obsSens-package {obsSens}

Perform sensitivity analysis on observational studies to explore possible effects of unmeasured (lurking) variables.

Description

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.

Details

Package:	obsSens
Type:	Package
Version:	1.0
Date:	2007-12-21
License:	Artistic-2.0

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.

Author(s)

Greg Snow [e-mail]

References

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.

Examples

# 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 )