Invited commentary: mixing multiple imputation and bootstrapping for variance estimation.

Multiple imputation (MI) is commonly implemented to mitigate potential selection bias due to missing data. The accompanying article by Nguyen and Stuart (Am J Epidemiol. 2024;193(10):1470-1476) examines the statistical consistency of several ways of integrating MI with propensity scores. As Nguyen and Stuart noted, variance estimation for these different approaches remains to be developed. One common option is the nonparametric bootstrap, which can provide valid inference when closed-form variance estimators are not available. However, there is no consensus on how to implement MI and nonparametric bootstrapping in analyses. To complement Nguyen and Stuart's article on MI and propensity score analyses, we review some currently available approaches on variance estimation with MI and nonparametric bootstrapping.

Conducting density-sampled case-control studies using survey data with complex sampling designs: A simulation study.

PURPOSE: Population-based surveys are possible sources from which to draw representative control data for case-control studies. However, these surveys involve complex sampling that could lead to biased estimates of measures of association if not properly accounted for in analyses. Approaches to incorporating complex-sampled controls in density-sampled case-control designs have not been examined. METHODS: We used a simulation study to evaluate the performance of different approaches to estimating incidence density ratios (IDR) from case-control studies with controls drawn from complex survey data using risk-set sampling. In simulated population data, we applied four survey sampling approaches, with varying survey sizes, and assessed the performance of four analysis methods for incorporating survey-based controls. RESULTS: Estimates of the IDR were unbiased for methods that conducted risk-set sampling with probability of selection proportional to survey weights. Estimates of the IDR were biased when sampling weights were not incorporated, or only included in regression modeling. The unbiased analysis methods performed comparably and produced estimates with variance comparable to biased methods. Variance increased and confidence interval coverage decreased as survey size decreased. CONCLUSIONS: Unbiased estimates are obtainable in risk-set sampled case-control studies using controls drawn from complex survey data when weights are properly incorporated.

Actomyosin-Mediated Tension Orchestrates Uncoupled Respiration in Adipose Tissues.

The activation of brown/beige adipose tissue (BAT) metabolism and the induction of uncoupling protein 1 (UCP1) expression are essential for BAT-based strategies to improve metabolic homeostasis. Here, we demonstrate that BAT utilizes actomyosin machinery to generate tensional responses following adrenergic stimulation, similar to muscle tissues. The activation of actomyosin mechanics is critical for the acute induction of oxidative metabolism and uncoupled respiration in UCP1+ adipocytes. Moreover, we show that actomyosin-mediated elasticity regulates the thermogenic capacity of adipocytes via the mechanosensitive transcriptional co-activators YAP and TAZ, which are indispensable for normal BAT function. These biomechanical signaling mechanisms may inform future strategies to promote the expansion and activation of brown/beige adipocytes.