Directed Acyclic Graphs: Alternative tool for causal inference in epidemiology and biostatistics research and teaching

MedPharmRes ◽  
2018 ◽  
Vol 2 (3) ◽  
pp. 12-16 ◽  
Author(s):  
Dang Tran ◽  
Long Khuong ◽  
Tram Huynh ◽  
Hong Le ◽  
Tuan Vo
Keyword(s):  
Traditional Approach ◽  
Univariate Analysis ◽  
Experimental Studies ◽  
Directed Acyclic Graphs ◽  
Causal Effects ◽  
P Value ◽  
Causal Inferences ◽  
Disease Patterns ◽  
Research And Teaching ◽  
Acyclic Graphs

The issue of causation is one of the major challenges for epidemiologists who aim to understand the association between an exposure and an outcome to explain disease patterns and potentially provide a basis for intervention. Suitably designed experimental studies can offer robust evidence of the causal relationships. The experimental studies, however, are not popular, difficult or even unethical and impossible to conduct; it would be desirable if there is a methodology for reducing bias or strengthening the causal inferences drawn from observational studies. The traditional approach of estimating causal effects in such studies is to adjust for a set of variables judged to be confounders by including them in a multiple regression. However, which variables should be adjusted for as confounders in a regression model has long been a controversial issue in epidemiology. From my observation, the adjustments using only "statistical artifacts" methods such as the p-value<0.2 in univariate analysis, stepwise (forward/backward) are widely used in research and teaching in Epidemiology and Statistics but without appropriated notice on the biological or clinical relationships between exposure and outcome which may induce the bias in estimating causal effects. In this mini-review, we introduce an interesting method, namely Directed Acyclic Graphs (DAGs), which can be used to reduce the bias in estimating causal effects; it is also a good application for Epidemiology and Biostatistics teaching.

scholarly journals Use of directed acyclic graphs (DAGs) to identify confounders in applied health research: review and recommendations

2020 ◽  
Author(s):  
Peter W G Tennant ◽  
Eleanor J Murray ◽  
Kellyn F Arnold ◽  
Laurie Berrie ◽  
Matthew P Fox ◽  
...  
Keyword(s):  
Health Research ◽  
Web Of Science ◽  
Directed Acyclic Graphs ◽  
Causal Effects ◽  
Research Review ◽  
Future Research ◽  
Substantial Variation ◽  
Acyclic Graphs ◽  
Confounding Variables ◽  
Applied Health Research

Abstract Background Directed acyclic graphs (DAGs) are an increasingly popular approach for identifying confounding variables that require conditioning when estimating causal effects. This review examined the use of DAGs in applied health research to inform recommendations for improving their transparency and utility in future research. Methods Original health research articles published during 1999–2017 mentioning ‘directed acyclic graphs’ (or similar) or citing DAGitty were identified from Scopus, Web of Science, Medline and Embase. Data were extracted on the reporting of: estimands, DAGs and adjustment sets, alongside the characteristics of each article’s largest DAG. Results A total of 234 articles were identified that reported using DAGs. A fifth (n = 48, 21%) reported their target estimand(s) and half (n = 115, 48%) reported the adjustment set(s) implied by their DAG(s). Two-thirds of the articles (n = 144, 62%) made at least one DAG available. DAGs varied in size but averaged 12 nodes [interquartile range (IQR): 9–16, range: 3–28] and 29 arcs (IQR: 19–42, range: 3–99). The median saturation (i.e. percentage of total possible arcs) was 46% (IQR: 31–67, range: 12–100). 37% (n = 53) of the DAGs included unobserved variables, 17% (n = 25) included ‘super-nodes’ (i.e. nodes containing more than one variable) and 34% (n = 49) were visually arranged so that the constituent arcs flowed in the same direction (e.g. top-to-bottom). Conclusion There is substantial variation in the use and reporting of DAGs in applied health research. Although this partly reflects their flexibility, it also highlights some potential areas for improvement. This review hence offers several recommendations to improve the reporting and use of DAGs in future research.

scholarly journals Graphical Causal Models for Survey Inference

2019 ◽  
Author(s):  
Julian Schuessler ◽  
Peter Selb
Keyword(s):  
Survey Methodology ◽  
Directed Acyclic Graphs ◽  
Observational Research ◽  
Population Parameters ◽  
Causal Inferences ◽  
Survey Nonresponse ◽  
Survey Error ◽  
Acyclic Graphs ◽  
Data Collection Process ◽  
Theoretical Assumptions

