183 related articles for article (PubMed ID: 15961628)
1. Regression modelling and other methods to control confounding.
McNamee R
Occup Environ Med; 2005 Jul; 62(7):500-6, 472. PubMed ID: 15961628
[No Abstract] [Full Text] [Related]
2. The performance of random coefficient regression in accounting for residual confounding.
Gustafson P; Greenland S
Biometrics; 2006 Sep; 62(3):760-8. PubMed ID: 16984318
[TBL] [Abstract][Full Text] [Related]
3. Adjustment for multiple cardiovascular risk factors using a summary risk score.
Arbogast PG; Kaltenbach L; Ding H; Ray WA
Epidemiology; 2008 Jan; 19(1):30-7. PubMed ID: 18091000
[TBL] [Abstract][Full Text] [Related]
4. [Common errors in analysis of the relationship between variables].
Moczko JA
Przegl Lek; 2006; 63(10):1153-8. PubMed ID: 17288244
[TBL] [Abstract][Full Text] [Related]
5. Response to invited commentary. Rose et al. respond to "G-computation and standardization in epidemiology".
Rose S; Snowden JM; Mortimer KM
Am J Epidemiol; 2011 Apr; 173(7):743-4. PubMed ID: 21415030
[No Abstract] [Full Text] [Related]
6. Regression modelling in hospital epidemiology: a statistical note.
Wolkewitz M; Beyersmann J; Gastmeier P; Schumacher M
Crit Care; 2008; 12(5):427. PubMed ID: 18828871
[No Abstract] [Full Text] [Related]
7. Systematic differences in treatment effect estimates between propensity score methods and logistic regression.
Martens EP; Pestman WR; de Boer A; Belitser SV; Klungel OH
Int J Epidemiol; 2008 Oct; 37(5):1142-7. PubMed ID: 18453634
[TBL] [Abstract][Full Text] [Related]
8. [Epidemiology and biostatistics in medical research. Series 3. Confounding and interaction: concept and application of multivariate analysis].
Kuwabara Y; Saito T; Inagaki Y
Kokyu To Junkan; 1992 Apr; 40(4):333-40. PubMed ID: 1570421
[No Abstract] [Full Text] [Related]
9. Efficient sampling approaches to address confounding in database studies.
Hanley JA; Dendukuri N
Stat Methods Med Res; 2009 Feb; 18(1):81-105. PubMed ID: 18815164
[TBL] [Abstract][Full Text] [Related]
10. Marginal structural models for estimating effect modification.
Chiba Y; Azuma K; Okumura J
Ann Epidemiol; 2009 May; 19(5):298-303. PubMed ID: 19362275
[TBL] [Abstract][Full Text] [Related]
11. Assessment and control of confounding in trauma research.
Kurth T; Sonis J
J Trauma Stress; 2007 Oct; 20(5):807-20. PubMed ID: 17955531
[TBL] [Abstract][Full Text] [Related]
12. Re: "Variable selection for propensity score models".
Shrier I; Platt RW; Steele RJ
Am J Epidemiol; 2007 Jul; 166(2):238-9. PubMed ID: 17526863
[No Abstract] [Full Text] [Related]
13. Confounding: regression adjustment.
Fitzmaurice G
Nutrition; 2006 May; 22(5):581-3. PubMed ID: 16600821
[No Abstract] [Full Text] [Related]
14. [Shortly about cohort studies].
Ludvigsson JF
Lakartidningen; 2006 Mar 22-28; 103(12):943-4. PubMed ID: 16618039
[No Abstract] [Full Text] [Related]
15. A positive or a negative confounding variable? A simple teaching aid for clinicians and students.
Mehio-Sibai A; Feinleib M; Sibai TA; Armenian HK
Ann Epidemiol; 2005 Jul; 15(6):421-3. PubMed ID: 15967387
[TBL] [Abstract][Full Text] [Related]
16. Quantitative assessment of unobserved confounding is mandatory in nonrandomized intervention studies.
Groenwold RH; Hak E; Hoes AW
J Clin Epidemiol; 2009 Jan; 62(1):22-8. PubMed ID: 18619797
[TBL] [Abstract][Full Text] [Related]
17. On the estimation and use of propensity scores in case-control and case-cohort studies.
MÃ¥nsson R; Joffe MM; Sun W; Hennessy S
Am J Epidemiol; 2007 Aug; 166(3):332-9. PubMed ID: 17504780
[TBL] [Abstract][Full Text] [Related]
18. Methods in pharmacoepidemiology: an invited series.
Schneeweiss S
Pharmacoepidemiol Drug Saf; 2005 Jul; 14(7):453-4. PubMed ID: 15981303
[No Abstract] [Full Text] [Related]
19. Supplementary data collection with case-cohort analysis to address potential confounding in a cohort study of thromboembolism in oral contraceptive initiators matched on claims-based propensity scores.
Eng PM; Seeger JD; Loughlin J; Clifford CR; Mentor S; Walker AM
Pharmacoepidemiol Drug Saf; 2008 Mar; 17(3):297-305. PubMed ID: 18215000
[TBL] [Abstract][Full Text] [Related]
20. Marginal structural models might overcome confounding when analyzing multiple treatment effects in observational studies.
Suarez D; Haro JM; Novick D; Ochoa S
J Clin Epidemiol; 2008 Jun; 61(6):525-30. PubMed ID: 18471655
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]