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.

Trial Design for Cancer Immunotherapy: A Methodological Toolkit.

Immunotherapy with checkpoint inhibitors (CPIs) and cell-based products has revolutionized the treatment of various solid tumors and hematologic malignancies. These agents have shown unprecedented response rates and long-term benefits in various settings. These clinical advances have also pointed to the need for new or adapted approaches to trial design and assessment of efficacy and safety, both in the early and late phases of drug development. Some of the conventional statistical methods and endpoints used in other areas of oncology appear to be less appropriate in immuno-oncology. Conversely, other methods and endpoints have emerged as alternatives. In this article, we discuss issues related to trial design in the early and late phases of drug development in immuno-oncology, with a focus on CPIs. For early trials, we review the most salient issues related to dose escalation, use and limitations of tumor response and progression criteria for immunotherapy, the role of duration of response as an endpoint in and of itself, and the need to conduct randomized trials as early as possible in the development of new therapies. For late phases, we discuss the choice of primary endpoints for randomized trials, review the current status of surrogate endpoints, and discuss specific statistical issues related to immunotherapy, including non-proportional hazards in the assessment of time-to-event endpoints, alternatives to the Cox model in these settings, and the method of generalized pairwise comparisons, which can provide a patient-centric assessment of clinical benefit and be used to design randomized trials.

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.

Bayesian biomarker-driven outcome-adaptive randomization with an imperfect biomarker assay.

OBJECTIVE: We investigate the impact of biomarker assay's accuracy on the operating characteristics of a Bayesian biomarker-driven outcome-adaptive randomization design. METHODS: In a simulation study, we assume a trial with two treatments, two biomarker-based strata, and a binary clinical outcome (response). Pbt denotes the probability of response for treatment t (t = 0 or 1) in biomarker stratum (b = 0 or 1). Four different scenarios in terms of true underlying response probabilities are considered: a null (P00 = P01 = 0.25, P10 = P11= 0.25) and consistent (P00 = P10 = 0.25, P01 = 0.5) treatment effect scenario, as well as a quantitative (P00 = P01 = P10 = 0.25, P11 = 0.5) and a qualitative (P00 = P11 = 0.5, P01 = P10 = 0.25) stratum-treatment interaction. For each scenario, we compare the case of a perfect with the case of an imperfect biomarker assay with sensitivity and specificity of 0.8 and 0.7, respectively. In addition, biomarker-positive prevalence values P(B = 1) = 0.2 and 0.5 are investigated. RESULTS: Results show that the use of an imperfect assay affects the operational characteristics of the Bayesian biomarker-based outcome-adaptive randomization design. In particular, the misclassification causes a substantial reduction in power accompanied by a considerable increase in the type-I error probability. The magnitude of these effects depends on the sensitivity and specificity of the assay, as well as on the distribution of the biomarker in the patient population. CONCLUSION: With an imperfect biomarker assay, the decision to apply a biomarker-based outcome-adaptive randomization design may require careful reflection.

Estimation of diagnostic accuracy of a combination of continuous biomarkers allowing for conditional dependence between the biomarkers and the imperfect reference-test.

Estimating biomarker-index accuracy when only imperfect reference-test information is available is usually performed under the assumption of conditional independence between the biomarker and imperfect reference-test values. We propose to define a latent normally-distributed tolerance-variable underlying the observed dichotomous imperfect reference-test results. Subsequently, we construct a Bayesian latent-class model based on the joint multivariate normal distribution of the latent tolerance and biomarker values, conditional on latent true disease status, which allows accounting for conditional dependence. The accuracy of the continuous biomarker-index is quantified by the AUC of the optimal linear biomarker-combination. Model performance is evaluated by using a simulation study and two sets of data of Alzheimer's disease patients (one from the memory-clinic-based Amsterdam Dementia Cohort and one from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database). Simulation results indicate adequate model performance and bias in estimates of the diagnostic-accuracy measures when the assumption of conditional independence is used when, in fact, it is incorrect. In the considered case studies, conditional dependence between some of the biomarkers and the imperfect reference-test is detected. However, making the conditional independence assumption does not lead to any marked differences in the estimates of diagnostic accuracy.

Protein Kinase Activity Decreases with Higher Braak Stages of Alzheimer's Disease Pathology.

