Design Sensitivity in Observational Studies
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Penn collection
Statistics Papers
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autoregressive models
Bayesian forecasting
call center
cubic smoothing spline
inhomogeneous Poisson process
Markov chain Monte Carlo
multiplicative model
sequential Monte Carlo
state-space model
Statistics and Probability
Bayesian forecasting
call center
cubic smoothing spline
inhomogeneous Poisson process
Markov chain Monte Carlo
multiplicative model
sequential Monte Carlo
state-space model
Statistics and Probability
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Brown, Lawrence D
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Abstract
Outside the field of statistics, the literature on observational studies offers advice about research designs or strategies for judging whether or not an association is causal, such as multiple operationalism or a dose-response relationship. These useful suggestions are typically informal and qualitative. A quantitative measure, design sensitivity, is proposed for measuring the contribution such strategies are then evaluated in terms of their contribution to design sensitivity. A related method for computing the power of a sensitivity analysis is also developed.
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2007-01-01
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Journal of the American Statistical Association