Of limited interest to most users, function mscorev() computes M-scores. A similar function function is in the package sensitivitymv.
mscorev(ymat, inner = 0, trim = 3, lambda = 0.5)Generally, a matrix with the same dimensions as ymat containing the M-scores.
If there are I matched sets and the largest matched set contains J individuals, then y is an I by J matrix with one row for each matched set. If matched set i contains one treated individual and k controls, where k is at least 1 and at most J-1, then y[i,1] is the treated individual's response, y[i,2],...,y[i,k+1] are the responses of the k controls, and y[i,k+2],...,y[i,J] are equal to NA.
Although y can contain NA's, y[i,1] and y[i,2] must not be NA for every i. That is, every matched set must have at least one treated subject and one control.
inner and trim together define the \(\psi\)-function for the M-statistic. The default values yield a version of Huber's \(\psi\)-function, while setting inner = 0 and trim = Inf uses the mean within each matched set. The \(\psi\)-function is an odd function, so \(\psi(w) = -\psi(-w)\). For \(w \ge 0\), the \(\psi\)-function is \(\psi(w)=0\) for \(0 \le w \le \) inner, is \(\psi(w)= \) trim for \(w \ge \) trim, and rises linearly from 0 to trim for inner < w < trim.
If uncertain about inner, trim and lambda, then use the defaults.
An error will result unless \(0 \le \) inner \( \le \) trim.
Taking trim < Inf limits the influence of outliers; see Huber (1981). Taking trim < Inf and inner = 0 uses Huber's psi function. Taking trim = Inf does no trimming and is similar to a weighted mean; see TonT. Taking inner > 0 often increases design sensitivity; see Rosenbaum (2013).
inner and trim together define the \(\psi\)-function for the M-statistic. See inner.
Before applying the \(\psi\)-function to treated-minus-control differences, the differences are scaled by dividing by the lambda quantile of all within set absolute differences. Typically, lambda = 1/2 for the median. The value of lambda has no effect if trim=Inf and inner=0. See Maritz (1979) for the paired case and Rosenbaum (2007) for matched sets.
An error will result unless 0 < lambda < 1.
Paul R. Rosenbaum
Huber, P. (1981) Robust Statistics. New York: John Wiley. (M-estimates based on M-statistics.)
Maritz, J. S. (1979). A note on exact robust confidence intervals for location. Biometrika 66 163--166. (Introduces exact permutation tests based on M-statistics by redefining the scaling parameter.)
Rosenbaum, P. R. (2007) Sensitivity analysis for m-estimates, tests and confidence intervals in matched observational studies. Biometrics, 2007, 63, 456-464. <doi:10.1111/j.1541-0420.2006.00717.x>
Rosenbaum, P. R. (2013). Impact of multiple matched controls on design sensitivity in observational studies. Biometrics 69 118-127. (Introduces inner trimming.) <doi:10.1111/j.1541-0420.2012.01821.x>
Rosenbaum, P. R. (2015). Two R packages for sensitivity analysis in observational studies. Observational Studies, v. 1. (Free on-line.)
# The example reproduces Table 3 in Rosenbaum (2007).
data(tbmetaphase)
mscorev(tbmetaphase,trim=1)
Run the code above in your browser using DataLab