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Published in:

01-10-2015

Bayesian Methods for Prevention Research

Author: Joseph B. Kadane

Published in: Prevention Science | Issue 7/2015

Abstract

Bayesian statistics represents a paradigm shift in statistical reasoning and an approach to analysis that is applicable to prevention trials with small samples. This paper introduces the reader to the philosophy behind Bayesian statistics. This introduction is followed by a review of some issues that arise in sampling statistics and how Bayesian methods address them. Finally, the article provides an extended illustration of the application of Bayesian statistics to data from a prevention trial that tested a family-focused intervention.

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Congdon, P. (2003). Applied Bayesian modelling. Probability and statistics. Chichester: Wiley.CrossRef

Congdon, P. (2005). Bayesian models for categorical data. Probability and statistics. Chichester: Wiley.CrossRef

Congdon, P. (2010). Applied Bayesian hierarchical methods. Boca Raton: Chapman and Hall.CrossRef

Dallal, S., & Hall, W. (1983). Approximating priors by mixtures of conjugate priors. Journal of the Royal Statistical Society: Series B, 45, 278–286.

Dawid, A., Stone, M., Zidek, J. (1974). Marginal paradoxes in Bayesian and structural inferences. Journal of the Royal Statistical Society: Series B, 35, 189–233.

deFinetti, B. (1980). Foresight: Its logical laws, its subjective sources. In Kyburg, H.E., & Smokler, H. (Eds.), Studies in subjective probability, (pp. 55–118). Huntington: Krieger.

Diaconis, P., & Ylvisaker, D. (1985). Quantifying prior opinion In Bernardo, J., DeGroot, M., Lindley, D., Smith, A. (Eds.), Bayesian statistics (Vol. 2, pp. 133–156). North Holland: Amsterdam.

Fisher, F. (1966). The identification problem in econometrics. New York: McGraw-Hill.

Geisser, S. (1993). Predictive inference: An introduction. Chapman and Hall: Boca Raton.CrossRef

Gelman, A., Carlin, J., Stern, H., Dunson, D., Vehtari, A., Rubin, D. (2013). Bayesian data analysis., 3rd edn. Chapman and Hall: Boca Raton.

Gelman, A., & Hill, J. (2006). Data analysis using regression and multilevel/hierarchical models. Cambridge: Cambridge University Press.CrossRef

Gill, J. (2007). Bayesian methods: A social and behavioral sciences approach, 2nd edn. Boca Raton: Chapman and Hall.

Jackman, S. (2009). Bayesian analysis for the social sciences. Chichester: Wiley.CrossRef

Kadane, J. (2011). Principles of uncertainty. Boca Raton: Chapman & Hall/CRC.CrossRef

Kadane, J., Dickey, J., Winkler, R., Smith, W., Peters, S. (1980). Interactive elicitation of opinion for a normal linear model. Journal of the American Statistical Association, 75, 845–854.CrossRef

Lancaster, T. (2004). An introduction to modern Bayesian econometrics. Blackwell: Malden.

Lindley, D. (1985). Making decisions., 2nd edn. New York: Wiley.

Lindley, D. (2014). Understanding uncertainty., 2nd edn. Hoboken: Wiley.

Lindley, D., & El-Sayyad, G. (1968). The Bayesian estimation of a linear functional relationship. Journal of the Royal Statistical Society: Series B, 30, 190–202.

O’Hagan, A., & Forster, J. (2004). Bayesian inference.. Kendall’s advanced theory of statistics, 2nd edn. (Vol. 2, p. 325). London: Arnold.

Raiffa, H., & Schlaifer, R. (1961). Applied statistical decision theory. MIT Press: Cambridge.

Rubin, D. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66, 688–701.CrossRef

Rubin, D. (2005). Causal inference using potential outcomes. Journal of the American Statistical Association, 100, 322–331.CrossRef

Silver, N. (2012). The signal and the noise: Why so many predictions fail - and some don’t. London: Penguin Group.

Title: Bayesian Methods for Prevention Research
Author: Joseph B. Kadane
Publication date: 01-10-2015
Publisher: Springer US
Published in: Prevention Science / Issue 7/2015
Print ISSN: 1389-4986
Electronic ISSN: 1573-6695
DOI: https://doi.org/10.1007/s11121-014-0531-x

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