help biasmis (Quantitative Bais Aanlysis in Epidemiology)

Title

biasmis -- performs bias analysis for misclassification

Syntax

biasmis
depvar indepvar
[if] [in]
[, options]

Options
sa(#) Specify the sensitivity and the default is 0.75

sb(#) Specify the specificity/sensitivity. The default is 0.95

sc(#) Specify the sensitivity/specificity. The default is 0.75

sd(#) Specify the specificity and the default value is 0.95

mistype(#) Specify the type of the misclassification. The default is 1, which indicates the exposure Misclassification

generate generate newvar containing the predicted binary true classification variable for each individual;

seed specifies the initial value of the random-number seed;

biasmisi
a b c d
[if] [in]
[, options]

Options
sa(#) Specify the sensitivity and the default is 0.75

sb(#) Specify the specificity/sensitivity. The default is 0.95

sc(#) Specify the sensitivity/specificity. The default is 0.75

sd(#) Specify the specificity and the default value is 0.95

mistype(#) Specify the type of the misclassification. The default is 1, which indicates the exposure Misclassification

Description

Command biasmis, which is one of the command among the package biasepi, performs simple bias analysis and multidimensional bias analysis for misclassification.

Combining the three commands (biasselect, biascon, biasmis) is able to perform multiple bias modelinging.
Combining the existing Stata commands for probalistic distributions, biascon is able to perform probalistic bias analysis

Options

sa sa is the sensitivity for the exposure among the cases when mistype=1. sa is the sensitivity for the cases among the exposed when mistype=2.

sb sb is the specificity for the unexposed among the cases when mistype=1. sb is the sensitivity for the cases among the unexposed when mistype=2.

sc sc is the sensitivity for the exposure among the noncases when mistype=1. sc is the specificity for the noncases among the exposed when mistype=2.

sd sd is the specificity for the unexposure among the noncases when mistype=1. sd is specificity for the noncases among the unexposed when mistype=2.

mistype Specifies the type of misclassification: mistype=1 indicates the exposure misclassification, which is the default.
mistype=2 indicates the outcome misclassification.

Examples

1. Exposure misclassification:

.biasmisi 232 133 4677 6031, sa(0.75) sb(0.95) sc(0.75) sd(0.95) mistype(1)

More examples

click on her

References

Lash, Timothy L., Fox, Matthew P., Fink, Aliza K. 2009.

Applying Quantitative Bias Analysis to Epidemiologic Data

The online resource for the reference textbook.

Author

Chunsen Wu, the University of Southern Denmark; Odense University Hospital, Denmark

cwu@health.sdu.dk

chunsen.wu@rsyd.dk

Also see

Help: biasepi, biasselect, biascon, biasmis, biassurv, biastab2

Stored results

biasmis and biasmisi store the following in r():

Scalars

r(illegal) indicator for illegal value (0: no, 1:yes)

r(RR_Observed) Risk ratio for the observed 2*2 table

r(OR_Observed) Odds ratio for the observed 2*2 table

r(RD_Observed) Risk difference for the observed 2*2 table

r(RR_Corrected) Risk ratio for the corrected 2*2 table

r(OR_Corrected) Odds ratio for the corrected 2*2 table

r(RD_Corrected) Risk difference for the corrected 2*2 table

Matrices

r(O) Observed 2*2 table

r(SE) Stardard error for the observed 2*2 table

r(C) Corrected 2*2 table

r(P) probability to be corrected classification

r(S) clasification table