Statistical methods for down-selection of treatment regimens based on multiple endpoints, with application to HIV vaccine trials

Predicting neutralization susceptibility to combination HIV-1 monoclonal broadly neutralizing antibody regimens

Combination monoclonal broadly neutralizing antibodies (bnAbs) are currently being developed for preventing HIV-1 infection. Recent work has focused on predicting in vitro neutralization potency of both individual bnAbs and combination regimens against HIV-1 pseudoviruses using Env sequence features. To predict in vitro combination regimen neutralization potency against a given HIV-1 pseudovirus, previous approaches have applied mathematical models to combine individual-bnAb neutralization and have predicted this combined neutralization value; we call this the combine-then-predict (CP) approach. However, prediction performance for some individual bnAbs has exceeded that for the combination, leading to another possibility: combining the individual-bnAb predicted values and using these to predict combination regimen neutralization; we call this the predict-then-combine (PC) approach. We explore both approaches in both simulated data and data from the Los Alamos National Laboratory's Compile, Neutralize, and Tally NAb Panels repository. The CP approach is superior to the PC approach when the neutralization outcome of interest is binary (e.g., neutralization susceptibility, defined as inhibitory concentration < 1 μg/mL. For continuous outcomes, the CP approach performs at least as well as the PC approach, and is superior to the PC approach when the individual-bnAb prediction algorithms have poor performance. This knowledge may be used when building prediction models for novel antibody combinations in the absence of in vitro neutralization data for the antibody combination; this, in turn, will aid in the evaluation and down-selection of these antibody combinations into prevention efficacy trials.

Methods for Feature Selection in Down-Selection of Vaccine Regimens Based on Multivariate Immune Response Endpoints

In clinical trials, it is often of interest to compare and order several candidate regimens based on multiple endpoints. For example, in HIV vaccine development, immune response profiles induced by vaccination are key for selecting vaccine regimens to advance to efficacy evaluation. Motivated by the need to rank and choose a few vaccine regimens based on their immunogenicity in phase I trials, Huang et al. (Biostatistics 18(2):230–243, 2017) proposed a ranking/filtering/selection algorithm that down-selects vaccine regimens to satisfy the superiority and non-redundancy criteria, based on multiple immune response endpoints. In practice, many candidate immune response endpoints can be correlated with each other. An important question that remains to be addressed is how to choose a parsimonious set of the available immune response endpoints to effectively compare regimens. In this paper, we propose novel algorithms for selecting immune response endpoints to be used in regimen down-selection, based on importance weights assigned to individual endpoints and their correlation structure. We show through extensive simulation studies that pre-selection of endpoints can substantially improve performance of the subsequent regimen down-selection process. The application of the proposed method is demonstrated using a real example in HIV vaccine research, although the methods are also applicable in general to clinical research for dimension reduction when comparing regimens based on multiple candidate endpoints.

Inference on covariate effect types for treatment effectiveness in a randomized trial with a binary outcome

BACKGROUND/AIMS

Some randomized clinical trials seek to establish covariate effect types that indicate whether a covariate is predictive and/or prognostic, in addition to endpoint evaluation. Here, for a case with a binary outcome, we propose that the covariate effect type should be assessed in terms of four types of potential responses: activated- (always-), inert- (never-), causative-, and preventive-responder.

METHODS

We introduce a new concept of covariate effect types differing from the commonly used "prediction" and "prognosis." We summarize the covariate effect types by inspecting the proportions of subjects in each response type in two subgroups of a covariate, and indicate whether the fractions are augmented, depleted, or neutral as one changes the level of the covariate. Although these proportions cannot generally be identified, we can derive the posterior distributions of the proportions by applying a recently developed Bayesian method. On the basis of the distributions, we would say that the covariate is "augmented-causative" if the difference between the proportions of causative-responders (who would respond if they received the treatment but would not if they did not) in two subgroups is positive, rather than that it is predictive. Similarly, we would say that the covariate is "neutral-activated" if the difference in the proportion of activated-responders (who would respond regardless of their randomized treatment assignment) is close to zero, rather than saying that the covariate is not prognostic. We further describe the relationship between our approach and standard subgroup analysis.

RESULTS

We applied our approach to data from a randomized clinical trial comparing nivolumab and docetaxel for subjects with advanced nonsquamous non-small-cell lung cancer; we assessed the covariate effect type of PD-L1 status, where PD-L1 is a ligand of the programmed death 1 (PD-1) receptor expressed by activated T cells. When the endpoint was the overall response rate, the posterior distributions for the differences between the proportions of subjects in response types in the PD-L1-positive and negative subgroups yielded an expected-a-posteriori estimate of 0.243 (95% credible interval (CI): 0.094, 0.374) for causative-responders and 0.014 (95% CI: -0.087, 0.125) for activated-responders. Thus, PD-L1 status was augmented-causative for nivolumab effectiveness, to an extent of 24.3%, and was neutral-activated.

CONCLUSION

Our approach characterizes the covariate effect types in terms of the response types, and to what extent. In a randomized clinical trial with a binary outcome, our approach is a potentially valuable addition to standard subgroup or regression analysis.

Identifying treatment responders using counterfactual modeling and potential outcomes

Individualizing treatment according to patients' characteristics is central for personalized or precision medicine. There has been considerable recent research in developing statistical methods to determine optimal personalized treatment strategies by modeling the outcome of patients according to relevant covariates under each of the alternative treatments, and then relying on so-called predicted individual treatment effects. In this paper, we use potential outcomes and principal stratification frameworks and develop a multinomial model for left and right-censored data to estimate the probability that a patient is a responder given a set of baseline covariates. The model can apply to RCT or observational study data. This method is based on the monotonicity assumption, which implies that no patients would respond to the control treatment but not to the experimental one. We conduct a simulation study to evaluate the properties of the proposed estimation method. Results showed that the predictions of the probability of being a responder were well calibrated even if we observed variability and a small bias when many parameters were estimated. We finally applied the method to a cohort study on the selection of patients for additional radiotherapy after resection of a soft-tissue sarcoma.