sen: Sensivity analysis with signed score test

In qingyuanzhao/CrossScreening: Cross-Screening in Observational Studies that Test Many Hypotheses

Description

This function implements Rosenbaum's sensitivity analysis for pair-matched observational study with general signed score test. It is faster and more flexible than the psens function in the package rbounds.

Usage

sen(d, mm = NULL, gamma = 1, alternative = c("greater", "less"),
  score.method = c("approximate", "exact"), tau = 0)

Arguments

d	a vector of treatment-minus-control differences
mm	a vector (m, munder, mover) or a matrix, each column a vector (m, munder, mover) that indicates the U-statistic.s NULL means Wilcoxon's signed rank test.
gamma	a vector of sensitivity parameters (must be >= 1).
alternative	report p-value corresponds to the maximum ("upper") or minimum ("lower") bound
score.method	either approximate score or exact score
tau	a scalar, null hypothesis is the additive effect is tau (default 0)

Value

A list

p.value

p-values corresponding to each entry of gamma

p.value2

two sided p-values

gamma.hat

estimate of design sensitivity

T

test statistic

E

Means of the test statistic under sensivity gamma

V

Variances of the test statistic under sensitivity gamma

eff.size

Effect size of T compared to E and V

E.gamma1

Expectation of T under null at Gamma = 1

Author(s)

Paul Rosenbaum, Qingyuan Zhao

References

• Rosenbaum, Paul R. Observational Studies. Springer New York, 2002.

• Rosenbaum, P. R. (2011). A New u-Statistic with Superior Design Sensitivity in Matched Observational Studies. Biometrics, 67(3), 1017-1027.

Examples

data(lead)
d.lead <- lead$exposed - lead$control
sen(d.lead, gamma = c(1, 2, 3, 4, 5, 6))

qingyuanzhao/CrossScreening documentation built on May 26, 2019, 11:35 a.m.