Accurate Estimates Of Effects Research Articles

TAD-SIE: sample size estimation for clinical randomized controlled trials using a Trend-Adaptive Design with a Synthetic-Intervention-Based Estimator

BackgroundPhase-3 clinical trials provide the highest level of evidence on drug safety and effectiveness needed for market approval by implementing large randomized controlled trials (RCTs). However, 30–40% of these trials fail mainly because such studies have inadequate sample sizes, stemming from the inability to obtain accurate initial estimates of average treatment effect parameters.MethodsTo remove this obstacle from the drug development cycle, we present a new algorithm called Trend-Adaptive Design with a Synthetic-Intervention-Based Estimator (TAD-SIE) that powers a parallel-group trial, a standard RCT design, by leveraging a state-of-the-art hypothesis testing strategy and a novel trend-adaptive design (TAD). Specifically, TAD-SIE uses synthetic intervention (SI) to estimate individual treatment effects and thereby simulate a cross-over design, which makes it easier for a trial to reach target power within trial constraints (e.g., sample size limits). To estimate sample sizes, TAD-SIE implements a new TAD tailored to SI given that using it violates assumptions under standard TADs. In addition, our TAD overcomes the ineffectiveness of standard TADs by allowing sample sizes to be increased across iterations without any condition while controlling significance level with futility stopping. Our TAD also introduces a hyperparameter that enables trial designers to trade off between accuracy and efficiency (sample size and number of iterations) of the solution.ResultsOn a real-world Phase-3 clinical RCT (i.e., a two-arm parallel-group superiority trial with an equal number of subjects per arm), TAD-SIE obtains operating points ranging between 63% to 84% power and 3% to 6% significance level in contrast to baseline algorithms that get at best 49% power and 6% significance level.ConclusionTAD-SIE is a superior TAD that can be used to reach typical target operating points but only for trials with rapidly measurable primary outcomes due to its sequential nature. The framework is useful to practitioners interested in leveraging the SI algorithm for their study design.

Long-term trends in the incidence of male breast cancer and nomogram for predicting survival in male breast cancer patients: a population-based epidemiologic study

Male breast cancer (MBC) is rare, and due to the absence of male-specific screening programs, many patients are diagnosed at advanced stages and older ages. This study aims to analyze the long-term trend of MBC incidence and develop a competing risk model to improve survival rates. MBC data from the Surveillance, Epidemiology, and End Results (SEER) database (1975–2019) were analyzed using the Age-Period-Cohort (APC) model to examine trends in age, period, and birth cohort effects of MBC incidence. A competing risk model was used to build a nomogram predicting breast cancer-specific survival (BCSS) for MBC patients, with model accuracy assessed using the concordance index (C-index) and calibration curves. These results were compared with the nomogram established by Cox model. Comparisons were made with traditional AJCC staging system using net reclassification improvement (NRI) and integrated discrimination improvement (IDI). APC analysis showed MBC incidence increases with age, while period and birth cohort effects were not significant. A total of 2,057 patients were included in the competing risk analysis, which identified factors like older age, estrogen receptor (ER) negative, progesterone receptor (PR) negative, and advanced AJCC stage as being associated with shorter survival. The competing risk model demonstrated excellent predictive ability, surpassing the AJCC staging system in both accuracy and clinical utility. In contrast, the Cox regression model overestimated the risk of endpoint events and failed to provide accurate effect estimates. The high incidence and poor prognosis of MBC in the elderly population emphasize the need for improved screening and early diagnosis in high-risk groups. Our competing risk model, compared to the Cox model, more accurately reflects real-world conditions. Additionally, we have developed a competing risk nomogram to assist in identifying high-risk individuals and guiding clinical decision-making.

Discovering Root Causal Genes with High Throughput Perturbations.

Root causal gene expression levels - or root causal genes for short - correspond to the initial changes to gene expression that generate patient symptoms as a downstream effect. Identifying root causal genes is critical towards developing treatments that modify disease near its onset, but no existing algorithms attempt to identify root causal genes from data. RNA-sequencing (RNA-seq) data introduces challenges such as measurement error, high dimensionality and non-linearity that compromise accurate estimation of root causal effects even with state-of-the-art approaches. We therefore instead leverage Perturb-seq, or high throughput perturbations with single cell RNA-seq readout, to learn the causal order between the genes. We then transfer the causal order to bulk RNA-seq and identify root causal genes specific to a given patient for the first time using a novel statistic. Experiments demonstrate large improvements in performance. Applications to macular degeneration and multiple sclerosis also reveal root causal genes that lie on known pathogenic pathways, delineate patient subgroups and implicate a newly defined omnigenic root causal model.

