scholarly journals Causal effects of motor control on gait kinematics after orthopedic surgery in cerebral palsy: a machine-learning approach

2022 ◽  
Author(s):  
Katherine M Steele ◽  
Michael H Schwartz
Keyword(s):  
Cerebral Palsy ◽  
Motor Control ◽  
Orthopedic Surgery ◽  
Causal Effect ◽  
Causal Modeling ◽  
Causal Effects ◽  
Mathematical Framework ◽  
Test Model ◽  
Causal Relationships ◽  
The Impact

Background Altered motor control is common in cerebral palsy (CP). Understanding how altered motor control effects movement and treatment outcomes is important, but challenging due to complex interactions between impairments. While regression can be used to examine associations between impairments and gait, causal modeling provides a mathematical framework to specify assumed causal relationships, identify covariates that may introduce bias, and test model plausibility. The goal of this research was to quantify the causal effects of altered motor control and other impairments on gait, before and after single-event multi-level orthopedic surgery (SEMLS). Methods We evaluated the impact of SEMLS on change in Gait Deviation Index (GDI) between gait analyses. We constructed our causal model with a Directed Acyclic Graph that included the assumed causal relationships between SEMLS, change in GDI, baseline GDI (GDIpre), baseline neurologic and orthopedic impairments (Imppre), age, and surgical history. We identified the adjustment set to evaluate the causal effect of SEMLS on change in GDI and the impact of Imppre on change in GDI and GDIpre. We used Bayesian Additive Regression Trees (BART) and accumulated local effects to assess relative effects. Results We prospectively recruited a cohort of children with bilateral CP undergoing SEMLS (N=54, 35 males, age: 10.5+/-3.1 years) and identified a control cohort with bilateral CP who did not undergo SEMLS (N=55, 30 males, age: 10.0+/-3.4 years). There was a small positive causal effect of SEMLS on change in GDI (1.68 GDI points). Altered motor control (i.e., dynamic and static motor control) and strength had strong effects on GDIpre, but minimal effects on change in GDI. Spasticity and orthopedic impairments had minimal effects on GDIpre or change in GDI. Conclusions Altered motor control and other baseline impairments did have a strong effect on GDIpre, indicating that these impairments do have a causal effect on a child's gait pattern but minimal effect on expected changes in GDI after SEMLS. Heterogeneity in outcomes suggests there are other factors contributing to changes in gait. Identifying these factors and employing causal methods to examine the complex relationships between impairments and movement will be required to advance our understanding and care of children with CP.

You Can’t Drive a Car With Only Three Wheels

2019 ◽  
Vol 188 (9) ◽  
pp. 1682-1685 ◽  
Author(s):  
Hailey R Banack
Keyword(s):  
Causal Inference ◽  
Observational Data ◽  
Causal Effect ◽  
Causal Effects ◽  
Measurement Bias ◽  
Exposure Misclassification ◽  
Unmeasured Confounding ◽  
Obstetrical Care ◽  
The Impact ◽  
Fundamental Requirement

Abstract Authors aiming to estimate causal effects from observational data frequently discuss 3 fundamental identifiability assumptions for causal inference: exchangeability, consistency, and positivity. However, too often, studies fail to acknowledge the importance of measurement bias in causal inference. In the presence of measurement bias, the aforementioned identifiability conditions are not sufficient to estimate a causal effect. The most fundamental requirement for estimating a causal effect is knowing who is truly exposed and unexposed. In this issue of the Journal, Caniglia et al. (Am J Epidemiol. 2019;000(00):000–000) present a thorough discussion of methodological challenges when estimating causal effects in the context of research on distance to obstetrical care. Their article highlights empirical strategies for examining nonexchangeability due to unmeasured confounding and selection bias and potential violations of the consistency assumption. In addition to the important considerations outlined by Caniglia et al., authors interested in estimating causal effects from observational data should also consider implementing quantitative strategies to examine the impact of misclassification. The objective of this commentary is to emphasize that you can’t drive a car with only three wheels, and you also cannot estimate a causal effect in the presence of exposure misclassification bias.

scholarly journals The Impact of Education and Occupation on Temporary and Permanent Work Incapacity

2016 ◽  
Vol 16 (2) ◽  
pp. 577-617 ◽  
Author(s):  
Nabanita Datta Gupta ◽  
Daniel Lau ◽  
Dario Pozzoli
Keyword(s):  
Sickness Absence ◽  
Older Workers ◽  
Factor Model ◽  
Causal Effect ◽  
Causal Effects ◽  
Model Data ◽  
Temporary Work ◽  
Quasi Maximum Likelihood ◽  
The Impact ◽  
Permanent Work

