Biomaterial Database

Marginal structural models in clinical research: when and how to use them? 2017

Tyler Williamson, and Pietro Ravani

O'Brien Institute of Public Health, Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.

Marginal structural models are a multi-step estimation procedure designed to control for the effect of confounding variables that change over time, and are affected by previous treatment. When a time-varying confounder is affected by prior treatment standard methods for confounding control are inappropriate, because over time the covariate plays both the role of confounder and mediator of the effect of treatment on outcome. Marginal structural models first calculate a weight to assign to each observation. These weights reflect the extent to which observations with certain characteristics (covariate values) are under-represented or over-represented in the sample with the respect to a target population in which these characteristics are balanced across treatment groups. Then, marginal structural models estimate the outcome of interest taking into account these weights. Marginal structural models are a powerful method for confounding control in longitudinal study designs that collect time-varying information on exposure, outcome and other covariates.

UI	MeSH Term	Description	Entries
D008137	Longitudinal Studies	Studies in which variables relating to an individual or group of individuals are assessed over a period of time.	Bogalusa Heart Study,California Teachers Study,Framingham Heart Study,Jackson Heart Study,Longitudinal Survey,Tuskegee Syphilis Study,Bogalusa Heart Studies,California Teachers Studies,Framingham Heart Studies,Heart Studies, Bogalusa,Heart Studies, Framingham,Heart Studies, Jackson,Heart Study, Bogalusa,Heart Study, Framingham,Heart Study, Jackson,Jackson Heart Studies,Longitudinal Study,Longitudinal Surveys,Studies, Bogalusa Heart,Studies, California Teachers,Studies, Jackson Heart,Studies, Longitudinal,Study, Bogalusa Heart,Study, California Teachers,Study, Longitudinal,Survey, Longitudinal,Surveys, Longitudinal,Syphilis Studies, Tuskegee,Syphilis Study, Tuskegee,Teachers Studies, California,Teachers Study, California,Tuskegee Syphilis Studies
D009203	Myocardial Infarction	NECROSIS of the MYOCARDIUM caused by an obstruction of the blood supply to the heart (CORONARY CIRCULATION).	Cardiovascular Stroke,Heart Attack,Myocardial Infarct,Cardiovascular Strokes,Heart Attacks,Infarct, Myocardial,Infarction, Myocardial,Infarctions, Myocardial,Infarcts, Myocardial,Myocardial Infarctions,Myocardial Infarcts,Stroke, Cardiovascular,Strokes, Cardiovascular
D006801	Humans	Members of the species Homo sapiens.	Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D000319	Adrenergic beta-Antagonists	Drugs that bind to but do not activate beta-adrenergic receptors thereby blocking the actions of beta-adrenergic agonists. Adrenergic beta-antagonists are used for treatment of hypertension, cardiac arrhythmias, angina pectoris, glaucoma, migraine headaches, and anxiety.	Adrenergic beta-Antagonist,Adrenergic beta-Receptor Blockader,Adrenergic beta-Receptor Blockaders,beta-Adrenergic Antagonist,beta-Adrenergic Blocker,beta-Adrenergic Blocking Agent,beta-Adrenergic Blocking Agents,beta-Adrenergic Receptor Blockader,beta-Adrenergic Receptor Blockaders,beta-Adrenoceptor Antagonist,beta-Blockers, Adrenergic,beta-Adrenergic Antagonists,beta-Adrenergic Blockers,beta-Adrenoceptor Antagonists,Adrenergic beta Antagonist,Adrenergic beta Antagonists,Adrenergic beta Receptor Blockader,Adrenergic beta Receptor Blockaders,Adrenergic beta-Blockers,Agent, beta-Adrenergic Blocking,Agents, beta-Adrenergic Blocking,Antagonist, beta-Adrenergic,Antagonist, beta-Adrenoceptor,Antagonists, beta-Adrenergic,Antagonists, beta-Adrenoceptor,Blockader, Adrenergic beta-Receptor,Blockader, beta-Adrenergic