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Causal inference in outcome‐dependent two‐phase sampling designs

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  • Weiwei Wang
  • Daniel Scharfstein
  • Zhiqiang Tan
  • Ellen J. MacKenzie

Abstract

Summary. We consider estimation of the causal effect of a treatment on an outcome from observational data collected in two phases. In the first phase, a simple random sample of individuals is drawn from a population. On these individuals, information is obtained on treatment, outcome and a few low dimensional covariates. These individuals are then stratified according to these factors. In the second phase, a random subsample of individuals is drawn from each stratum, with known stratum‐specific selection probabilities. On these individuals, a rich set of covariates is collected. In this setting, we introduce five estimators: simple inverse weighted; simple doubly robust; enriched inverse weighted; enriched doubly robust; locally efficient. We evaluate the finite sample performance of these estimators in a simulation study. We also use our methodology to estimate the causal effect of trauma care on in‐hospital mortality by using data from the National Study of Cost and Outcomes of Trauma.

Suggested Citation

  • Weiwei Wang & Daniel Scharfstein & Zhiqiang Tan & Ellen J. MacKenzie, 2009. "Causal inference in outcome‐dependent two‐phase sampling designs," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(5), pages 947-969, November.
    • Handle: RePEc:bla:jorssb:v:71:y:2009:i:5:p:947-969
    DOI: 10.1111/j.1467-9868.2009.00712.x
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    References listed on IDEAS

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    1. Cosslett, Stephen R, 1981. "Maximum Likelihood Estimator for Choice-Based Samples," Econometrica, Econometric Society, vol. 49(5), pages 1289-1316, September.
    2. Chatterjee N. & Chen Y-H. & Breslow N.E., 2003. "A Pseudoscore Estimator for Regression Problems With Two-Phase Sampling," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 158-168, January.
    3. Newey, Whitney K, 1994. "The Asymptotic Variance of Semiparametric Estimators," Econometrica, Econometric Society, vol. 62(6), pages 1349-1382, November.
    4. Cosslett, Stephen R, 1983. "Distribution-Free Maximum Likelihood Estimator of the Binary Choice Model," Econometrica, Econometric Society, vol. 51(3), pages 765-782, May.
    5. Tan, Zhiqiang, 2006. "A Distributional Approach for Causal Inference Using Propensity Scores," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1619-1637, December.
    6. J. F. Lawless & J. D. Kalbfleisch & C. J. Wild, 1999. "Semiparametric methods for response‐selective and missing data problems in regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(2), pages 413-438, April.
    7. Weaver, Mark A. & Zhou, Haibo, 2005. "An Estimated Likelihood Method for Continuous Outcome Regression Models With Outcome-Dependent Sampling," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 459-469, June.
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    Cited by:

    1. Rose Sherri & van der Laan Mark J., 2011. "A Targeted Maximum Likelihood Estimator for Two-Stage Designs," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-21, March.
    2. Cao, Yongxiu & Yu, Jichang, 2023. "Adjusting for unmeasured confounding in survival causal effect using validation data," Computational Statistics & Data Analysis, Elsevier, vol. 180(C).

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