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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
HighlyActive:methods
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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1Relaxing the rule of ten events per variable in logistic and Cox regression.
E. Vittinghoff, and C. McCulloch. American journal of epidemiology, 165 (6): 710-8 (March 2007)4832<m:linebreak></m:linebreak>JID: 7910653; 2006/12/20 aheadofprint; ppublish;.
2 years ago by @jepcastel
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Bias(Epidemiology)
Case-ControlStudies
ConfidenceIntervals
ConfoundingFactors(Epidemiology)
DataInterpretation
FactorAnalysis
Humans
LogisticModels
MultivariateAnalysis
PredictiveValueofTests
Prevalence
ProportionalHazardsModels
SampleSize
SensitivityandSpecificity
Statistical
Bias(Epidemiology)Case-ControlStudiesConfidenceIntervalsConfoundingFactors(Epidemiology)DataInterpretationFactorAnalysisHumansLogisticModelsMultivariateAnalysisPredictiveValueofTestsPrevalenceProportionalHazardsModelsSampleSizeSensitivityandSpecificityStatistical
(0)
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1Clustered environments and randomized genes: a fundamental distinction between conventional and genetic epidemiology.
G. Smith, D. Lawlor, R. Harbord, N. Timpson, I. Day, and S. Ebrahim. PLoS medicine, 4 (12): e352 (December 2007)4891<m:linebreak></m:linebreak>LR: 20081121; JID: 101231360; OID: NLM: PMC2121108; 2006/11/17 received; 2007/10/30 accepted; ppublish;<m:linebreak></m:linebreak>Epidemiologia genètica.
2 years ago by @jepcastel
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Aged
ClusterAnalysis
ConfoundingFactors(Epidemiology)
Cross-SectionalStudies
Environment
EpidemiologicMethods
Female
GeneticPredispositiontoDisease
GeneticVariation
Genotype
GreatBritain
HealthBehavior
HeartDiseases
HeartDiseases:epidemiology
HeartDiseases:etiology
HeartDiseases:genetics
HeartDiseases:physiopathology
Humans
LifeStyle
MiddleAged
Phenotype
Polymorphism
ReproducibilityofResults
RiskAssessment
RiskFactors
SingleNucleotide
SocioeconomicFactors
AgedClusterAnalysisConfoundingFactors(Epidemiology)Cross-SectionalStudiesEnvironmentEpidemiologicMethodsFemaleGeneticPredispositiontoDiseaseGeneticVariationGenotypeGreatBritainHealthBehaviorHeartDiseasesHeartDiseases:epidemiologyHeartDiseases:etiologyHeartDiseases:geneticsHeartDiseases:physiopathologyHumansLifeStyleMiddleAgedPhenotypePolymorphismReproducibilityofResultsRiskAssessmentRiskFactorsSingleNucleotideSocioeconomicFactors
(0)
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1Controlling confounding when studying large pharmacoepidemiologic databases: a case study of the two-stage sampling design.
J. Collet, D. Schaubel, J. Hanley, C. Sharpe, and J. Boivin. Epidemiology (Cambridge, Mass.), 9 (3): 309-15 (May 1998)2503.
2 years ago by @jepcastel
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Case-ControlStudies
ConfoundingFactors(Epidemiology)
Databases
Drug-RelatedSideEffectsandAdverseReactions
Factual
Humans
ResearchDesign
SamplingStudies
Case-ControlStudiesConfoundingFactors(Epidemiology)DatabasesDrug-RelatedSideEffectsandAdverseReactionsFactualHumansResearchDesignSamplingStudies
(0)
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1That confounded P-value.
J. Lang, K. Rothman, and C. Cann. Epidemiology (Cambridge, Mass.), 9 (1): 7-8 (January 1998)2500<m:linebreak></m:linebreak>Comment on: Epidemiology 1998 Jan;9(1):21-8; Comment in: Epidemiology 1999 May;10(3):345-7;.
2 years ago by @jepcastel
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ConfoundingFactors(Epidemiology)
EpidemiologicMethods
Humans
ConfoundingFactors(Epidemiology)EpidemiologicMethodsHumans
(0)
