tag :: Models ConfoundingFactors(Epidemiology)

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1Implementation of G-computation on a simulated data set: demonstration of a causal inference technique.
J. Snowden, S. Rose, and K. Mortimer. American journal of epidemiology, 173 (7): 731-8 (April 2011)6073<m:linebreak></m:linebreak>JID: 7910653; aheadofprint;.
2 years ago by @jepcastel
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Causality
ComputerSimulation
ConfoundingFactors(Epidemiology)
EpidemiologicResearchDesign
Humans
Models
RegressionAnalysis
Statistical
CausalityComputerSimulationConfoundingFactors(Epidemiology)EpidemiologicResearchDesignHumansModelsRegressionAnalysisStatistical
(0)
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1Some old and some new statistical tools for outcomes research.
S. Normand. Circulation, 118 (8): 872-84 (August 2008)4708<m:linebreak></m:linebreak>GR: R01-MH054693/MH/NIMH NIH HHS/United States; GR: R01-MH061434/MH/NIMH NIH HHS/United States; JID: 0147763; RF: 49; NIHMS46125; OID: NLM: NIHMS46125 Available on 02/19/09; OID: NLM: PMC2535854 Available on 02/19/09; ppublish;<m:linebreak></m:linebreak>Mesures d'associació; Variables instrumentals; Propensity score; Dades censurades.
2 years ago by @jepcastel
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BiomedicalResearch
BiomedicalResearch:statistics&numericaldata
ClusterAnalysis
ConfoundingFactors(Epidemiology)
DataInterpretation
Humans
Models
Statistical
StatisticsasTopic
StatisticsasTopic:methods
StatisticsasTopic:standards
TreatmentOutcome
BiomedicalResearchBiomedicalResearch:statistics&numericaldataClusterAnalysisConfoundingFactors(Epidemiology)DataInterpretationHumansModelsStatisticalStatisticsasTopicStatisticsasTopic:methodsStatisticsasTopic:standardsTreatmentOutcome
(0)
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1Treatment effects in the presence of unmeasured confounding: dealing with observations in the tails of the propensity score distribution--a simulation study.
T. Stürmer, K. Rothman, J. Avorn, and R. Glynn. American journal of epidemiology, 172 (7): 843-54 (October 2010)5732<m:linebreak></m:linebreak>JID: 7910653; aheadofprint;<m:linebreak></m:linebreak>Propensity score.
2 years ago by @jepcastel
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Bias(Epidemiology)
Calibration
CohortStudies
ComputerSimulation
ConfoundingFactors(Epidemiology)
EpidemiologicMethods
Humans
Models
RegressionAnalysis
ResearchDesign
ResearchDesign:statistics&numericaldata
Statistical
SurvivalRate
Bias(Epidemiology)CalibrationCohortStudiesComputerSimulationConfoundingFactors(Epidemiology)EpidemiologicMethodsHumansModelsRegressionAnalysisResearchDesignResearchDesign:statistics&numericaldataStatisticalSurvivalRate
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1Instrumental variables: application and limitations.
E. Martens, W. Pestman, A. de Boer, S. Belitser, and O. Klungel. Epidemiology (Cambridge, Mass.), 17 (3): 260-7 (May 2006)4042<m:linebreak></m:linebreak>Variables instrumentals.
2 years ago by @jepcastel
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Bias(Epidemiology)
ConfoundingFactors(Epidemiology)
EpidemiologicStudies
Models
Statistical
UnitedStates
Bias(Epidemiology)ConfoundingFactors(Epidemiology)EpidemiologicStudiesModelsStatisticalUnitedStates
(0)
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1Equivalence of the mediation, confounding and suppression effect.
D. MacKinnon, J. Krull, and C. Lockwood. Prevention science : the official journal of the Society for Prevention Research, 1 (4): 173-81 (December 2000)3530<m:linebreak></m:linebreak>Mesures d'associació.
2 years ago by @jepcastel
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Bias(Epidemiology)
Causality
ConfidenceIntervals
ConfoundingFactors(Epidemiology)
DataInterpretation
EffectModifier
Epidemiologic
Humans
Least-SquaresAnalysis
Models
ReproducibilityofResults
Statistical
Bias(Epidemiology)CausalityConfidenceIntervalsConfoundingFactors(Epidemiology)DataInterpretationEffectModifierEpidemiologicHumansLeast-SquaresAnalysisModelsReproducibilityofResultsStatistical
(0)
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1Use of poisson regression and box-jenkins models to evaluate the short-term effects of environmental noise levels on daily emergency admissions in Madrid, Spain.
A. Tobias, J. Díaz, M. Saez, and J. Alberdi. European journal of epidemiology, 17 (8): 765-71 (January 2001)4331<m:linebreak></m:linebreak>LR: 20041117; PUBM: Print; JID: 8508062; 0 (Air Pollutants); ppublish;<m:linebreak></m:linebreak>Sèries temporals.
2 years ago by @jepcastel
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AirPollutants