Disentangled latent factors for muti-cause treatment effect estimation

Abstract Existing methods estimate treatment effects from observational data and assume that covariates are all confounders. However, observed covariates may not directly represent confounding variables that influence both treatment and outcome. They always include variables that only affect the treatment or the outcome. In addition, for multi-dimensional binary treatments, disentangled methods are mainly designed for binary variables and ignore the impact of multi-cause treatment variables on the inference of latent factors. To address these two issues, based on variable decomposition and proxy inference, we propose the Disentangled Latent Factors for Multi-cause Treatment Estimation (DEMTE) algorithm. It utilizes an identifiable autoencoder to infer and disentangle latent factors based on the joint distribution of variables in observational data. DEMTE evaluates the treatment effect on the disentangled factors. Synthetic experiments and semi-synthetic experiments demonstrate the effectiveness of the inference and disentanglement techniques and our method achieves more accurate treatment effect estimation.

A Bayesian multivariate hierarchical model for developing a treatment benefit index using mixed types of outcomes

BackgroundPrecision medicine has led to the development of targeted treatment strategies tailored to individual patients based on their characteristics and disease manifestations. Although precision medicine often focuses on a single health outcome for individualized treatment decision rules (ITRs), relying only on a single outcome rather than all available outcomes information leads to suboptimal data usage when developing optimal ITRs.MethodsTo address this limitation, we propose a Bayesian multivariate hierarchical model that leverages the wealth of correlated health outcomes collected in clinical trials. The approach jointly models mixed types of correlated outcomes, facilitating the “borrowing of information” across the multivariate outcomes, and results in a more accurate estimation of heterogeneous treatment effects compared to using single regression models for each outcome. We develop a treatment benefit index, which quantifies the relative benefit of the experimental treatment over the control treatment, based on the proposed multivariate outcome model.ResultsWe demonstrate the strengths of the proposed approach through extensive simulations and an application to an international Coronavirus Disease 2019 (COVID-19) treatment trial. Simulation results indicate that the proposed method reduces the occurrence of erroneous treatment decisions compared to a single regression model for a single health outcome. Additionally, the sensitivity analyses demonstrate the robustness of the model across various study scenarios. Application of the method to the COVID-19 trial exhibits improvements in estimating the individual-level treatment efficacy (indicated by narrower credible intervals for odds ratios) and optimal ITRs.ConclusionThe study jointly models mixed types of outcomes in the context of developing ITRs. By considering multiple health outcomes, the proposed approach can advance the development of more effective and reliable personalized treatment.

Empirical Monte Carlo evidence on estimation of timing-of-events models

This article builds on the Empirical Monte Carlo simulation approach to study the estimation of Timing-of-Events (ToE) models. We exploit rich Swedish data of unemployed job seekers with information on participation in a training program to simulate placebo treatment durations. We first use these simulations to examine which covariates are key confounders to be included in dynamic selection models for training participation. The joint inclusion of specific short-term employment history indicators (notably, the share of time spent in employment), together with baseline socio-economic characteristics, regional and inflow timing information, is important to deal with selection bias. Next, we omit subsets of explanatory variables and estimate ToE models with discrete distributions for the ensuing systematic unobserved heterogeneity. In many cases, the ToE approach provides accurate effect estimates, especially if time-varying variation in the unemployment rate of the local labor market is taken into account. However, assuming too many or too few support points for unobserved heterogeneity may lead to large biases. Information criteria, in particular those penalizing parameter abundance, are useful to select the number of support points. A comparison with other duration models shows that a Stratified Cox model performs well with abundant multiple spells but less well when multiple spells are uncommon. The standard Cox regression model performs poorly in all configurations as it is unable to account for unobserved heterogeneity.

Discovering Ancestral Instrumental Variables for Causal Inference From Observational Data.

