Heterogeneous Causal Effect of Polysubstance Usage on Drug Overdose (Preprint)

2021 ◽  
Author(s):  
Mohammad Arif Ul Alam
Keyword(s):  
Drug Overdose ◽  
Causal Effect ◽  
Association Studies ◽  
Causal Models ◽  
Causal Effects ◽  
Opioid Overdose ◽  
Critical Question ◽  
Electronic Health Record Data ◽  
Drug Usage ◽  
Health Crisis

BACKGROUND Drug overdose has become a public health crisis in United States with devastating consequences. However, most of the drug overdose incidences are the consequence of recitative polysubstance usage over a defined period of time which can be happened by either the intentional usage of required drug with other drugs or by accident. Thus, predicting the effects of polysubstance usage is extremely important for clinicians to decide which combination of drugs should be prescribed. Although, machine learning community has made great progress toward using such rich models for supervised prediction, precision medicine problem such as polysubstance usage effects on drug overdose requires heterogeneous causal models, for which there is significantly less theoretical and practical guidance available. Recent advancement of structural causal models can provide ample insights of causal effects from observational data via identifiable causal directed graphs. OBJECTIVE Develop a system to identify heterogeneous causal effect of polysubstance usage from large electronic health record data METHODS We propose a system to estimate heterogeneous concurrent drug usage effects on overdose estimation, that consists of efficient co-variate selection, subgroup selection, generation of and heterogeneous causal effect estimation. Although, there has been several association studies have been proposed in the state-of-art methods, heterogeneous causal effects have never been studied in concurrent drug usage and drug overdose problem. We apply our framework to answer a critical question, ”can concurrent usage of benzodiazepines and opioids has heterogeneous causal effects on opioid overdose epidemic?” RESULTS Using Truven MarketScan claim data collected from 2001 to 2013 have shown significant promise of our proposed framework’s efficacy. Our efficient causal inference model estimated that the causal effect is higher (19%) than the regression studies (15%) to estimate the risks associated with the concurrent usage of opioid and benzodiazepines on opioid overdose. CONCLUSIONS Our generic framework can be a foundation of investigating concurrent events’ causal effects on any outcome that involves heterogeneity

scholarly journals Causal inference under over-simplified longitudinal causal models

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Lola Étiévant ◽  
Vivian Viallon
Keyword(s):  
Causal Inference ◽  
Causal Effect ◽  
Sufficient Conditions ◽  
Repeated Measurements ◽  
Causal Models ◽  
Causal Effects ◽  
Sensitivity Analyses ◽  
Time Varying ◽  
Weighted Averages

Abstract Many causal models of interest in epidemiology involve longitudinal exposures, confounders and mediators. However, repeated measurements are not always available or used in practice, leading analysts to overlook the time-varying nature of exposures and work under over-simplified causal models. Our objective is to assess whether – and how – causal effects identified under such misspecified causal models relates to true causal effects of interest. We derive sufficient conditions ensuring that the quantities estimated in practice under over-simplified causal models can be expressed as weighted averages of longitudinal causal effects of interest. Unsurprisingly, these sufficient conditions are very restrictive, and our results state that the quantities estimated in practice should be interpreted with caution in general, as they usually do not relate to any longitudinal causal effect of interest. Our simulations further illustrate that the bias between the quantities estimated in practice and the weighted averages of longitudinal causal effects of interest can be substantial. Overall, our results confirm the need for repeated measurements to conduct proper analyses and/or the development of sensitivity analyses when they are not available.

scholarly journals Understanding the nature of association between anxiety phenotypes and anorexia nervosa: a triangulation approach

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
E. Caitlin Lloyd ◽  
Hannah M. Sallis ◽  
Bas Verplanken ◽  
Anne M. Haase ◽  
Marcus R. Munafò
Keyword(s):  
Anorexia Nervosa ◽  
Longitudinal Study ◽  
Anxiety Disorder ◽  
Anxiety Disorders ◽  
Causal Effect ◽  
Causal Effects ◽  
Observational Research ◽  
Genome Wide Association Studies ◽  
Genetic Liability ◽  
Depressed Affect

