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1. Full Text Missing Data in Marginal Structural Models
by Liu, Shao-Hsien and Chrysanthopoulou, Stavroula A and Chang, Qiuzhi and Hunnicutt, Jacob N and Lapane, Kate L
Medical Care, ISSN 0025-7079, 03/2019, Volume 57, Issue 3, pp. 237 - 243
multiple imputation | marginal structural models | plasmode simulation | inverse probability weighting | missing data
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2. Full Text On Bayesian estimation of marginal structural models
by Saarela, Olli and Stephens, David A and Moodie, Erica E. M and Klein, Marina B
Biometrics, ISSN 0006-341X, 06/2015, Volume 71, Issue 2, pp. 279 - 288
The purpose of inverse probability of treatment (IPT) weighting in estimation of marginal treatment effects is to construct a pseudo-population without... 
Causal inference | Marginal structural models | Variance estimation | Inverse probability weighting | Longitudinal data | Bayesian inference | Posterior predictive inference | PROPENSITY SCORE | WEIGHTED LIKELIHOOD | BIOLOGY | MATHEMATICAL & COMPUTATIONAL BIOLOGY | STATISTICS & PROBABILITY | Liver Cirrhosis - etiology | Humans | Probability | Models, Statistical | Biometry | Disease Progression | Anti-HIV Agents - administration & dosage | Canada | Likelihood Functions | Algorithms | Bayes Theorem | HIV Infections - complications | HIV Infections - drug therapy | Hepatitis C - complications | Cohort Studies | Analysis | Censorship | Models | Bayesian analysis | Index Medicus
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3. Full Text A test for the correct specification of marginal structural models
by Sall, Alioune and Aubé, Karine and Trudel, Xavier and Brisson, Chantal and Talbot, Denis
Statistics in Medicine, ISSN 0277-6715, 07/2019, Volume 38, Issue 17, pp. 3168 - 3183
Marginal structural models (MSMs) allow estimating the causal effect of a time‐varying exposure on an outcome in the presence of time‐dependent confounding.... 
model specification | causal inference | marginal structural models | EFFORT-REWARD IMBALANCE | MEDICINE, RESEARCH & EXPERIMENTAL | MEDICAL INFORMATICS | RISK | STATISTICS & PROBABILITY | INVERSE PROBABILITY WEIGHTS | BLOOD-PRESSURE | PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH | WORK STRESS | MATHEMATICAL & COMPUTATIONAL BIOLOGY | INFORMATION CRITERION | Mathematical models | Statistical inference | Causality | Medical statistics
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4. Full Text An information criterion for marginal structural models
by Platt, Robert W and Brookhart, M. Alan and Cole, Stephen R and Westreich, Daniel and Schisterman, Enrique F
Statistics in Medicine, ISSN 0277-6715, 04/2013, Volume 32, Issue 8, pp. 1383 - 1393
Marginal structural models were developed as a semiparametric alternative to the G‐computation formula to estimate causal effects of exposures. In practice,... 
model specification | causal inference | marginal structural model | bias | regression analysis | Causal inference | Regression analysis | Marginal structural model | Bias | Model specification | DEFINITION | MEDICINE, RESEARCH & EXPERIMENTAL | INVERSE PROBABILITY | TREATMENT REGIMES | MEDICAL INFORMATICS | LARGE RANDOMIZED-TRIAL | SURVIVAL ANALYSIS | STATISTICS & PROBABILITY | TIME | POTENT ANTIRETROVIRAL THERAPY | PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH | ASSUMPTION | MATHEMATICAL & COMPUTATIONAL BIOLOGY | ASSOCIATION | Data Interpretation, Statistical | Body Weight | Humans | Breast Feeding | Infant | Male | CD4 Lymphocyte Count | Models, Statistical | HIV Infections - immunology | Likelihood Functions | Antiretroviral Therapy, Highly Active - standards | Computer Simulation | Adult | Female | HIV Infections - drug therapy | Highly active antiretroviral therapy | Antiviral agents | Models | Breast feeding | Analysis | Babies | Breastfeeding & lactation | Simulation | Human immunodeficiency virus--HIV | Weight | Index Medicus
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5. Full Text Missing Data in Marginal Structural Models: A Plasmode Simulation Study Comparing Multiple Imputation and Inverse Probability Weighting
by Liu, Shao-Hsien and Chrysanthopoulou, Stavroula A and Chang, Qiuzhi and Hunnicutt, Jacob N and Lapane, Kate L
Medical Care, ISSN 0025-7079, 03/2019, Volume 57, Issue 3, pp. 237 - 243
BACKGROUND:The use of marginal structural models (MSMs) to adjust for time-varying confounding has increased in epidemiologic studies. However, in the setting... 