Instrumental variable (IV) is a powerful approach to inferring the causal effect of a treatment on an outcome of interest from observational data even when there exist latent confounders between the treatment and the outcome. However, existing IV methods require that an IV is selected and justified with domain knowledge. An invalid IV may lead to biased estimates. Hence, discovering a valid IV is critical to the applications of IV methods. In this article, we study and design a data-driven algorithm to discover valid IVs from data under mild assumptions. We develop the theory based on partial ancestral graphs (PAGs) to support the search for a set of candidate ancestral IVs (AIVs), and for each possible AIV, the identification of its conditioning set. Based on the theory, we propose a data-driven algorithm to discover a pair of IVs from data. The experiments on synthetic and real-world datasets show that the developed IV discovery algorithm estimates accurate estimates of causal effects in comparison with the state-of-the-art IV-based causal effect estimators.

CutFEM-based MEG forward modeling improves source separability and sensitivity to quasi-radial sources: A somatosensory group study.

Source analysis of magnetoencephalography (MEG) data requires the computation of the magnetic fields induced by current sources in the brain. This so-called MEG forward problem includes an accurate estimation of the volume conduction effects in the human head. Here, we introduce the Cut finite element method (CutFEM) for the MEG forward problem. CutFEM's meshing process imposes fewer restrictions on tissue anatomy than tetrahedral meshes while being able to mesh curved geometries contrary to hexahedral meshing. To evaluate the new approach, we compare CutFEM with a boundary element method (BEM) that distinguishes three tissue compartments and a 6-compartment hexahedral FEM in an n = 19 group study of somatosensory evoked fields (SEF). The neural generators of the 20 ms post-stimulus SEF components (M20) are reconstructed using both an unregularized and a regularized inversion approach. Changing the forward model resulted in reconstruction differences of about 1 centimeter in location and considerable differences in orientation. The tested 6-compartment FEM approaches significantly increase the goodness of fit to the measured data compared with the 3-compartment BEM. They also demonstrate higher quasi-radial contributions for sources below the gyral crowns. Furthermore, CutFEM improves source separability compared with both other approaches. We conclude that head models with 6 compartments rather than 3 and the new CutFEM approach are valuable additions to MEG source reconstruction, in particular for sources that are predominantly radial.

Random forests for the analysis of matched case–control studies

BackgroundConditional logistic regression trees have been proposed as a flexible alternative to the standard method of conditional logistic regression for the analysis of matched case–control studies. While they allow to avoid the strict assumption of linearity and automatically incorporate interactions, conditional logistic regression trees may suffer from a relatively high variability. Further machine learning methods for the analysis of matched case–control studies are missing because conventional machine learning methods cannot handle the matched structure of the data.ResultsA random forest method for the analysis of matched case–control studies based on conditional logistic regression trees is proposed, which overcomes the issue of high variability. It provides an accurate estimation of exposure effects while being more flexible in the functional form of covariate effects. The efficacy of the method is illustrated in a simulation study and within an application to real-world data from a matched case–control study on the effect of regular participation in cervical cancer screening on the development of cervical cancer.ConclusionsThe proposed random forest method is a promising add-on to the toolbox for the analysis of matched case–control studies and addresses the need for machine-learning methods in this field. It provides a more flexible approach compared to the standard method of conditional logistic regression, but also compared to conditional logistic regression trees. It allows for non-linearity and the automatic inclusion of interaction effects and is suitable both for exploratory and explanatory analyses.

A new two-step estimation approach for retrieving surface urban heat island intensity and footprint based on urban-rural temperature gradients

Past decades have seen substantial efforts devoted to observing, assessing, and documenting the urban heat island (UHI) phenomenon. However, the discrepant criteria of non-urban references and ambiguous distinctions between urban and rural landscapes pose great challenges in measuring UHI magnitudes and spatial extents. This study goes beyond the conventional urban-rural dichotomy and introduces a new two-step approach based on the continuous transition of thermal environments along urban-rural gradients. The approach is applied to quantify Surface UHI (SUHI) intensities and footprints across 283 Chinese cities from 2005 to 2018 using multiple satellite-derived data sources. The results include: 1) The two-step approach avoids the limitations in subjective rural reference selections and provides reliable quantification of SUHI characteristics in various cities over time. 2) The SUHI footprints extracted by our approach are more reasonable than those obtained by two existing methods, with footprint ratios generally ranging within 0 − 6 times the urban area. 3) The two-step approach provides more concentrated estimates of SUHI intensity. Typically, ignoring heat sources in non-built-up areas can cause an overestimation of SUHI effect and misidentification of remote rural areas with high temperatures. Overall, the two-step approach enables more accurate estimates of SUHI effect, thereby facilitating policy-making for SUHI mitigation.

