximum likelihood estimators of causal inference and variable importance parameters. The targeted maximum likelihood approach involves fluctuating an initial density estimate, (Q), in order to make a bias/variance tradeoff targeted towards a specific parameter in a semi-parametric model. The fluctuation involves estimation of a nuisance parameter portion of the likelihood, g. TMLE and other double robust estimators have been shown to be consistent and asymptotically normally distributed (CAN) under regularity conditions, when either one of these two factors of the likelihood of the data is correctly specified. In this article we provide a template for applying collaborative targeted maximum likelihood estimation (C-TMLE) to the estimation of pathwise differentiable parameters in semi-parametric models. The procedure creates a sequence of candidate targeted maximum likelihood estimators based on an initial estimate for Q coupled with a succession of increasingly non-parametric estimates for g. In a departure from current state of the art nuisance parameter estimation, C-TMLE estimates of g are constructed based on a loss function for the relevant factor Q_0, instead of a loss function for the nuisance parameter itself. Likelihood-based cross-validation is used to select the best estimator among all candidate TMLE estimators in this sequence. A penalized-likelihood loss function for Q_0 is suggested when the parameter of interest is borderline-identifiable. We present theoretical results for collaborative double robustness, demonstrating that the collaborative targeted maximum likelihood estimator is CAN when Q and g are both mis-specified, providing that g solves a specified score equation implied by the difference between the Q and the true Q_0. This marks an improvement over the current definition of double robustness in the estimating equation literature. We also establish an asymptotic linearity theorem for the C-DR-TMLE of the target parameter, showing that the C-DR-TMLE is more adaptive to the truth, and, as a consequence, can even be super efficient if the first stage density estimator does an excellent job itself with respect to the target parameter. This research provides a template for targeted efficient and robust loss-based learning of a particular target feature of the probability distribution of the data within large (infinite dimensional) semi-parametric models, while still providing statistical inference in terms of confidence intervals and p-values. This research also breaks with a taboo (e.g., in the propensity score literature in the field of causal inference) on using the relevant part of likelihood to fine-tune the fitting of the nuisance parameter/censoring mechanism/treatment mechanism

Targeted Maximum Likelihood Estimation: A Gentle Introduction

This paper provides a concise introduction to targeted maximum likelihood estimation (TMLE) of causal effect parameters. The interested analyst should gain sufficient understanding of TMLE from this introductory tutorial to be able to apply the method in practice. A program written in R is provided. This program implements a basic version of TMLE that can be used to estimate the effect of a binary point treatment on a continuous or binary outcome

Targeted Minimum Loss Based Estimation of an Intervention Specific Mean Outcome

Targeted minimum loss based estimation (TMLE) provides a template for the construction of semiparametric locally efficient double robust substitution estimators of the target parameter of the data generating distribution in a semiparametric censored data or causal inference model based on a sample of independent and identically distributed copies from this data generating distribution (van der Laan and Rubin (2006), van der Laan (2008), van der Laan and Rose (2011)). TMLE requires 1) writing the target parameter as a particular mapping from a typically infinite dimensional parameter of the probability distribution of the unit data structure into the parameter space, 2) computing the canonical gradient/efficient influence curve of the pathwise derivative of the target parameter mapping, 3) specifying a loss function for this parameter that is possibly indexed by unknown nuisance parameters, 4) a least favorable parametric submodel/path through an initial/current estimator of the parameter chosen so that the linear span of the generalized loss-based score at zero fluctuation includes the efficient influence curve, and 5) an updating algorithm involving the iterative minimization of the loss-specific empirical risk over the fluctuation parameters of the least favorable parametric submodel/path. By the generalized loss-based score condition 4) on the submodel and loss function, it follows that the resulting estimator of the infinite dimensional parameter solves the efficient influence curve (i.e., efficient score) equation, providing the basis for the double robustness and asymptotic efficiency of the corresponding substitution estimator of the target parameter obtained by plugging in the updated estimator of the infinite dimensional parameter in the target parameter mapping. To enhance the finite sample performance of the TMLE of the target parameter, it is of interest to choose the parameter and the nuisance parameter of the loss function as low dimensional as possible. Inspired by this goal, we present a particular closed form TMLE of an intervention specific mean outcome based on general longitudinal data structures. %We also present its generalization of this type of TMLE to other causal parameters. This TMLE provides an alternative to the closed form TMLE presented in van der Laan and Gruber (2010) and Stitelman and vanderLaan (2011) based on the log-likelihood loss function. The theoretical properties of the TMLE are also practically demonstrated with a small scale simulation study. The proposed TMLE builds upon a previously proposed estimator by Bang and Robins (2005) by integrating some of its key and innovative ideas into the TMLE framework