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1Design and statistical analysis of oral medicine studies: common pitfalls.
L. Baccaglini, J. Shuster, J. Cheng, D. Theriaque, V. Schoenbach, S. Tomar, and C. Poole. Oral diseases, 16 (3): 233-41 (April 2010)5654<m:linebreak></m:linebreak>GR: M01 RR000082-476042/RR/NCRR NIH HHS/United States; GR: M01RR00082/RR/NCRR NIH HHS/United States; GR: R21 DE018714-01A1/DE/NIDCR NIH HHS/United States; GR: R21DE018714/DE/NIDCR NIH HHS/United States; GR: U54RR025208/RR/NCRR NIH HHS/United States; JID: 9508565; RF: 33; 2009/10/23 aheadofprint; ppublish;<m:linebreak></m:linebreak>Recursos/Organització; Errors.
2 years ago by @jepcastel
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Bias(Epidemiology)
ConfoundingFactors(Epidemiology)
DataCollection
DataInterpretation
DentalResearch
DentalResearch:methods
DentalResearch:standards
Humans
InformationManagement
ObserverVariation
ResearchDesign
ResearchDesign:standards
SampleSize
Statistical
Bias(Epidemiology)ConfoundingFactors(Epidemiology)DataCollectionDataInterpretationDentalResearchDentalResearch:methodsDentalResearch:standardsHumansInformationManagementObserverVariationResearchDesignResearchDesign:standardsSampleSizeStatistical
(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
(0)
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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
(0)
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1Definition, sources, magnitude, effect modifiers, and strategies of reduction of the healthy worker effect.
B. Choi. Journal of occupational medicine. : official publication of the Industrial Medical Association, 34 (10): 979-88 (October 1992)2685<m:linebreak></m:linebreak>Mesures d'associació.
2 years ago by @jepcastel
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AgeFactors
Bias(Epidemiology)
CauseofDeath
ConfoundingFactors(Epidemiology)
Diagnosis
Differential
EffectModifier
Employment
Epidemiologic
HealthyWorkerEffect
Humans
Neoplasms
Neoplasms:epidemiology
OccupationalDiseases
OccupationalDiseases:epidemiology
OccupationalDiseases:mortality
OccupationalHealth
Occupations
Population
ResearchDesign
RiskFactors
SexFactors
SocialClass
SocialConditions
AgeFactorsBias(Epidemiology)CauseofDeathConfoundingFactors(Epidemiology)DiagnosisDifferentialEffectModifierEmploymentEpidemiologicHealthyWorkerEffectHumansNeoplasmsNeoplasms:epidemiologyOccupationalDiseasesOccupationalDiseases:epidemiologyOccupationalDiseases:mortalityOccupationalHealthOccupationsPopulationResearchDesignRiskFactorsSexFactorsSocialClassSocialConditions
(0)
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1Those confounded vitamins: what can we learn from the differences between observational versus randomised trial evidence?
D. Lawlor, G. Smith, D. Kundu, K. Bruckdorfer, and S. Ebrahim. Lancet, 363 (9422): 1724-7 (May 2004)3635<m:linebreak></m:linebreak>Tipus d'estudis.
2 years ago by @jepcastel
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Anthropometry
Antioxidants
Antioxidants:therapeuticuse
AscorbicAcid
AscorbicAcid:blood
AscorbicAcid:therapeuticuse
CardiovascularDiseases
CardiovascularDiseases:prevention&control
Causality
ConfoundingFactors(Epidemiology)
HealthBehavior
Humans
LifeStyle
Observation
ResearchDesign
SocioeconomicFactors
RCT
AnthropometryAntioxidantsAntioxidants:therapeuticuseAscorbicAcidAscorbicAcid:bloodAscorbicAcid:therapeuticuseCardiovascularDiseasesCardiovascularDiseases:prevention&controlCausalityConfoundingFactors(Epidemiology)HealthBehaviorHumansLifeStyleObservationResearchDesignSocioeconomicFactorsRCT
(0)
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1High-dimensional propensity score adjustment in studies of treatment effects using health care claims data.
