help blinding-------------------------------------------------------------------------------

Title

blinding-- Estimate blinding index

Syntax

blindingmatname [,options]

optionsDescription -------------------------------------------------------------------------bweight(matname)weights for the main data, to be used in Bang's index when the data is in the 2x5 formancillary(matname)ancillary dataancweight(matname)weights for the ancillary datalevel(#)confidence level

Description

blindingis an implementation of the blinding indexes introduced by James et al (1990) and Bang et al (2004), to assess the success of blinding in clinical trials. The command performs the estimation of both indexes, along with standard errors, confidence intervals and significance levels, based on the null hypothesis that the trial is successfully blinded.The usual procedure to measure the effectivness of blindness is to ask the participants for the treatment allocation they believe they are assigned. The command -blinding- works with the two most common structures for the responses, that is, the 2x3 setting and the 2x5 setting.

The argument

matnameis a matrix of dimensions either 2x3 or 2x5, where the rows correspond to the true allocation of the participants: the first row contains the data from the drug arm and the second row contains the data from the placebo arm; the columns correspond to the treatment allocation that participants believe they are assigned. In the 2x3 setting, the columns correspond to drug, placebo and "don't know" (DK) respectively, and in the 2x5 setting, the columns correspond to "strongly believe that the treatment is drug", "somehow believe that the treatment is drug", "somehow believe that the treatment is placebo", "strongly believe that the treatment is placebo" and "don't know", in this order.The Jame's index is only computed for the 2x3 setting. When the original data is entered in the 2x5 form, it is transformed to the 2x3 form in order to compute the James' index.

Options

bweight(matname)specifies the weights to be applied to the main data for computing the Bang's index when the data is in the 2x5 format. This option is used to assign weights to the "somewhat believe" responses and the "strongly believe" responses. It is a matrix in the same 2x5 format; the default isweight(1,0.5,0.5,1,0\1,0.5,0.5,1,0). According to Bang et al(2004), the weights matrix needs to verify the following conditions: the elements of this matrix should be numbers between 0 and 1; the first and the forth column should be equal; the second and the third column should be equal; the last column should only contain zeros.

ancillary(matname)specifies a 2x3 matrix of ancillary data (when available), to be used to compute the Bang's index. In some trials, participants who answer DK are asked to choose a treatment allocation anyway. The ancillary matrix contains this data. Therefore, the total of counts for each row in this matrix should be equal to the corresponding DK cell in the main data matrix.

ancweight(matname)specifies a 2x3 matrix containing the weights for the ancillary data. Requires the ancillary() option. The default isancweight(0.25,0.25,0\0.25,0.25,0). This matrix needs to verify: the entries should be numbers between 0 and 1; the first and the second column should be equal; the last column should only contain zeros.

level(#)specifies the level of the confidence intervals. The default islevel(95).

ExamplesSetup

. matrix define A=(2,5,3,4,6\2,4,3,5,8). matrix define B=(1,0.6,0.6,1,0\1,0.6,0.6,1,0). matrix define C=(3,3,0\3,4,1). matrix define D=(0.2,0.2,0\0.2,0.2,0)Estimate blinding indexes for A

. blinding AEstimate blinding indexes for A using the weight matrix B

. blinding A, bweight(B)Estimate blinding indexes for A using the ancillary data C

. blinding A, ancillary(C)Estimate blinding indexes for A using the ancillary data C and its weight D

. blinding A, ancillary(C) ancweight(D)Estimate blinding indexes for A using the weight matrix B and the ancillary data C

. blinding A, bweight(B) ancillary(C)Estimate blinding indexes for A using the weight matrix B, the ancillary data C and its weight D, and specify the level of confidence interval to 90

. blinding A, bweight(B) ancillary(C) ancweight(D) level(90)

Saved results

blindingsaves the following inr():Matrices

r(b)vector containing the reported indexesr(var)vector containing the variances of the indexes

ReferencesBang, H., L. Ni, and C. E. Davis. 2004. Assessment of blinding in clinical trial.

Controlled Clinical Trials25: 143-156.James, K. E., K. K. Lee, H. K. Kraemer, and R. K. Fuller. 1990. An index for assessing blindness in a multicenter clinical trial: disulfiram for alcohol cessation-a VA cooperative study.

Statistics in Medicine15: 1421-1434.

AuthorJiefeng Chen, Texas A&M University, Department of Statistics