A model-based approach for simulating adaptive clinical studies with surrogate endpoints used for interim decision-making

Using an early outcome as the sole source of information of interim decisions regarding treatment effect on a long-term endpoint: The non-Gaussian case

In randomized clinical trials that use a long-term efficacy endpoint, the follow-up time necessary to observe the endpoint may be substantial. In such trials, an attractive option is to consider an interim analysis based solely on an early outcome that could be used to expedite the evaluation of treatment's efficacy. Garcia Barrado et al. (Pharm Stat. 2022; 21: 209-219) developed a methodology that allows introducing such an early interim analysis for the case when both the early outcome and the long-term endpoint are normally-distributed, continuous variables. We extend the methodology to any combination of the early-outcome and long-term-endpoint types. As an example, we consider the case of a binary outcome and a time-to-event endpoint. We further evaluate the potential gain in operating characteristics (power, expected trial duration, and expected sample size) of a trial with such an interim analysis in function of the properties of the early outcome as a surrogate for the long-term endpoint.

Estimation of conditional power in the presence of auxiliary data

Conditional power (CP) is a commonly used tool to inform interim decision-making in clinical trials, but the conventional approach using only primary endpoint data to calculate CP may not perform well when the primary endpoint requires a long follow-up period, or the treatment effect size changes during the trial. Several methods have been proposed to use additional short term auxiliary data observed at the interim analysis to improve the CP estimation in these situations, however, they may rely on strong assumptions, have limited applications, or use ad hoc choices of information fraction. In this paper we propose a general framework where the true CP formula is first derived in the presence of auxiliary data, and CP estimation is obtained by substituting the unknown parameters with consistent estimators. We conducted extensive simulations to examine the performance of both proposed and conventional approaches using the true CP as the benchmark. As the proposed approach is based on the true underlying CP, the simulations confirmed its superiority over the conventional approach in terms of efficiency and accuracy, especially if observed auxiliary data reflect the change of treatment effect size. The simulations also indicate that the magnitude of improvement in CP estimation is associated with the correlation between auxiliary and primary endpoints and/or the magnitude of the effect size change during the trial.

Using a surrogate with heterogeneous utility to test for a treatment effect

The primary benefit of identifying a valid surrogate marker is the ability to use it in a future trial to test for a treatment effect with shorter follow-up time or less cost. However, previous work has demonstrated potential heterogeneity in the utility of a surrogate marker. When such heterogeneity exists, existing methods that use the surrogate to test for a treatment effect while ignoring this heterogeneity may lead to inaccurate conclusions about the treatment effect, particularly when the patient population in the new study has a different mix of characteristics than the study used to evaluate the utility of the surrogate marker. In this article, we develop a novel test for a treatment effect using surrogate marker information that accounts for heterogeneity in the utility of the surrogate. We compare our testing procedure to a test that uses primary outcome information (gold standard) and a test that uses surrogate marker information, but ignores heterogeneity. We demonstrate the validity of our approach and derive the asymptotic properties of our estimator and variance estimates. Simulation studies examine the finite sample properties of our testing procedure and demonstrate when our proposed approach can outperform the testing approach that ignores heterogeneity. We illustrate our methods using data from an AIDS clinical trial to test for a treatment effect using CD4 count as a surrogate marker for RNA.

Estimation of the proportion of treatment effect explained by a high-dimensional surrogate

Clinical studies examining the effectiveness of a treatment with respect to some primary outcome often require long-term follow-up of patients and/or costly or burdensome measurements of the primary outcome of interest. Identifying a surrogate marker for the primary outcome of interest may allow one to evaluate a treatment effect with less follow-up time, less cost, or less burden. While much clinical and statistical work has focused on identifying and validating surrogate markers, available approaches tend to focus on settings in which only a single surrogate marker is of interest. Limited work has been done to accommodate the high-dimensional surrogate marker setting where the number of potential surrogates is greater than the sample size. In this article, we develop methods to estimate the proportion of treatment effect explained by high-dimensional surrogates. We study the asymptotic properties of our proposed estimator, propose inference procedures, and examine finite sample performance via a simulation study. We illustrate our proposed methods using data from a randomized study comparing a novel whey-based oral nutrition supplement with a standard supplement with respect to change in body fat percentage over 12 weeks, where the surrogate markers of interest are gene expression probesets.

Using an interim analysis based exclusively on an early outcome in a randomized clinical trial with a long-term clinical endpoint

In RCTs with an interest in a long-term efficacy endpoint, the follow-up time necessary to observe the endpoint may be substantial. In order to reduce the expected duration of such trials, early-outcome data may be collected to enrich an interim analysis aimed at stopping the trial early for efficacy. We propose to extend such a design with an additional interim analysis using solely early-outcome data in order to expedite the evaluation of treatment's efficacy. We evaluate the potential gain in operating characteristics (power, expected trial duration, and expected sample size) when introducing such an early interim analysis, in function of the properties of the early outcome as a surrogate for the long-term endpoint. In the context of a longitudinal age-related macular degeneration (ARMD) ophthalmology trial, results show potentially substantial gains in both the expected trial duration and the expected sample size. A prerequisite, though, is that the treatment effect on the early outcome has to be strongly correlated with the treatment effect on the long-term endpoint, that is, that the early outcome is a validated surrogate for the long-term endpoint.