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Volumn 45, Issue 2, 2017, Pages 202-219

Estimating treatment effects in observational studies with both prevalent and incident cohorts

Author keywords

Incident cohort; MSC 2010: Primary 62N01; prevalent cohort; propensity score; sampling bias; secondary 62N02; selection bias

Indexed keywords


EID: 85017564647     PISSN: 03195724     EISSN: 1708945X     Source Type: Journal    
DOI: 10.1002/cjs.11317     Document Type: Article
Times cited : (10)

References (25)
  • 1
    • 30344432123 scopus 로고    scopus 로고
    • Asymptotic behavior of the unconditional npmle of the length-biased survivor function from right censored prevalent cohort data
    • Asgharian, M. & Wolfson, D. B. (2005). Asymptotic behavior of the unconditional npmle of the length-biased survivor function from right censored prevalent cohort data. The Annals of Statistics, 33, 2109–2131.
    • (2005) The Annals of Statistics , vol.33 , pp. 2109-2131
    • Asgharian, M.1    Wolfson, D.B.2
  • 2
    • 44649173785 scopus 로고    scopus 로고
    • A critical appraisal of propensity-score matching in the medical literature between 1996 and 2003
    • Austin, P. C. (2008). A critical appraisal of propensity-score matching in the medical literature between 1996 and 2003. Statistics in Medicine, 27, 2037–2049.
    • (2008) Statistics in Medicine , vol.27 , pp. 2037-2049
    • Austin, P.C.1
  • 3
    • 84866597106 scopus 로고    scopus 로고
    • Locally recurrent or metastatic breast cancer: Esmo clinical practice guidelines for diagnosis, treatment and follow-up
    • ESMO Guidelines Working Group
    • Cardoso, F., Harbeck, N., Fallowfield, L., Kyriakides, S., Senkus, E., & ESMO Guidelines Working Group (2012). Locally recurrent or metastatic breast cancer: Esmo clinical practice guidelines for diagnosis, treatment and follow-up. Annals of Oncology, 23, vii11–vii19.
    • (2012) Annals of Oncology , vol.23 , pp. vii11-vii19
    • Cardoso, F.1    Harbeck, N.2    Fallowfield, L.3    Kyriakides, S.4    Senkus, E.5
  • 4
    • 84882589400 scopus 로고    scopus 로고
    • Survival analysis without survival data: Connecting length-biased and case-control data
    • Chan, K. C. G. (2013). Survival analysis without survival data: Connecting length-biased and case-control data. Biometrika, 100, 1–7.
    • (2013) Biometrika , vol.100 , pp. 1-7
    • Chan, K.C.G.1
  • 5
    • 84866763792 scopus 로고    scopus 로고
    • Estimating propensity scores and causal survival functions using prevalent survival data
    • Cheng, Y.-J. & Wang, M.-C. (2012). Estimating propensity scores and causal survival functions using prevalent survival data. Biometrics, 68, 707–716.
    • (2012) Biometrics , vol.68 , pp. 707-716
    • Cheng, Y.-J.1    Wang, M.-C.2
  • 6
    • 3543135271 scopus 로고    scopus 로고
    • Tutorial in biostatistics: Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group
    • D'Agostino, R. B. (1998). Tutorial in biostatistics: Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group. Statistics in Medicine, 17, 2265–2281.
    • (1998) Statistics in Medicine , vol.17 , pp. 2265-2281
    • D'Agostino, R.B.1
  • 7
    • 84928914864 scopus 로고    scopus 로고
    • Propensity score estimation in the presence of length-biased sampling: A non-parametric adjustment approach
    • 3
    • Ertefaie, A., Asgharian, M., & Stephens, D. (2014). Propensity score estimation in the presence of length-biased sampling: A non-parametric adjustment approach. Stat, 3(1), 83–94.
    • (2014) Stat , Issue.1 , pp. 83-94
    • Ertefaie, A.1    Asgharian, M.2    Stephens, D.3
  • 10
    • 50849129626 scopus 로고    scopus 로고
    • Covariate balance in simple, stratified and clustered comparative studies
    • Hansen, B. B. & Bowers, J. (2008). Covariate balance in simple, stratified and clustered comparative studies. Statistical Science, 23, 219–236.
    • (2008) Statistical Science , vol.23 , pp. 219-236
    • Hansen, B.B.1    Bowers, J.2
  • 11
    • 40549104115 scopus 로고    scopus 로고
    • Misunderstandings between experimentalists and observationalists about causal inference
    • Imai, K., King, G., & Stuart, E. A. (2008). Misunderstandings between experimentalists and observationalists about causal inference. Journal of the Royal Statistical Society: Series A, 171, 481–502.
    • (2008) Journal of the Royal Statistical Society: Series A , vol.171 , pp. 481-502
    • Imai, K.1    King, G.2    Stuart, E.A.3
  • 12
    • 46249131752 scopus 로고    scopus 로고
    • Demystifying double robustness: A comparison of alternative strategies for estimating a population mean from incomplete data
