BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

312 related articles for article (PubMed ID: 24151187)

  • 1. On the joint use of propensity and prognostic scores in estimation of the average treatment effect on the treated: a simulation study.
    Leacy FP; Stuart EA
    Stat Med; 2014 Sep; 33(20):3488-508. PubMed ID: 24151187
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Prognostic score-based balance measures can be a useful diagnostic for propensity score methods in comparative effectiveness research.
    Stuart EA; Lee BK; Leacy FP
    J Clin Epidemiol; 2013 Aug; 66(8 Suppl):S84-S90.e1. PubMed ID: 23849158
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Model misspecification and robustness in causal inference: comparing matching with doubly robust estimation.
    Waernbaum I
    Stat Med; 2012 Jul; 31(15):1572-81. PubMed ID: 22359267
    [TBL] [Abstract][Full Text] [Related]  

  • 4. The performance of inverse probability of treatment weighting and full matching on the propensity score in the presence of model misspecification when estimating the effect of treatment on survival outcomes.
    Austin PC; Stuart EA
    Stat Methods Med Res; 2017 Aug; 26(4):1654-1670. PubMed ID: 25934643
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Metrics for covariate balance in cohort studies of causal effects.
    Franklin JM; Rassen JA; Ackermann D; Bartels DB; Schneeweiss S
    Stat Med; 2014 May; 33(10):1685-99. PubMed ID: 24323618
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Comparison of the ability of double-robust estimators to correct bias in propensity score matching analysis. A Monte Carlo simulation study.
    Nguyen TL; Collins GS; Spence J; Devereaux PJ; Daurès JP; Landais P; Le Manach Y
    Pharmacoepidemiol Drug Saf; 2017 Dec; 26(12):1513-1519. PubMed ID: 28984050
    [TBL] [Abstract][Full Text] [Related]  

  • 7. A comparison of the ability of different propensity score models to balance measured variables between treated and untreated subjects: a Monte Carlo study.
    Austin PC; Grootendorst P; Anderson GM
    Stat Med; 2007 Feb; 26(4):734-53. PubMed ID: 16708349
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Should a propensity score model be super? The utility of ensemble procedures for causal adjustment.
    Alam S; Moodie EEM; Stephens DA
    Stat Med; 2019 Apr; 38(9):1690-1702. PubMed ID: 30586681
    [TBL] [Abstract][Full Text] [Related]  

  • 9. On regression adjustment for the propensity score.
    Vansteelandt S; Daniel RM
    Stat Med; 2014 Oct; 33(23):4053-72. PubMed ID: 24825821
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Practical recommendations on double score matching for estimating causal effects.
    Zhang Y; Yang S; Ye W; Faries DE; Lipkovich I; Kadziola Z
    Stat Med; 2022 Apr; 41(8):1421-1445. PubMed ID: 34957585
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Improving causal inference with a doubly robust estimator that combines propensity score stratification and weighting.
    Linden A
    J Eval Clin Pract; 2017 Aug; 23(4):697-702. PubMed ID: 28116816
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Vector-based kernel weighting: A simple estimator for improving precision and bias of average treatment effects in multiple treatment settings.
    Garrido MM; Lum J; Pizer SD
    Stat Med; 2021 Feb; 40(5):1204-1223. PubMed ID: 33327037
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Subclassification estimation of the weighted average treatment effect.
    Choi BY
    Biom J; 2021 Dec; 63(8):1706-1728. PubMed ID: 34270815
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Genetic matching for time-dependent treatments: a longitudinal extension and simulation study.
    Weymann D; Chan B; Regier DA
    BMC Med Res Methodol; 2023 Aug; 23(1):181. PubMed ID: 37559105
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Machine learning outcome regression improves doubly robust estimation of average causal effects.
    Choi BY; Wang CP; Gelfond J
    Pharmacoepidemiol Drug Saf; 2020 Sep; 29(9):1120-1133. PubMed ID: 32716126
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Comparison of propensity score methods for pre-specified subgroup analysis with survival data.
    Izem R; Liao J; Hu M; Wei Y; Akhtar S; Wernecke M; MaCurdy TE; Kelman J; Graham DJ
    J Biopharm Stat; 2020 Jul; 30(4):734-751. PubMed ID: 32191555
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Targeted Maximum Likelihood Estimation for Causal Inference in Observational Studies.
    Schuler MS; Rose S
    Am J Epidemiol; 2017 Jan; 185(1):65-73. PubMed ID: 27941068
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Comparison of balancing scores using the ANCOVA approach for estimating average treatment effect: a simulation study.
    Tu C; Koh WY
    J Biopharm Stat; 2019; 29(3):508-515. PubMed ID: 30561245
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Applied comparison of large-scale propensity score matching and cardinality matching for causal inference in observational research.
    Fortin SP; Johnston SS; Schuemie MJ
    BMC Med Res Methodol; 2021 May; 21(1):109. PubMed ID: 34030640
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Evaluating the performance of propensity score matching based approaches in individual patient data meta-analysis.
    Johara FT; Benedetti A; Platt R; Menzies D; Viiklepp P; Schaaf S; Chan E
    BMC Med Res Methodol; 2021 Nov; 21(1):257. PubMed ID: 34814845
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 16.