To reduce the potentially confounding effects of unmeasured and/or unmodelled variables, surrogate variables (SVs) were estimated [ 21].
While our numerical study suggests building state-variable surrogates and using the relative error indicator for building log-likelihood surrogates, selecting appropriate type of surrogates and error indicators depends on the shapes of response surfaces.
When the correlation of the performance variable of surrogate variable exists, this article proposes a two-stage charting design to monitor either the performance variable or its surrogate variable in an alternating fashion rather than monitoring the performance variable alone.
Numerical results show that the proposed chart is insensitive on the correlation of the performance variable and surrogate variable even when the historical information on the correlation coefficient is not very accurate.
The addition of time as an input variable to surrogate models provides an extension of the methodology to safety studies where the resolution of time-varying output is important.
We observe an increase in the proportion of high priority areas when using a spatially variable cost surrogate.
Another way to reduce sample size requirements in rare disease studies is through selection of the outcome measure using a continuous outcome variable, a surrogate marker, a composite endpoint, or repeated measure outcome.
Univariate linear regression analysis was performed to identify associations between sRAGE and esRAGE as exposure variables and surrogate markers of atherosclerosis as outcome variables.
The contribution of the intrinsic subtypes was assessed by the change in log likelihood ratio χ, between a model containing only clinical variables (age, T-stage, N-stage), and a model including clinical variables and (surrogate) subtype information.
