Human well-being and causality in social epidemiology

This paper discusses the work of Ballas and Dorling on life events and happiness. I believe epidemiologists have things they could learn from economists (and vice versa). Here I emphasize the issue of how to establish causality, and try to suggest some ways forward

Large Sample Theory for Semiparametric Regression Models with Two-Phase, Outcome Dependent Sampling

Outcome-dependent, two-phase sampling designs can dramatically reduce the costs of observational studies by judicious selection of the most informative subjects for purposes of detailed covariate measurement. Here we derive asymptotic information bounds and the form of the efficient score and influence functions for the semiparametric regression models studied by Lawless, Kalbfleisch, and Wild (1999) under two-phase sampling designs. We show that the maximum likelihood estimators for both the parametric and nonparametric parts of the model are asymptotically normal and efficient. The efficient influence function for the parametric part aggress with the more general information bound calculations of Robins, Hsieh, and Newey (1995). By verifying the conditions of Murphy and Van der Vaart (2000) for a least favorable parametric submodel, we provide asymptotic justification for statistical inference based on profile likelihood

Mortality ascertainment of participants in the National Wilms Tumor Study using the National Death Index: comparison of active and passive follow-up results

Long term studies of childhood cancer survivors are hampered by difficulties in tracking young adult participants. After performing a National Death Index (NDI) search we sought to identify which factors best predicted a match among known decedents from the National Wilms Tumor Study (NWTS) and to determine if record linkage could substitute for missing follow-up in a cohort of NWTS survivors. To our knowledge, this is the first study to compare passive mortality follow-up using the NDI to active follow-up of a childhood and young adult population

Dietary Patterns and Prostate Cancer Risk in the National Health and Nutrition Examination Survey Epidemiological Follow-up Study Cohort

Ecological studies implicate a “Western” diet in prostate cancer development, but whether dietary patterns measured in individuals are associated with risk has not been studied previously. We examined this issue using prospective data from the nationally representative United States Health Examination Epidemiological Follow-up Study. Among 3,779 men followed from 1982– 84 to 1992, 136 incident cases were identified. Using principal component analysis on responses to a 105-item dietary questionnaire, the following three distinct patterns were identified: a vegetable-fruit pattern; a red meat-starch pattern characterized by red meats, potatoes, cheese, salty snacks, and desserts; and a Southern pattern characterized by such foods as cornbread, grits, sweet potatoes, okra, beans, and rice. In adjusted proportional hazards models, prostate cancer risk was not associated with the vegetable-fruit or red meat-starch pattern, but higher intake of the Southern pattern showed a reduction in risk (3rd versus 1st tertile relative risk, 0.6; 95% confidence interval, 0.4 –1.1; trend P= 0.08) that approached statistical significance. The inverse association was observed in black and non-black men and was not attributable to intake of any individual foods or nutrients. A Southern dietary pattern may reflect a history of living in the South and serve as an integrative marker of sunlight exposure and protection through 1,25- dihydroxyvitamin D production. Further evaluation and better characterization of the pattern would offer more information on potentially beneficial features of the diet or its associated lifestyle

OLA! A Scenario-Based Approach to Enhance Open Learning Through Accessibility

Open Educational Resources (OER) and Massive Open Online Courses (MOOC) have not developed with an inherent capacity to attend to the needs of disabled students. In our research, we aim to understand the social, contextual and organisational issues behind these inadequacies. Through this, interventions and best practices can be developed to improve the situation

A Recurrent Neural Network Survival Model: Predicting Web User Return Time

The size of a website's active user base directly affects its value. Thus, it is important to monitor and influence a user's likelihood to return to a site. Essential to this is predicting when a user will return. Current state of the art approaches to solve this problem come in two flavors: (1) Recurrent Neural Network (RNN) based solutions and (2) survival analysis methods. We observe that both techniques are severely limited when applied to this problem. Survival models can only incorporate aggregate representations of users instead of automatically learning a representation directly from a raw time series of user actions. RNNs can automatically learn features, but can not be directly trained with examples of non-returning users who have no target value for their return time. We develop a novel RNN survival model that removes the limitations of the state of the art methods. We demonstrate that this model can successfully be applied to return time prediction on a large e-commerce dataset with a superior ability to discriminate between returning and non-returning users than either method applied in isolation.Comment: Accepted into ECML PKDD 2018; 8 figures and 1 tabl

Crude incidence in two-phase designs in the presence of competing risks.

