*! version 1.1 29january2019

Title

pvw Predictive value weighting for covariate misclassification in logistic regression (pvw)

Syntax

pvw varlist, casesens(real) contsens(real) casespec(real) contspec(real) outcome(varname)
[ othercov(varlist) cohort reps(#) seed(#) ]

Main
*casesens(real) the assumed sensitivity in cases

*contsens(real) the assumed sensitivity in controls

*casespec(real) the assumed specificity in cases

*contspec(real) the assumed specificity in controls

*outcome(varname) the binary outcome variable

othercov(varlist) the other covariates in the analysis model

cohort specifies that bootstrap unstratified resampling be performed

reps(#) how many bootstrap samples to perform

seed(#) set the random number seed to the specified value

*denotes a required option

varlist specifies the covariates in the model for the misclassified variable conditional on outcome and other variables.

Description

pvw implements the predictive value weighting approach for adjustment for covariate misclassification, as proposed by Lyles and Lin (2010). Using the
notation of Lyles and Lin (2010), the main varlist lists the covariates in the model for the misclassified variable Z conditional on outcome Y and other variables C.
As noted by Lyles and Lin, correction specification of this model may require interactions between elements of (Y,C), and these can be passed in the varlist
using Stata's factor interaction notation (fvvarlist).

The pvw command performs predictive value weighting assuming the sensitivity and specificity values are known constants.

The covariates of the analysis model of interest (except the unobserved misclasified variable X) are specified using the othercov option, which
can also include factor variables and interactions (fvvarlist).

P-values and confidence intervals are calculated by pvw using bootstrapping. The default is to perform stratified resampling in the two levels (0,1) of
the outcome, which is appropriate for a case-control design. For a cohort study, the cohort option should be specified, which instead performs
unstratified bootstrap resampling.

Examples

Perform predictive value weighting on the low birthweight data, assuming that smoking is misclassified, with sensitivity and specificity assumed
to be 0.9 in low birthweight and normal birthweight observations.

use http://www.stata-press.com/data/r13/lbw.dta, clear

pvw low age lwt i.race ptl ht ui, casesens(0.9) contsens(0.9) casespec(0.9) contspec(0.9) misclass(smoke) outcome(low) othercov(age lwt i.race ptl ht ui) cohort seed(5412)

References

R H Lyles and J Lin. Sensitivity analysis for misclassification in logistic regression via likelihood methods and predictive value weighting. Statistics in Medicine 2010; 29: 2297-2309

Author

Jonathan Bartlett, University of Bath, UK

j.w.bartlett@bath.ac.uk

thestatsgeek.com