The Triangulation WIthin a STudy (TWIST) framework for causal inference within pharmacogenetic research.
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ABSTRACT:
SUBMITTER: Bowden J
PROVIDER: S-EPMC8452063 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ObjectivesTarget trial emulation is an intuitive design framework that encourages investigators to formulate their comparative effectiveness research (CER) question as a hypothetical randomised controlled trial (RCT). Our aim was to systematically review CER studies in rheumatoid arthritis (RA) to provide examples of design limitations that could be avoided using target trial emulation, and how these limitations might introduce bias.MethodsWe searched for head-to-head CER studies of biologic disease modifying anti-rheumatic drugs (DMARDs) in RA. Study designs were reviewed for seven components of the target trial emulation framework: eligibility criteria, treatment strategies, assignment procedures, follow-up period, outcome, causal contrasts of interest (ie, intention-to-treat (ITT) or per-protocol effect) and analysis plan. Hypothetical trials corresponding to the reported methods were assessed to identify design limitations that would have been avoided with an explicit target trial protocol. Analysis of the primary effectiveness outcome was chosen where multiple analyses were performed.ResultsWe found 31 CER studies, of which 29 (94%) had at least one design limitation belonging to seven components. The most common limitations related to: (1) eligibility criteria: 19/31 (61%) studies used post-baseline information to define baseline eligibility; (2) causal contrasts: 25 (81%) did not define whether ITT or per-protocol effects were estimated and (3) assignment procedures: 13 (42%) studies did not account for confounding by indication or relied solely on statistical confounder selection.ConclusionsDesign limitations were found in 94% of observational CER studies in RA. Target trial emulation is a structured approach for designing observational CER studies that helps to avoid potential sources of bias.
| S-EPMC8693471 | biostudies-literature
Project description:Background: T ransformer-based language models have delivered clear improvements in a wide range of natural language processing (NLP) tasks. However, those models have a significant limitation; specifically, they cannot infer causality, a prerequisite for deployment in pharmacovigilance, and health care. Therefore, these transformer-based language models should be developed to infer causality to address the key question of the cause of a clinical outcome. Results: In this study, we propose an innovative causal inference model-InferBERT, by integrating the A Lite Bidirectional Encoder Representations from Transformers (ALBERT) and Judea Pearl's Do-calculus to establish potential causality in pharmacovigilance. Two FDA Adverse Event Reporting System case studies, including Analgesics-related acute liver failure and Tramadol-related mortalities, were employed to evaluate the proposed InferBERT model. The InferBERT model yielded accuracies of 0.78 and 0.95 for identifying Analgesics-related acute liver failure and Tramadol-related death cases, respectively. Meanwhile, the inferred causes of the two clinical outcomes, (i.e. acute liver failure and death) were highly consistent with clinical knowledge. Furthermore, inferred causes were organized into a causal tree using the proposed recursive do-calculus algorithm to improve the model's understanding of causality. Moreover, the high reproducibility of the proposed InferBERT model was demonstrated by a robustness assessment. Conclusion: The empirical results demonstrated that the proposed InferBERT approach is able to both predict clinical events and to infer their causes. Overall, the proposed InferBERT model is a promising approach to establish causal effects behind text-based observational data to enhance our understanding of intrinsic causality. Availability and implementation: The InferBERT model and preprocessed FAERS data sets are available on GitHub at https://github.com/XingqiaoWang/DeepCausalPV-master.
| S-EPMC8202286 | biostudies-literature
Project description:A central question in neuroscience is how self-organizing dynamic interactions in the brain emerge on their relatively static structural backbone. Due to the complexity of spatial and temporal dependencies between different brain areas, fully comprehending the interplay between structure and function is still challenging and an area of intense research. In this paper we present a graph neural network (GNN) framework, to describe functional interactions based on the structural anatomical layout. A GNN allows us to process graph-structured spatio-temporal signals, providing a possibility to combine structural information derived from diffusion tensor imaging (DTI) with temporal neural activity profiles, like that observed in functional magnetic resonance imaging (fMRI). Moreover, dynamic interactions between different brain regions discovered by this data-driven approach can provide a multi-modal measure of causal connectivity strength. We assess the proposed model's accuracy by evaluating its capabilities to replicate empirically observed neural activation profiles, and compare the performance to those of a vector auto regression (VAR), like that typically used in Granger causality. We show that GNNs are able to capture long-term dependencies in data and also computationally scale up to the analysis of large-scale networks. Finally we confirm that features learned by a GNN can generalize across MRI scanner types and acquisition protocols, by demonstrating that the performance on small datasets can be improved by pre-training the GNN on data from an earlier study. We conclude that the proposed multi-modal GNN framework can provide a novel perspective on the structure-function relationship in the brain. Accordingly this approach appears to be promising for the characterization of the information flow in brain networks.