Abstract This paper investigates whether education and working in a physically demanding job causally impact temporary work incapacity (TWI), i. e. sickness absence and permanent work incapacity (PWI), i. e. the inflow to disability via sickness absence. Our contribution is to allow for endogeneity of both education and occupation by estimating a quasi-maximum-likelihood discrete factor model. Data on sickness absence and disability spells for the population of older workers come from the Danish administrative registers for 1998–2002. We generally find causal effects of both education and occupation on TWI. Once we condition on temporary incapacity, we find again a causal effect of education on PWI, but no effect of occupation. Our results confirm that workers in physically demanding jobs are broken down by their work over time (women more than men) but only in terms of TWI.

scholarly journals Estimating the causal effects of chronic disease combinations on 30-day hospital readmissions based on observational Medicaid data

2017 ◽  
Vol 25 (6) ◽  
pp. 670-678 ◽  
Author(s):  
Sabrina Casucci ◽  
Li Lin ◽  
Sharon Hewner ◽  
Alexander Nikolaev
Keyword(s):  
Chronic Kidney Disease ◽  
Chronic Disease ◽  
Kidney Disease ◽  
Causal Effect ◽  
Hospital Readmissions ◽  
Causal Analysis ◽  
Causal Effects ◽  
Day Hospital ◽  
Artery Disease ◽  
The Impact

Abstract Objective Demonstrate how observational causal inference methods can generate insights into the impact of chronic disease combinations on patients’ 30-day hospital readmissions. Materials and Methods Causal effect estimation was used to quantify the impact of each risk factor scenario (ie, chronic disease combination) associated with chronic kidney disease and heart failure (HF) for adult Medicaid beneficiaries with initial hospitalizations in 2 New York State counties. The experimental protocol: (1) created matched risk factor and comparator groups, (2) assessed covariate balance in the matched groups, and (3) estimated causal effects and their statistical significance. Causality lattices summarized the impact of chronic disease comorbidities on readmissions. Results Chronic disease combinations were ordered with respect to their causal impact on readmissions. Of disease combinations associated with HF, the combination of HF, coronary artery disease, and tobacco abuse (in that order) had the highest causal effect on readmission rate (+22.3%); of disease combinations associated with chronic kidney disease, the combination of chronic kidney disease, coronary artery disease, and diabetes had the highest effect (+9.5%). Discussion Multi-hypothesis causal analysis reveals the effects of chronic disease comorbidities on health outcomes. Understanding these effects will guide the development of health care programs that address unique care needs of different patient subpopulations. Additionally, these insights bring new attention to individuals at high risk for readmission based on chronic disease comorbidities, allowing for more personalized attention and prioritization of care. Conclusion Multi-hypothesis causal analysis, a new methodological tool, generates meaningful insights from health care claims data, guiding the design of care and intervention programs.

scholarly journals Adversarial balancing-based representation learning for causal effect inference with observational data

2021 ◽  
Author(s):  
Xin Du ◽  
Lei Sun ◽  
Wouter Duivesteijn ◽  
Alexander Nikolaev ◽  
Mykola Pechenizkiy
Keyword(s):  
Health Care ◽  
Observational Data ◽  
Causal Effect ◽  
Representation Learning ◽  
Average Treatment Effect ◽  
Treatment Selection ◽  
Causal Effects ◽  
Control Group ◽  
Confounding Bias ◽  
The Impact

AbstractLearning causal effects from observational data greatly benefits a variety of domains such as health care, education, and sociology. For instance, one could estimate the impact of a new drug on specific individuals to assist clinical planning and improve the survival rate. In this paper, we focus on studying the problem of estimating the Conditional Average Treatment Effect (CATE) from observational data. The challenges for this problem are two-fold: on the one hand, we have to derive a causal estimator to estimate the causal quantity from observational data, in the presence of confounding bias; on the other hand, we have to deal with the identification of the CATE when the distributions of covariates over the treatment group units and the control units are imbalanced. To overcome these challenges, we propose a neural network framework called Adversarial Balancing-based representation learning for Causal Effect Inference (ABCEI), based on recent advances in representation learning. To ensure the identification of the CATE, ABCEI uses adversarial learning to balance the distributions of covariates in the treatment and the control group in the latent representation space, without any assumptions on the form of the treatment selection/assignment function. In addition, during the representation learning and balancing process, highly predictive information from the original covariate space might be lost. ABCEI can tackle this information loss problem by preserving useful information for predicting causal effects under the regularization of a mutual information estimator. The experimental results show that ABCEI is robust against treatment selection bias, and matches/outperforms the state-of-the-art approaches. Our experiments show promising results on several datasets, encompassing several health care (and other) domains.