Abstract Background Evidence from observational studies suggests an association between anxiety disorders and anorexia nervosa (AN), but causal inference is complicated by the potential for confounding in these studies. We triangulate evidence across a longitudinal study and a Mendelian randomization (MR) study, to evaluate whether there is support for anxiety disorder phenotypes exerting a causal effect on AN risk. Methods Study One assessed longitudinal associations of childhood worry and anxiety disorders with lifetime AN in the Avon Longitudinal Study of Parents and Children cohort. Study Two used two-sample MR to evaluate: causal effects of worry, and genetic liability to anxiety disorders, on AN risk; causal effects of genetic liability to AN on anxiety outcomes; and the causal influence of worry on anxiety disorder development. The independence of effects of worry, relative to depressed affect, on AN and anxiety disorder outcomes, was explored using multivariable MR. Analyses were completed using summary statistics from recent genome-wide association studies. Results Study One did not support an association between worry and subsequent AN, but there was strong evidence for anxiety disorders predicting increased risk of AN. Study Two outcomes supported worry causally increasing AN risk, but did not support a causal effect of anxiety disorders on AN development, or of AN on anxiety disorders/worry. Findings also indicated that worry causally influences anxiety disorder development. Multivariable analysis estimates suggested the influence of worry on both AN and anxiety disorders was independent of depressed affect. Conclusions Overall our results provide mixed evidence regarding the causal role of anxiety exposures in AN aetiology. The inconsistency between outcomes of Studies One and Two may be explained by limitations surrounding worry assessment in Study One, confounding of the anxiety disorder and AN association in observational research, and low power in MR analyses probing causal effects of genetic liability to anxiety disorders. The evidence for worry acting as a causal risk factor for anxiety disorders and AN supports targeting worry for prevention of both outcomes. Further research should clarify how a tendency to worry translates into AN risk, and whether anxiety disorder pathology exerts any causal effect on AN.

scholarly journals A comprehensive evaluation of methods for Mendelian randomization using realistic simulations and an analysis of 38 biomarkers for risk of type 2 diabetes

2021 ◽  
Author(s):  
Guanghao Qi ◽  
Nilanjan Chatterjee
Keyword(s):  
Type 2 Diabetes ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Real Data ◽  
Causal Effects ◽  
Type I ◽  
Genome Wide Association Studies ◽  
Simulation Studies ◽  
Sample Sizes

Abstract Background Previous studies have often evaluated methods for Mendelian randomization (MR) analysis based on simulations that do not adequately reflect the data-generating mechanisms in genome-wide association studies (GWAS) and there are often discrepancies in the performance of MR methods in simulations and real data sets. Methods We use a simulation framework that generates data on full GWAS for two traits under a realistic model for effect-size distribution coherent with the heritability, co-heritability and polygenicity typically observed for complex traits. We further use recent data generated from GWAS of 38 biomarkers in the UK Biobank and performed down sampling to investigate trends in estimates of causal effects of these biomarkers on the risk of type 2 diabetes (T2D). Results Simulation studies show that weighted mode and MRMix are the only two methods that maintain the correct type I error rate in a diverse set of scenarios. Between the two methods, MRMix tends to be more powerful for larger GWAS whereas the opposite is true for smaller sample sizes. Among the other methods, random-effect IVW (inverse-variance weighted method), MR-Robust and MR-RAPS (robust adjust profile score) tend to perform best in maintaining a low mean-squared error when the InSIDE assumption is satisfied, but can produce large bias when InSIDE is violated. In real-data analysis, some biomarkers showed major heterogeneity in estimates of their causal effects on the risk of T2D across the different methods and estimates from many methods trended in one direction with increasing sample size with patterns similar to those observed in simulation studies. Conclusion The relative performance of different MR methods depends heavily on the sample sizes of the underlying GWAS, the proportion of valid instruments and the validity of the InSIDE assumption. Down-sampling analysis can be used in large GWAS for the possible detection of bias in the MR methods.

scholarly journals Restoration of breathing after opioid overdose and spinal cord injury using temporal interference stimulation

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Michael D. Sunshine ◽  
Antonino M. Cassarà ◽  
Esra Neufeld ◽  
Nir Grossman ◽  
Thomas H. Mareci ◽  
...  
Keyword(s):  
Spinal Cord ◽  
Spinal Cord Injury ◽  
Motor Neurons ◽  
Drug Overdose ◽  
Cervical Spinal Cord ◽  
Opioid Overdose ◽  
Electrode Position ◽  
Spinal Motor Neurons ◽  
Cord Injury ◽  
Low Amplitude

AbstractRespiratory insufficiency is a leading cause of death due to drug overdose or neuromuscular disease. We hypothesized that a stimulation paradigm using temporal interference (TI) could restore breathing in such conditions. Following opioid overdose in rats, two high frequency (5000 Hz and 5001 Hz), low amplitude waveforms delivered via intramuscular wires in the neck immediately activated the diaphragm and restored ventilation in phase with waveform offset (1 Hz or 60 breaths/min). Following cervical spinal cord injury (SCI), TI stimulation via dorsally placed epidural electrodes uni- or bilaterally activated the diaphragm depending on current and electrode position. In silico modeling indicated that an interferential signal in the ventral spinal cord predicted the evoked response (left versus right diaphragm) and current-ratio-based steering. We conclude that TI stimulation can activate spinal motor neurons after SCI and prevent fatal apnea during drug overdose by restoring ventilation with minimally invasive electrodes.

