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Background Longitudinal matching can mitigate confounding in observational, real-world studies of time-dependent treatments. To date, these methods have required iterative, manual re-specifications to achieve covariate balance. We propose a longitudinal extension of genetic matching, a machine learning approach that automates balancing of covariate histories. We examine performance by comparing the proposed extension against baseline propensity score matching and time-dependent propensity score matching. Methods To evaluate comparative performance, we developed a Monte Carlo simulation framework that reflects a static treatment assigned at multiple time points. Data generation considers a treatment assignment model, a continuous outcome model, and underlying covariates. In simulation, we generated 1,000 datasets, each consisting of 1,000 subjects, and applied: (1) nearest neighbour matching on time-invariant, baseline propensity scores; (2) sequential risk set matching on time-dependent propensity scores; and (3) longitudinal genetic matching on time-dependent covariates. To measure comparative performance, we estimated covariate balance, efficiency, bias, and root mean squared error (RMSE) of treatment effect estimates. In scenario analysis, we varied underlying assumptions for assumed covariate distributions, correlations, treatment assignment models, and outcome models. Results In all scenarios, baseline propensity score matching resulted in biased effect estimation in the presence of time-dependent confounding, with mean bias ranging from 29.7% to 37.2%. In contrast, time-dependent propensity score matching and longitudinal genetic matching achieved stronger covariate balance and yielded less biased estimation, with mean bias ranging from 0.7% to 13.7%. Across scenarios, longitudinal genetic matching achieved similar or better performance than time-dependent propensity score matching without requiring manual re-specifications or normality of covariates. Conclusions While the most appropriate longitudinal method will depend on research questions and underlying data patterns, our study can help guide these decisions. Simulation results demonstrate the validity of our longitudinal genetic matching approach for supporting future real-world assessments of treatments accessible at multiple time points.

In order to study longitudinal movement and intramural shearing of the arterial wall with a Lagrangian viewpoint using ultrafast ultrasound imaging, a new tracking scheme is required. We propose the use of an iterative tracking scheme based on temporary down-sampling of the frame-rate, anteroposterior tracking, and unbiased block-matching using two kernels per position estimate. The tracking scheme was evaluated on phantom B-mode cine loops and considered both velocity and displacement for a range of down-sampling factors (k = 1–128) at the start of the iterations. The cine loops had a frame rate of 1300–1500 Hz and were beamformed using delay-and-sum. The evaluation on phantom showed that both the mean estimation errors and the standard deviations decreased with an increasing initial down-sampling factor, while they increased with an increased velocity or larger pitch. A limited in vivo study shows that the major pattern of movement corresponds well with state-of-the-art low frame rate motion estimates, indicating that the proposed tracking scheme could enable the study of longitudinal movement of the intima–media complex using ultrafast ultrasound imaging, and is one step towards estimating the propagation velocity of the longitudinal movement of the arterial wall.

Objectives Given the challenges of conducting experimental studies in criminology and criminal justice, propensity score matching (PSM) represents one of the most commonly used techniques for evaluating the efficacy of treatment conditions on future behavior. Nevertheless, current iterations of PSM fail to adjust for the effects of longitudinal clustering on participant exposure to treatment conditions. The current study presents and evaluates longitudinal PSM (LPSM) as an alternative method for assessing the effects of a treatment condition on future behavior. LPSM adjusts for the effects of longitudinal clustering (i.e., clustered error) by assuming that the association between a cross-sectional predictor and a treatment condition varies depending upon the time at which the treatment was administered. Methods Two general steps were taken to evaluate the validity of LPSM. First, we conducted a series of simulation analyses to illustrate the LPSM method. Second, we further demonstrate the method using data from 63,899 inmates incarcerated in Ohio prisons by assessing the effects of prison programming on recidivism over a three-year post-release period. Disparities in treatment effects were compared between cross-sectional PSM and LPSM. Results The simulation and demonstration analyses produced evidence of disparities in results between LPSM and cross-sectional PSM. LPSM appeared to provide the superior adjustment for longitudinal clustering relative to cross-sectional PSM. Conclusions LPSM provides a useful alternative to cross-sectional PSM when the probability of exposure to a treatment condition varies by the time at which the treatment was administered.

The two-way linear fixed effects regression (2FE) has become a default method for estimating causal effects from panel data. Many applied researchers use the 2FE estimator to adjust for unobserved unit-specific and time-specific confounders at the same time. Unfortunately, we demonstrate that the ability of the 2FE model to simultaneously adjust for these two types of unobserved confounders critically relies upon the assumption of linear additive effects. Another common justification for the use of the 2FE estimator is based on its equivalence to the difference-in-differences estimator under the simplest setting with two groups and two time periods. We show that this equivalence does not hold under more general settings commonly encountered in applied research. Instead, we prove that the multi-period difference-in-differences estimator is equivalent to the weighted 2FE estimator with some observations having negative weights. These analytical results imply that in contrast to the popular belief, the 2FE estimator does not represent a design-based, nonparametric estimation strategy for causal inference. Instead, its validity fundamentally rests on the modeling assumptions.

