Perturbation and scaled Cook's distance

Cook's distance [Technometrics 19 (1977) 15-18] is one of the most important diagnostic tools for detecting influential individual or subsets of observations in linear regression for cross-sectional data. However, for many complex data structures (e.g., longitudinal data), no rigorous approach has been developed to address a fundamental issue: deleting subsets with different numbers of observations introduces different degrees of perturbation to the current model fitted to the data, and the magnitude of Cook's distance is associated with the degree of the perturbation. The aim of this paper is to address this issue in general parametric models with complex data structures. We propose a new quantity for measuring the degree of the perturbation introduced by deleting a subset. We use stochastic ordering to quantify the stochastic relationship between the degree of the perturbation and the magnitude of Cook's distance. We develop several scaled Cook's distances to resolve the comparison of Cook's distance for different subset deletions. Theoretical and numerical examples are examined to highlight the broad spectrum of applications of these scaled Cook's distances in a formal influence analysis.Comment: Published in at http://dx.doi.org/10.1214/12-AOS978 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Bayesian Influence Diagnostic Methods for Parametric Regression Models

The goals of assessing the influence of individual observations in statistical analysis are not only to identify influential observations such as outliers and high leverage points, but also to determine the importance of each observation in the analysis for a better model fit. Thus, assessing the influence of individual observations on a model, choosing an appropriate dimensionality of a model and selecting the best model for a given dataset are very important and highly relevant problems in any formal statistical analysis. Recently, Bayesian methodologies have been getting enormous attention in biomedical research due to the potential advantages of fitting a vast array of complex models posed by modern data. As the demand for Bayesian data analysis and modeling increases, we need good diagnostic methods for model assessment and selection. In this dissertation, we develop Bayesian diagnostic measures based on case-deletion to assess the influence of each observation to model fit and model complexity. First, we propose Bayesian case influence diagnostics for complex survival models. In detail, we develop case deletion influence diagnostics for both the joint and marginal posterior distributions based on the Kullback-Leibler divergence. Second, we introduce three types of Bayesian case influence measures based on case deletion, namely the Φ-divergence, Cook's posterior mode distance and Cook's posterior mean distance to evaluate the effects of deleting a set of observations in general Bayesian parametric models. We also examine the statistical properties of these three Bayesian case influence measures and their applications to identification of influential sets and model complexity. In any deletion diagnostic, "size matters" issue persists and it is a fundamental issue of influence analysis, because the size of the deletion diagnostic is associated with the size of the perturbation. For Cook's distance, that is Cook's distance is a monotonic function of the size of perturbation. Thus, we develop a scaled version of Cook's distance to address the size issue for deletion diagnostics in general parametric models.Doctor of Philosoph

Bayesian Case Influence Diagnostics for Survival Models

We propose Bayesian case influence diagnostics for complex survival models. We develop case deletion influence diagnostics for both the joint and marginal posterior distributions based on the Kullback–Leibler divergence (K–L divergence). We present a simplified expression for computing the K–L divergence between the posterior with the full data and the posterior based on single case deletion, as well as investigate its relationships to the conditional predictive ordinate. All the computations for the proposed diagnostic measures can be easily done using Markov chain Monte Carlo samples from the full data posterior distribution. We consider the Cox model with a gamma process prior on the cumulative baseline hazard. We also present a theoretical relationship between our case-deletion diagnostics and diagnostics based on Cox’s partial likelihood. A simulated data example and two real data examples are given to demonstrate the methodology

Bayesian Case Influence Measures for Statistical Models With Missing Data

We examine three Bayesian case influence measures including the φ-divergence, Cook's posterior mode distance and Cook's posterior mean distance for identifying a set of influential observations for a variety of statistical models with missing data including models for longitudinal data and latent variable models in the absence/presence of missing data. Since it can be computationally prohibitive to compute these Bayesian case influence measures in models with missing data, we derive simple first-order approximations to the three Bayesian case influence measures by using the Laplace approximation formula and examine the applications of these approximations to the identification of influential sets. All of the computations for the first-order approximations can be easily done using Markov chain Monte Carlo samples from the posterior distribution based on the full data. Simulated data and an AIDS dataset are analyzed to illustrate the methodology