On the inaccuracies of point-particle approach for char conversion modeling

Char conversion is a complex phenomenon that involves not only heterogeneous reactions but also external and internal heat and mass transfer. Reactor-scale simulations often use a point-particle approach (PP approach) as sub-models for char conversion because of its low computational cost. Despite a number of simplifications involved in the PP approach, there are very few studies that systematically investigate the inaccuracies of the PP approach. This study aims to compare and identify when and why the PP approach deviates from resolved-particle simulations (RP approach). Simulations have been carried out for CO2 gasification of a char particle under zone II conditions (i.e., pore diffusion control) using both PP and RP approaches. Results showed significant deviations between the two approaches for the effectiveness factor, gas compositions, particle temperature, and particle diameter. The most significant sources of inaccuracies in the PP approach are negligence of the non-uniform temperature inside the particle and the inability to accurately model external heat transfer. Under the conditions with low effectiveness factors, the errors of intra-particle processes were dominant while the errors of external processes became dominant when effectiveness factors were close to unity. Because it assumes uniform internal temperature, the models applying the PP approach always predict higher effectiveness factors than the RP approach, despite its accurate estimation of intra-particle mass diffusion effects. As a consequence, the PP approach failed to predict the particle size changes accurately. Meanwhile, no conventional term for external heat transfer could explain the inaccuracy, indicating the importance of other sources of errors such as 2D/3D asymmetry or penetration of external flows inside the particles.

Approaches to estimate bidirectional causal effects using Mendelian randomization with application to body mass index and fasting glucose.

Mendelian randomization (MR) is an epidemiological framework using genetic variants as instrumental variables (IVs) to examine the causal effect of exposures on outcomes. Statistical methods based on unidirectional MR (UMR) are widely used to estimate the causal effects of exposures on outcomes in observational studies. To estimate the bidirectional causal effects between two phenotypes, investigators have naively applied UMR methods separately in each direction. However, bidirectional causal effects between two phenotypes create a feedback loop that biases the estimation when UMR methods are naively applied. To overcome this limitation, we proposed two novel approaches to estimate bidirectional causal effects using MR: BiRatio and BiLIML, which are extensions of the standard ratio, and limited information maximum likelihood (LIML) methods, respectively. We compared the performance of the two proposed methods with the naive application of UMR methods through extensive simulations of several scenarios involving varying numbers of strong and weak IVs. Our simulation results showed that when multiple strong IVs are used, the proposed methods provided accurate bidirectional causal effect estimation in terms of median absolute bias and relative median absolute bias. Furthermore, compared to the BiRatio method, the BiLIML method provided a more accurate estimation of causal effects when weak IVs were used. Therefore, based on our simulations, we concluded that the BiLIML should be used for bidirectional causal effect estimation. We applied the proposed methods to investigate the potential bidirectional relationship between obesity and diabetes using the data from the Multi-Ethnic Study of Atherosclerosis cohort. We used body mass index (BMI) and fasting glucose (FG) as measures of obesity and type 2 diabetes, respectively. Our results from the BiLIML method revealed the bidirectional causal relationship between BMI and FG in across all racial populations. Specifically, in the White/Caucasian population, a 1 kg/m2 increase in BMI increased FG by 0.70 mg/dL (95% confidence interval [CI]: 0.3517-1.0489; p = 8.43×10-5), and 1 mg/dL increase in FG increased BMI by 0.10 kg/m2 (95% CI: 0.0441-0.1640; p = 6.79×10-4). Our study provides novel findings and quantifies the effect sizes of the bidirectional causal relationship between BMI and FG. However, further studies are needed to understand the biological and functional mechanisms underlying the bidirectional pathway.