Although widely used in policy debates, the literature on children’s outcomes when raised by same-sex parents mostly relies on small selective samples or samples based on cross-sectional survey data. This has led to a lack of statistical power and the inability to distinguish children born to same-sex parents from children of separated parents. We address these issues by using unique administrative longitudinal data from the Netherlands, which was the first country to legalize same-sex marriage. These data include 2,971 children with same-sex parents (2,786 lesbian couples and 185 gay male couples) and over a million children with different-sex parents followed from birth. The results indicate that children raised by same-sex parents from birth perform better than children raised by different-sex parents in both primary and secondary education. Our findings are robust to use of cousin fixed effects and coarsened exact matching to improve covariate balance and to reduce model dependence. Further analyses using a novel bounding estimator suggest the selection on unobserved characteristics would have to be more than three times higher than the selection on observed characteristics to reduce the positive estimates to zero.

Mild cognitive impairment (MCI), as a stage in the cognitive continuum between normal ageing and dementia, is mainly characterized by memory impairment. The aims of this study were to examine CANTAB measures of temporal changes of visual memory in MCI and to evaluate the usefulness of the baseline scores for predicting changes in cognitive status. The study included 201 participants aged over 50 years with subjective cognitive complaints. Visual memory was assessed with four CANTAB tests [paired associates learning (PAL), delayed matching to sample (DMS), pattern recognition memory (PRM) and spatial span (SSP)] administered at baseline and on two further occasions, with a follow-up interval of 18-24 months. Participants were divided into three groups according to the change in their cognitive status: participants with subjective cognitive complaints who remained stable, MCI participants who remained stable (MCI-Stable) and MCI participants whose cognitive deterioration continued (MCI-Worsened). Linear mixed models were used to model longitudinal changes, with evaluation time as a fixed variable, and multinomial regression models were used to predict changes in cognitive status. Isolated significant effects were obtained for age and group with all CANTAB tests used. Interactions between evaluation time and group were identified in the PAL and DMS tests, indicating different temporal patterns depending on the changes in cognitive status. Regression models also indicated that CANTAB scores were good predictors of changes in cognitive status. Decline in visual memory measured by PAL and DMS tests can successfully distinguish different types of MCI, and considered together PAL, DMS, PRM and SSP can predict changes in cognitive status.

Women are underrepresented in many science, technology, engineering, and mathematics (STEM) majors and in some non-STEM majors (e.g., philosophy). Combining newly gathered data on students' perceptions of college major traits with data from the Education Longitudinal Study of 2002 (ELS:2002), we find that perceived gender bias against women emerges as the dominant predictor of the gender balance in college majors. The perception of the major being math or science oriented is less important. We replicate these findings using a separate sample to measure college major traits. Results suggest the need to incorporate major-level traits in research on gender gaps in college major choices and the need to recognize the impact of perceptions of potential gender discrimination on college major choices.

Marijuana use has been proposed to serve as a “gateway” that increases the likelihood that users will engage in subsequent use of harder and more harmful substances, known as the marijuana gateway hypothesis (MGH). The current study refines and extends the literature on the MGH by testing the hypothesis using rigorous quasi-experimental, propensity score-matching methodology in a nationally representative sample. Using three waves of data from the National Longitudinal Study of Adolescent to Adult Health (1994–2002), eighteen propensity score-matching tests of the marijuana gateway hypothesis were conducted. Six of the eighteen tests were statistically significant; however, only three were substantively meaningful. These three tests found weak effects of frequent marijuana use on illicit drug use but they were also sensitive to hidden bias. Results from this study indicate that marijuana use is not a reliable gateway cause of illicit drug use. As such, prohibition policies are unlikely to reduce illicit drug use.

To increase the numbers of underrepresented racial minority students in science, technology, engineering, and mathematics (STEM), federal and private agencies have allocated significant funding to undergraduate research programs, which have been shown to increase students' intentions of enrolling in graduate or professional school. Analyzing a longitudinal sample of 4,152 aspiring STEM majors who completed the 2004 Freshman Survey and 2008 College Senior Survey, this study utilizes multinomial hierarchical generalized linear modeling and propensity score matching techniques to examine how participation in undergraduate research affects STEM students' intentions to enroll in STEM and non-STEM graduate and professional programs. Findings indicate that participation in an undergraduate research program significantly improved students' probability of indicating plans to enroll in a STEM graduate program.

Limited longitudinal research examining developmental changes in visuospatial working memory (WM) among children and adolescents with autism spectrum disorder (ASD) has prompted our investigation. We assessed 123 autistic children and adolescents and 145 typically developing controls (TDC) using the Cambridge Neuropsychological Test Automated Battery at baseline (Time 1 [mean age ± SD]: ASD: 13.04 ± 2.86; TDC: 11.53 ± 2.81) and 2-9 years later (Time 2: ASD: 18.08 ± 3.17; TDC: 16.41 ± 3.09) to measure changes of visuospatial (working) memory over time. The linear mixed model was used to compare the differences between ASD and TDC and estimate the effect of changes over time, age, ASD diagnosis, and interactions of Time×Age×ASD. The overall Age×ASD effect was calculated in the spline regression. Autistic children and adolescents exhibited significantly poorer performance on all spatial tasks and some visual tasks than their TDC counterparts at Time 1 and Time 2, after adjusting for sex, age, attention deficit/hyperactivity disorder (ADHD), and full-scale intelligence quotient. There was an overall improvement from Time 1 to Time 2 across all tasks with significant Age×Time interactions. Significant Age×ASD interactions were observed in the delayed matching to sample, pattern recognition memory (PRM), spatial span (SSP), and spatial working memory (SWM) tasks with no significant Time×ASD interactions. In the quadratic nonlinear model, Age×ASD interactions were significant in PRM and SSP. Despite significant improvements during the follow-up period, autistic children and adolescents continue to experience persistent deficits in SWM, with a weaker age-related improvement in visuospatial WM than TDC.