tmle: An R Package for Targeted Maximum Likelihood Estimation

Targeted maximum likelihood estimation (TMLE) presents an approach for construction of an efficient double-robust semi-parametric substitution estimator of a target feature of the data generating distribution, such as a statistical association measure or a causal effect parameter. tmle is a recently developed R package that implements TMLE for estimation of the effect of a binary treatment at a single point in time on an outcome of interest, controlling for user supplied covariates: the additive treatment effect, the relative risk, the odds ratio. The package allows outcome data with missingness, and experimental units that contribute repeated records of the point-treatment data structure, thereby allowing this package to analyze longitudinal data structures. The TMLE of the direct effect of the binary treatment, controlling for a binary intermediate variable on the pathway from treatment to the outcome, is also implemented. Estimation of the parameters of a marginal structural model for binary treatments is also provided. Relevant factors of the likelihood may be modeled or fit by user-specified commands, or fit data-adaptively internally. Effect estimates, variances, p-values, and 95% confidence intervals are provided by the software

Comparing the Impact of Bullying and Sexual Harassment Victimization on the Mental and Physical Health of Adolescents

A sample of 522 middle and high school students from a school district in a northeastern state in the U.S. was used to address two questions about bullying and sexual harassment: Is one more frequent than the other, and are there gender or sexual orientation differences in this regard? And, does one have greater adverse health effects than the other, and, if so, for whom? Bullying occurred more frequently than sexual harassment for both girls and boys but not among sexual minorities. Girls were bullied or harassed as frequently as boys, but sexual minorities experienced higher levels of both. Compared to bullying, sexual harassment had adverse effects on more health outcomes. These adverse effects were especially notable among girls and sexual minorities.[ABSTRACT FROM AUTHOR

Cheaper By the Dozen: Using Sibling Discounts at Catholic Schools to Estimate the Price Elasticity of Private School Attendance

The effect of vouchers on sorting between private and public schools depends upon the price elasticity of demand for private schooling. Estimating this elasticity is empirically challenging because prices and quantities are jointly determined in the market for private schooling. We exploit a unique and previously undocumented source of variation in private school tuition to estimate this key parameter. A majority of Catholic elementary schools offer discounts to families that enroll more than one child in the school in a given year. Catholic school tuition costs therefore depend upon the interaction of the number and spacing of a family’s children with the pricing policies of the local school. This within-neighborhood variation in tuition prices allows us to control for unobserved determinants of demand with a set fine geographic group fixed effects while still identifying the price parameter. We analyze this variation by using data on over 3700 school tuition schedules collected from Catholic schools around the nation, matched to restricted Census data that identifies precise location that can be matched to the nearest Catholic school. We find that a standard deviation decrease in tuition prices increases the probability that a family will send its children to private school by one half percentage point, which translates into an elasticity of Catholic school attendance with respect to tuition costs of -0.19. Our subgroup results suggest that a voucher program would disproportionately induce into private schools those who, along observable dimensions, are unlike those who currently attend private school.

Readings in Targeted Maximum Likelihood Estimation

This is a compilation of current and past work on targeted maximum likelihood estimation. It features the original targeted maximum likelihood learning paper as well as chapters on super (machine) learning using cross validation, randomized controlled trials, realistic individualized treatment rules in observational studies, biomarker discovery, case-control studies, and time-to-event outcomes with censored data, among others. We hope this collection is helpful to the interested reader and stimulates additional research in this important area

Intrinsic Frontolimbic Connectivity and Mood Symptoms in Young Adult Cannabis Users.

Objective: The endocannbinoid system and cannabis exposure has been implicated in emotional processing. The current study examined whether regular cannabis users demonstrated abnormal intrinsic (a.k.a. resting state) frontolimbic connectivity compared to non-users. A secondary aim examined the relationship between cannabis group connectivity differences and self-reported mood and affect symptoms. Method: Participants included 79 cannabis-using and 80 non-using control emerging adults (ages of 18-30), balanced for gender, reading ability, and age. Standard multiple regressions were used to predict if cannabis group status was associated with frontolimbic connectivity after controlling for site, past month alcohol and nicotine use, and days of abstinence from cannabis. Results: After controlling for research site, past month alcohol and nicotine use, and days of abstinence from cannabis, cannabis users demonstrated significantly greater connectivity between left rACC and the following: right rACC (p = 0.001; corrected p = 0.05; f 2 = 0.55), left amygdala (p = 0.03; corrected p = 0.47; f 2 = 0.17), and left insula (p = 0.03; corrected p = 0.47; f 2 = 0.16). Among cannabis users, greater bilateral rACC connectivity was significantly associated with greater subthreshold depressive symptoms (p = 0.02). Conclusions: Cannabis using young adults demonstrated greater connectivity within frontolimbic regions compared to controls. In cannabis users, greater bilateral rACC intrinsic connectivity was associated with greater levels of subthreshold depression symptoms. Current findings suggest that regular cannabis use during adolescence is associated with abnormal frontolimbic connectivity, especially in cognitive control and emotion regulation regions