Receptor,Blockaders, Adrenergic beta-Receptor,Blockaders, beta-Adrenergic Receptor,Blocker, beta-Adrenergic,Blockers, beta-Adrenergic,Blocking Agent, beta-Adrenergic,Blocking Agents, beta-Adrenergic,Receptor Blockader, beta-Adrenergic,Receptor Blockaders, beta-Adrenergic,beta Adrenergic Antagonist,beta Adrenergic Antagonists,beta Adrenergic Blocker,beta Adrenergic Blockers,beta Adrenergic Blocking Agent,beta Adrenergic Blocking Agents,beta Adrenergic Receptor Blockader,beta Adrenergic Receptor Blockaders,beta Adrenoceptor Antagonist,beta Adrenoceptor Antagonists,beta Blockers, Adrenergic,beta-Antagonist, Adrenergic,beta-Antagonists, Adrenergic,beta-Receptor Blockader, Adrenergic,beta-Receptor Blockaders, Adrenergic
D015233	Models, Statistical	Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc.	Probabilistic Models,Statistical Models,Two-Parameter Models,Model, Statistical,Models, Binomial,Models, Polynomial,Statistical Model,Binomial Model,Binomial Models,Model, Binomial,Model, Polynomial,Model, Probabilistic,Model, Two-Parameter,Models, Probabilistic,Models, Two-Parameter,Polynomial Model,Polynomial Models,Probabilistic Model,Two Parameter Models,Two-Parameter Model
D015982	Bias	Any deviation of results or inferences from the truth, or processes leading to such deviation. Bias can result from several sources: one-sided or systematic variations in measurement from the true value (systematic error); flaws in study design; deviation of inferences, interpretations, or analyses based on flawed data or data collection; etc. There is no sense of prejudice or subjectivity implied in the assessment of bias under these conditions.	Aggregation Bias,Bias, Aggregation,Bias, Ecological,Bias, Statistical,Bias, Systematic,Ecological Bias,Outcome Measurement Errors,Statistical Bias,Systematic Bias,Bias, Epidemiologic,Biases,Biases, Ecological,Biases, Statistical,Ecological Biases,Ecological Fallacies,Ecological Fallacy,Epidemiologic Biases,Experimental Bias,Fallacies, Ecological,Fallacy, Ecological,Scientific Bias,Statistical Biases,Truncation Bias,Truncation Biases,Bias, Experimental,Bias, Scientific,Bias, Truncation,Biase, Epidemiologic,Biases, Epidemiologic,Biases, Truncation,Epidemiologic Biase,Error, Outcome Measurement,Errors, Outcome Measurement,Outcome Measurement Error
D015986	Confounding Factors, Epidemiologic	Factors that can cause or prevent the outcome of interest but are not intermediate variables of the factor(s) under investigation.	Confounding Factor, Epidemiologic,Confounding Factors, Epidemiological,Confounding Factors, Epidemiology,Confounding Variables,Confounding Variables, Epidemiologic,Confounding Variables, Epidemiological,Confounding Factor, Epidemiological,Confounding Factor, Epidemiology,Confounding Variable,Confounding Variable, Epidemiologic,Confounding Variable, Epidemiological,Epidemiologic Confounding Factor,Epidemiologic Confounding Factors,Epidemiologic Confounding Variable,Epidemiologic Confounding Variables,Epidemiological Confounding Factor,Epidemiological Confounding Factors,Epidemiological Confounding Variable,Epidemiological Confounding Variables,Epidemiology Confounding Factor,Epidemiology Confounding Factors,Variable, Confounding,Variable, Epidemiologic Confounding,Variable, Epidemiological Confounding,Variables, Confounding,Variables, Epidemiologic Confounding,Variables, Epidemiological Confounding
D016019	Survival Analysis	A class of statistical procedures for estimating the survival function (function of time, starting with a population 100% well at a given time and providing the percentage of the population still well at later times). The survival analysis is then used for making inferences about the effects of treatments, prognostic factors, exposures, and other covariates on the function.	Analysis, Survival,Analyses, Survival,Survival Analyses