Testing the feasibility of quantifying change in agricultural soil carbon stocks through empirical sampling

There is disagreement about the potential for regenerative management practices to sequester sufficient soil organic carbon (SOC) to help mitigate climate change. Measuring change in SOC stocks following practice adoption at the grain of farm fields, within the extent of regional agriculture, could help resolve this disagreement. Yet sampling demands to quantify change are considered infeasible primarily because within-field variation in stock sizes is thought to obscure accurate quantification of management effects on incremental SOC accrual. We evaluate this ‘infeasibility assumption’ using high-density (e.g., 0.1 ha sample–1), within-field, sampling data from 45 cropland fields inventoried for SOC. We explore how more typical within-field sampling densities, as well as field numbers and magnitude of simulated change in SOC stocks, impacts the ability to accurately quantify management effects on SOC change. We find that (1) stock change estimates for individual fields are inaccurate and variable, where marked losses and gains in SOC stocks are frequently estimated even when no change has occurred. Higher sampling densities (e.g., 1.2 versus 4.0 ha sample–1) narrow the range of estimated stock changes but inaccuracies remain large. (2) The accuracy of stock change estimates at the project level (i.e., multiple fields) were similarly sensitive to sampling density. In contrast to individual fields, however, higher sampling densities, as well as a greater number of fields (e.g., 30), generated robust and accurate, mean project-level estimates of carbon accrual, with ∼ 80 % of the estimates falling within 20 % of the simulated stock change. Yet such monitoring designs do not account for dynamic baselines, which necessitates measurement of stock changes in control, non-regenerative fields. We find (3) that higher sampling densities (e.g., 1.2 versus 4.0 ha sample–1), field numbers (e.g., 30 versus 10 pairs of fields), and magnitudes of simulated SOC stock change (+3 and +5 versus +1 Mg C ha−1 10 y–1) are then collectively required to make accurate estimates of management effects on stock change at the project level. The simulated effect sizes that could be consistently detected under these conditions included rates of SOC accrual considered achievable and meaningful for climate mitigation (e.g., 3 Mg C ha−1 10 y–1), with field numbers and sampling densities that are reasonable given current sampling methods. Our findings reveal the potential to use empirical approaches to accurately quantify, at project scales, SOC stock responses to practice change. We provide recommendations for data that government, farmer and corporate entities should measure and share to build confidence in the effects of regenerative practices, freeing the SOC debate from overreliance on theory and data collected at scales mismatched with agricultural management.

The Effects of the 2021 Child Tax Credit on Food Insecurity and Financial Hardship

We review the literature on the expansion of the Child Tax Credit in 2021, as it relates to food and financial hardship among households with children in the U.S. Extant scholarship consistently finds that receipt of the expanded tax credit is associated with an increase in food purchases and declines in food insufficiency and food insecurity. The effects of the tax credit expansion also vary by the socioeconomic characteristics of families. There are important differences, though, in effect sizes across studies, indicating that data sources, timeframe of analysis, and the way in which food hardship is measured all matter to the accurate estimation of effects. The effect of the credit on financial hardship is less conclusive, with the literature finding generally insignificant effects on measures such as difficulty paying rent or bills.

Testing the feasibility of quantifying change in agricultural soil carbon stocks through empirical sampling.

Abstract There is disagreement about the potential for regenerative management practices to sequester sufficient soil organic carbon (SOC) to help mitigate climate change. Measuring change in SOC stocks following practice adoption at the grain of farm fields, within the extent of regional agriculture, could help resolve this disagreement. Yet sampling demands to quantify change are considered infeasible primarily because within-field variation in stock sizes is thought to obscure accurate quantification of management effects on incremental SOC accrual. We evaluate this 'infeasibility assumption' using high-density (e.g., 0.1 ha sample -1 ), within-field, sampling data from 45 cropland fields inventoried for SOC. We explore how more typical within-field sampling densities, as well as field numbers and magnitude of simulated change in SOC stocks, impacts the ability to accurately quantify management effects on SOC change. We find that (1) stock change estimates for individual fields are inaccurate and variable, where marked losses and gains in SOC stocks are frequently estimated even when no change has occurred. Higher sampling densities (e.g., 1.2 versus 4.0 ha sample -1 ) narrow the range of estimated stock changes but inaccuracies remain large. (2) The accuracy of stock change estimates at the project level (i.e., multiple fields) were similarly sensitive to sampling density. In contrast to individual fields, however, higher sampling densities, as well as a greater number of fields (e.g., 30), generated robust and accurate, mean project-level estimates of carbon accrual, with ~80% of the estimates falling within 20% of the simulated stock change. Yet such monitoring designs do not account for dynamic baselines, which necessitates measurement of stock changes in control, non-regenerative fields. We find (3) that higher sampling densities (e.g., 1.2 versus 4.0 ha sample -1 ), field numbers (e.g., 30 vs. 10 pairs of fields), and magnitudes of simulated SOC stock change (+3 and +5 versus +1 Mg C ha -1 10 y -1 ) are then collectively required to make accurate estimates of management effects on stock change at the project level. The simulated effect sizes that could be consistently detected under these conditions included rates of SOC accrual considered achievable and meaningful for climate mitigation (e.g., 3 Mg C ha -1 10 y -1 ), with field numbers and sampling densities that are reasonable given current sampling methods. Our findings reveal the potential to use empirical approaches to accurately quantify, at project scales, SOC stock responses to practice change. We provide recommendations for data that government, farmer and corporate entities should measure and share to build confidence in the effects of regenerative practices, freeing the SOC debate from overreliance on theory and data collected at scales mismatched with agricultural management.

