Abstract
In randomized studies with missing outcomes, non-identifiable assumptions are required to hold for valid data analysis. As a result, statisticians have been advocating the use of sensitivity analysis to evaluate the effect of varying assumptions on study conclusions. While this approach may be useful in assessing the sensitivity of treatment comparisons to missing data assumptions, it may be dissatisfying to some researchers/decision makers because a single summary is not provided. In this paper, we present a fully Bayesian methodology that allows the investigator to draw a 'single' conclusion by formally incorporating prior beliefs about non-identifiable, yet interpretable, selection bias parameters. Our Bayesian model provides robustness to prior specification of the distributional form of the continuous outcomes.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 495-512 |
| Number of pages | 18 |
| Journal | Biostatistics |
| Volume | 4 |
| Issue number | 4 |
| State | Published - Oct 2003 |
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ASJC Scopus subject areas
- Medicine(all)
- Statistics and Probability
- Statistics, Probability and Uncertainty
Cite this
Incorporating prior beliefs about selection bias into the analysis of randomized trials with missing outcomes. / Scharfstein, Daniel O; Daniels, Michael J.; Robins, James M.
In: Biostatistics, Vol. 4, No. 4, 10.2003, p. 495-512.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Incorporating prior beliefs about selection bias into the analysis of randomized trials with missing outcomes.
AU - Scharfstein, Daniel O
AU - Daniels, Michael J.
AU - Robins, James M.
PY - 2003/10
Y1 - 2003/10
N2 - In randomized studies with missing outcomes, non-identifiable assumptions are required to hold for valid data analysis. As a result, statisticians have been advocating the use of sensitivity analysis to evaluate the effect of varying assumptions on study conclusions. While this approach may be useful in assessing the sensitivity of treatment comparisons to missing data assumptions, it may be dissatisfying to some researchers/decision makers because a single summary is not provided. In this paper, we present a fully Bayesian methodology that allows the investigator to draw a 'single' conclusion by formally incorporating prior beliefs about non-identifiable, yet interpretable, selection bias parameters. Our Bayesian model provides robustness to prior specification of the distributional form of the continuous outcomes.
AB - In randomized studies with missing outcomes, non-identifiable assumptions are required to hold for valid data analysis. As a result, statisticians have been advocating the use of sensitivity analysis to evaluate the effect of varying assumptions on study conclusions. While this approach may be useful in assessing the sensitivity of treatment comparisons to missing data assumptions, it may be dissatisfying to some researchers/decision makers because a single summary is not provided. In this paper, we present a fully Bayesian methodology that allows the investigator to draw a 'single' conclusion by formally incorporating prior beliefs about non-identifiable, yet interpretable, selection bias parameters. Our Bayesian model provides robustness to prior specification of the distributional form of the continuous outcomes.
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M3 - Article
C2 - 14557107
AN - SCOPUS:2942658011
VL - 4
SP - 495
EP - 512
JO - Biostatistics
JF - Biostatistics
SN - 1465-4644
IS - 4
ER -