AirPollutants:analysis
Chi-SquareDistribution
Climate
ConfoundingFactors(Epidemiology)
EmergencyService
EnvironmentalExposure
EnvironmentalExposure:adverseeffects
Hospital
Hospital:utilization
Humans
Models
Noise
Noise:adverseeffects
PatientAdmission
PatientAdmission:statistics&numericaldata
PoissonDistribution
Spain
Statistical
UrbanPopulation
AirPollutantsAirPollutants:analysisChi-SquareDistributionClimateConfoundingFactors(Epidemiology)EmergencyServiceEnvironmentalExposureEnvironmentalExposure:adverseeffectsHospitalHospital:utilizationHumansModelsNoiseNoise:adverseeffectsPatientAdmissionPatientAdmission:statistics&numericaldataPoissonDistributionSpainStatisticalUrbanPopulation
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1Bias formulas for sensitivity analysis of unmeasured confounding for general outcomes, treatments, and confounders.
T. Vanderweele, and O. Arah. Epidemiology (Cambridge, Mass.), 22 (1): 42-52 (January 2011)6262<m:linebreak></m:linebreak>LR: 20110412; GR: R03 HD060696-01A1/HD/NICHD NIH HHS/United States; JID: 9009644; NIHMS239676; OID: NLM: NIHMS239676 Available on 01/01/12; OID: NLM: PMC3073860 Available on 01/01/12; PMCR: 2012/01/01; ppublish;.
2 years ago by @jepcastel
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Bias(Epidemiology)
ConfoundingFactors(Epidemiology)
EpidemiologicStudies
Humans
Models
SensitivityandSpecificity
Statistical
Bias(Epidemiology)ConfoundingFactors(Epidemiology)EpidemiologicStudiesHumansModelsSensitivityandSpecificityStatistical
(0)
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1Doubly robust estimation of causal effects.
M. Funk, D. Westreich, C. Wiesen, T. Stürmer, M. Brookhart, and M. Davidian. American journal of epidemiology, 173 (7): 761-7 (April 2011)6075<br/>JID: 7910653; aheadofprint;<br/><br/>Causalitat; Propensity score; IPTW.
2 years ago by @jepcastel
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Causality
ComputerSimulation
ConfidenceIntervals
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EpidemiologicMethods
Humans
LikelihoodFunctions
Models
MonteCarloMethod
PropensityScore
RegressionAnalysis
Statistical
CausalityComputerSimulationConfidenceIntervalsConfoundingFactors(Epidemiology)EpidemiologicMethodsHumansLikelihoodFunctionsModelsMonteCarloMethodPropensityScoreRegressionAnalysisStatistical
(0)
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1On the distinction between interaction and effect modification.
T. VanderWeele. Epidemiology (Cambridge, Mass.), 20 (6): 863-71 (November 2009)6414<m:linebreak></m:linebreak>LR: 20110917; JID: 9009644; CIN: Epidemiology. 2010 Jul;21(4):587; author reply 587-8. PMID: 20539118; EIN: Epidemiology. 2010 Jan;21(1):162; EIN: Epidemiology. 2011 Sep;22(5):752; ppublish;<m:linebreak></m:linebreak>Mesures d'associació; Interacció.
2 years ago by @jepcastel
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Causality
ConfoundingFactors(Epidemiology)
EpidemiologicStudies
Models
Statistical
CausalityConfoundingFactors(Epidemiology)EpidemiologicStudiesModelsStatistical
(0)
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1Measures of effect based on the sufficient causes model. 2. Risks and rates of disease associated with a single preventive agent.
R. Allard, and J. Boivin. Epidemiology (Cambridge, Mass.), 4 (6): 517-23 (November 1993)3147<m:linebreak></m:linebreak>Mesures d'associació.
2 years ago by @jepcastel
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AIDS-RelatedComplex
AIDS-RelatedComplex:epidemiology
AIDS-RelatedComplex:prevention&control
AIDS-RelatedOpportunisticInfections
AIDS-RelatedOpportunisticInfections:epidemiolog
AcquiredImmunodeficiencySyndrome
AcquiredImmunodeficiencySyndrome:epidemiology
AcquiredImmunodeficiencySyndrome:prevention&c
Biometry
Case-ControlStudies
ConfoundingFactors(Epidemiology)
EpidemiologicMethods
Humans
Models
Probability
RiskFactors
Statistical
Zidovudine
Zidovudine:therapeuticuse
AIDS-RelatedComplexAIDS-RelatedComplex:epidemiologyAIDS-RelatedComplex:prevention&controlAIDS-RelatedOpportunisticInfectionsAIDS-RelatedOpportunisticInfections:epidemiologAcquiredImmunodeficiencySyndromeAcquiredImmunodeficiencySyndrome:epidemiologyAcquiredImmunodeficiencySyndrome:prevention&cBiometryCase-ControlStudiesConfoundingFactors(Epidemiology)EpidemiologicMethodsHumansModelsProbabilityRiskFactorsStatisticalZidovudineZidovudine:therapeuticuse
(0)
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1Marginal structural models and causal inference in epidemiology.
J. Robins, M. Hernán, and B. Brumback. Epidemiology (Cambridge, Mass.), 11 (5): 550-60 (September 2000)4777<m:linebreak></m:linebreak>LR: 20071114; GR: R01-AI32475/AI/NIAID NIH HHS/United States; JID: 9009644; 0 (Anti-HIV Agents); 30516-87-1 (Zidovudine); ppublish;<m:linebreak></m:linebreak>Causalitat; Dades longitudinals; Marginal structural models.