S. Schneeweiss, J. Rassen, R. Glynn, J. Avorn, H. Mogun, and M. Brookhart. Epidemiology (Cambridge, Mass.), 20 (4): 512-22 (July 2009)5216<m:linebreak></m:linebreak>GR: R01-AG018833/AG/NIA NIH HHS/United States; GR: R01-AG021950/AG/NIA NIH HHS/United States; GR: R01-AG023178/AG/NIA NIH HHS/United States; GR: R01-AG027400/AG/NIA NIH HHS/United States; GR: R01-MH078708/MH/NIMH NIH HHS/United States; JID: 9009644; CIN: Epidemiology. 2009 Jul;20(4):523-4. PMID: 19525688; ppublish;<m:linebreak></m:linebreak>Propensity score.
2 years ago by @jepcastel
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80andover
Aged
Algorithms
Anti-InflammatoryAgents
ConfoundingFactors(Epidemiology)
Cyclooxygenase2Inhibitors
Cyclooxygenase2Inhibitors:adverseeffects
Cyclooxygenase2Inhibitors:therapeuticuse
Female
Humans
InsuranceClaimReview
InsuranceClaimReview:statistics&numericaldat
Male
Medicare
Medicare:statistics&numericaldata
Non-Steroidal
Non-Steroidal:adverseeffects
Non-Steroidal:therapeuticuse
Pharmacoepidemiology
Pharmacoepidemiology:statistics&numericaldata
RiskAssessment
TreatmentOutcome
UnitedStates
UpperGastrointestinalTract
UpperGastrointestinalTract:drugeffects
80andoverAgedAlgorithmsAnti-InflammatoryAgentsConfoundingFactors(Epidemiology)Cyclooxygenase2InhibitorsCyclooxygenase2Inhibitors:adverseeffectsCyclooxygenase2Inhibitors:therapeuticuseFemaleHumansInsuranceClaimReviewInsuranceClaimReview:statistics&numericaldatMaleMedicareMedicare:statistics&numericaldataNon-SteroidalNon-Steroidal:adverseeffectsNon-Steroidal:therapeuticusePharmacoepidemiologyPharmacoepidemiology:statistics&numericaldataRiskAssessmentTreatmentOutcomeUnitedStatesUpperGastrointestinalTractUpperGastrointestinalTract:drugeffects
(0)
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1Statistical analysis of air pollution panel studies: an illustration.
H. Janes, L. Sheppard, and K. Shepherd. Annals of epidemiology, 18 (10): 792-802 (October 2008)4973<m:linebreak></m:linebreak>JID: 9100013; 0 (Air Pollutants); 2008/01/09 received; 2008/05/17 revised; 2008/06/18 accepted; ppublish;.
2 years ago by @jepcastel
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Adolescent
AirPollutants
AirPollutants:adverseeffects
AirPollution
AirPollution:adverseeffects
AirPollution:statistics&numericaldata
Child
ConfoundingFactors(Epidemiology)
DataInterpretation
EnvironmentalExposure
EnvironmentalExposure:adverseeffects
Female
Humans
LongitudinalStudies
Male
Models
Statistical
Theoretical
TimeFactors
Washington
Washington:epidemiology
AdolescentAirPollutantsAirPollutants:adverseeffectsAirPollutionAirPollution:adverseeffectsAirPollution:statistics&numericaldataChildConfoundingFactors(Epidemiology)DataInterpretationEnvironmentalExposureEnvironmentalExposure:adverseeffectsFemaleHumansLongitudinalStudiesMaleModelsStatisticalTheoreticalTimeFactorsWashingtonWashington:epidemiology
(0)
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1A method for detection of residual confounding in time-series and other observational studies.
W. Flanders, M. Klein, L. Darrow, M. Strickland, S. Sarnat, J. Sarnat, L. Waller, A. Winquist, and P. Tolbert. Epidemiology (Cambridge, Mass.), 22 (1): 59-67 (January 2011)6261<m:linebreak></m:linebreak>GR: R01ES11294/ES/NIEHS NIH HHS/United States; JID: 9009644; 10028-15-6 (Ozone); ppublish;.
2 years ago by @jepcastel
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Asthma
Asthma:epidemiology
Asthma:etiology
ConfoundingFactors(Epidemiology)
EmergencyService
EnvironmentalExposure
EpidemiologicStudies
Georgia
Georgia:epidemiology
Hospital
Hospital:utilization
Humans
Ozone
Ozone:adverseeffects
PopulationSurveillance
PopulationSurveillance:methods
