These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

137 related articles for article (PubMed ID: 28230909)

  • 1. A general instrumental variable framework for regression analysis with outcome missing not at random.
    Tchetgen Tchetgen EJ; Wirth KE
    Biometrics; 2017 Dec; 73(4):1123-1131. PubMed ID: 28230909
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Using interviewer random effects to remove selection bias from HIV prevalence estimates.
    McGovern ME; Bärnighausen T; Salomon JA; Canning D
    BMC Med Res Methodol; 2015 Feb; 15():8. PubMed ID: 25656226
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Implementation of Instrumental Variable Bounds for Data Missing Not at Random.
    Marden JR; Wang L; Tchetgen EJT; Walter S; Glymour MM; Wirth KE
    Epidemiology; 2018 May; 29(3):364-368. PubMed ID: 29394191
    [TBL] [Abstract][Full Text] [Related]  

  • 4. On the assumption of bivariate normality in selection models: a Copula approach applied to estimating HIV prevalence.
    McGovern ME; Bärnighausen T; Marra G; Radice R
    Epidemiology; 2015 Mar; 26(2):229-37. PubMed ID: 25643102
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Semiparametric Estimation with Data Missing Not at Random Using an Instrumental Variable.
    Sun B; Liu L; Miao W; Wirth K; Robins J; Tchetgen Tchetgen EJ
    Stat Sin; 2018 Oct; 28(4):1965-1983. PubMed ID: 33335381
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Correcting HIV prevalence estimates for survey nonparticipation using Heckman-type selection models.
    Bärnighausen T; Bor J; Wandira-Kazibwe S; Canning D
    Epidemiology; 2011 Jan; 22(1):27-35. PubMed ID: 21150352
    [TBL] [Abstract][Full Text] [Related]  

  • 7. National HIV prevalence estimates for sub-Saharan Africa: controlling selection bias with Heckman-type selection models.
    Hogan DR; Salomon JA; Canning D; Hammitt JK; Zaslavsky AM; Bärnighausen T
    Sex Transm Infect; 2012 Dec; 88 Suppl 2(Suppl_2):i17-23. PubMed ID: 23172342
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Adjusting HIV prevalence estimates for non-participation: an application to demographic surveillance.
    McGovern ME; Marra G; Radice R; Canning D; Newell ML; Bärnighausen T
    J Int AIDS Soc; 2015; 18(1):19954. PubMed ID: 26613900
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Instrumental Variable Methods for Continuous Outcomes That Accommodate Nonignorable Missing Baseline Values.
    Ertefaie A; Flory JH; Hennessy S; Small DS
    Am J Epidemiol; 2017 Jun; 185(12):1233-1239. PubMed ID: 28338946
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Folic acid supplementation and malaria susceptibility and severity among people taking antifolate antimalarial drugs in endemic areas.
    Crider K; Williams J; Qi YP; Gutman J; Yeung L; Mai C; Finkelstain J; Mehta S; Pons-Duran C; Menéndez C; Moraleda C; Rogers L; Daniels K; Green P
    Cochrane Database Syst Rev; 2022 Feb; 2(2022):. PubMed ID: 36321557
    [TBL] [Abstract][Full Text] [Related]  

  • 11. The productivity of mental health care: an instrumental variable approach.
    Lu M
    J Ment Health Policy Econ; 1999 Jun; 2(2):59-71. PubMed ID: 11967410
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Causal inference accounting for unobserved confounding after outcome regression and doubly robust estimation.
    Genbäck M; de Luna X
    Biometrics; 2019 Jun; 75(2):506-515. PubMed ID: 30430543
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Instrumental variable estimation of the causal hazard ratio.
    Wang L; Tchetgen Tchetgen E; Martinussen T; Vansteelandt S
    Biometrics; 2023 Jun; 79(2):539-550. PubMed ID: 36377509
    [TBL] [Abstract][Full Text] [Related]  

  • 14. A Two-Step Approach for Analysis of Nonignorable Missing Outcomes in Longitudinal Regression: an Application to Upstate KIDS Study.
    Liu D; Yeung EH; McLain AC; Xie Y; Buck Louis GM; Sundaram R
    Paediatr Perinat Epidemiol; 2017 Sep; 31(5):468-478. PubMed ID: 28767145
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Using Inverse Probability Bootstrap Sampling to Eliminate Sample Induced Bias in Model Based Analysis of Unequal Probability Samples.
    Nahorniak M; Larsen DP; Volk C; Jordan CE
    PLoS One; 2015; 10(6):e0131765. PubMed ID: 26126211
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Identification of causal effects in the presence of nonignorable missing outcome values.
    Mattei A; Mealli F; Pacini B
    Biometrics; 2014 Jun; 70(2):278-88. PubMed ID: 24447366
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Binary variable multiple-model multiple imputation to address missing data mechanism uncertainty: application to a smoking cessation trial.
    Siddique J; Harel O; Crespi CM; Hedeker D
    Stat Med; 2014 Jul; 33(17):3013-28. PubMed ID: 24634315
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Discrete Choice Models for Nonmonotone Nonignorable Missing Data: Identification and Inference.
    Tchetgen EJT; Wang L; Sun B
    Stat Sin; 2018 Oct; 28(4):2069-2088. PubMed ID: 33994754
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Empirical Likelihood in Nonignorable Covariate-Missing Data Problems.
    Xie Y; Zhang B
    Int J Biostat; 2017 Apr; 13(1):. PubMed ID: 28441139
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Analyses of Sensitivity to the Missing-at-Random Assumption Using Multiple Imputation With Delta Adjustment: Application to a Tuberculosis/HIV Prevalence Survey With Incomplete HIV-Status Data.
    Leacy FP; Floyd S; Yates TA; White IR
    Am J Epidemiol; 2017 Feb; 185(4):304-315. PubMed ID: 28073767
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.