2 years ago by @jepcastel
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Anti-HIVAgents
Anti-HIVAgents:therapeuticuse
Causality
ConfoundingFactors(Epidemiology)
EpidemiologicMethods
HIVInfections
HIVInfections:drugtherapy
HIVInfections:mortality
Humans
Models
RiskFactors
Statistical
TimeFactors
Zidovudine
Zidovudine:therapeuticuse
Anti-HIVAgentsAnti-HIVAgents:therapeuticuseCausalityConfoundingFactors(Epidemiology)EpidemiologicMethodsHIVInfectionsHIVInfections:drugtherapyHIVInfections:mortalityHumansModelsRiskFactorsStatisticalTimeFactorsZidovudineZidovudine:therapeuticuse
(0)
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1Multivariable analysis in cerebrovascular research: practical notes for the clinician.
G. Reboldi, F. Angeli, and P. Verdecchia. Cerebrovascular diseases (Basel, Switzerland), 35 (2): 187-93 (January 2013)7435<m:linebreak></m:linebreak>CI: Copyright (c) 2013; JID: 9100851; CIN: Cerebrovasc Dis. 2013;35(2):186. PMID: 23429278; 2012/08/07 received; 2012/10/24 accepted; 2013/02/21 aheadofprint; ppublish;<m:linebreak></m:linebreak>Anàlisi de dades; Introductori; Neurologia.
2 years ago by @jepcastel
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BiomedicalResearch
BiomedicalResearch:statistics&numericaldata
CerebrovascularDisorders
CerebrovascularDisorders:diagnosis
CerebrovascularDisorders:mortality
CerebrovascularDisorders:therapy
Chi-SquareDistribution
ConfoundingFactors(Epidemiology)
DataInterpretation
Humans
LinearModels
LogisticModels
Models
MultivariateAnalysis
OddsRatio
Prognosis
ProportionalHazardsModels
RiskFactors
Statistical
SurvivalAnalysis
BiomedicalResearchBiomedicalResearch:statistics&numericaldataCerebrovascularDisordersCerebrovascularDisorders:diagnosisCerebrovascularDisorders:mortalityCerebrovascularDisorders:therapyChi-SquareDistributionConfoundingFactors(Epidemiology)DataInterpretationHumansLinearModelsLogisticModelsModelsMultivariateAnalysisOddsRatioPrognosisProportionalHazardsModelsRiskFactorsStatisticalSurvivalAnalysis
(0)
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1Designs for the combination of group- and individual-level data.
S. Haneuse, and S. Bartell. Epidemiology (Cambridge, Mass.), 22 (3): 382-9 (May 2011)6098<m:linebreak></m:linebreak>Review; NLM Journal Code: a2t, 9009644;.
2 years ago by @jepcastel
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Bias(Epidemiology)
BirthWeight
ConfoundingFactors(Epidemiology)
DataCollection
DataInterpretation
EffectModifier
Epidemiologic
EpidemiologicResearchDesign
EthnicGroups
EthnicGroups:statistics&numericaldata
Female
Humans
Infant
LowBirthWeight
Male
Models
Newborn
NorthCarolina
RiskFactors
SexFactors
Statistical
Bias(Epidemiology)BirthWeightConfoundingFactors(Epidemiology)DataCollectionDataInterpretationEffectModifierEpidemiologicEpidemiologicResearchDesignEthnicGroupsEthnicGroups:statistics&numericaldataFemaleHumansInfantLowBirthWeightMaleModelsNewbornNorthCarolinaRiskFactorsSexFactorsStatistical
(0)
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1Marginal Structural Models: unbiased estimation for longitudinal studies.
E. Moodie, and D. Stephens. International journal of public health, 56 (1): 117-9 (February 2011)6250<m:linebreak></m:linebreak>GR: Canadian Institutes of Health Research/Canada; JID: 101304551; 2010/05/07 received; 2010/09/12 accepted; 2010/08/01 revised; 2010/10/08 aheadofprint; ppublish;<m:linebreak></m:linebreak>Propensity score; Marginal structural models; Introductori.
2 years ago by @jepcastel
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Bias(Epidemiology)
ConfoundingFactors(Epidemiology)
DataInterpretation
EpidemiologicResearchDesign
Humans
LikelihoodFunctions
LongitudinalStudies
LongitudinalStudies:standards
LongitudinalStudies:statistics&numericaldata
Models
Probability
Statistical
TimeFactors
Bias(Epidemiology)ConfoundingFactors(Epidemiology)DataInterpretationEpidemiologicResearchDesignHumansLikelihoodFunctionsLongitudinalStudiesLongitudinalStudies:standardsLongitudinalStudies:statistics&numericaldataModelsProbabilityStatisticalTimeFactors
(0)
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1Opportunities for minimization of confounding in observational research.
G. Quartey, M. Feudjo-Tepie, J. Wang, and J. Kim. Pharmaceutical statistics, 10 (6): 539-47 (November 2011)6482<m:linebreak></m:linebreak>CI: Copyright (c) 2011; JID: 101201192; aheadofprint;<m:linebreak></m:linebreak>Propensity score; Bias; Variables instrumentals; Introductori; Tipus d'estudis.
2 years ago by @jepcastel
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ConfoundingFactors(Epidemiology)