AsthmaAsthma:epidemiologyAsthma:etiologyConfoundingFactors(Epidemiology)EmergencyServiceEnvironmentalExposureEpidemiologicStudiesGeorgiaGeorgia:epidemiologyHospitalHospital:utilizationHumansOzoneOzone:adverseeffectsPopulationSurveillancePopulationSurveillance:methods
(0)
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1Loss to follow-up in cohort studies: how much is too much?
V. Kristman, M. Manno, and P. Côté. European journal of epidemiology, 19 (8): 751-60 (January 2004)7137<br/>LR: 20061115; JID: 8508062; ppublish;<br/>Mesures d'associació; Cohorts; Bias.
2 years ago by @jepcastel
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Bias(Epidemiology)
Canada
CohortStudies
ConfidenceIntervals
ConfoundingFactors(Epidemiology)
EpidemiologicMethods
Follow-UpStudies
Humans
OddsRatio
SensitivityandSpecificity
Bias(Epidemiology)CanadaCohortStudiesConfidenceIntervalsConfoundingFactors(Epidemiology)EpidemiologicMethodsFollow-UpStudiesHumansOddsRatioSensitivityandSpecificity
(0)
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1Use of instrumental variable in prescription drug research with observational data: a systematic review.
Y. Chen, and B. Briesacher. Journal of clinical epidemiology, 64 (6): 687-700 (June 2011)6272<m:linebreak></m:linebreak>CI: Copyright (c) 2011; GR: K01AG031836/AG/NIA NIH HHS/United States; JID: 8801383; 0 (Prescription Drugs); NIHMS243351; OID: NLM: NIHMS243351 Available on 06/01/12; OID: NLM: PMC3079803 Available on 06/01/12; PMCR: 2012/06/01; 2010/01/20 received; 2010/09/02 revised; 2010/09/19 accepted; 2010/12/16 aheadofprint; ppublish;<m:linebreak></m:linebreak>Variables instrumentals.
2 years ago by @jepcastel
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ComparativeEffectivenessResearch
ComparativeEffectivenessResearch:standards
ConfoundingFactors(Epidemiology)
Evidence-BasedMedicine
Evidence-BasedMedicine:standards
Humans
Models
PrescriptionDrugs
PrescriptionDrugs:therapeuticuse
Statistical
UnitedStates
UnitedStates:epidemiology
ComparativeEffectivenessResearchComparativeEffectivenessResearch:standardsConfoundingFactors(Epidemiology)Evidence-BasedMedicineEvidence-BasedMedicine:standardsHumansModelsPrescriptionDrugsPrescriptionDrugs:therapeuticuseStatisticalUnitedStatesUnitedStates:epidemiology
(0)
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1Instrumental variable methods in comparative safety and effectiveness research.
M. Brookhart, J. Rassen, and S. Schneeweiss. Pharmacoepidemiology and drug safety, 19 (6): 537-54 (June 2010)5676<m:linebreak></m:linebreak>GR: 1 K01 HS018088/HS/AHRQ HHS/United States; GR: 290-2005-0016-I-TO3-WA1/PHS HHS/United States; GR: K25AG027400/AG/NIA NIH HHS/United States; JID: 9208369; NIHMS205457; OID: NLM: NIHMS205457 Available on 06/01/11; OID: NLM: PMC2886161 Available on 06/01/11; ppublish;<m:linebreak></m:linebreak>Variables instrumentals.
2 years ago by @jepcastel
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ComparativeEffectivenessResearch
ComparativeEffectivenessResearch:methods
ConfoundingFactors(Epidemiology)
EpidemiologicResearchDesign
Humans
Models
PharmaceuticalPreparations
PharmaceuticalPreparations:adverseeffects
Statistical
ComparativeEffectivenessResearchComparativeEffectivenessResearch:methodsConfoundingFactors(Epidemiology)EpidemiologicResearchDesignHumansModelsPharmaceuticalPreparationsPharmaceuticalPreparations:adverseeffectsStatistical
(0)
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1A potential pitfall in control of covariates in epidemiologic studies.
H. Brenner. Epidemiology (Cambridge, Mass.), 9 (1): 68-71 (January 1998)2498.
2 years ago by @jepcastel
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Case-ControlStudies
ConfoundingFactors(Epidemiology)
EffectModifier
Epidemiologic
EpidemiologicMethods
Humans
Case-ControlStudiesConfoundingFactors(Epidemiology)EffectModifierEpidemiologicEpidemiologicMethodsHumans