    • Kang, J. D. & Schafer, J. L. (2007). Demystifying double robustness: A comparison of alternative strategies for estimating a population mean from incomplete data. Statistical Science, 22, 523–539.
    • (2007) Statistical Science , vol.22 , pp. 523-539
    • Kang, J.D.1    Schafer, J.L.2
  • 13
    • 0000286861 scopus 로고
    • Rank regression methods for left-truncated and right-censored data
    • Lai, T. L. & Ying, Z. (1991). Rank regression methods for left-truncated and right-censored data. The Annals of Statistics, 10, 531–556.
    • (1991) The Annals of Statistics , vol.10 , pp. 531-556
    • Lai, T.L.1    Ying, Z.2
  • 14
    • 84896544710 scopus 로고    scopus 로고
    • A weighting analogue to pair matching in propensity score analysis
    • Li, L. & Greene, T. (2013). A weighting analogue to pair matching in propensity score analysis. The International Journal of Biostatistics, 9, 215–234.
    • (2013) The International Journal of Biostatistics , vol.9 , pp. 215-234
    • Li, L.1    Greene, T.2
  • 15
    • 83655183094 scopus 로고    scopus 로고
    • Buckley–james-type estimator with right-censored and length-biased data
    • Ning, J., Qin, J., & Shen, Y. (2011). Buckley–james-type estimator with right-censored and length-biased data. Biometrics, 67, 1369–1378.
    • (2011) Biometrics , vol.67 , pp. 1369-1378
    • Ning, J.1    Qin, J.2    Shen, Y.3
  • 16
    • 77952996245 scopus 로고    scopus 로고
    • Statistical methods for analyzing right-censored length-biased data under cox model
    • Qin, J. & Shen, Y. (2010). Statistical methods for analyzing right-censored length-biased data under cox model. Biometrics, 66, 382–392.
    • (2010) Biometrics , vol.66 , pp. 382-392
    • Qin, J.1    Shen, Y.2
  • 17
    • 77951622706 scopus 로고
    • The central role of the propensity score in observational studies for causal effects
    • Rosenbaum, P. R. & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70, 41–55.
    • (1983) Biometrika , vol.70 , pp. 41-55
    • Rosenbaum, P.R.1    Rubin, D.B.2
  • 18
    • 84945581878 scopus 로고
    • Constructing a control group using multivariate matched sampling methods that incorporate the propensity score
    • Rosenbaum, P. R. & Rubin, D. B. (1985). Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. The American Statistician, 39, 33–38.
    • (1985) The American Statistician , vol.39 , pp. 33-38
    • Rosenbaum, P.R.1    Rubin, D.B.2
  • 20
    • 0000394113 scopus 로고
    • Matching to remove bias in observational studies
    • Rubin, D. B. (1973). Matching to remove bias in observational studies. Biometrics, 29, 159–183.
    • (1973) Biometrics , vol.29 , pp. 159-183
    • Rubin, D.B.1
  • 21
    • 85144841000 scopus 로고
    • Using multivariate matched sampling and regression adjustment to control bias in observational studies
    • Rubin, D. B. (1979). Using multivariate matched sampling and regression adjustment to control bias in observational studies. Journal of the American Statistical Association, 74, 318–328.
    • (1979) Journal of the American Statistical Association , vol.74 , pp. 318-328
    • Rubin, D.B.1
  • 22
    • 70349761736 scopus 로고    scopus 로고
    • Analyzing length-biased data with semiparametric transformation and accelerated failure time models
    • Shen, Y., Ning, J., & Qin, J. (2009). Analyzing length-biased data with semiparametric transformation and accelerated failure time models. Journal of the American Statistical Association, 104, 1192–1202.
    • (2009) Journal of the American Statistical Association , vol.104 , pp. 1192-1202
    • Shen, Y.1    Ning, J.2    Qin, J.3
  • 23
    • 0000267144 scopus 로고
    • Nonparametric estimation in the presence of length bias
    • Vardi, Y. (1982). Nonparametric estimation in the presence of length bias. The Annals of Statistics, 10, 616–620.
    • (1982) The Annals of Statistics , vol.10 , pp. 616-620
    • Vardi, Y.1
  • 24
    • 0027313977 scopus 로고
    • Statistical models for prevalent cohort data
    • Wang, M.-C., Brookmeyer, R., & Jewell, N. P. (1993). Statistical models for prevalent cohort data. Biometrics, 49, 1–11.
    • (1993) Biometrics , vol.49 , pp. 1-11
    • Wang, M.-C.1    Brookmeyer, R.2    Jewell, N.P.3
  • 25
    • 85018177420 scopus 로고    scopus 로고
    • Forward and backward recurrence times and length biased sampling: Age specific models
    • In, Springer, New York, USA
    • Zelen, M. (2006). Forward and backward recurrence times and length biased sampling: Age specific models. In Probability, Statistics and Modelling in Public Health, Springer, New York, USA, 1–11.
    • (2006) Probability, Statistics and Modelling in Public Health , pp. 1-11
    • Zelen, M.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.