Directed acyclic graphs (DAGs) are an increasingly popular tool to inform causal inferences in observational research. We demonstrate how DAGs can be used to encode and communicate theoretical assumptions about nonprobability samples and survey nonresponse, determine whether typical population parameters of interest to survey researchers can be identified from a sample, and support the choice of adjustment strategies. Following an introduction to basic concepts in graph and probability theory, we discuss sources of bias and assumptions for eliminating it in selection scenarios familiar from the missing data literature. We then introduce and analyze graphical representations of the multiple selection stages in the survey data collection process, in line with the Total Survey Error approach. Finally, we identify areas for future survey methodology research that can benefit from advances in causal graph theory.

scholarly journals Qualitative approximations to causality: Non-randomizable factors in clinical psychology

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Michael Höfler ◽  
Sebastian Trautmann ◽  
Philipp Kanske
Keyword(s):  
Clinical Psychology ◽  
Quantitative Methods ◽  
Quantitative Estimation ◽  
False Negative ◽  
Directed Acyclic Graphs ◽  
Causal Effects ◽  
Qualitative Approaches ◽  
Acyclic Graphs ◽  
Negative Conclusion ◽  
Causal Conclusion

Background Causal quests in non-randomized studies are unavoidable just because research questions are beyond doubt causal (e.g., aetiology). Large progress during the last decades has enriched the methodical toolbox. Aims Summary papers mainly focus on quantitative and highly formal methods. With examples from clinical psychology, we show how qualitative approaches can inform on the necessity and feasibility of quantitative analysis and may yet sometimes approximate causal answers. Results Qualitative use is hidden in some quantitative methods. For instance, it may yet suffice to know the direction of bias for a tentative causal conclusion. Counterfactuals clarify what causal effects of changeable factors are, unravel what is required for a causal answer, but do not cover immutable causes like gender. Directed acyclic graphs (DAGs) address causal effects in a broader sense, may give rise to quantitative estimation or indicate that this is premature. Conclusion No method is generally sufficient or necessary. Any causal analysis must ground on qualification and should balance the harms of a false positive and a false negative conclusion in a specific context.

scholarly journals Use of directed acyclic graphs (DAGs) in applied health research: review and recommendations

2019 ◽  
Author(s):  
Peter WG Tennant ◽  
Wendy J Harrison ◽  
Eleanor J Murray ◽  
Kellyn F Arnold ◽  
Laurie Berrie ◽  
...  
Keyword(s):  
Health Research ◽  
Web Of Science ◽  
Directed Acyclic Graphs ◽  
Causal Effects ◽  
Research Review ◽  
Future Research ◽  
Acyclic Graphs ◽  
Confounding Variables ◽  
Applied Health Research ◽  
Consistent Direction

ABSTRACTBackgroundDirected acyclic graphs (DAGs) are an increasingly popular approach for identifying confounding variables that require adjustment when estimating causal effects. This review examined the use of DAGs in applied health research to inform recommendations for improving their transparency and utility in future research.MethodsOriginal health research articles published during 1999-2017 mentioning “directed acyclic graphs” or similar or citing DAGitty were identified from Scopus, Web of Science, Medline, and Embase. Data were extracted on the reporting of: estimands, DAGs, and adjustment sets, alongside the characteristics of each article’s largest DAG.ResultsA total of 234 articles were identified that reported using DAGs. A fifth (n=48, 21%) reported their target estimand(s) and half (n=115, 48%) reported the adjustment set(s) implied by their DAG(s).Two-thirds of the articles (n=144, 62%) made at least one DAG available. Diagrams varied in size but averaged 12 nodes (IQR: 9-16, range: 3-28) and 29 arcs (IQR: 19-42, range: 3-99). The median saturation (i.e. percentage of total possible arcs) was 46% (IQR: 31-67, range: 12-100). 37% (n=53) of the DAGs included unobserved variables, 17% (n=25) included super-nodes (i.e. nodes containing more than one variable, and a 34% (n=49) were arranged so the constituent arcs flowed in a consistent direction.ConclusionsThere is substantial variation in the use and reporting of DAGs in applied health research. Although this partly reflects their flexibility, it also highlight some potential areas for improvement. This review hence offers several recommendations to improve the reporting and use of DAGs in future research.

