This package provides functions for doing sensitivity analysis on coefficients of regression type models (regression, logistic regression, cox proportional hazards). These assume a true model of the form: g(y)=beta*x+gamma*u+theta*z where u is an unmeasured potential lurking variable, x is the main variable of interest (treatment) and z represents other potential variables in the model. The response variable (y) can be continuous, binary, or a survival object. These functions examine the effect of u on beta for different values of gamma and the relationship between u and y.
The key functions are all of the form obsSensYXU where Y specifies the type of variable used as the response variable (y), X specifies the type of variable used as the main predictor variable to be tested (x), and U specifies the type of unmeasured variable to use. They can take on the following values: S - survival analysis (Y only), C - Categorical (logistic regression, currently only handles 2 levels), or N - normal (or continuous variables).
All the functions take either a fitted model object (lm, glm, or coxph) or a coefficient value and its confidence interval. You then specify values (vector) for the possible relationship between Y and U and X and U. The return value is a list with a matrix or array with the adjusted coefficients and upper and lower confidence limits.
Greg Snow [email protected]
Lin, DY and Psaty, BM and Kronmal, RA. (1998): Assessing the Sensitivity of Regression Results to Unmeasured Confounders in Observational Studies. Biometrics, 54 (3), Sep, pp. 948-963.
Baer, VL et. als (2007): Do Platelet Transfusions in the NICU Adversely Affect Survival? Analysis of 1600 Thrombocytopenic neonates in a mulihospital healthcare system. Journal of Perinatology, 27, pp. 790-796.
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# Recreate tables from above references obsSensCCC( log(23.1), log(c(6.9, 77.7)), g0=c(2,6,10), p0=seq(0,.5,.1), p1=seq(0,1,.2) ) obsSensSCC( log(1.21), log(c(1.09,1.25)), p0=seq(0,.5,.1), p1=seq(0,1,.1), g0=3 ) obsSensCNN( log(1.14), log(c(1.10,1.18)), rho=c(0,.5, .75, .85, .9, .95, .98, .99), gamma=seq(0,1,.2), sdx=4.5 )
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