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Finding the best treatment under heavy censoring and hidden bias

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  • Myoung‐jae Lee
  • Unto Häkkinen
  • Gunnar Rosenqvist

Abstract

Summary. We analyse male survival duration after hospitalization following an acute myocardial infarction with a large (N=11024) Finnish data set to find the best performing hospital district (and to disseminate its treatment protocol). This is a multiple‐treatment problem with 21 treatments (i.e. 21 hospital districts). The task of choosing the best treatment is difficult owing to heavy right censoring (73%), which makes the usual location measures (the mean and median) unidentified; instead, only lower quantiles are identified. There is also a sample selection issue that only those who made it to a hospital alive are observed (54%); this becomes a problem if we wish to know their potential survival duration after hospitalization, if they had survived to a hospital contrary to the fact. The data set is limited in its covariates—only age is available—but includes the distance to the hospital, which plays an interesting role. Given that only age and distance are observed, it is likely that there are unobserved confounders. To account for them, a sensitivity analysis is conducted following pair matching. All estimators employed point to a clear winner and the sensitivity analysis indicates that the finding is fairly robust.

Suggested Citation

  • Myoung‐jae Lee & Unto Häkkinen & Gunnar Rosenqvist, 2007. "Finding the best treatment under heavy censoring and hidden bias," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(1), pages 133-147, January.
    • Handle: RePEc:bla:jorssa:v:170:y:2007:i:1:p:133-147
    DOI: 10.1111/j.1467-985X.2006.00442.x
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    References listed on IDEAS

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    1. Powell, James L, 1986. "Symmetrically Trimmed Least Squares Estimation for Tobit Models," Econometrica, Econometric Society, vol. 54(6), pages 1435-1460, November.
    2. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    3. Powell, James L., 1986. "Censored regression quantiles," Journal of Econometrics, Elsevier, vol. 32(1), pages 143-155, June.
    4. M.‐J. Lee & H. Kim, 1998. "Semiparametric econometric estimators for a truncated regression model: a review with an extension," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 52(2), pages 200-225, June.
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    Cited by:

    1. Chang, Pao-Li & Lee, Myoung-Jae, 2011. "The WTO trade effect," Journal of International Economics, Elsevier, vol. 85(1), pages 53-71, September.
    2. Myoung-jae Lee, 2007. "Difference in Generalized-Differences with Panel Data: Effects of Moving from Private to Public School on Test Scores," Discussion Paper Series 0721, Institute of Economic Research, Korea University.
    3. Choi, Jin-young & Lee, Myoung-jae, 2019. "Twins are more different than commonly believed, but made less different by compensating behaviors," Economics & Human Biology, Elsevier, vol. 35(C), pages 18-31.
    4. Myoung‐jae Lee & Jin‐young Choi, 2022. "Finding mover–stayer quantile difference due to unobservables using quantile selection corrections," Bulletin of Economic Research, Wiley Blackwell, vol. 74(3), pages 704-721, July.

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