(0)
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1In search of fewer independent risk factors.
D. Brotman, E. Walker, M. Lauer, and R. O'Brien. Archives of internal medicine, 165 (2): 138-45 (January 2005)3841<m:linebreak></m:linebreak>Mesures d'associació.
2 years ago by @jepcastel
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AgeFactors
CardiovascularDiseases
CardiovascularDiseases:diagnosis
CardiovascularDiseases:epidemiology
ConfoundingFactors(Epidemiology)
EpidemiologicMethods
Female
Humans
Male
Models
MultivariateAnalysis
OddsRatio
PredictiveValueofTests
RiskFactors
SensitivityandSpecificity
SexFactors
Statistical
RCT
AgeFactorsCardiovascularDiseasesCardiovascularDiseases:diagnosisCardiovascularDiseases:epidemiologyConfoundingFactors(Epidemiology)EpidemiologicMethodsFemaleHumansMaleModelsMultivariateAnalysisOddsRatioPredictiveValueofTestsRiskFactorsSensitivityandSpecificitySexFactorsStatisticalRCT
(0)
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1Interpreting observational studies -- look before you leap.
A. Bavry, and D. Bhatt. Journal of clinical epidemiology, 59 (8): 763-4 (August 2006)4211<m:linebreak></m:linebreak>PUBM: Print-Electronic; DEP: 20060515; JID: 8801383; 0 (Antilipemic Agents); CON: J Clin Epidemiol. 2006 Aug;59(8):819-28. PMID: 16828675; 2005/12/06 received; 2005/12/18 accepted; 2006/05/15 aheadofprint; ppublish;<m:linebreak></m:linebreak>Tipus d'estudis.
2 years ago by @jepcastel
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Aged
Bias(Epidemiology)
CardiovascularDiseases
CardiovascularDiseases:drugtherapy
CardiovascularDiseases:mortality
ClinicalTrialsasTopic
ConfoundingFactors(Epidemiology)
Female
Humans
HypolipidemicAgents
HypolipidemicAgents:therapeuticuse
Male
PatientSelection
AgedBias(Epidemiology)CardiovascularDiseasesCardiovascularDiseases:drugtherapyCardiovascularDiseases:mortalityClinicalTrialsasTopicConfoundingFactors(Epidemiology)FemaleHumansHypolipidemicAgentsHypolipidemicAgents:therapeuticuseMalePatientSelection
(0)
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1Effect modification by time-varying covariates.

1Marginal structural models for estimating effect modification.

1Relaxing the rule of ten events per variable in logistic and Cox regression.

1Clustered environments and randomized genes: a fundamental distinction between conventional and genetic epidemiology.

1Controlling confounding when studying large pharmacoepidemiologic databases: a case study of the two-stage sampling design.

1That confounded P-value.

1Design and statistical analysis of oral medicine studies: common pitfalls.

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

1Reducing bias through directed acyclic graphs.

1Definition, sources, magnitude, effect modifiers, and strategies of reduction of the healthy worker effect.

1Those confounded vitamins: what can we learn from the differences between observational versus randomised trial evidence?

1High-dimensional propensity score adjustment in studies of treatment effects using health care claims data.

1Statistical analysis of air pollution panel studies: an illustration.

1A method for detection of residual confounding in time-series and other observational studies.

1Loss to follow-up in cohort studies: how much is too much?

1Use of instrumental variable in prescription drug research with observational data: a systematic review.

1Instrumental variable methods in comparative safety and effectiveness research.

1A potential pitfall in control of covariates in epidemiologic studies.

1In search of fewer independent risk factors.

1Interpreting observational studies -- look before you leap.

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