Tuesday, Aug 5: 2:00 PM - 3:50 PM
4120
Contributed Posters
Music City Center
Room: CC-Hall B
Main Sponsor
Section on Statistical Consulting
Presentations
Human trafficking remains a global issue, with traffickers using online platforms to advertise victims disguised as consensual services. Law enforcement struggles to distinguish between sex work and trafficking, especially on Adult Service Websites (ASWs), which traffickers exploit for anonymity and broad reach (Cotterill, 2023). This study examines whether trafficking-related arrests reduce online prostitution ads in Detroit, Seattle, Chicago, Boston, and Houston. Data from all five cities were filtered for relevant offenses and analyzed using linear regression scatterplots to assess relationships between arrests and ad volumes. The scatterplots and cross-correlation function plots revealed no strong correlation between these variables. Further analysis using autocorrelation functions and change point analysis showed significant shifts in data patterns, corresponding to events such as major police operations. This suggests traffickers quickly adapt to law enforcement actions, though certain events may temporarily disrupt their activity. This analysis aids law enforcement in understanding links between arrests and online ads, providing a basis for further research and strategy.
Keywords
ASWs
cross correlation function
autocorrelation function
change point analysis
human trafficking
time series analysis
Introduction. Serious complications like central line-associated bloodstream infection and venous thromboembolism are associated with central venous access devices and can be life-threatening. In this study, we used Classification and Regression Tree (CART) analysis to model the rate of these complications.
Methods. Children from Pediatric Health Information System (PHIS) 2017-2021 database were included in the study. CART analysis was performed to examine important risk factors of serious complications with central line. Data were randomly split into developmental (50%) and validation samples (50%). Gini index was used as splitting criterion. Parent/terminal nodes were set to be 10/5.
Results. Of 67,830 children hospitalized who had at least one central line placed during study period, 4,688 (6.9%) experienced serious complications. The CART model with disease severity, receiving total parenteral nutrition, central line type and complex chronic condition had an area under the receiver operating characteristic curve of 0.77 with sensitivity of 66.8% and specificity of 77.4% for the developmental samples.
Keywords
Classification and Regression Tree analysis
Predictive modeling
Sensitivity
Specificity
area under the receiver operating characteristic curve
Co-Author(s)
Melodee Liegl, Medical College of Wisconsin
Alina Burek, Medical College of Wisconsin
First Author
Amy Pan, Medical College of Wisconsin
Presenting Author
Amy Pan, Medical College of Wisconsin
Inverse Probability of Treatment Weighting (IPTW) is a key method in causal inference for estimating treatment effects while addressing confounding. While both stratification and adjustment are used to control for confounders, stratification may be superior when a confounder is strongly correlated with treatment. In this study, we emulated a target trial comparing surgical versus endovascular lower extremity revascularization for major adverse limb events. Given that chronic limb-threatening ischemia (CLTI) strongly influences treatment choice, we compared results between including the CLTI in the propensity score model and stratifying the data by CLTI and then running the propensity score model separately within the strata of CLTI. Our findings highlight the need for stratification when a confounder is strongly correlated with treatment.
Keywords
Inverse Probability of Treatment Weighting (IPTW)
Causal Inference
Stratification
Adjustment
Introduction: To address the gap in knowledge of developmental outcomes of infants ≥34 weeks gestation requiring NICU care, 1183 infants were evaluated. Variables collected for each infant included demographics, delivery course, NICU diagnoses and management, and developmental screening results. Of interest was which variables predict abnormal developmental screening. Methods: Traditional logistic regression analyses results were unstable because of the multicollinearity problem between the large number of predictor variables. CART analysis was ultimately used. Other machine learning methods, including random forest, TreeNet gradient boosting, MARS and regularized logistic regression methods, were also used for comparison. Results: NICU variables selected as most important in the CART analysis were PO feeding, number of medications prescribed, and number of follow-up appointments at NICU discharge. Other four machine learning models produced similar results to the CART model with slightly higher AUC and specificity, but lower sensitivity. Conclusion: Machine learning algorithms can provide useful tool in health care to assist providers in making complex clinical decisions.
Keywords
Machine learning
Prediction
Health care
Osteoarthritis affects the tissues and cells across the whole joint and results in cartilage degradation. Human chondrocytes are the only cell type in articular cartilage and are responsible for cartilage repair and homeostasis through metabolism. An increase in temperature surrounding these cells results in faster metabolic processes which will in turn generate heat as a byproduct. This study analyzes heat measurements of three-dimensionally encapsulated chondrocytes suspended in agarose hydrogels for four carbon sources. The heat is measured every second for 48 hours for each gel. Heat measurement profiles can be analyzed using the total heat over time by integrating instantaneous heat measurements. Or the heat curves themselves can be analyzed using a functional response linear model, which allows more detailed assessments of when the groups might differ and possibly more power to detect differences. We compare the functional data approach to using a conventional linear model for the aggregated heat responses. Pairwise follow-up tests to assess differences between groups and/or in particular regions of time are considered.
Keywords
biostatistics
functional data
functional ANOVA
follow-up tests
Chimeric antigen receptor (CAR) T-cell therapy efficacy is limited by toxicities and high cost (median $350k) in relapsed/refractory large B-cell lymphoma (R/R LBCL). This study explores patient features predicting 90-day readmission and hospital length of stay (LOS). A retrospective review of 66 patients with R/R LBCL treated at UC San Diego (2016–2022) included 46 clinical variables (e.g., clinical/visits/readmission parameters & adverse events). The targets were 90-day readmission (29%) and related LOS (mean 18.3 days [SD 19.5]). Data were pre-processed for lasso logistic/linear regression (L1-LG/LR), random forest (RF), extreme gradient boosting (XGB), and support vector machine (SVM) with a 75/25 train/test split, 100 randomly created test sets, GridSearchCV tuning, and 5-fold cross-validation. For 90-day readmission, top predictor was Day-60 Readmission LOS (22%-35% from RF to XGB; L1-LG coefficient 2.5), and highly weighted for predicting the associated LOS (35%-70% across models; L1-LR coefficient 2.9). Large variations (Min F1s: 0.33-0.75) and low R2 (about 0.55) were observed. This study highlights key predictors; larger datasets are needed for clinical generalization.
Keywords
chimeric antigen receptor (CAR) T-cell
90-day readmission
machine learning
important features
Co-Author(s)
Aaron Trando, University of California San Diego School of Medicine,
Ah-Reum Jeong, Department of Medicine, Division of Blood and Marrow Transplantation
Dimitrios Tzachanis, Department of Medicine, Division of Blood and Marrow Transplantation
First Author
Philip Yeung, Kansas University Medical Center/ MPKey, LLC.
Presenting Author
Philip Yeung, Kansas University Medical Center/ MPKey, LLC.