Humans
Models
ObserverVariation
ResearchDesign
ResearchDesign:statistics&numericaldata
Statistical
ConfoundingFactors(Epidemiology)HumansModelsObserverVariationResearchDesignResearchDesign:statistics&numericaldataStatistical
(0)
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1Bias formulas for external adjustment and sensitivity analysis of unmeasured confounders.
O. Arah, Y. Chiba, and S. Greenland. Annals of epidemiology, 18 (8): 637-46 (August 2008)4616<m:linebreak></m:linebreak>JID: 9100013; 2007/09/14 received; 2008/03/10 revised; 2008/04/02 accepted; ppublish;<m:linebreak></m:linebreak>Mesures d'associació.
2 years ago by @jepcastel
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Bias(Epidemiology)
Case-ControlStudies
ConfoundingFactors(Epidemiology)
EpidemiologicMethods
Humans
Models
MonteCarloMethod
OddsRatio
SensitivityandSpecificity
Statistical
Bias(Epidemiology)Case-ControlStudiesConfoundingFactors(Epidemiology)EpidemiologicMethodsHumansModelsMonteCarloMethodOddsRatioSensitivityandSpecificityStatistical
(0)
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1Effect modification by time-varying covariates.
J. Robins, M. Hernán, and A. Rotnitzky. American journal of epidemiology, 166 (9): 994-1002; discussion 1003-4 (November 2007)4950<m:linebreak></m:linebreak>GR: R01-HL080644/HL/NHLBI NIH HHS/United States; GR: R37-AI032475/AI/NIAID NIH HHS/United States; JID: 7910653; CON: Am J Epidemiol. 2007 Nov 1;166(9):985-93. PMID: 17875580; 2007/09/17 aheadofprint; ppublish;.
2 years ago by @jepcastel
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AntiretroviralTherapy
CohortStudies
ConfoundingFactors(Epidemiology)
HIVInfections
HIVInfections:drugtherapy
HIVInfections:epidemiology
HIVInfections:immunology
HighlyActive
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Humans
LogisticModels
LongitudinalStudies
MathematicalComputing
Models
ProportionalHazardsModels
Statistical
TimeFactors
TreatmentOutcome
UnitedStates
UnitedStates:epidemiology
AntiretroviralTherapyCohortStudiesConfoundingFactors(Epidemiology)HIVInfectionsHIVInfections:drugtherapyHIVInfections:epidemiologyHIVInfections:immunologyHighlyActiveHighlyActive:methodsHumansLogisticModelsLongitudinalStudiesMathematicalComputingModelsProportionalHazardsModelsStatisticalTimeFactorsTreatmentOutcomeUnitedStatesUnitedStates:epidemiology
(0)
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1Marginal structural models for estimating effect modification.
Y. Chiba, K. Azuma, and J. Okumura. Annals of epidemiology, 19 (5): 298-303 (May 2009)5047.
2 years ago by @jepcastel
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Case-ControlStudies
CohortStudies
ConfoundingFactors(Epidemiology)
EffectModifier
Epidemiologic
Female
Humans
LogisticModels
Models
Observation
Observation:methods
Pregnancy
Statistical
Case-ControlStudiesCohortStudiesConfoundingFactors(Epidemiology)EffectModifierEpidemiologicFemaleHumansLogisticModelsModelsObservationObservation:methodsPregnancyStatistical
(0)
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1Addressing the issue of channeling bias in observational studies with propensity scores analysis.
F. Lobo, S. Wagner, C. Gross, and J. Schommer. Research in social & administrative pharmacy : RSAP, 2 (1): 143-51 (March 2006)6524<m:linebreak></m:linebreak>LR: 20071115; JID: 101231974; ppublish;<m:linebreak></m:linebreak>Propensity score; Introductori.
2 years ago by @jepcastel
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Bias(Epidemiology)
Causality
ConfoundingFactors(Epidemiology)
DataInterpretation
DrugTherapy
Humans
Models
PopulationSurveillance
PopulationSurveillance:methods
ResearchDesign
Statistical
TreatmentOutcome
RCT
Bias(Epidemiology)CausalityConfoundingFactors(Epidemiology)DataInterpretationDrugTherapyHumansModelsPopulationSurveillancePopulationSurveillance:methodsResearchDesignStatisticalTreatmentOutcomeRCT
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1Reducing bias through directed acyclic graphs.
I. Shrier, and R. Platt. BMC medical research methodology, (January 2008)5089<m:linebreak></m:linebreak>JID: 100968545; OID: NLM: PMC2601045; 2008/03/11 received; 2008/10/30 accepted; 2008/10/30 aheadofprint; epublish;<m:linebreak></m:linebreak>Mesures d'associació.
2 years ago by @jepcastel
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Bias(Epidemiology)
ComputerGraphics
ConfoundingFactors(Epidemiology)
Humans
Models
Statistical
Bias(Epidemiology)ComputerGraphicsConfoundingFactors(Epidemiology)HumansModelsStatistical
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1Implementation of G-computation on a simulated data set: demonstration of a causal inference technique.

