Data analysis methods and the reliability of analytic epidemiologic research.

Publications that compare randomized controlled trial and cohort study results on the effects of postmenopausal estrogen-plus-progestin therapy are reviewed. The 2 types of studies agree in identifying an early elevation in coronary heart disease risk, and a later developing elevation in breast cancer risk. Effects among women who begin hormone therapy within a few years after the menopause may be comparatively more favorable for coronary heart disease and less favorable for breast cancer. These analyses illustrate the potential of modern data analysis methods to enhance the reliability and interpretation of epidemiologic data

Implementation and evaluation of a new methane model within a dynamic global vegetation model: LPJ-WHyMe v1.3.1

For the first time, a model that simulates methane emissions from northern peatlands is incorporated directly into a dynamic global vegetation model. The model, LPJ-WHyMe (LPJ <B>W</B>etland <B>Hy</B>drology and <B>Me</B>thane), was previously modified in order to simulate peatland hydrology, permafrost dynamics and peatland vegetation. LPJ-WHyMe simulates methane emissions using a mechanistic approach, although the use of some empirical relationships and parameters is unavoidable. The model simulates methane production, three pathways of methane transport (diffusion, plant-mediated transport and ebullition) and methane oxidation. A sensitivity test was conducted to identify the most important factors influencing methane emissions, followed by a parameter fitting exercise to find the best combination of parameter values for individual sites and over all sites. A comparison of model results to observations from seven sites resulted in normalised root mean square errors (NRMSE) of 0.40 to 1.15 when using the best site parameter combinations and 0.68 to 1.42 when using the best overall parameter combination

Clinical validity assessment of a breast cancer risk model combining genetic and clinical information

_Background:_ The extent to which common genetic variation can assist in breast cancer (BCa) risk assessment is unclear. We assessed the addition of risk information from a panel of BCa-associated single nucleotide polymorphisms (SNPs) on risk stratification offered by the Gail Model.

_Methods:_ We selected 7 validated SNPs from the literature and genotyped them among white women in a nested case-control study within the Women’s Health Initiative Clinical Trial. To model SNP risk, previously published odds ratios were combined multiplicatively. To produce a combined clinical/genetic risk, Gail Model risk estimates were multiplied by combined SNP odds ratios. We assessed classification performance using reclassification tables and receiver operating characteristic (ROC) curves. 

_Results:_ The SNP risk score was well calibrated and nearly independent of Gail risk, and the combined predictor was more predictive than either Gail risk or SNP risk alone. In ROC curve analysis, the combined score had an area under the curve (AUC) of 0.594 compared to 0.557 for Gail risk alone. For reclassification with 5-year risk thresholds at 1.5% and 2%, the net reclassification index (NRI) was 0.085 (Z = 4.3, P = 1.0×10^-5^). Focusing on women with Gail 5-year risk of 1.5-2% results in an NRI of 0.195 (Z = 3.8, P = 8.6×10^−5^).

_Conclusions:_ Combining clinical risk factors and validated common genetic risk factors results in improvement in classification of BCa risks in white, postmenopausal women. This may have implications for informing primary prevention and/or screening strategies. Future research should assess the clinical utility of such strategies.
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On Two-Stage Hypothesis Testing Procedures Via Asymptotically Independent Statistics

Kooperberg and LeBlanc (2008) proposed a two-stage testing procedure to screen for significant interactions in genome-wide association (GWA) studies by a soft threshold on marginal associations (MA), though its theoretical properties and generalization have not been elaborated. In this article, we discuss conditions that are required to achieve strong control of the Family-Wise Error Rate (FWER) by such procedures for low or high-dimensional hypothesis testing. We provide proof of asymptotic independence of marginal association statistics and interaction statistics in linear regression, logistic regression, and Cox proportional hazard models in a randomized clinical trial (RCT) with a rare event. In case-control studies nested within a RCT, a complementary criterion, namely deviation from baseline independence (DBI) in the case-control sample, is advocated as a screening tool for discovering significant interactions or main effects. Simulations and an application to a GWA study in Women’s Health Initiative (WHI) are presented to show utilities of the proposed two-stage testing procedures in pharmacogenetic studies

Statistical Aspects of the Use of Biomarkers in Nutritional Epidemiology Research

Few strong and consistent associations have arisen from observational studies of dietary consumption in relation to chronic disease risk. Measurement error in self-reported dietary assessment may be obscuring many such associations. Attempts to correct for measurement error have mostly used a second self-report assessment in a subset of a study cohort to calibrate the self-report assessment used throughout the cohort, under the dubious assumption of uncorrelated measurement errors between the two assessments. The use, instead, of objective biomarkers of nutrient consumption to produce calibrated consumption estimates provides a promising approach to enhance study reliability. As summarized here, we have recently applied this nutrient biomarker approach to examine energy, protein, and percent of energy from protein, in relation to disease incidence in Women’s Health Initiative cohorts, and find strong associations that are not evident without biomarker calibration. A major bottleneck for the broader use of a biomarker-calibration approach is the rather few nutrients for which a suitable biomarker has been developed. Some methodologic approaches to the development of additional pertinent biomarkers, including the possible use of a respiratory quotient from indirect calorimetry for macronutrient biomarker development, and the potential of human feeding studies for the evaluation of a range of urine- and blood-based potential biomarkers, will briefly be described