scholarly journals The Pice Effects of Competition from Parallel Imports and Therapeutic Alternatives: Using Dynamic Models to Estimate the Causal Effect on the Extensive and Intensive Margins

2021 ◽  
Author(s):  
David Granlund
Keyword(s):  
Dynamic Models ◽  
Causal Effect ◽  
The Other ◽  
Causal Effects ◽  
Parallel Imports ◽  
Extensive And Intensive Margins ◽  
Price Effects ◽  
Short And Long Term ◽  
Therapeutic Alternatives

AbstractThis paper studies responses to competition with the use of dynamic models that distinguish between short- and long-term price effects. The dynamic models also allow lagged numbers of competitors to become valid and strong instruments for the current numbers, which enables studying the causal effects using flexible specifications. A first parallel trader is found to decrease prices of exchangeable products by 7% in the long term. On the other hand, prices do not respond to the first competitor that sells therapeutic alternatives; but competition from four or more competitors that sell on-patent therapeutic alternatives decreases prices by about 10% in the long term.

scholarly journals Bias in two-sample Mendelian randomization when using heritable covariable-adjusted summary associations

2021 ◽  
Author(s):  
Fernando Pires Hartwig ◽  
Kate Tilling ◽  
George Davey Smith ◽  
Deborah A Lawlor ◽  
Maria Carolina Borges
Keyword(s):  
Waist Circumference ◽  
Genetic Variants ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Association Studies ◽  
Real Data ◽  
Sensitivity Analyses ◽  
Effect Estimate ◽  
Genome Wide Association Studies ◽  
Residual Confounding

Abstract Background Two-sample Mendelian randomization (MR) allows the use of freely accessible summary association results from genome-wide association studies (GWAS) to estimate causal effects of modifiable exposures on outcomes. Some GWAS adjust for heritable covariables in an attempt to estimate direct effects of genetic variants on the trait of interest. One, both or neither of the exposure GWAS and outcome GWAS may have been adjusted for covariables. Methods We performed a simulation study comprising different scenarios that could motivate covariable adjustment in a GWAS and analysed real data to assess the influence of using covariable-adjusted summary association results in two-sample MR. Results In the absence of residual confounding between exposure and covariable, between exposure and outcome, and between covariable and outcome, using covariable-adjusted summary associations for two-sample MR eliminated bias due to horizontal pleiotropy. However, covariable adjustment led to bias in the presence of residual confounding (especially between the covariable and the outcome), even in the absence of horizontal pleiotropy (when the genetic variants would be valid instruments without covariable adjustment). In an analysis using real data from the Genetic Investigation of ANthropometric Traits (GIANT) consortium and UK Biobank, the causal effect estimate of waist circumference on blood pressure changed direction upon adjustment of waist circumference for body mass index. Conclusions Our findings indicate that using covariable-adjusted summary associations in MR should generally be avoided. When that is not possible, careful consideration of the causal relationships underlying the data (including potentially unmeasured confounders) is required to direct sensitivity analyses and interpret results with appropriate caution.

Causal Returns to Education

2006 ◽  
Vol 226 (1) ◽  
Author(s):  
Anton L. Flossmann ◽  
Winfried Pohlmeier
Keyword(s):  
Educational Attainment ◽  
Educational Policy ◽  
Empirical Evidence ◽  
Returns To Education ◽  
Causal Effect ◽  
Causal Effects ◽  
Continuous Treatment ◽  
Treatment Variable ◽  
Secondary Schooling ◽  
Upper Secondary

SummaryThis paper surveys the empirical evidence on causal effects of education on earnings for Germany and compares alternative studies in the light of their underlying identifying assumptions. We work out the different assumptions taken by various studies, which lead to rather different interpretations of the estimated causal effect. In particular, we are interested in the question to what extend causal return estimates are informative regarding educational policy advice. Despite the substantial methodological differences, we have to conclude that the empirical findings for Germany are quite robust and do not deviate substantially from each other. This also holds for the few studies which rely on ignorability conditions, regardless of whether they use educational attainment as a continuous treatment variable or as a discrete treatment indicator. Own estimates based on the matching approach indicate that the selection into upper secondary schooling is suboptimal

scholarly journals Emergency physicians’ attitudes and perceived barriers to the implementation of take-home naloxone programs in Canadian emergency departments

