help nonparmdeVersion 1.0.0

Title

nonparmde -- Calculates the minimum detectable effect (MDE) using a Horvitz-Thompson estimator and a size (and covariate) adjusted Raj estimator for cluster randomized controlled experiments.

Syntax

nonparmde varlist ,
mtreatment(clusternum)
mcontrol(clusternum)
[n(clustersizevar)
kx(numlist)
averages
power(num)
ci(num)
crossfold(num)
avgclustersize(num)]

Description

nonparmde is a method for calculating the minimum detectable effect (MDE) using the nonparametric estimators proposed in Middleton & Aronow (2011). This program is for use on cluster-level data to calculate the minimum effect that a cluster randomized experiment would be able to reliably detect at a given level of statistical power.

Options

varlist the name of the prior DV variable, optionally followed by a list of covariates that are expected to explain variance in the DV variable (all variables must be either cluster averages or cluster totals)

mtreatment(clusternum) number of clusters assigned to treatment group

mcontrol(clusternum) number of clusters assigned to control group

n(clustersizevar) name of the variable that denotes cluster size (if left unspecified avgclustersize(num) must be specified)

kx(numlist) regression coefficients of covariates regressed on prior DV variable, controlling for cluster size (calculated automatically if left unspecified)

averages forces the program to treat all variables as cluster averages (n(clustersizevar) must be specified if this option is used)

power(num) statistical power (if left unspecified assumes 80% power)

ci(num) confidence interval (if left unspecified assumes 95% CI)

crossfold(num) crossfolds used for the calculation of kx; can be either an integer or a variable specifying cutoffs (if left unspecified no crossfolding is done). The command xvalols must be installed.

avgclustersize(num) average cluster size (use only if n(clustersizevar) is left unspecified)

Examples

. nonparmde Y_total, mtreatment(50) mcontrol(50) avgclustersize(20.61)

. nonparmde y_avg x_avg n, n(n) averages mtreatment(50) mcontrol(50)

. nonparmde Y_total X_total Z_total, mtreatment(50) mcontrol(50) kx(.2 .1) averageclustersize(20.61)

. nonparmde Y_total X_total Z_total, mtreatment(50) mcontrol(50) averageclustersize(20.61)

. nonparmde Y_total X_total Z_total, mtreatment(50) mcontrol(50) n(n)

. nonparmde Y_total X_total Z_total n, mtreatment(50) mcontrol(50) n(n)

. nonparmde Y_total X_total Z_total n, mtreatment(50) mcontrol(50) n(n) crossfold(cut)

. nonparmde Y_total X_total Z_total n, mtreatment(50) mcontrol(50) n(n) crossfold(2)

Saved Results

nonparmde saves the following in e():

Scalars

e(Vht) Horvitz-Thompson variance

e(Vraj) Raj variance

e(Vrajcovars) covariate adjusted Raj variance

References

Middleton, Joel A. and Aronow, Peter M., Unbiased Estimation of the Average Treatment Effect in Cluster-Randomized Experiments (April 5, 2011). Available at SSRN: http://ssrn.com/abstract=1803849

Authors

Joel Middleton

New York University

joel.middleton@gmail.com

John Ternovski

Analyst Institute

johnt1@gmail.com