Abstract
In some clinical studies, assessing covariate effect types indicating whether a covariate is predictive and/or prognostic is of interest, in addition to the study endpoint. Recently, for a case with a binary outcome, Chiba (Clinical Trials, 2019; 16: 237–245) proposed the new concept of covariate effect type, which is assessed in terms of four response types, and showed that standard subgroup or regression analysis is applicable only in certain cases. Although this concept could be useful for supplementing conventional standard analysis, its application is limited to cases with a binary outcome. In this article, we aim to generalize Chiba’s concept to continuous and time-to-event outcomes. We define covariate effect types based on four response types. It is difficult to estimate the response types from the observed data without making certain assumptions, so we propose a simple method to estimate them under the assumption of independent potential outcomes. Our approach is illustrated using data from a clinical study with a time-to-event outcome.
Highlights
In certain clinical studies, it is important to assess covariate effect types to indicate whether a covariate is predictive and/or prognostic in the context of treatment effectiveness
For cases with a binary outcome, Chiba [3] proposed assessing covariate effect types based on four response types [4] defined in terms of the potential outcome of Y if X is x, Y(x), rather than δ and γ as above
We explored the covariate effect type in the context of radiation effectiveness in the pancreatic cancer site, which was subclassified as pancreatic head (Z = 1) or “other” (Z = 0)
Summary
It is important to assess covariate effect types to indicate whether a covariate is predictive and/or prognostic in the context of treatment effectiveness. For cases with a binary outcome, Chiba [3] proposed assessing covariate effect types based on four response types [4] defined in terms of the potential outcome of Y if X is x, Y(x), rather than δ and γ as above. Based on the four response types, measures of the covariate effect types are defined as follows [3]: ηkl ≡ Pr Y(1) = k, Y(0) = l Z = 1 − Pr Y(1) = k, Y(0) = l Z = 0. Η10 can be used instead of a current common measure for predicting the effectiveness of the experimental treatment, i.e., δ in (1).
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