Early treatment-related changes in diabetes and cardiovascular disease risk markers in first episode psychosis subjects

To examine prospective changes in cardiovascular disease (CVD) and type-2 diabetes risk factors in young adult first episode psychotic (FEP) patients treated with second generation antipsychotic medications

Infection-related and lifestyle-related cancer burden in Kampala, Uganda: Projection of the future cancer incidence up to 2030

OBJECTIVES: In Uganda, infection-related cancers have made the greatest contribution to cancer burden in the past; however, burden from lifestyle-related cancers has increased recently. Using the Kampala Cancer Registry data, we projected incidence of top five cancers, namely, Kaposi sarcoma (KS), cervical, breast and prostate cancer, and non-Hodgkin\u27s lymphoma (NHL) in Uganda. DESIGN: Trend analysis of cancer registry data. SETTING: Kampala Cancer Registry, Uganda. MAIN OUTCOME MEASURE: Cancer incidence data from 2001 to 2015 were used and projected to 2030. Population data were obtained from the Uganda Bureau of Statistics. Age-standardised incidence rates (ASRs) and their trends over the observed and projected period were calculated. Percentage change in cancer incidence was calculated to determine whether cancer incidence changes were attributable to cancer risk changes or population changes. RESULTS: It was projected that the incidence rates of KS and NHL continue to decrease by 22.6% and 37.3%, respectively. The ASR of KS was expected to decline from 29.6 per 100 000 population to 10.4, while ASR of NHL was expected to decrease from 7.6 to 3.2. In contrast, cervical, breast and prostate cancer incidence were projected to increase by 35.3%, 57.7% and 33.4%, respectively. The ASRs of cervical and breast were projected to increase up to 66.1 and 48.4 per 100 000 women. The ASR of prostate cancer was estimated to increase from 41.6 to 60.5 per 100 000 men. These changes were due to changes in risk factors and population growth. CONCLUSION: Our results suggest a rapid shift in the profile of common cancers in Uganda, reflecting a new trend emerging in low/middle-income countries. This change in cancer spectrum, from infection-related to lifestyle-related, yields another challenge to cancer control programmes in resource-limited countries. Forthcoming cancer control programmes should include a substantial focus on lifestyle-related cancers, while infectious disease control programmes should be maintained

INK4/ARF Transcript Expression Is Associated with Chromosome 9p21 Variants Linked to Atherosclerosis

Genome-wide association studies (GWAS) have linked common single nucleotide polymorphisms (SNPs) on chromosome 9p21 near the INK4/ARF (CDKN2A/B) tumor suppressor locus with risk of atherosclerotic diseases and type 2 diabetes mellitus. To explore the mechanism of this association, we investigated whether expression of proximate transcripts (p16(INK4a), p15(INK4b), ARF, ANRIL and MTAP) correlate with genotype of representative 9p21 SNPs.We analyzed expression of 9p21 transcripts in purified peripheral blood T-cells (PBTL) from 170 healthy donors. Samples were genotyped for six selected disease-related SNPs spanning the INK4/ARF locus. Correlations among these variables were determined by univariate and multivariate analysis. Significantly reduced expression of all INK4/ARF transcripts (p15(INK4b), p16(INK4a), ARF and ANRIL) was found in PBTL of individuals harboring a common SNP (rs10757278) associated with increased risk of coronary artery disease, stroke and aortic aneurysm. Expression of MTAP was not influenced by rs10757278 genotype. No association of any these transcripts was noted with five other tested 9p21 SNPs.Genotypes of rs10757278 linked to increased risk of atherosclerotic diseases are also associated with decreased expression in PBTL of the INK4/ARF locus, which encodes three related anti-proliferative transcripts of known importance in tumor suppression and aging