CutFEM forward modeling for EEG source analysis.

Source analysis of Electroencephalography (EEG) data requires the computation of the scalp potential induced by current sources in the brain. This so-called EEG forward problem is based on an accurate estimation of the volume conduction effects in the human head, represented by a partial differential equation which can be solved using the finite element method (FEM). FEM offers flexibility when modeling anisotropic tissue conductivities but requires a volumetric discretization, a mesh, of the head domain. Structured hexahedral meshes are easy to create in an automatic fashion, while tetrahedral meshes are better suited to model curved geometries. Tetrahedral meshes, thus, offer better accuracy but are more difficult to create. We introduce CutFEM for EEG forward simulations to integrate the strengths of hexahedra and tetrahedra. It belongs to the family of unfitted finite element methods, decoupling mesh and geometry representation. Following a description of the method, we will employ CutFEM in both controlled spherical scenarios and the reconstruction of somatosensory-evoked potentials. CutFEM outperforms competing FEM approaches with regard to numerical accuracy, memory consumption, and computational speed while being able to mesh arbitrarily touching compartments. CutFEM balances numerical accuracy, computational efficiency, and a smooth approximation of complex geometries that has previously not been available in FEM-based EEG forward modeling.

Causal Disentangled Recommendation against User Preference Shifts

Recommender systems easily face the issue of user preference shifts. User representations will become out-of-date and lead to inappropriate recommendations if user preference has shifted over time. To solve the issue, existing work focuses on learning robust representations or predicting the shifting pattern. There lacks a comprehensive view to discover the underlying reasons for user preference shifts. To understand the preference shift, we abstract a causal graph to describe the generation procedure of user interaction sequences. Assuming user preference is stable within a short period, we abstract the interaction sequence as a set of chronological environments. From the causal graph, we find that the changes of some unobserved factors (e.g., becoming pregnant) cause preference shifts between environments. Besides, the fine-grained user preference over item categories sparsely affects the interactions with different items. Inspired by the causal graph, our key considerations to handle preference shifts lie in modeling the interaction generation procedure by: (1) capturing the preference shifts across environments for accurate preference prediction and (2) disentangling the sparse influence from user preference to interactions for accurate effect estimation of preference. To this end, we propose a Causal Disentangled Recommendation (CDR) framework, which captures preference shifts via a temporal variational autoencoder and learns the sparse influence from multiple environments. Specifically, an encoder is adopted to infer the unobserved factors from user interactions while a decoder is to model the interaction generation process. Besides, we introduce two learnable matrices to disentangle the sparse influence from user preference to interactions. Last, we devise a multi-objective loss to optimize CDR. Extensive experiments on three datasets show the superiority of CDR in enhancing the generalization ability under user preference shifts.