CJEM ◽  
2017 ◽  
Vol 20 (1) ◽  
pp. 46-52 ◽  
Author(s):  
Lauren Lacroix ◽  
Lisa Thurgur ◽  
Aaron M. Orkin ◽  
Jeffrey J. Perry ◽  
Ian G. Stiell
Keyword(s):  
At Risk ◽  
Perceived Barriers ◽  
Face Validity ◽  
Opioid Overdose ◽  
Emergency Physicians ◽  
Web Based ◽  
Health Crisis ◽  
Canadian Association ◽  
National Public Health

AbstractObjectivesRates of opioid-related deaths have reached the level of national public health crisis in Canada. Community-based opioid overdose education and naloxone distribution (OEND) programs distribute naloxone to people at risk, and the emergency department (ED) may be an underutilized setting to deliver naloxone to these people. The goal of this study was to identify Canadian emergency physicians’ attitudes and perceived barriers to the implementation of take-home naloxone programs.MethodsThis was an anonymous Web-based survey of members of the Canadian Association of Emergency Physicians. Survey questions were developed by the research team and piloted for face validity and clarity. Two reminder emails were sent to non-responders at 2-week intervals. Respondent demographics were collected, and Likert scales were used to assess attitudes and barriers to the prescription of naloxone from the ED.ResultsA total of 459 physicians responded. The majority of respondents were male (64%), worked in urban tertiary centres (58.3%), and lived in Ontario (50.6%). Overall, attitudes to OEND were strongly positive; 86% identified a willingness to prescribe naloxone from the ED. Perceived barriers included support for patient education (57%), access to follow-up (44%), and inadequate time (37%). In addition to people at risk of overdose, 77% of respondents identified that friends and family members may also benefit.ConclusionsCanadian emergency physicians are willing to distribute take-home naloxone, but thoughtful systems are required to facilitate opioid OEND implementation. These data will inform the development of these programs, with emphasis on multidisciplinary training and education.

Causal inference via string diagram surgery

2021 ◽  
pp. 1-22
Author(s):  
Bart Jacobs ◽  
Aleks Kissinger ◽  
Fabio Zanasi
Keyword(s):  
Causal Inference ◽  
Probabilistic Reasoning ◽  
Causal Effect ◽  
Sufficient Conditions ◽  
Causal Effects ◽  
Stochastic Matrices ◽  
Counterfactual Reasoning ◽  
Special Cases ◽  
String Diagram ◽  
Set Up

Abstract Extracting causal relationships from observed correlations is a growing area in probabilistic reasoning, originating with the seminal work of Pearl and others from the early 1990s. This paper develops a new, categorically oriented view based on a clear distinction between syntax (string diagrams) and semantics (stochastic matrices), connected via interpretations as structure-preserving functors. A key notion in the identification of causal effects is that of an intervention, whereby a variable is forcefully set to a particular value independent of any prior propensities. We represent the effect of such an intervention as an endo-functor which performs ‘string diagram surgery’ within the syntactic category of string diagrams. This diagram surgery in turn yields a new, interventional distribution via the interpretation functor. While in general there is no way to compute interventional distributions purely from observed data, we show that this is possible in certain special cases using a calculational tool called comb disintegration. We demonstrate the use of this technique on two well-known toy examples: one where we predict the causal effect of smoking on cancer in the presence of a confounding common cause and where we show that this technique provides simple sufficient conditions for computing interventions which apply to a wide variety of situations considered in the causal inference literature; the other one is an illustration of counterfactual reasoning where the same interventional techniques are used, but now in a ‘twinned’ set-up, with two version of the world – one factual and one counterfactual – joined together via exogenous variables that capture the uncertainties at hand.

scholarly journals Novel bounds for causal effects based on sensitivity parameters on the risk difference scale

2021 ◽  
Vol 9 (1) ◽  
pp. 190-210
Author(s):  
Arvid Sjölander ◽  
Ola Hössjer
Keyword(s):  
Risk Ratio ◽  
Simulation Study ◽  
Observational Studies ◽  
Causal Effect ◽  
Real Data ◽  
Risk Difference ◽  
Causal Effects ◽  
Ratio Scale ◽  
Unmeasured Confounding ◽  
Range Of Values

Abstract Unmeasured confounding is an important threat to the validity of observational studies. A common way to deal with unmeasured confounding is to compute bounds for the causal effect of interest, that is, a range of values that is guaranteed to include the true effect, given the observed data. Recently, bounds have been proposed that are based on sensitivity parameters, which quantify the degree of unmeasured confounding on the risk ratio scale. These bounds can be used to compute an E-value, that is, the degree of confounding required to explain away an observed association, on the risk ratio scale. We complement and extend this previous work by deriving analogous bounds, based on sensitivity parameters on the risk difference scale. We show that our bounds can also be used to compute an E-value, on the risk difference scale. We compare our novel bounds with previous bounds through a real data example and a simulation study.

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