scholarly journals The impact of orthopedic surgery on parents of children affected by cerebral palsy: a preliminary study in Greece

Folia Medica ◽  
2019 ◽  
Vol 61 (3) ◽  
pp. 384-388 ◽  
Author(s):  
Assimina Tsibidaki ◽  
Haridimos Tsibidakis ◽  
Anastasia Tsamparli ◽  
Doxa Kotzia ◽  
Artemisia Panou
Keyword(s):  
Cerebral Palsy ◽  
Orthopedic Surgery ◽  
Medical Problem ◽  
Structured Interviews ◽  
Entire Family ◽  
Intact Families ◽  
Economic Class ◽  
Preliminary Study ◽  
The Impact

Background: Cerebral palsy (CP) is a serious disorder with an important impact not only on the affected person but also on parents and the entire family. CP children often undergo surgery with long stay hospitalization. Aim: The aim of the study was to highlight the impact of orthopedic surgery on parents of children affected by cerebral palsy in Greece. Materials and methods: The semi-structured interviews of 80 parents (40 fathers and 40 mothers) of nuclear and intact families were collected. All parents were of Greek nationality, belonged to the middle socio-economic class and had at least one child affected by CP candidate to orthopedic surgery. Results: The majority of parents’ perception was that CP is a condition requiring special education, need for specialized services and a medical problem that affects the entire family. Their expectations after orthopedic surgery were focused mainly on child’s healing and hope to have a “healthy” child, while expectations from future surgery were focused on improving child’s quality of life, movement and gait. Conclusions: Parents of CP children have different perceptions of the clinical condition and a variety of expectations about orthopedic surgery and its outcomes.

scholarly journals Causal effects in microbiomes using interventional calculus

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Musfiqur Sazal ◽  
Vitalii Stebliankin ◽  
Kalai Mathee ◽  
Changwon Yoo ◽  
Giri Narasimhan
Keyword(s):  
Causal Inference ◽  
Causal Effect ◽  
Synthetic Data ◽  
Real Data ◽  
Outcome Variable ◽  
Data Sets ◽  
Causal Network ◽  
Causal Relationships ◽  
Microbial Taxon ◽  
The Impact

AbstractCausal inference in biomedical research allows us to shift the paradigm from investigating associational relationships to causal ones. Inferring causal relationships can help in understanding the inner workings of biological processes. Association patterns can be coincidental and may lead to wrong conclusions about causality in complex systems. Microbiomes are highly complex, diverse, and dynamic environments. Microbes are key players in human health and disease. Hence knowledge of critical causal relationships among the entities in a microbiome, and the impact of internal and external factors on microbial abundance and their interactions are essential for understanding disease mechanisms and making appropriate treatment recommendations. In this paper, we employ causal inference techniques to understand causal relationships between various entities in a microbiome, and to use the resulting causal network to make useful computations. We introduce a novel pipeline for microbiome analysis, which includes adding an outcome or “disease” variable, and then computing the causal network, referred to as a “disease network”, with the goal of identifying disease-relevant causal factors from the microbiome. Internventional techniques are then applied to the resulting network, allowing us to compute a measure called the causal effect of one or more microbial taxa on the outcome variable or the condition of interest. Finally, we propose a measure called causal influence that quantifies the total influence exerted by a microbial taxon on the rest of the microiome. Our pipeline is robust, sensitive, different from traditional approaches, and able to predict interventional effects without any controlled experiments. The pipeline can be used to identify potential eubiotic and dysbiotic microbial taxa in a microbiome. We validate our results using synthetic data sets and using results on real data sets that were previously published.

