Covariate Balancing Inverse Probability Weights for Time-Varying Continuous Interventions

Huffman, Curtis; Gameren, Edwin van

doi:10.1515/jci-2017-0002

Cited by 7 publications

(3 citation statements)

References 24 publications

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“…First, only the time-invariant confounders can be adjusted using this approach, such that dynamic ICU covariates cannot be evaluated for confounding using this method. 66 Nevertheless, the DoC level was routinely ascertained off sedation per institutional guideline. Second, this confound-isolating approach requires categorical bins for continuous confounders such as age, such that residual confounding can be preserved within each category.…”

Section: Discussionmentioning

confidence: 99%

Resting-State Electroencephalography for Continuous, Passive Prediction of Coma Recovery After Acute Brain Injury

Zabihi

Rubin

Ack

et al. 2022

Preprint

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Accurately predicting emergence from disorders of consciousness (DoC) after acute brain injury can profoundly influence mortality, acute management, and rehabilitation planning. While recent advances in functional neuroimaging and stimulus-based EEG offer the potential to enrich shared decision-making, their procedural sophistication and expense limit widespread availability or repeated performance. We investigated continuous EEG (cEEG) within a passive, resting-state framework to provide continuously updated predictions of DoC recovery at 24-, 48-, and 72-hour prediction horizons. To develop robust, continuous prediction models from a large population of patients with acute brain injury (ABI), we leveraged a recently described pragmatic approach transforming Glasgow Coma Scale assessment sub-score combinations into frequently assessed DoC diagnoses: coma, vegetative state, minimally conscious state with or without language, and post-injury confusional or recovered states. We retrospectively identified consecutive patients undergoing cEEG following acute traumatic brain injury (TBI), subarachnoid hemorrhage (SAH), or intracerebral hemorrhage (ICH). Models continuously predicting DoC diagnosis for multiple prediction horizons were evaluated utilizing recent clinical assessments with or without cEEG information, which comprised a comprehensive EEG feature set of 288 time, frequency, and time-frequency characteristics computed from consecutive 5-minute EEG epochs, with 6 additional features capturing each EEG feature's temporal dynamics. Features were fed into a predictive model developed with cross-validation; the ordinal DoC diagnosis was discriminated using an ensemble of XGBoost binary classifiers. For 201 ABI patients (46 TBI, 140 SAH, 15 ICH patients comprising 27,280 cEEG-hours with concomitant clinical assessments), cEEG-augmented models accurately predicted the future DoC diagnosis at 24 hours (one-vs-rest AU-ROC, 92.4%; weighted-F1 84.1%), 48 hours (one-vs-rest AU-ROC=88%, weighted-F1=80%) and 72 hours (one-vs-rest AU-ROC=86.3%, weighted-F1=76.6%). Models were robust to utilizing different ordinal cut-points for the DoC prediction target and evaluating additional models derived from specific sub-populations using a confound-isolating cross-validation framework. The most robust features across evaluation configurations included Petrosian fractal dimension, relative power of high to low (gamma-beta to delta-alpha) EEG frequency spectra, energy within the 12-35 Hz frequency band in the short-time Fourier transform domain, and wavelet entropy. The cEEG-augmented model exceeded the performance of models using preceding clinical assessments, continuously predicting future DoC diagnosis with one-vs-rest AU-ROC in the range of 84.3-92.4% while utilizing approaches to limit overfitting. The proposed continuous, resting-state cEEG prediction method represents a promising tool to predict DoC emergence in ABI patients. Enabling these methods prospectively would represent a new paradigm of continuous prognostic monitoring for predicting coma recovery and assessing treatment response.