scholarly journals Structural learning and estimation of joint causal effects among network-dependent variables

2021 ◽  
Author(s):  
Federico Castelletti ◽  
Alessandro Mascaro
Keyword(s):  
Network Structure ◽  
Causal Effect ◽  
Directed Acyclic Graphs ◽  
Causal Effects ◽  
Structural Learning ◽  
Acyclic Graphs ◽  
Dependent Variables ◽  
Underlying Network ◽  
Dag Model ◽  
Joint Posterior Distribution

AbstractBayesian networks in the form of Directed Acyclic Graphs (DAGs) represent an effective tool for modeling and inferring dependence relations among variables, a process known as structural learning. In addition, when equipped with the notion of intervention, a causal DAG model can be adopted to quantify the causal effect on a response due to a hypothetical intervention on some variable. Observational data cannot distinguish between DAGs encoding the same set of conditional independencies (Markov equivalent DAGs), which however can be different from a causal perspective. In addition, because causal effects depend on the underlying network structure, uncertainty around the DAG generating model crucially affects the causal estimation results. We propose a Bayesian methodology which combines structural learning of Gaussian DAG models and inference of causal effects as arising from simultaneous interventions on any given set of variables in the system. Our approach fully accounts for the uncertainty around both the network structure and causal relationships through a joint posterior distribution over DAGs, DAG parameters and then causal effects.

scholarly journals An Overview of Causal Directed Acyclic Graphs for Substance Abuse Researchers

2017 ◽  
Author(s):  
Michael Lewis ◽  
Alexis Kuerbis
Keyword(s):  
Substance Abuse ◽  
Epidemiological Data ◽  
Experimental Manipulation ◽  
Directed Acyclic Graphs ◽  
Causal Effects ◽  
Model Specification ◽  
Design Data ◽  
Logistic Regression Models ◽  
Gateway Hypothesis ◽  
Acyclic Graphs

Background. Within substance abuse research, quantitative methodologists tend to view randomized controlled trials (RCTs) as the “gold standard” for estimating causal effects, in part due to experimental manipulation and random assignment. Such methods are not always possible due to ethical and other reasons. Causal directed acyclic graphs (causal DAGs) are mathematical tools for (1) precisely stating researchers' causal assumptions and (2) providing guidance regarding the specification of statistical models for causal inference with nonexperimental data (such as epidemiological data). Purpose. This manuscript describes causal DAGs and illustrates their use in regards to a long standing theory within the field of substance use: the gateway hypothesis. Design. Data from the 2013 National Survey of Drug Use and Health are utilized to illustrate the application of causal DAGs in model specification. Then using the model specification constructed via causal DAGs, logistic regression models are used to generate odds ratios of the likelihood of trying heroin, given that one has tried alcohol, marijuana, and/or tobacco. Conclusion. Granting the assumptions encoded in specific causal DAGs, researchers, even in the absence of RCTs, can identify and estimate causal effects of interest.

HUBUNGAN LAMA WAKTU MENDERITA STROKE DENGAN TINGKAT KEMANDIRIAN KLIEN DALAM MELAKUKAN AKTIVITAS PEMENUHAN KEBUTUHAN SEHARI-HARI

2018 ◽  
Vol 1 (2) ◽  
pp. 58
Author(s):  
Setia Budi ◽  
Ria Dila Syahfitri
Keyword(s):  
Data Analysis ◽  
Univariate Analysis ◽  
Sampling Technique ◽  
P Value ◽  
Cross Sectional ◽  
Stroke Incidence ◽  
Bivariate Data ◽  
Cross Sectional Design ◽  
Interviewing Techniques ◽  
Need For Rehabilitation