BackgroundIn many studies, some information might not be available for the whole cohort, some covariates, or even the outcome, might be ascertained in selected subsamples. These studies are part of a broad category termed two-phase studies. Common examples include the nested case-control and the case-cohort designs. For two-phase studies, appropriate weighted survival estimates have been derived; however, no estimator of cumulative incidence accounting for competing events has been proposed. This is relevant in the presence of multiple types of events, where estimation of event type specific quantities are needed for evaluating outcome.MethodsWe develop a non parametric estimator of the cumulative incidence function of events accounting for possible competing events. It handles a general sampling design by weights derived from the sampling probabilities. The variance is derived from the influence function of the subdistribution hazard.ResultsThe proposed method shows good performance in simulations. It is applied to estimate the crude incidence of relapse in childhood acute lymphoblastic leukemia in groups defined by a genotype not available for everyone in a cohort of nearly 2000 patients, where death due to toxicity acted as a competing event. In a second example the aim was to estimate engagement in care of a cohort of HIV patients in resource limited setting, where for some patients the outcome itself was missing due to lost to follow-up. A sampling based approach was used to identify outcome in a subsample of lost patients and to obtain a valid estimate of connection to care.ConclusionsA valid estimator for cumulative incidence of events accounting for competing risks under a general sampling design from an infinite target population is derived

Association of Exposure to Phthalates with Endometriosis and Uterine Leiomyomata: Findings from NHANES, 1999-2004

BACKGROUND. Phthalates are ubiquitous chemicals used in consumer products. Some phthalates are reproductive toxicants in experimental animals, but human data are limited. OBJECTIVE. We conducted a cross-sectional study of urinary phthalate metabolite concentrations in relation to self-reported history of endometriosis and uterine leiomyomata among 1,227 women 20-54 years of age from three cycles of the National Health and Nutrition Examination Survey (NHANES), 1999-2004. METHODS. We examined four phthalate metabolites: mono(2-ethylhexyl) phthalate (MEHP), monobutyl phthalate (MBP), monoethyl phthalate (MEP), and monobenzyl phthalate (MBzP). From the last two NHANES cycles, we also examined mono(2-ethyl-5-hydroxyhexyl) phthalate (MEHHP) and mono(2-ethyl-5-oxohexyl) phthalate (MEOHP). We used logistic regression to estimate odds ratios (ORs) and 95% confidence intervals (CIs), adjusting for potential confounders. RESULTS. Eighty-seven (7%) and 151 (12%) women reported diagnoses of endometriosis and leiomyomata, respectively. The ORs comparing the highest versus lowest three quartiles of urinary MBP were 1.36 (95% CI, 0.77-2.41) for endometriosis, 1.56 (95% CI, 0.93-2.61) for leiomyomata, and 1.71 (95% CI, 1.07-2.75) for both conditions combined. The corresponding ORs for MEHP were 0.44 (95% CI, 0.19-1.02) for endometriosis, 0.63 (95% CI, 0.35-1.12) for leiomyomata, and 0.59 (95% CI, 0.37-0.95) for both conditions combined. Findings for MEHHP and MEOHP agreed with findings for MEHP with respect to endometriosis only. We observed null associations for MEP and MBzP. Associations were similar when we excluded women diagnosed > 7 years before their NHANES evaluation. CONCLUSION. The positive associations for MBP and inverse associations for MEHP in relation to endometriosis and leiomyomata warrant investigation in prospective studies