Quality of Life and Socioeconomic Indicators Associated with Survival of Myeloid Leukemias in Canada

Understanding how patient‐reported quality of life (QoL) and socioeconomic status (SES) relate to survival of acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS) may improve prognostic information sharing. This study explores associations among QoL, SES, and survival through administration of the Euro‐QoL 5‐Dimension, 3‐level and Functional Assessment of Cancer Therapy‐Leukemia and financial impact questionnaires to 138 adult participants with newly diagnosed AML or MDS in a longitudinal, pan‐Canadian study. Cox regression and lasso variable selection models were used to explore associations among QoL, SES, and established predictors of survival. Secondary outcomes were changes in QoL, performance of the QoL instruments, and lost income. We found that higher QoL and SES were positively associated with survival. The Lasso model selected the visual analog scale of the EQ‐5D‐3L as the most important predictor among all other variables (P = .03; 92% selection). Patients with AML report improved QoL after treatment, despite higher mean out‐of‐pocket expenditures compared with MDS (up to $599 CDN/month for AML vs$239 for MDS; P = .05), greater loss of productivity‐related income (reaching id="mce_marker"786/month for AML vs $709 for MDS; P < .05), and greater caregiver effects (65% vs 35% caregiver productivity losses for AML vs MDS; P < .05). Our results suggest that including patient‐reported QoL and socioeconomic indicators can improve the accuracy of survival models

Sparse Multivariate Reduced-Rank Regression with Covariance Estimation

Multivariate multiple linear regression is multiple linear regression, but with multiple responses. Standard approaches assume that observations from different subjects are uncorrelated and so estimates of the regression parameters can be obtained through separate univariate regressions, regardless of whether the responses are correlated within subjects. There are three main extensions to the simplest model. The first assumes a low rank structure on the coefficient matrix that arises from a latent factor model linking predictors to responses. The second reduces the number of parameters through variable selection. The third allows for correlations between response variables in the low rank model. Chen and Huang propose a new model that falls under the reduced-rank regression framework, employs variable selection, and estimates correlations among error terms. This project reviews their model, describes its implementation, and reports the results of a simulation study evaluating its performance. The project concludes with ideas for further research

Sparse Multivariate Reduced-Rank Regression with Covariance Estimation Title: Sparse Multivariate Reduced-Rank Regression with Covariance Estimation

Abstract Abstract Multivariate multiple linear regression is multiple linear regression, but with multiple responses. Standard approaches assume that observations from different subjects are uncorrelated and so estimates of the regression parameters can be obtained through separate univariate regressions, regardless of whether the responses are correlated within subjects. There are three main extensions to the simplest model. The first assumes a low rank structure on the coefficient matrix that arises from a latent factor model linking predictors to responses. The second reduces the number of parameters through variable selection. The third allows for correlations between response variables in the low rank model. Chen and Huang propose a new model that falls under the reduced-rank regression framework, employs variable selection, and estimates correlations among error terms. This project reviews their model, describes its implementation, and reports the results of a simulation study evaluating its performance. The project concludes with ideas for further research

Late first acute rejection in pediatric kidney transplantation: A North American Pediatric Renal Trials and Collaborative Studies special study

Rates of early AR in pediatric kidney transplantation have declined in every era but the most recent NAPRTCS cohort has shown an increase in late first AR rates. We hypothesized this was due to an increased proportion of deceased donor utilization and early steroid taper utilization. Using the NAPRTCS database, we compared the most recent three cohorts of patients transplanted between 2002-2006, 2007-2011, and 2012-2017. To determine variables that predict late first AR, we used two multivariable models: a standard Cox regression model and LASSO model. From the LASSO model, deceased donor source (P = .002), higher recipient age (P = .019), black race (P = .010), and transplant cohort 2012-17 (P = .014) were all significant predictors of more late first AR. On standard Cox regression analysis, those same variables, minus donor source, were significant, in addition to mycophenolates usage (P = .007) and lower eGFR at 12 months (P = .02). The most recent 2012-2017 cohort remains an independently significant risk factor for late first AR, suggesting unmeasured variables. Further research is needed to determine whether these higher late first AR rates will impact long-term graft survival in the most recent cohort