Proxy Pattern-Mixture Analysis for a Binary Variable Subject to Nonresponse.

We consider assessment of the impact of nonresponse for a binary survey variable Y subject to nonresponse, when there is a set of covariates observed for nonrespondents and respondents. To reduce dimensionality and for simplicity we reduce the covariates to a continuous proxy variable X that has the highest correlation with Y, estimated from a probit regression analysis of respondent data. We extend our previously proposed proxy-pattern mixture analysis (PPMA) for continuous outcomes to the binary outcome using a latent variable approach. The method does not assume data are missing at random, and creates a framework for sensitivity analyses. Maximum likelihood, Bayesian, and multiple imputation versions of PPMA are described, and robustness of these methods to model assumptions are discussed. Properties are demonstrated through simulation and with data from the Ohio Family Health Survey (OFHS)

Health Disparities in Liver Cancer: An Analysis of the Ohio Cancer Incidence Surveillance System

Background: We explored associations between neighborhood deprivation and tumor characteristics, treatment, and 5-year survival among primary hepatocellular carcinoma (HCC) patients in Ohio diagnosed between 2008 and 2016. Methods: We used data from the Ohio Cancer Incidence Surveillance System and limited our analysis to adult (>18 years of age) HCC patients with known census tract information based on address at diagnosis. Using principal components analysis, we created a neighborhood deprivation index (NDI) using 9 census tract-level variables. We examined associations between tumor characteristics (stage and tumor size) and NDI quintile using chi-square tests and analysis of variance (ANOVA). Associations between guideline-concordant care and NDI using log-binomial regression adjusted for sex, race, age at diagnosis, metropolitan status, cancer stage, and year of diagnosis were conducted. For 5-year survival, we utilized Cox proportional hazards models with a similar adjustment set. Results: Neighborhood deprivation index was not associated with stage or tumor size. Individuals living in the most deprived neighborhoods were 16% less likely to receive guideline-concordant care as compared to individuals living in the least deprived neighborhoods (adjusted prevalence ratio [PR]: 0.84; 95% confidence interval [CI]: 0.74-0.94). Similarly, individuals living in the most deprived neighborhoods were 15% less likely to survive 5 years compared to individuals living in the least deprived neighborhoods (adjusted Hazard Ratio: 1.15; 95% CI: 1.01-1.29). Conclusion: Our results suggest a negative association between neighborhood deprivation on guideline-concordant care and survival among HCC patients. Interventions targeting disparities of HCC should focus not only on individual-level factors but address larger neighborhood level factors as well

Indices of nonâ ignorable selection bias for proportions estimated from nonâ probability samples

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151805/1/rssc12371_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151805/2/rssc12371.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151805/3/rssc12371-sup-0001-SupInfo.pd

A simulation study of diagnostics for bias in non-probability samples

A non-probability sampling mechanism is likely to bias estimates of parameters with respect to a target population of interest. This bias poses a unique challenge when selection is \u27non-ignorable\u27, i.e. dependent upon the unobserved outcome of interest, since it is then undetectable and thus cannot be ameliorated. We extend a simulation study by Nishimura et al. [International Statistical Review, 84, 43--62 (2016)], adding a recently published statistic, the so-called \u27standardized measure of unadjusted bias\u27, which explicitly quantifies the extent of bias under the assumption that a specified amount of non-ignorable selection exists. Our findings suggest that this new sensitivity diagnostic is considerably correlated with, and more predictive of, the true, unknown extent of selection bias than other diagnostics, even when the underlying assumed level of non-ignorability is incorrect

Continuous Cognitive Dynamics of the Evaluation of Trustworthiness in Williams Syndrome

