  Increasingly, medical research is dependent on data collected for
non-research purposes, such as electronic health records data (EHR). EHR data
and other large databases can be prone to measurement error in key exposures,
and unadjusted analyses of error-prone data can bias study results. Validating
a subset of records is a cost-effective way of gaining information on the error
structure, which in turn can be used to adjust analyses for this error and
improve inference. We extend the mean score method for the two-phase analysis
of discrete-time survival models, which uses the unvalidated covariates as
auxiliary variables that act as surrogates for the unobserved true exposures.
This method relies on a two-phase sampling design and an estimation approach
that preserves the consistency of complete case regression parameter estimates
in the validated subset, with increased precision leveraged from the auxiliary
data. Furthermore, we develop optimal sampling strategies which minimize the
variance of the mean score estimator for a target exposure under a fixed cost
constraint. We consider the setting where an internal pilot is necessary for
the optimal design so that the phase two sample is split into a pilot and an
adaptive optimal sample. Through simulations and data example, we evaluate
efficiency gains of the mean score estimator using the derived optimal
validation design compared to balanced and simple random sampling for the phase
two sample. We also empirically explore efficiency gains that the proposed
discrete optimal design can provide for the Cox proportional hazards model in
the setting of a continuous-time survival outcome.