| S-EPMC8044149 | biostudies-literature
Project description:We propose a Bayesian non-parametric (BNP) framework for estimating causal effects of mediation, the natural direct, and indirect, effects. The strategy is to do this in two parts. Part 1 is a flexible model (using BNP) for the observed data distribution. Part 2 is a set of uncheckable assumptions with sensitivity parameters that in conjunction with Part 1 allows identification and estimation of the causal parameters and allows for uncertainty about these assumptions via priors on the sensitivity parameters. For Part 1, we specify a Dirichlet process mixture of multivariate normals as a prior on the joint distribution of the outcome, mediator, and covariates. This approach allows us to obtain a (simple) closed form of each marginal distribution. For Part 2, we consider two sets of assumptions: (a) the standard sequential ignorability (Imai et al., 2010) and (b) weakened set of the conditional independence type assumptions introduced in Daniels et al. (2012) and propose sensitivity analyses for both. We use this approach to assess mediation in a physical activity promotion trial.
| S-EPMC5288310 | biostudies-literature
Project description:This work proposes a new approach to determine the spatial location and orientation of an object using measurements performed on the object itself. The on-board triangulation algorithm we outline could be implemented in lieu of, or in addition to, well-known alternatives such as Global Positioning System (GPS) or standard triangulation, since both of these correspond to significantly different geometric pictures and necessitate different hardware and algorithms. We motivate the theory by describing situations in which on-board triangulation would be useful and even preferable to standard methods. The on-board triangulation algorithm we outline involves utilizing dumb beacons which broadcast omnidirectional single frequency radio waves, and smart antenna arrays on the object itself to infer the direction of the beacon signals, which may be used for onboard calculation of the position and orientation of the object. Numerical examples demonstrate the utility of the method and its noise tolerance.
| S-EPMC5482487 | biostudies-literature
Project description:In this issue, Naimi et al. (Am J Epidemiol. XXXX;XXX(XX):XXXX-XXXX) discuss a critical topic in public health and beyond: obtaining valid statistical inference when using machine learning in causal research. In doing so, the authors review recent prominent methodological work and recommend: (i) double robust estimators, such as targeted maximum likelihood estimation (TMLE); (ii) ensemble methods, such as Super Learner, to combine predictions from a diverse library of algorithms, and (iii) sample-splitting to reduce bias and improve inference. We largely agree with these recommendations. In this commentary, we highlight the critical importance of the Super Learner library. Specifically, in both simulation settings considered by the authors, we demonstrate that low bias and valid statistical inference can be achieved using TMLE without sample-splitting and with a Super Learner library that excludes tree-based methods but includes regression splines. Whether extremely data-adaptive algorithms and sample-splitting are needed depends on the specific problem and should be informed by simulations reflecting the specific application. More research is needed on practical recommendations for selecting among these options in common situations arising in epidemiology.
| S-EPMC10472326 | biostudies-literature
Project description:Causality plays an essential role in multiple scientific disciplines, including the social, behavioral, and biological sciences and portions of statistics and artificial intelligence. Manual-based causality assessment from a large number of free text-based documents is very time-consuming, labor-intensive, and sometimes even impractical. Herein, we proposed a general causal inference framework named DeepCausality to empirically estimate the causal factors for suspected endpoints embedded in the free text. The proposed DeepCausality seamlessly incorporates AI-powered language models, named entity recognition and Judea Pearl's Do-calculus, into a general framework for causal inference to fulfill different domain-specific applications. We exemplified the utility of the proposed DeepCausality framework by employing the LiverTox database to estimate idiosyncratic drug-induced liver injury (DILI)-related causal terms and generate a knowledge-based causal tree for idiosyncratic DILI patient stratification. Consequently, the DeepCausality yielded a prediction performance with an accuracy of 0.92 and an F-score of 0.84 for the DILI prediction. Notably, 90% of causal terms enriched by the DeepCausality were consistent with the clinical causal terms defined by the American College of Gastroenterology (ACG) clinical guideline for evaluating suspected idiosyncratic DILI (iDILI). Furthermore, we observed a high concordance of 0.91 between the iDILI severity scores generated by DeepCausality and domain experts. Altogether, the proposed DeepCausality framework could be a promising solution for causality assessment from free text and is publicly available through https://github.com/XingqiaoWang/https-github.com-XingqiaoWang-DeepCausality-LiverTox.