The decision to approach or avoid an unfamiliar person is based in part on one’s evaluation of facial expressions. Individuals with Williams syndrome (WS) are characterized in part by an excessive desire to approach people, but they display deficits in identifying facial emotional expressions. Likert-scale ratings are generally used to examine approachability ratings in WS, but these measures only capture an individual’s final approach/avoid decision. The present study expands on previous research by utilizing mouse-tracking methodology to visually display the nature of approachability decisions via the motor movement of a computer mouse. We recorded mouse movement trajectories while participants chose to approach or avoid computer-generated faces that varied in terms of trustworthiness. We recruited 30 individuals with WS and 30 chronological age-matched controls (mean age = 20 years). Each participant performed 80 trials (20 trials each of four face types: mildly and extremely trustworthy; mildly and extremely untrustworthy). We found that individuals with WS were significantly more likely than controls to choose to approach untrustworthy faces. In addition, WS participants considered approaching untrustworthy faces significantly more than controls, as evidenced by their larger maximum deviation, before eventually choosing to avoid the face. Both the WS and control participants were able to discriminate between mild and extreme degrees of trustworthiness and were more likely to make correct approachability decisions as they grew older. These findings increase our understanding of the cognitive processing that underlies approachability decisions in individuals with WS

Cellphone Laws and Teens\u27 Calling While Driving: Analysis of Repeated Cross-Sectional Surveys in 2013, 2015, 2017, and 2019

BACKGROUND: Distracted driving among teens is a public health and safety concern. Most states in the U.S. have sought to restrict cellphone use while driving by enacting laws. This study examines the difference in prevalence of self-reported calling while driving (CWD) between states with different cellphone bans. METHODS: Demographics and CWD data were extracted from state Youth Risk Behavior Surveys (YRBS) from 14 states in 2013, 2015, 2017, and 2019. The state YRBS is conducted every 2 years with a representative sample of 9th through 12th grade students attending public school. States were grouped by type of cellphone law(s): no ban (the absence of both handheld calling ban and young driver ban), young driver ban (a ban on all forms of cellphone use while driving, for young drivers only), or concurrent ban (a young driver ban plus a ban on handheld calling for all drivers irrespective of age). Poisson regression models with robust variance were used to estimate prevalence ratios comparing CWD prevalence across ban types. RESULTS: In total, 157,423 high school students participated in the surveys, and 65,044 (45%) participants reached the minimum age to obtain an intermediate license and drove during the 30 days prior the survey. Approximately 53% of participants reported CWD at least once during the previous 30 days, and the percentages varied widely by states (range: 51-55%). Compared to students from states with no ban, those from states with concurrent bans were 19%(95% CI: 14-24%) less likely to engage in CWD. Students in states with concurrent bans were 23% less likely to engage in CWD compared to students in states with young driver bans (95% CI:17-27%). CONCLUSIONS: Engaging in CWD is common among teen drivers. The concurrent implementation of a handheld calling ban and a young driver ban was associated with a lower prevalence of CWD

Self-Compassion and Depressive Symptoms as Determinants of Sensitive Parenting: Associations with Sociodemographic Characteristics in a Sample of Mothers and Toddlers

Parenting that is sensitive and responsive to children’s needs has been shown to support children’s optimal growth and development in many cultural contexts. Numerous studies suggest that self-compassion is positively related to sensitive parenting. Despite growing research interest linking self-compassion to responsive parenting, there are considerable gaps in the literature. The current study examined the associations between self-compassion, depressive symptoms, socioeconomic status, and sensitive parenting. Data was obtained from a cohort study of 300 families in central Ohio enrolled when children were a mean (SD) calendar age of 18.2 (0.7) months. Children of all gestational ages at birth are included, and 37% were born preterm (<37 weeks’ gestation). Observational protocols were used to determine maternal sensitivity in a semi-structured play setting. Self-compassion was assessed with the Self-Compassion Scale when children were 24 months old. Self-compassion was not associated with sociodemographic characteristics including maternal education, household income, child sex and gestational age. In unadjusted regression models, depressive symptoms were related to sensitive parenting (B = −0.036, SE = 0.016, p = 0.03), but self-compassion was not a statistically significant predictor (p = 0.35) of sensitivity, and neither self-compassion nor depressive symptoms were statistically significant predictors of sensitive parenting after adjustment for covariates. Considerations for future studies are discussed