cmsens
is used to conduct sensitivity analysis for unmeasured confounding via
the Evalue approach by Vanderweele et al. (2017) and Smith et al. (2019), and
sensitivity analysis for measurement error via regression calibration by Carroll
et al. (1995) and SIMEX by Cook et al. (1994) and Küchenhoff et al. (2006).
cmsens( object = NULL, sens = "uc", MEmethod = "simex", MEvariable = NULL, MEvartype = NULL, MEerror = NULL, lambda = c(0.5, 1, 1.5, 2), B = 200, nboot.rc = 400 ) # S3 method for cmsens.uc print(x, ...) # S3 method for cmsens.me print(x, ...) # S3 method for cmsens.me summary(object, ...) # S3 method for summary.cmsens.me print(x, digits = 4, ...)
object  an object of class 

sens  sensitivity analysis for unmeasured confounding or measurement error.

MEmethod  method for measurement error correction. 
MEvariable  variable measured with error. 
MEvartype  type of the variable measured with error. Can be 
MEerror  a vector of standard deviations of the measurement error (when 
lambda  a vector of lambdas for SIMEX. Default is 
B  number of simulations for SIMEX. Default is 
nboot.rc  number of boots for correcting the varcov matrix of coefficients
with regression calibration. Default is 
x  an object of class 
...  other arguments. 
digits  minimal number of significant digits. See print.default. 
If sens
is uc
, an object of class cmsens.uc
is returned:
the function call,
a data frame in which the first three columns are point estimates, lower limits of 95% confidence intervals and upper limits of 95% confidence intervals of causal effects on the risk or rate ratio scale and the last three columns are Evalues on the risk or rate ratio scale,
the function call,
a list which might contain MEmethod
, MEvariable
, MEvartype
,
MEerror
, lambda
, B
, nboot.rc
and reliability ratio (which is calculated
by 1  MEerror[i]/sd(data[, MEvariable])
for i=1,...,length(MEerror)
when
MEvartype
is continuous
),
naive causal mediation analysis results,
a list of causal mediation analysis results after correcting errors in MEerror
,
Sensitivity Analysis for Unmeasured Confounding
Currently, sensitivity analysis for unmeasured confounding are available when the outcome regression model is fitted by lm, glm, glm.nb, gam, multinom, polr.
All Evalues are reported on the risk or rate ratio scale. If the causal effects are estimated on the difference scale (i.e., the outcome is continuous), they are transformed into risk ratios using the transformation described by Vanderweele et al. (2017).
Sensitivity Analysis for Measurement Error
Currently, sensitivity analysis for measurement error are available:
1) when the regression
model involving the variable measured with error is fitted by lm,
glm (with family gaussian
, binomial
or poisson
), multinom,
polr, coxph or survreg and model
is
rb
or gformula
;
2) when estimation
is paramfunc
.
Sensitivity analysis for measurement error only supports a single variable measured with error. Regression calibration requires that the variable measured with error be an independent continuous variable in the regression it's involved in. SIMEX supports a continuous or categorical variable measured with error. Quadratic extrapolation method is implemented for SIMEX.
print
: Print results of cmsens.uc
nicely
print
: Print results of cmsens.me
nicely
summary
: Summarize results of cmsens.me
nicely
print
: Print the summary of cmsens.me
nicely
VanderWeele TJ, Ding P (2017). Sensitivity analysis in observational research: introducing the EValue. Annals of Internal Medicine. 167(4): 268  274.
Smith LH, VanderWeele TJ (2019). Mediational Evalues: Approximate sensitivity analysis for unmeasured mediatoroutcome confounding. Epidemiology. 30(6): 835  837.
Carrol RJ, Ruppert D, Stefanski LA, Crainiceanu C (2006). Measurement Error in Nonlinear Models: A Modern Perspective, Second Edition. London: Chapman & Hall.
Cook JR, Stefanski LA (1994). Simulationextrapolation estimation in parametric measurement error models. Journal of the American Statistical Association, 89(428): 1314  1328.
Küchenhoff H, Mwalili SM, Lesaffre E (2006). A general method for dealing with misclassification in regression: the misclassification SIMEX. Biometrics. 62(1): 85  96.
Stefanski LA, Cook JR (1995). Simulationextrapolation: the measurement error jackknife. Journal of the American Statistical Association. 90(432): 1247  56.
Valeri L, Lin X, VanderWeele TJ (2014). Mediation analysis when a continuous mediator is measured with error and the outcome follows a generalized linear model. Statistics in medicine, 33(28): 4875 – 4890.
if (FALSE) { library(CMAverse) # 10 boots are used for illustration naive < cmest(data = cma2020, model = "rb", outcome = "contY", exposure = "A", mediator = c("M1", "M2"), basec = c("C1", "C2"), EMint = TRUE, mreg = list("logistic", "multinomial"), yreg = "linear", astar = 0, a = 1, mval = list(0, "M2_0"), estimation = "imputation", inference = "bootstrap", nboot = 10) exp1 < cmsens(object = naive, sens = "uc") exp2 < cmsens(object = naive, sens = "me", MEmethod = "rc", MEvariable = "C1", MEvartype = "con", MEerror = c(0.1, 0.2)) summary(exp2) # B = 10 is used for illustration exp3 < cmsens(object = naive, sens = "me", MEmethod = "simex", MEvariable = "M1", MEvartype = "cat", MEerror = list(matrix(c(0.95,0.05,0.05,0.95), nrow = 2), matrix(c(0.9,0.1,0.1,0.9), nrow = 2)), B = 10) summary(exp3) }