| S-EPMC9763446 | biostudies-literature
Project description:Often, a community becomes alarmed when high rates of cancer are noticed, and residents suspect that the cancer cases could be caused by a known source of hazard. In response, the US Centers for Disease Control and Prevention recommend that departments of health perform a standardized incidence ratio (SIR) analysis to determine whether the observed cancer incidence is higher than expected. This approach has several limitations that are well documented in the existing literature. In this paper we propose a novel causal inference framework for cancer cluster investigations, rooted in the potential outcomes framework. Assuming that a source of hazard representing a potential cause of increased cancer rates in the community is identified a priori, we focus our approach on a causal inference estimand which we call the causal SIR (cSIR). The cSIR is a ratio defined as the expected cancer incidence in the exposed population divided by the expected cancer incidence for the same population under the (counterfactual) scenario of no exposure. To estimate the cSIR we need to overcome two main challenges: 1) identify unexposed populations that are as similar as possible to the exposed one to inform estimation of the expected cancer incidence under the counterfactual scenario of no exposure, and 2) publicly available data on cancer incidence for these unexposed populations are often available at a much higher level of spatial aggregation (e.g. county) than what is desired (e.g. census block group). We overcome the first challenge by relying on matching. We overcome the second challenge by building a Bayesian hierarchical model that borrows information from other sources to impute cancer incidence at the desired level of spatial aggregation. In simulations, our statistical approach was shown to provide dramatically improved results, i.e., less bias and better coverage, than the current approach to SIR analyses. We apply our proposed approach to investigate whether trichloroethylene vapor exposure has caused increased cancer incidence in Endicott, New York.
| S-EPMC8276584 | biostudies-literature
Project description:BackgroundFour models are commonly used to adjust for energy intake when estimating the causal effect of a dietary component on an outcome: 1) the "standard model" adjusts for total energy intake, 2) the "energy partition model" adjusts for remaining energy intake, 3) the "nutrient density model" rescales the exposure as a proportion of total energy, and 4) the "residual model" indirectly adjusts for total energy by using a residual. It remains underappreciated that each approach evaluates a different estimand and only partially accounts for confounding by common dietary causes.ObjectivesWe aimed to clarify the implied causal estimand and interpretation of each model and evaluate their performance in reducing dietary confounding.MethodsSemiparametric directed acyclic graphs and Monte Carlo simulations were used to identify the estimands and interpretations implied by each model and explore their performance in the absence or presence of dietary confounding.ResultsThe "standard model" and the mathematically identical "residual model" estimate the average relative causal effect (i.e., a "substitution" effect) but provide biased estimates even in the absence of confounding. The "energy partition model" estimates the total causal effect but only provides unbiased estimates in the absence of confounding or when all other nutrients have equal effects on the outcome. The "nutrient density model" has an obscure interpretation but attempts to estimate the average relative causal effect rescaled as a proportion of total energy. Accurate estimates of both the total and average relative causal effects may instead be derived by simultaneously adjusting for all dietary components, an approach we term the "all-components model."ConclusionsLack of awareness of the estimand differences and accuracy of the 4 modeling approaches may explain some of the apparent heterogeneity among existing nutritional studies. This raises serious questions regarding the validity of meta-analyses where different estimands have been inappropriately pooled.
| S-EPMC8755101 | biostudies-literature
Project description:Population health researchers from different fields often address similar substantive questions but rely on different study designs, reflecting their home disciplines. This is especially true in studies involving causal inference, for which semantic and substantive differences inhibit interdisciplinary dialogue and collaboration. In this paper, we group nonrandomized study designs into two categories: those that use confounder-control (such as regression adjustment or propensity score matching) and those that rely on an instrument (such as instrumental variables, regression discontinuity, or differences-in-differences approaches). Using the Shadish, Cook, and Campbell framework for evaluating threats to validity, we contrast the assumptions, strengths, and limitations of these two approaches and illustrate differences with examples from the literature on education and health. Across disciplines, all methods to test a hypothesized causal relationship involve unverifiable assumptions, and rarely is there clear justification for exclusive reliance on one method. Each method entails trade-offs between statistical power, internal validity, measurement quality, and generalizability. The choice between confounder-control and instrument-based methods should be guided by these tradeoffs and consideration of the most important limitations of previous work in the area. Our goals are to foster common understanding of the methods available for causal inference in population health research and the tradeoffs between them; to encourage researchers to objectively evaluate what can be learned from methods outside one's home discipline; and to facilitate the selection of methods that best answer the investigator's scientific questions.
| S-EPMC6926350 | biostudies-literature