The bidirectional association between Internet addiction and depression: a large-sample longitudinal study among Chinese university students (Preprint)

2020 ◽  
Author(s):  
Wan-Jun Guo ◽  
Xia Yang ◽  
Yu-Jie Tao ◽  
Ya-Jing Meng ◽  
Hui-Yao Wang ◽  
...  
Keyword(s):  
Longitudinal Study ◽  
Internet Addiction ◽  
Causal Effects ◽  
Causal Relationships ◽  
Depression Severity ◽  
Large Sample ◽  
Panel Model ◽  
Bidirectional Association ◽  
Predictive Effect

BACKGROUND Evidence indicates that Internet addiction (IA) is associated with depression, but longitudinal studies have rarely been reported, and no studies have yet investigated potential common vulnerability or a possible specific causal relationship between these disorders. OBJECTIVE To overcome these gaps, the present 12-month longitudinal study based on a large-sample employed a cross-lagged panel model (CLPM) approach to investigate the potential common vulnerability and specific cross-causal relationships between IA and CSD (or depression). METHODS IA and clinically-significant depression (CSD) among 12 043 undergraduates were surveyed at baseline (as freshmen) and in follow-up after 12 months (as sophomores). Application of CLPM revealed two well-fitted design between IA and CSD, and between severities of IA and depression, adjusting for demographics. RESULTS Rates of baseline IA and CSD were 5.47% and 3.85%, respectively; increasing to 9.47% and 5.58%, respectively at follow-up. Among those with baseline IA and CSD, 44.61% and 34.48% remained stable at the time of the follow-up survey, respectively. Rates of new-incidences of IA and CSD over 12 months were 7.43% and 4.47%, respectively. Application of a cross-lagged panel model approach (CLPM, a discrete time structural equation model used primarily to assess causal relationships in real-world settings) revealed two well-fitted design between IA and CSD, and between severities of IA and depression, adjusting for demographics. Models revealed that baseline CSD (or depression severity) had a significant net-predictive effect on follow-up IA (or IA severity), and baseline IA (or IA severity) had a significant net-predictive effect on follow-up CSD (or depression severity). CONCLUSIONS These correlational patterns using a CLPM indicate that both common vulnerability and bidirectional specific cross-causal effects between them may contribute to the association between IA and depression. As the path coefficients of the net-cross-predictive effects were significantly smaller than those of baseline to follow-up cross-section associations, vulnerability may play a more significant role than bidirectional-causal effects. CLINICALTRIAL Ethics Committee of West China Hospital, Sichuan University (NO. 2016-171)

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 Determinants of participation and quality of life of young adults with cerebral palsy: longitudinal approach and comparison with the general population – SPARCLE 3 study protocol

BMC Neurology ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Catherine Arnaud ◽  
Carine Duffaut ◽  
Jérôme Fauconnier ◽  
Silke Schmidt ◽  
Kate Himmelmann ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Young Adults ◽  
Cerebral Palsy ◽  
Young People ◽  
General Population ◽  
Transition Period ◽  
Well Being ◽  
Personal Factors ◽  
The Impact

Abstract Background Effective inclusion in society for young people with disabilities is increasingly seen as generating opportunities for self-development, and improving well-being. However, significant barriers remain in the vast majority of activities meaningful for young adults. Research argues that various personal (disabilities, health) and environmental (access to the resources needed, accessible environment, discrimination, lack of personal economic independence) factors contribute to limited participation. However, previous studies conducted in young people with cerebral palsy (CP) mainly investigated the transition period to adulthood, and did not fully consider the whole range of impairment severity profiles or environmental barriers. In this study, we will use the follow-up of the SPARCLE cohort and a comparison group from the general population (1) to investigate the impact of the environment on participation and quality of life of young adults with CP, (2) to determine predictors of a successful young adulthood in educational, professional, health and social fields, (3) to compare quality of life and frequency of participation in social, work and recreational activities with the general population, (4) to document on participation and quality of life in those with severe disabilities. Methods The SPARCLE3 study has a combined longitudinal and cross-sectional design. Young adults with CP aged 22 to 27 years in 6 European regions previously enrolled in the SPARCLE cohort or newly recruited will be invited to self-complete a comprehensive set of questionnaires exploring participation (daily life and discretionary activities), health-related quality of life, body function, personal factors (health, personal resources), and contextual factors (availability of needed environmental items, family environment, services provision) during home visits supervised by trained researchers. Proxy-reports or adapted questionnaires will be used for those with the most severe impairments. The recruitment of a large group from the general population (online survey) will enable to identify life areas where the discrepancies between young people with CP and their able-bodied peers are the most significant. Discussion This study will help identify to what extent disabilities and barriers in environment negatively affect participation and quality of life, and how previous valued experiences during childhood or adolescence might modulate these effects.

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.

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