Abstract:
Comparison of the accuracy of a new screening test to that of the standard one can
be implemented by administering both screening tests to a group of asymptomatic
subjects for which the disease status can be determined using a gold standard (GS)
test. Nevertheless, the GS test may be too costly or too invasive hence unethical to
administer to all the study subjects, including those who screen negative on all the
screening tests. When this is the case, relative accuracy of the two screening tests
can be estimated when a randomized paired screen positive (RPSP) design is used
to collect the data. However, this design contains cells with missing data, thus the
likelihood function is not available. The objective of this study is to demonstrate a
parsimonious way of estimating relative accuracy of the screening tests when the
determination of the disease status of the subjects who screened negative on the new
and the standard screening tests was not conducted due to ethical concerns.
Markov Chain Monte Carlo simulation technique is used to parsimoniously address
the aforementioned shortcoming of the RPSP design when subjective approach to
estimation is used. Multiple data imputation using Gibbs sampler is performed. Monte
Carlo point and interval estimates of the missing data, measures of accuracy and the
relative rates are computed when the tests are treated to be:
I. Conditionally independent
II. Dependent
The accuracy when the tests are dependent is higher than when the tests are
conditionally independent. This is apparent from the narrower credible bounds and
smaller standard errors of the true and false positive rates under this setting. Secondly,
the estimates obtained under the two assumptions are better than when the missing
data are ignored. Whether these differences were true or just due to chance warrant
statistical investigation under different assumptions