The rate of stroke incidence is about 200 per 100,000 people throughout the world. This study aims to determine the Relation Suffer Stroke With Independence Level In Neurology Polyclinic TK II DR Ak Gani Palembang Year Hospital 2017. The research method used is descriptive quantitative with cross sectional design that is done by interviewing techniques with questionnaires on 42 respondents with Accidental sampling technique. This research was conducted in August 2017. Data analysis used is univariate data analysis and bivariate data analysis with one way anova test result. The results of univariate analysis showed that the duration of the respondents suffering from stroke was between 2.10 years to 3.38 years. Also found that most respondents were at the level of independence f; independent, except bathing, dressing, moving, and one other function with a total of 12 respondents. The results showed that there was a significant relationship between the long suffering stroke with the level of independence with the value of p value 0.025. For that the need for rehabilitation to patients and families of patients in order to help improve the independence of stroke patients in doing their daily activities. Keywords : Long Suffer Stroke, Level of Independence

HUBUNGAN ANTARA USIA DAN PENDIDIKAN IBU DENGAN PEMERIKSAAN KEHAMILAN DI KLINIK BUDI MULIA PALEMBANG TAHUN 2014

2019 ◽  
Vol 5 (1) ◽  
Author(s):  
Rubiyati Rubiyati
Keyword(s):  
Prenatal Care ◽  
Antenatal Care ◽  
Gestational Age ◽  
Significant Relationship ◽  
Univariate Analysis ◽  
Survey Method ◽  
Bivariate Analysis ◽  
P Value ◽  
Chi Square ◽  
Cross Sectional

ABSTRACT Antenatal Care is the care given to pregnant woman to monitor, support maternal health and maternal detect, whether normal or troubled pregnant women. Aki in Indonesia amounted to 359 in 100.000 live births. The purpose of the study was to determine the relationship between age and education in the clinic Budi Mulia Medika 2014. This study used a survey method whit cross sectional analytic. This is the overall study population of women with gestational age ≥36 weeks who come to visit the clinic Budi Mulia Medika Palembang on February 10 to 18. The study sample was taken in non-random with the technique of “accidental smapling “ with respondents who happens to be there or variable. The obtained using univariate and bivariate analysis using Chi-Square test statistic. The results of the univariate analysis showed that 83,3% of respondents did according to the standard prenatal care, high risk age 40,0 %, 60,0% lower risk of age, higher education 70,0%, 30,0% low education. Bivariate analysis showed that there was no significant relationship betwee age and pregnancy tests wit p value= 0,622, and significant relationship between education and prenatal care with p value= 0,019. From the results of this study are expected to need to increase outreach activities to the community about the importance of examination of pregnancy according to gestational age in an effort to reduse maternal mortality.   ABSTRAK Antenatal Care merupakan pelayanan  yang di berikan pada ibu hamil untuk memonitor, mendukung kesehatan ibu dan mendeteksi ibu, apakah ibu hamil normal atau bermasalah. Di Indonesia AKI berjumlah 359 per 100.000 kelahiran hidup. Tujuan penelitian adalah untuk mengetahui hubungan antara usia dan pendidikan dengan pemeriksaan kehamilan di klinik budi mulia medika tahun 2014. Penelitian ini menggunakan metode survey analitik dengan pendekatan cross sectional. Populasi penelitian ini adalahseluruh ibu dengan usia kehamilan ≥ 36 minggu yang dating berkunjung ke Klinik Budi Mulia Medika pada tanggal 10-18 Februari. Sampel penelitian ini di ambil secara non random dengan tekhnik ‘’ Accidental Sampling’’ dengan responden yang kebetulan ada atau tersedia. Data yang di peroleh menggunakan analisis univariat dan bivariat menggunakan uji statistik Chi-Square. Hasil analisis univariat ini menunjukan bahwa 83,8% responden melakukan pemeriksaan kehamilan sesuai standar, 16,7% tidak melakukan pemeriksaan kehamilan sesuai standar, usia resiko tinggi 40,0%, usia resiko rendah 60,0%, pendidikan tinggi 70,0 %, pendidikan rendah 30,0 %. Analisis bivariat menunjukan bahwa tidak ada hubungan bermakna antara usia dengan pemeriksaan kehamilan dengan p value =0,622, ada hubunngan bermakana antara pendidikan dengan pemeriksaan kehamilan dengan p value = 0,019. Dari hasil penelitian ini di harapkan perlu meningkatkan kegiatan penyuluhan kepada masyarakat tentang pentingnya dilakukan pemeriksaan kehamilan sesuai dengan umur kehamilan sebagai upaya menurunkan angka kematian ibu.    

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