Expression of p16 INK4a in peripheral blood T-cells is a biomarker of human aging

Expression of the p16INK4a tumor suppressor sharply increases with age in most mammalian tissues, and contributes to an age-induced functional decline of certain self-renewing compartments. These observations have suggested that p16INK4a expression could be a biomarker of mammalian aging. To translate this notion to human use, we determined p16INK4a expression in cellular fractions of human whole blood, and found highest expression in peripheral blood T-lymphocytes (PBTL). We then measured INK4/ARF transcript expression in PBTL from two independent cohorts of healthy humans (170 donors total), and analyzed their relationship with donor characteristics. Expression of p16INK4a, but not other INK4/ARF transcripts, appeared to exponentially increase with donor chronologic age. Importantly, p16INK4a expression did not independently correlate with gender or body-mass index, but was significantly associated with tobacco use and physical inactivity. In addition, p16INK4a expression was associated with plasma interleukin-6 concentration, a marker of human frailty. These data suggest that p16INK4a expression in PBTL is an easily measured, peripheral blood biomarker of molecular age

Multiple-Case Studies of Hand-on Breast Massage Techniques used by Breastfeeding Experts

PURPOSE: The aim of this study was to understand the hand-on breast massage techniques used by well-known experts in breastfeeding clinics. METHODS: A qualitative multiple-case design was applied that involved a feasibility test. Four experts sampling qualitative data collected by observing participants and in individual interviews were analyzed by content analysis, linking data to the propositions, and cross-case pattern matching. This study explored differences within and between cases, and the possibilities of replicating findings across cases. Thirty-nine postpartum women participated voluntarily in the feasibility test, which investigated the usability of four massage techniques. RESULTS: The four techniques showed considerable similarities in terms of the application of stimulation to the breast base and increased flexibility of the wired flexible body, which was the core mechanism underlying the techniques. The breast management strategies were consistent with existing practice guidelines with the exception of using cold cabbage to control engorgement pain. There was insufficient scientific evidence for supporting the massage techniques used by the experts. All of the techniques showed 100% education completeness, but application rates were higher for self-control-oriented techniques. CONCLUSION: The massage techniques applied by experts in breastfeeding were based on hypotheses and self-control techniques are feasible to apply in practice

Multimorbidity patterns by health-related quality of life status in older adults: an association rules and network analysis utilizing the Korea National Health and Nutrition Examination Survey

OBJECTIVES Improved life expectancy has increased the prevalence of older adults living with multimorbidity which likely deteriorates their health-related quality of life (HRQoL). However, relatively little is known about patterns and the relationships of multimorbidity by HRQoL status in older adults. METHODS Individuals aged 65 or older from the Korea National Health and Nutrition Examination Survey V-VII (2010-2018) were analyzed. HRQoL was assessed by the EuroQoL-5 dimensions questionnaire and categorized as poor, normal, or good. The impact of multimorbidity on HRQoL was evaluated using logistic regression. The patterns and inter-relationships between multimorbidity, stratified by HRQoL groups, were analyzed using the association rules and network analysis approach. RESULTS Multimorbidity was significantly associated with poor HRQoL (3 or more diseases vs. none; adjusted odds ratio, 2.70; 95% confidence interval, 2.10 to 3.46). Hypertension, arthritis, hyperlipidemia, and diabetes were the most prevalent diseases across all HRQoL groups. Complex interrelationships of morbidities, higher prevalence, and node strengths in all diseases were observed in the poor HRQoL group, particularly for arthritis, depression, and stroke, compared to other groups (1.5-3.0 times higher, p<0.05 for all). Apart from hypertension, arthritis and hyperlipidemia had a higher prevalence and stronger connections with other diseases in females, whereas this was the case for diabetes and stroke in males with poor HRQoL. CONCLUSIONS Multimorbidity patterns formed complicatedly inter-correlated disease networks in the poor HRQoL group with differences according to sex. These findings enhance the understanding of multimorbidity connections and provide information on the healthcare needs of older adults, especially those with poor HRQoL