Check out some of the work by Dr. Banack and the Aging Epi research group
There is emerging evidence that cancer and its treatments may accelerate the normal aging process, increasing the magnitude and rate of decline in functional capacity. This accelerated aging process is hypothesized to hasten the occurrence of common adverse age-related outcomes in cancer survivors, including loss of muscle mass and decrease in physical function. However, there is no data describing age-related loss of muscle mass and its relation to physical function in the long-term in cancer survivors.
We describe the use of Apisensr, a web-based application that can be used to implement quantitative bias analysis for misclassification, selection bias, and unmeasured confounding. We apply Apisensr using an example of exposure misclassification bias due to use of self-reported body mass index (BMI) to define obesity status in an analysis of the relationship between obesity and diabetes.
Abdominal adiposity, including visceral and subcutaneous abdominal adipose tissue (VAT and SAT), is recognized as a strong risk factor for cardiometabolic disease, cancer, and mortality.
To discuss possible explanations for the obesity paradox and explore whether the paradox can be attributed to a form of selection bias known as collider stratification bias.
Body mass index (BMI) is a widely used indicator of obesity status in clinical settings and population health research. However, there are concerns about the validity of BMI as a measure of obesity in postmenopausal women. Unlike BMI, which is an indirect measure of obesity and does not distinguish lean from fat mass, dual-energy x-ray absorptiometry (DXA) provides a direct measure of body fat and is considered a gold standard of adiposity measurement. The goal of this study is to examine the validity of using BMI to identify obesity in postmenopausal women relative to total body fat percent measured by DXA scan.
Obesity and smoking are independently associated with a higher mortality risk, but previous studies have reported conflicting results about the relationship between these 2 time-varying exposures. Using prospective longitudinal data (1987-2007) from the Atherosclerosis Risk in Communities Study, our objective in the present study was to estimate the joint effects of obesity and smoking on all-cause mortality and investigate whether there were additive or multiplicative interactions. We fit a joint marginal structural Poisson model to account for time-varying confounding affected by prior exposure to obesity and smoking.
The use of relative and absolute effect estimates has important implications for the interpretation of study findings. Likewise, examining additive and multiplicative interaction can lead to differing conclusions about the joint effects of two exposure variables. The aim of this paper is to examine the relationship between BMI and mortality on the relative and absolute scales and investigate interaction between BMI and age.
Concerns about reverse causality and selection bias complicate the interpretation of studies of body mass index (BMI, calculated as weight (kg)/height (m)2) and mortality in older adults. The objective of this study was to investigate methodological explanations for the apparent attenuation of obesity-related risks in older adults. We used data from 68,132 participants in the Women’s Health Initiative (WHI) clinical trial for this analysis.
Selection bias is a well-known concern in research on older adults. We discuss two common forms of selection bias in aging research: (1) survivor bias and (2) bias due to loss to follow-up. Our objective was to review these two forms of selection bias in geriatrics research. In clinical aging research, selection bias is a particular concern because all participants must have survived to old age, and be healthy enough, to take part in a research study in geriatrics.
Quantitative bias analysis can be used to empirically assess how far study estimates are from the truth (i.e., an estimate that is free of bias). These methods can be used to explore the potential impact of confounding bias, selection bias (collider stratification bias), and information bias. Quantitative bias analysis includes methods that can be used to check the robustness of study findings to multiple types of bias and methods that use simulation studies to generate data and understand the hypothetical impact of specific types of bias in a simulated data set.
There is widespread concern about the use of body mass index (BMI) to define obesity status in postmenopausal women because it may not accurately represent an individual’s true obesity status. The objective of the present study is to examine and adjust for exposure misclassification bias from using an indirect measure of obesity (BMI) compared with a direct measure of obesity (percent body fat).
Study question: What is the association between reproductive health history (e.g. age at menarche, menopause, reproductive lifespan) with abdominal adiposity in postmenopausal women?
Summary answer: Higher visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) tissue levels were observed among women with earlier menarche, earlier menopause, and greater parity.
The D3-creatine (D3Cr) dilution method provides a direct measure of skeletal muscle. The aim of this study was to compare the association of D3Cr muscle mass with lean body mass (LBM) measured by dual-energy x-ray absorptiometry (DXA) and examine its relation with physical function in postmenopausal women.
The objective of this manuscript is to identify longitudinal trajectories of change in body mass index (BMI) after menopause and investigate the association of BMI trajectories with risk of diabetes and cardiovascular disease (CVD) among postmenopausal women.
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