FLEXIBLE PROPENSITY SCORE ESTIMATION STRATEGIES FOR CLUSTERED DATA IN NON-EXPERIMENTAL STUDIES

Propensity score methods are a popular tool for reducing confounding bias of treatment effect estimates in non-experimental studies. Existing studies have demonstrated superior performance of nonparametric machine learning over logistic regression for propensity score estimation. However, that work has been done with just individual-level data. In many medical, behavioral, and educational settings, however, individuals are clustered into groups; it is unclear whether the advantages of nonparametric propensity score modeling carry to multilevel data settings. In addition, a particular question arises when there might be unmeasured cluster-level confounding, which is likely in clustered data settings. In this work, we describe a set of parametric and nonparametric propensity score estimation procedures: multilevel logistic regression with fixed or random cluster effects, Bayesian additive regression trees (BART) with indicators for clusters or random cluster effects, generalized boosted modeling (GBM) with indicators for clusters, as well as logistic regression, BART, and GBM models that ignore the clustered structure. We then compare the methods’ performance in a two-level clustered data context where treatment is administered at the individual level. We simulated data for three hypothetical observational studies of varying sample and cluster sizes (20 clusters of size 200 to 500; 100 clusters of size 50; 20 clusters of size 100), each with six individual-level confounders, two cluster-level confounders, and an additional cluster-level confounder that is unobserved in the data analyses. A binary treatment indicator and a continuous outcome are generated based on seven scenarios with different relationships between the treatment and confounders (linear and additive, non-linear/non-additive in the observed confounders, non-additive with the unobserved cluster-level confounder). Simulation results suggest that when both the sample and cluster sizes are sufficiently large (e.g., 20 clusters of size 200 to 500), nonparametric propensity scores tend to outperform parametric propensity scores in terms of covariate balance, bias reduction, and 95% confidence interval coverage, regardless of the degree of non-linearity or non-additivity in the true propensity score model. When the sample or cluster sizes are small, however, nonparametric models may become more vulnerable to unmeasured cluster-level confounding and thus may not provide better performance compared to their parametric counterparts

Design of Titanium Alloys Insensitive to Thermal History for Additive Manufacturing

Powder bed fusion is the most common technology used for 3D printing, where thermal energy is used to selectively melt/sinter granular materials into solid shapes. The build platform is then lowered, more powder is added, and the process is repeated for the next layer to fully print the design. As a result, the built-up part is repeatedly heated. Therefore, materials that are not sensitive to thermal history are preferred for this process. The Ti–Zr system forms a continuous solid solution for both β- and α-phases. The presence of Fe in Ti alloys is inevitable; however, it provides some beneficial effects. The purpose of this work was to prepare Ti–Zr–Fe alloys and investigate their heat treatment behaviour. Ti-xmass%Zr-1mass%Fe alloys (x = 0, 5, 10) were prepared with arc melting. The formation of a solid solution of Zr in Ti was confirmed on the basis of X-ray diffraction peak shifts and hardening effects. A small amount of β-phase precipitation was suggested by the change in electrical resistivity after isothermal ageing at 673 and 773 K. However, no obvious phase or microstructural changes were observed. The laser scanning increased the volume of the precipitates and also coarsened them, but the effect was limited.Ueda M., Ting Hsuan C., Ikeda M., et al. Design of Titanium Alloys Insensitive to Thermal History for Additive Manufacturing. Crystals 13, 568 (2023); https://doi.org/10.3390/cryst13040568

The in Vivo Deleterious Effects of Ethanol

Oxidative stress, which is defined as an imbalance between pro-oxidants and antioxidants, has been demonstrated to mediate the pathogenesis of ethanol-induced injury. Senescence-accelerated mice prone P8 (SAMP8) is considered an excellent model for rode

Experimental and Numerical Study of Tsunami Wave Propagation and Run-Up on Sloping Beaches

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

Methods for Integrating Trials and Non-Experimental Data to Examine Treatment Effect Heterogeneity

Estimating treatment effects conditional on observed covariates can improve the ability to tailor treatments to particular individuals. Doing so effectively requires dealing with potential confounding, and also enough data to adequately estimate effect moderation. A recent influx of work has looked into estimating treatment effect heterogeneity using data from multiple randomized controlled trials and/or observational datasets. With many new methods available for assessing treatment effect heterogeneity using multiple studies, it is important to understand which methods are best used in which setting, how the methods compare to one another, and what needs to be done to continue progress in this field. This paper reviews these methods broken down by data setting: aggregate-level data, federated learning, and individual participant-level data. We define the conditional average treatment effect and discuss differences between parametric and nonparametric estimators, and we list key assumptions, both those that are required within a single study and those that are necessary for data combination. After describing existing approaches, we compare and contrast them and reveal open areas for future research. This review demonstrates that there are many possible approaches for estimating treatment effect heterogeneity through the combination of datasets, but that there is substantial work to be done to compare these methods through case studies and simulations, extend them to different settings, and refine them to account for various challenges present in real data