Mixed Discrete and Continuous Cox Regression Model

The Cox (1972) regression model is extended to include discrete and mixed continuous/discrete failure time data by retaining the multiplicative hazard rate form of the absolutely continuous model. Application of martingale arguments to the regression parameter estimating function show the Breslow (1974) estimator to be consistent and asymptotically Gaussian under this model. A computationally convenient estimator of the variance of the score function can be developed, again using martingale arguments. This estimator reduces to the usual hypergeometric form in the special case of testing equality of several survival curves, and it leads more generally to a convenient consistent variance estimator for the regression parameter. A small simulation study is carried out to study the regression parameter estimator and its variance estimator under the discrete Cox model special case and an application to a bladder cancer recurrence dataset is provided.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46860/1/10985_2004_Article_5119440.pd

Targetable Mechanical Properties by Switching between Self-Sorting and Co-assembly with In Situ Formed Tripodal Ketoenamine Supramolecular Hydrogels

A new family of supramolecular hydrogelators are introduced in which self-sorting and co-assembly can be utilised in the tuneability of the mechanical properties of the materials, a property closely tied to the nanostructure of the gel network. The in situ reactivity of the components of the gelators allows for system chemistry concepts to be applied to the formation of the gels and shows that molecular properties, and not necessarily the chemical identity, determines some gel properties in these family of gels

Options for basing Dietary Reference Intakes (DRIs) on chronic disease endpoints: report from a joint US-/Canadian-sponsored working group.

Dietary Reference Intakes (DRIs) are used in Canada and the United States in planning and assessing diets of apparently healthy individuals and population groups. The approaches used to establish DRIs on the basis of classical nutrient deficiencies and/or toxicities have worked well. However, it has proved to be more challenging to base DRI values on chronic disease endpoints; deviations from the traditional framework were often required, and in some cases, DRI values were not established for intakes that affected chronic disease outcomes despite evidence that supported a relation. The increasing proportions of elderly citizens, the growing prevalence of chronic diseases, and the persistently high prevalence of overweight and obesity, which predispose to chronic disease, highlight the importance of understanding the impact of nutrition on chronic disease prevention and control. A multidisciplinary working group sponsored by the Canadian and US government DRI steering committees met from November 2014 to April 2016 to identify options for addressing key scientific challenges encountered in the use of chronic disease endpoints to establish reference values. The working group focused on 3 key questions: 1) What are the important evidentiary challenges for selecting and using chronic disease endpoints in future DRI reviews, 2) what intake-response models can future DRI committees consider when using chronic disease endpoints, and 3) what are the arguments for and against continuing to include chronic disease endpoints in future DRI reviews? This report outlines the range of options identified by the working group for answering these key questions, as well as the strengths and weaknesses of each option

The Gambian Bone and Muscle Ageing Study: Baseline Data from a Prospective Observational African Sub-Saharan Study

The Gambian Bone and Muscle Ageing Study is a prospective observational study investigating bone and muscle ageing in men and women from a poor, subsistence farming community of The Gambia, West Africa. Musculoskeletal diseases, including osteoporosis and sarcopenia, form a major part of the current global non-communicable disease burden. By 2050, the vast majority of the world’s ageing population will live in low- and middle-income countries with an estimated two-fold rise in osteoporotic fracture. The study design was to characterise change in bone and muscle outcomes and to identify possible preventative strategies for fracture and sarcopenia in the increasing ageing population. Men and women aged ≥40 years from the Kiang West region of The Gambia were recruited with stratified sampling by sex and age. Baseline measurements were completed in 488 participants in 2012 who were randomly assigned to follow-up between 1.5 and 2 years later. Follow-up measurements were performed on 465 participants approximately 1.7 years after baseline measurements. The data set comprises a wide range of measurements on bone, muscle strength, anthropometry, biochemistry, and dietary intake. Questionnaires were used to obtain information on health, lifestyle, musculoskeletal pain, and reproductive status. Baseline cross-sectional data show preliminary evidence for bone mineral density and muscle loss with age. Men had greater negative differences in total body lean mass with age than women following adjustments for body size. From peripheral quantitative computed tomography scans, greater negative associations between bone outcomes and age at the radius and tibia were shown in women than in men. Ultimately, the findings from The Gambian Bone and Muscle Ageing Study will contribute to the understanding of musculoskeletal health in a transitioning population and better characterise fracture and sarcopenia incidence in The Gambia with an aim to the development of preventative strategies against both