CAUSAL INFERENCE | multiple imputation | FULLY CONDITIONAL SPECIFICATION | missing data | PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH | HEALTH CARE SCIENCES & SERVICES | HEALTH POLICY & SERVICES | marginal structural models | plasmode simulation | VALUES | inverse probability weighting | KNEE OSTEOARTHRITIS | EXPOSURE | Knee | Computer simulation | Bias | Knee (anatomy) | Epidemiology | Estimates | Empirical analysis | Structural models | Missing data | Biomedical materials | Weighting | Pain | Simulation | Biocompatibility | Osteoarthritis
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6. Full Text Marginal Structural Models for Sufficient Cause Interactions
by VanderWeele, Tyler J and Vansteelandt, Stijn and Robins, James M
American Journal of Epidemiology, ISSN 0002-9262, 2/2010, Volume 171, Issue 4, pp. 506 - 514
Sufficient cause interactions concern cases in which there is a particular causal mechanism for some outcome that requires the presence of 2 or more specific... 
Causal inference | Sufficient causes | Weighting | Marginal structural models | Synergism | Interaction | DEFINITION | INFERENCE | weighting | STATISTICAL INTERACTIONS | PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH | ASSUMPTION | DISEASE | interaction | synergism | marginal structural models | causal inference | sufficient causes | PROPORTION | EXPOSURE | EPIDEMIOLOGY | Epidemiologic Studies | Confounding Factors (Epidemiology) | Models, Statistical | Causality | Humans | Models | Research | Epidemiologic methods | Confounding factors | Analysis | Causation | Variables | Epidemiology | Regression analysis | Index Medicus | Practice of Epidemiology
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7. Full Text Using marginal structural models to adjust for treatment drop‐in when developing clinical prediction models
by Sperrin, Matthew and Martin, Glen P and Pate, Alexander and Van Staa, Tjeerd and Peek, Niels and Buchan, Iain
Statistics in Medicine, ISSN 0277-6715, 12/2018, Volume 37, Issue 28, pp. 4142 - 4154
Clinical prediction models (CPMs) can inform decision making about treatment initiation, which requires predicted risks assuming no treatment is given.... 
marginal structural models | clinical prediction models | treatment drop‐in | counterfactual causal inference | validation | longitudinal data | treatment drop-in | MEDICINE, RESEARCH & EXPERIMENTAL | TRIALS | PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH | MEDICAL INFORMATICS | MATHEMATICAL & COMPUTATIONAL BIOLOGY | STATISTICS & PROBABILITY | Cardiovascular disease | Index Medicus
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8. Full Text Marginal Structural Models: An Application to Incarceration and Marriage DuringYoungAdulthood
by Bacak, Valerio and Kennedy, Edward H
Journal of Marriage and Family, ISSN 0022-2445, 02/2015, Volume 77, Issue 1, pp. 112 - 125
Quantitative methodology | Incarceration | Marginal structural models | Marriage | Family life | Social science research | Family studies | Adulthood | Research | Imprisonment | Social aspects
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9. Full Text Assessing mediation using marginal structural models in the presence of confounding and moderation
by Coffman, Donna L and Zhong, Wei
Psychological Methods, ISSN 1082-989X, 12/2012, Volume 17, Issue 4, pp. 642 - 664
This article presents marginal structural models with inverse propensity weighting (IPW) for assessing mediation. Generally, individuals are not randomly... 
Inverse propensity weighting | Marginal structural model | Mediation | PROPENSITY SCORE | CAUSAL INFERENCE | BIAS | inverse propensity weighting | POTENTIAL OUTCOMES | PRINCIPAL STRATIFICATION | marginal structural model | IDENTIFICATION | PSYCHOLOGY, MULTIDISCIPLINARY | INTERVENTIONS | OBSERVATIONAL DATA | PREVENTION RESEARCH | mediation | KINDERGARTEN RETENTION POLICY | Intervention | Causal Models | Self Efficacy | Job Search Methods | Computation | Mediation Theory | Statistical Analysis | Depression (Psychology) | Effect Modifier, Epidemiologic | Propensity Score | Regression Analysis | Confounding Factors (Epidemiology) | Models, Statistical | Humans | Methodology | Mathematical statistics | Analysis | Research | Regression analysis | Confounding factors | Index Medicus
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10. Full Text Exploring the Subtleties of Inverse Probability Weighting and Marginal Structural Models
by Breskin, Alexander and Cole, Stephen R and Westreich, Daniel
Epidemiology, ISSN 1044-3983, 05/2018, Volume 29, Issue 3, pp. 352 - 355
Since being introduced to epidemiology in 2000, marginal structural models have become a commonly used method for causal inference in a wide range of... 
PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH | CAUSAL INFERENCE | ANTIRETROVIRAL THERAPY | TIME | DEATH | ADJUSTED SURVIVAL CURVES | EPIDEMIOLOGY | Probability | Data Interpretation, Statistical | Confounding Factors (Epidemiology) | Models, Statistical | Causality | Usage | Statistical models | Epidemiology | Analysis | Index Medicus | epidemiological methods | marginal structural models | inverse probability weighting | causal inference | statistics
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11. Full Text Impact of childhood abuse on suicide-related behavior: Analysis using marginal structural models
by Obikane, Erika and Shinozaki, Tomohiro and Takagi, Daisuke and Kawakami, Norito
Journal of Affective Disorders, ISSN 0165-0327, 07/2018, Volume 234, pp. 224 - 230
Childhood abuse is associated with adult suicidal behaviors, partially mediated by mental disorders. However, the direct effect of childhood abuse not mediated... 
