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Analysis of survival data from a randomized trial with all-or-none compliance: Estimating the cost-effectiveness of a cancer screening program
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Accounting for no-shows in experimental evaluation designs
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Maximum likelihood estimation from incomplete data using the EM algorithm
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Dempster, A., Laird, N., and Rubin, D. B. (1977). Maximum likelihood estimation from incomplete data using the EM algorithm. Journal of the Royal Statistical Society, Series B 39, 1-38.
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Addressing complications of intent-to-treat analysis in the combined presence of all-or-none treatment-noncompliance and subsequent missing outcomes
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Frangakis, C. E. and Rubin, D. B. (1999). Addressing complications of intent-to-treat analysis in the combined presence of all-or-none treatment-noncompliance and subsequent missing outcomes. Biometrika 86, 365-379.
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Principle stratification in causal inference
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Clustered encouragement designs with individual noncompliance: Bayesian inference with randomization, and application to advance directive forms
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Frangakis, C. E., Rubin, D. B., and Zhou, X. H. (2002). Clustered encouragement designs with individual noncompliance: Bayesian inference with randomization, and application to advance directive forms (with discussion and rejoinder). Biostatistics 3, 147-177.
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Inference from iterative simulation using multiple sequences
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Gelman, A. and Rubin, D. B. (1992). Inference from iterative simulation using multiple sequences (with discussion). Statistical Science 7, 457-472.
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Non-compliance, ethical imperative or mission impossible
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Choosing among alternative non-experimental method for estimating the impact of social programs: The case of manpower training
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Bayesian inference for causal effects in randomized experiments with noncompliance
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Imbens, G. W. and Rubin, D. B. (1996). Bayesian inference for causal effects in randomized experiments with noncompliance. The Annals of Statistics 25, 305-327.
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Estimating outcome distributions for compliers in instrumental variables models
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Imbens, G. W. and Rubin, D. B. (1997). Estimating outcome distributions for compliers in instrumental variables models. Review of Economic Studies 64, 555-574.
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Statistical techniques for analyzing data from prevention trials: Treatment of no-shows using Rubin's causal model
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Estimating the causal effect of smoking cessation in the presence of confounding factors using a rank preserving structural failure time model
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Multivariate correlation models with mixed discrete and continuous variables
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Inference and missing data
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