1Some old and some new statistical tools for outcomes research.

1Treatment effects in the presence of unmeasured confounding: dealing with observations in the tails of the propensity score distribution--a simulation study.

1Instrumental variables: application and limitations.

1Equivalence of the mediation, confounding and suppression effect.

1Use of poisson regression and box-jenkins models to evaluate the short-term effects of environmental noise levels on daily emergency admissions in Madrid, Spain.

1Bias formulas for sensitivity analysis of unmeasured confounding for general outcomes, treatments, and confounders.

1Doubly robust estimation of causal effects.

1On the distinction between interaction and effect modification.

1Measures of effect based on the sufficient causes model. 2. Risks and rates of disease associated with a single preventive agent.

1Marginal structural models and causal inference in epidemiology.

1Multivariable analysis in cerebrovascular research: practical notes for the clinician.

1Designs for the combination of group- and individual-level data.

1Marginal Structural Models: unbiased estimation for longitudinal studies.

1Opportunities for minimization of confounding in observational research.

1Bias formulas for external adjustment and sensitivity analysis of unmeasured confounders.

1Effect modification by time-varying covariates.

1Marginal structural models for estimating effect modification.

1Addressing the issue of channeling bias in observational studies with propensity scores analysis.

1Reducing bias through directed acyclic graphs.

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