Suicidal behaviors | Mental health, marginal structural model | Child abuse | Suicide | DEPRESSION | SEXUAL-ABUSE | PSYCHIATRY | RISK | CLINICAL NEUROLOGY | EXPERIENCES | HOUSEHOLD DYSFUNCTION | HEALTH | ONSET | LIFE | Analysis | Models | Children | Behavior | Social aspects | Child psychopathology | Risk factors | Index Medicus
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12. Full Text Association of long-duration breastfeeding and dental marginal structural models caries estimated with
by Chaffee, BW and Feldens, CA and Vitolo, MR
ANNALS OF EPIDEMIOLOGY, ISSN 1047-2797, 06/2014, Volume 24, Issue 6, pp. 448 - 454
Purpose: To estimate the association between breastfeeding 24 months or beyond and severe early childhood caries (S-ECC). Methods: Within a birth cohort (n =... 
POPULATION | Feeding behavior | RISK-FACTORS | CARIOGENICITY | Prospective studies | EARLY-CHILDHOOD CARIES | CHILDRENS ORAL-HEALTH | Epidemiologic methods | Dental caries | IMPACT | PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH | HUMAN-MILK | Marginal structural models | DISPARITIES | PATTERN | DISEASE | Breastfeeding
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13. Full Text Association of long-duration breastfeeding and dental caries estimated with marginal structural models
by Chaffee, Benjamin W., DDS, MPH, PhD and Feldens, Carlos Alberto, PhD and Vítolo, Márcia Regina, PhD
Annals of Epidemiology, ISSN 1047-2797, 2014, Volume 24, Issue 6, pp. 448 - 454
Abstract Purpose To estimate the association between breastfeeding 24 months or beyond and severe early childhood caries (S-ECC). Methods Within a birth cohort... 
Internal Medicine | Dental caries | Feeding behavior | Prospective studies | Marginal structural models | Breastfeeding | Epidemiologic methods | Prevalence | Dental Caries - diagnosis | Humans | Infant | Breast Feeding - statistics & numerical data | Models, Statistical | Social Class | Pregnancy | Dental Caries - epidemiology | Infant Welfare - statistics & numerical data | Female | Causality | Odds Ratio | Brazil - epidemiology | Infant, Newborn | Cohort Studies | Breast feeding | Models | Index Medicus | dental caries | marginal structural models | epidemiologic methods | breastfeeding | feeding behavior | prospective studies
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14. Full Text The impact of social housing on mental health: longitudinal analyses using marginal structural models and machine learning-generated weights
by Bentley, Rebecca and Bentley, Rebecca and Baker, Emma and Simons, Koen and Simons, Koen and Simpson, Julie A and Simpson, Julie A and Blakely, Tony and Blakely, Tony
International journal of epidemiology, ISSN 0300-5771, 10/2018, Volume 47, Issue 5, pp. 1414 - 1422
Background: Social housing may provide an affordable and secure residential environment, but has also been associated with stigma, poor housing conditions and... 
Social housing | Marginal structural models | Machine learning | Mental health | AUSTRALIA | POLICY | PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH | PUBLIC-HEALTH | mental health | marginal structural models | machine learning | INFERENCE | Index Medicus
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15. Full Text Marginal structural models and other analyses allow multiple estimates of treatment effects in randomized clinical trials: Meta-epidemiological analysis
by Ewald, Hannah and Speich, Benjamin and Ladanie, Aviv and Bucher, Heiner C and Ioannidis, John P.A and Hemkens, Lars G
Journal of Clinical Epidemiology, ISSN 0895-4356, 03/2019, Volume 107, pp. 12 - 26
To determine how marginal structural models (MSMs), which are increasingly used to estimate causal effects, are used in randomized clinical trials (RCTs) and... 
Vibration of effects | Marginal structural models | Prespecification | Reporting | Randomized controlled trial | Selective reporting | Intention-to-treat | MAINTENANCE BEVACIZUMAB | PER-PROTOCOL ANALYSES | PHASE-III | MEDICAL MALE CIRCUMCISION | PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH | STAGE IIIB | HEALTH CARE SCIENCES & SERVICES | CARDIOVASCULAR-DISEASE | ESTROGEN PLUS PROGESTIN | PRIMARY PREVENTION | WOMENS HEALTH | Medicine, Experimental | Clinical trials | Medical research | Models | Epidemiology | Stroke | Aspirin | Statistical analysis | Decision making | Embolisms | Mortality | Bias | Cardiovascular disease | Statistics | Estimates | Structural models | Confidence intervals | Randomization | Motivation | Medical prognosis | Diabetes | Median (statistics) | Significance
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