Multi-response Mendelian randomization: Identification of shared and distinct exposures for multimorbidity and multiple related disease outcomes

The existing framework of Mendelian randomization (MR) infers the causal effect of one or multiple exposures on one single outcome. It is not designed to jointly model multiple outcomes, as would be necessary to detect causes of more than one outcome and would be relevant to model multimorbidity or other related disease outcomes. Here, we introduce multi-response Mendelian randomization (MR2), an MR method specifically designed for multiple outcomes to identify exposures that cause more than one outcome or, conversely, exposures that exert their effect on distinct responses. MR2 uses a sparse Bayesian Gaussian copula regression framework to detect causal effects while estimating the residual correlation between summary-level outcomes, i.e., the correlation that cannot be explained by the exposures, and vice versa. We show both theoretically and in a comprehensive simulation study how unmeasured shared pleiotropy induces residual correlation between outcomes irrespective of sample overlap. We also reveal how non-genetic factors that affect more than one outcome contribute to their correlation. We demonstrate that by accounting for residual correlation, MR2 has higher power to detect shared exposures causing more than one outcome. It also provides more accurate causal effect estimates than existing methods that ignore the dependence between related responses. Finally, we illustrate how MR2 detects shared and distinct causal exposures for five cardiovascular diseases in two applications considering cardiometabolic and lipidomic exposures and uncovers residual correlation between summary-level outcomes reflecting known relationships between cardiovascular diseases.

The unrecognized role of fidelity in effectiveness-implementation hybrid trials: simulation study and guidance for implementation researchers

BackgroundEffectiveness-implementation hybrid designs are a relatively new approach to evaluate efficacious interventions in real-world settings while concurrently gathering information on the implementation. Intervention fidelity can significantly influence the effectiveness of an intervention during implementation. However little guidance exists for applied researchers conducting effectiveness-implementation hybrid trials regarding the impact of fidelity on intervention effects and power.MethodsWe conducted a simulation study based on parameters from a clinical example study. For the simulation, we explored parallel and stepped-wedge cluster randomized trials (CRTs) and hypothetical patterns of fidelity increase during implementation: slow, linear, and fast. Based on fixed design parameters, i.e., the number of clusters (C = 6), time points (T = 7), and patients per cluster (n = 10) we used linear mixed models to estimate the intervention effect and calculated the power for different fidelity patterns. Further, we conducted a sensitivity analysis to compare outcomes based on different assumptions for the intracluster-correlation coefficient and the cluster size.ResultsEnsuring high fidelity from the beginning is central to achieve accurate intervention effect estimates in stepped-wedge and parallel CRTs. The importance of high fidelity in the earlier stages is more emphasized in stepped-wedge designs than in parallel CRTs. In contrast, if the increase of fidelity is too slow despite relatively high starting levels, the study will likely be underpowered and the intervention effect estimates will also be biased. This effect is more accentuated in parallel CRTs, here reaching 100% fidelity within the next measurement points is crucial.ConclusionsThis study discusses the importance of intervention fidelity for the study`s power and highlights different recommendations to deal with low fidelity in parallel and stepped-wedge CRTs from a design perspective. Applied researchers should consider the detrimental effect of low fidelity in their evaluation design. Overall, there are fewer options to adjust the trial design after the fact in parallel CRT as compared to stepped-wedge CRTs. Particular emphasis should be placed on the selection of contextually relevant implementation strategies.

Verification, calibration, and validation of stall delay models using NREL phase VI and MEXICO data

An accurate estimation of rotational effects is critical during the preliminary design of wind turbines. For this purpose, different stall delay models were developed based on the centrifugal pumping mechanism. However, their generality is not yet thoroughly evaluated. In this work, we investigated the causal relationship between the radial flow, the pressure reduction, and forces augmentation. Three stall delay models, which represent different solutions of the centrifugal pumping mechanism, were verified and modified to accurately predict the radial flow, the pressure coefficient, and normal and tangential force coefficients. Then, the three modified stall delay models were calibrated using radial flow data available from the literature. Finally, they were validated against the experimental data of the NREL phase VI and MEXICO rotors. The results showed that the centrifugal pumping produces a small chordwise pressure gradient in the separated boundary layer, which produces a small augmentation in the normal and tangential force coefficients. In contrast, the measured pressure coefficient and the normal and tangential force coefficients showed a large augmentation compared to the three modified stall delay models. Consequently, the lack of generality of current stall delay models is mainly due to the centrifugal pumping assumption. Furthermore, the verification and calibration of these stall delay models allowed us to isolate large errors in the model's output due to the model's assumptions. Thus, the importance of a rigorous verification and calibration before performing the validation.