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Section: Discussionmentioning

confidence: 99%

Resting-State Electroencephalography for Continuous, Passive Prediction of Coma Recovery After Acute Brain Injury

Zabihi

Rubin

Ack

et al. 2022

Preprint

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“…Possible extensions include replacing the multivariate normal distribution with non-parametric or semi-parametric alternatives as has been done recently with univariate methods [24,45,49]. Other possible extensions include allowing for time-varying outcomes and exposures such has been recently proposed for CBGPS [19]. Additionally, SUTVA might be relaxed to test for potential geographic interference [48,50].…”

Section: Discussionmentioning

confidence: 99%

Causal inference for multiple continuous exposures via the multivariate generalized propensity score

Williams¹,

Crespi²

2020

Preprint

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The generalized propensity score (GPS) is an extension of the propensity score for use with quantitative or continuous exposures (e.g., dose of medication or years of education). Current GPS methods allow estimation of the dose-response relationship between a single continuous exposure and an outcome. However, in many real-world settings, there are multiple exposures occurring simultaneously that could be causally related to the outcome. We propose a multivariate GPS method (mvGPS) that allows estimation of a dose-response surface that relates the joint distribution of multiple continuous exposure variables to an outcome. The method involves generating weights under a multivariate normality assumption on the exposure variables. Focusing on scenarios with two exposure variables, we show via simulation that the mvGPS method can achieve balance across sets of confounders that may differ for different exposure variables and reduces bias of the treatment effect estimates under a variety of data generating scenarios. We apply the mvGPS method to an analysis of the joint effect of two types of intervention strategies to reduce childhood obesity rates.Keywords childhood obesity • dose response • observational studies • combination therapy • estimable regions • convex hull

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“…[21][22][23][24] Two decades ago, Imbens 25 and Imai and van Dyk 26 generalized the propensity score framework from the binary treatment setting to the setting of multi-valued treatments. Generalized propensity score (GPS) methods have since been proposed for the case of a single continuous treatment, [27][28][29][30] and multiple continuous [31][32][33] or multi-valued [34][35][36] treatments. Yang et al 37 proposed subclassification or matching on the GPS to estimate pairwise average causal effects, and Li and Li 38 introduce generalized overlap weights for pairwise comparisons that focus on the target population with the most covariate overlap across multiple levels of treatment.…”

Section: Introductionmentioning

confidence: 99%

Targeted learning in observational studies with multi‐valued treatments: An evaluation of antipsychotic drug treatment safety

Poulos,

Horvitz‐Lennon,

Zelevinsky

et al. 2024

Statistics in Medicine

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We investigate estimation of causal effects of multiple competing (multi‐valued) treatments in the absence of randomization. Our work is motivated by an intention‐to‐treat study of the relative cardiometabolic risk of assignment to one of six commonly prescribed antipsychotic drugs in a cohort of nearly 39 000 adults with serious mental illnesses. Doubly‐robust estimators, such as targeted minimum loss‐based estimation (TMLE), require correct specification of either the treatment model or outcome model to ensure consistent estimation; however, common TMLE implementations estimate treatment probabilities using multiple binomial regressions rather than multinomial regression. We implement a TMLE estimator that uses multinomial treatment assignment and ensemble machine learning to estimate average treatment effects. Our multinomial implementation improves coverage, but does not necessarily reduce bias, relative to the binomial implementation in simulation experiments with varying treatment propensity overlap and event rates. Evaluating the causal effects of the antipsychotics on 3‐year diabetes risk or death, we find a safety benefit of moving from a second‐generation drug considered among the safest of the second‐generation drugs to an infrequently prescribed first‐generation drug known for having low cardiometabolic risk.

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Covariate Balancing Inverse Probability Weights for Time-Varying Continuous Interventions

Cited by 7 publications

References 24 publications

Resting-State Electroencephalography for Continuous, Passive Prediction of Coma Recovery After Acute Brain Injury

Resting-State Electroencephalography for Continuous, Passive Prediction of Coma Recovery After Acute Brain Injury

Causal inference for multiple continuous exposures via the multivariate generalized propensity score

Targeted learning in observational studies with multi‐valued treatments: An evaluation of antipsychotic drug treatment safety

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