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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
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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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1Association vs. causality in transfusion medicine: understanding multivariable analysis in prediction vs. etiologic research.
S. Zalpuri, R. Middelburg, L. van de Watering, E. Vamvakas, J. Zwaginga, and J. van der Bom. Transfusion medicine reviews, 27 (2): 74-81 (April 2013)7527<m:linebreak></m:linebreak>CI: Copyright (c) 2013; JID: 8709027; 2013/01/04 received; 2013/02/13 revised; 2013/02/15 accepted; 2013/03/13 aheadofprint; ppublish;.
2 years ago by @jepcastel
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BiomedicalResearch
BiomedicalResearch:statistics&numericaldata
BloodTransfusion
BloodTransfusion:statistics&numericaldata
Causality
Comorbidity
Comprehension
ConfoundingFactors(Epidemiology)
Female
Forecasting
Forecasting:methods
Humans
Male
MultivariateAnalysis
Prognosis
Questionnaires
RegenerativeMedicine
RegenerativeMedicine:statistics&numericaldata
RiskFactors
BiomedicalResearchBiomedicalResearch:statistics&numericaldataBloodTransfusionBloodTransfusion:statistics&numericaldataCausalityComorbidityComprehensionConfoundingFactors(Epidemiology)FemaleForecastingForecasting:methodsHumansMaleMultivariateAnalysisPrognosisQuestionnairesRegenerativeMedicineRegenerativeMedicine:statistics&numericaldataRiskFactors
(0)
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1Combining ecological and individual variables to reduce confounding by indication: case study--subarachnoid hemorrhage treatment.
S. Johnston. Journal of clinical epidemiology, 53 (12): 1236-41 (December 2000)2724<m:linebreak></m:linebreak>Mesures d'associació.
2 years ago by @jepcastel
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Bias(Epidemiology)
CohortStudies
ConfoundingFactors(Epidemiology)
EpidemiologicResearchDesign
Female
HospitalMortality
Humans
Male
MiddleAged
Models
OutcomeAssessment(HealthCare)
PatientDischarge
PatientDischarge:statistics&numericaldata
SelectionBias
Statistical
SubarachnoidHemorrhage
SubarachnoidHemorrhage:mortality
SubarachnoidHemorrhage:therapy
UnitedStates
UnitedStates:epidemiology
Bias(Epidemiology)CohortStudiesConfoundingFactors(Epidemiology)EpidemiologicResearchDesignFemaleHospitalMortalityHumansMaleMiddleAgedModelsOutcomeAssessment(HealthCare)PatientDischargePatientDischarge:statistics&numericaldataSelectionBiasStatisticalSubarachnoidHemorrhageSubarachnoidHemorrhage:mortalitySubarachnoidHemorrhage:therapyUnitedStatesUnitedStates:epidemiology
(0)
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1Control sampling strategies for case-crossover studies: an assessment of relative efficiency.
M. Mittleman, M. Maclure, and J. Robins. American journal of epidemiology, 142 (1): 91-8 (July 1995)2446.
2 years ago by @jepcastel
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80andover
Adult
Aged
Bias(Epidemiology)
Case-ControlStudies
ConfoundingFactors(Epidemiology)
Cross-OverStudies
Exercise
Female
Humans
Male
MiddleAged
Models
MyocardialInfarction
MyocardialInfarction:epidemiology
Risk
Statistical
80andoverAdultAgedBias(Epidemiology)Case-ControlStudiesConfoundingFactors(Epidemiology)Cross-OverStudiesExerciseFemaleHumansMaleMiddleAgedModelsMyocardialInfarctionMyocardialInfarction:epidemiologyRiskStatistical
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1Mega-trials vs. meta-analysis: precision vs. heterogeneity?
I. Shrier, R. Platt, and R. Steele. Contemporary clinical trials, 28 (3): 324-8 (May 2007)4811<m:linebreak></m:linebreak>LR: 20071115; JID: 101242342; 2006/04/28 received; 2006/10/31 revised; 2006/11/12 accepted; 2006/11/21 aheadofprint; ppublish;<m:linebreak></m:linebreak>Metaanàlisi.
2 years ago by @jepcastel
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ClinicalTrialsasTopic
ClinicalTrialsasTopic:statistics&numericald
ConfoundingFactors(Epidemiology)
DataInterpretation
Humans
Meta-AnalysisasTopic
ResearchSupportasTopic
Statistical
ClinicalTrialsasTopicClinicalTrialsasTopic:statistics&numericaldConfoundingFactors(Epidemiology)DataInterpretationHumansMeta-AnalysisasTopicResearchSupportasTopicStatistical
(0)
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1Disease risk score as a confounder summary method: systematic review and recommendations.
M. Tadrous, J. Gagne, T. Stürmer, and S. Cadarette. Pharmacoepidemiology and drug safety, 22 (2): 122-9 (February 2013)7198<m:linebreak></m:linebreak>CI: Copyright (c) 2012; GR: R01 AG023178/AG/NIA NIH HHS/United States; JID: 9208369; 2012/06/21 received; 2012/10/20 revised; 2012/10/23 accepted; 2012/11/21 aheadofprint; ppublish;<m:linebreak></m:linebreak>Propensity score; Disease risk score.
2 years ago by @jepcastel
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Animals
ConfoundingFactors(Epidemiology)
Disease
Disease:etiology
HealthPlanningGuidelines
Humans
ResearchDesign
ResearchDesign:standards
RiskFactors
AnimalsConfoundingFactors(Epidemiology)DiseaseDisease:etiologyHealthPlanningGuidelinesHumansResearchDesignResearchDesign:standardsRiskFactors
(0)
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1Common problems related to the use of number needed to treat.
A. Stang, C. Poole, and R. Bender. Journal of clinical epidemiology, 63 (8): 820-5 (August 2010)5754<m:linebreak></m:linebreak>CI: Copyright (c) 2010; JID: 8801383; 2008/09/10 received; 2009/05/25 revised; 2009/08/03 accepted; 2009/10/31 aheadofprint; ppublish;<m:linebreak></m:linebreak>NNT.
2 years ago by @jepcastel
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ConfoundingFactors(Epidemiology)
DataInterpretation
Evidence-BasedMedicine
Humans
RandomizedControlledTrialsasTopic:standards
SampleSize
Statistical
RCT
ConfoundingFactors(Epidemiology)DataInterpretationEvidence-BasedMedicineHumansRandomizedControlledTrialsasTopic:standardsSampleSizeStatisticalRCT
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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.

1Association vs. causality in transfusion medicine: understanding multivariable analysis in prediction vs. etiologic research.

1Combining ecological and individual variables to reduce confounding by indication: case study--subarachnoid hemorrhage treatment.

1Control sampling strategies for case-crossover studies: an assessment of relative efficiency.

1Mega-trials vs. meta-analysis: precision vs. heterogeneity?

1Disease risk score as a confounder summary method: systematic review and recommendations.

1Common problems related to the use of number needed to treat.

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