Alzheimer's disease (AD) is characterized by a long pre-clinical phase (20-30 years), during which significant brain pathology manifests itself. Disease mechanisms associated with pathological hallmarks remain elusive. Most processes associated with AD pathogenesis, such as inflammation, synaptic dysfunction, and hyper-phosphorylation of tau are dependent on protein kinase activity. The objective of this study was to determine the involvement of protein kinases in AD pathogenesis. Protein kinase activity was determined in postmortem hippocampal brain tissue of 60 patients at various stages of AD and 40 non-demented controls (Braak stages 0-VI) using a peptide-based microarray platform. We observed an overall decrease of protein kinase activity that correlated with disease progression. The phosphorylation of 96.7% of the serine/threonine peptides and 37.5% of the tyrosine peptides on the microarray decreased significantly with increased Braak stage (p-value <0.01). Decreased activity was evident at pre-clinical stages of AD pathology (Braak I-II). Increased phosphorylation was not observed for any peptide. STRING analysis in combination with pathway analysis and identification of kinases responsible for peptide phosphorylation showed the interactions between well-known proteins in AD pathology, including the Ephrin-receptor A1 (EphA1), a risk gene for AD, and sarcoma tyrosine kinase (Src), which is involved in memory formation. Additionally, kinases that have not previously been associated with AD were identified, e.g., protein tyrosine kinase 6 (PTK6/BRK), feline sarcoma oncogene kinase (FES), and fyn-associated tyrosine kinase (FRK). The identified protein kinases are new biomarkers and potential drug targets for early (pre-clinical) intervention.

Transferring cut-off values between assays for cerebrospinal fluid Alzheimer's disease biomarkers.

Current technologies quantifying cerebrospinal fluid biomarkers to identify subjects with Alzheimer's disease pathology report different concentrations in function of technology and suffer from between-laboratory variability. Hence, lab- and technology-specific cut-off values are required. It is common practice to establish cut-off values on small datasets and, in the absence of well-characterized samples, to transfer the cut-offs to another assay format using 'side-by-side' testing of samples with both assays. We evaluated the uncertainty in cut-off estimation and the performance of two methods of cut-off transfer by using two clinical datasets and simulated data. The cut-off for the new assay was transferred by applying the commonly-used linear regression approach and a new Bayesian method, which consists of using prior information about the current assay for estimation of the biomarker's distributions for the new assay. Simulations show that cut-offs established with current sample sizes are insufficiently precise and also show the effect of increasing sample sizes on the cut-offs' precision. The Bayesian method results in unbiased and less variable cut-offs with substantially narrower 95% confidence intervals compared to the linear-regression transfer. For the BIODEM datasets, the transferred cut-offs for INNO-BIA Aß1-42 are 167.5 pg/mL (95% credible interval [156.1, 178.0] and 172.8 pg/mL (95% CI [147.6, 179.6]) with Bayesian and linear regression methods, respectively. For the EUROIMMUN assay, the estimated cut-offs are 402.8 pg/mL (95% credible interval [348.0, 473.9]) and 364.4 pg/mL (95% CI [269.7, 426.8]). Sample sizes and statistical methods used to establish and transfer cut-off values have to be carefully considered to guarantee optimal diagnostic performance of biomarkers.

Development of a diagnostic test based on multiple continuous biomarkers with an imperfect reference test.

Ignoring the fact that the reference test used to establish the discriminative properties of a combination of diagnostic biomarkers is imperfect can lead to a biased estimate of the diagnostic accuracy of the combination. In this paper, we propose a Bayesian latent-class mixture model to select a combination of biomarkers that maximizes the area under the ROC curve (AUC), while taking into account the imperfect nature of the reference test. In particular, a method for specification of the prior for the mixture component parameters is developed that allows controlling the amount of prior information provided for the AUC. The properties of the model are evaluated by using a simulation study and an application to real data from Alzheimer's disease research. In the simulation study, 100 data sets are simulated for sample sizes ranging from 100 to 600 observations, with a varying correlation between biomarkers. The inclusion of an informative as well as a flat prior for the diagnostic accuracy of the reference test is investigated. In the real-data application, the proposed model was compared with the generally used logistic-regression model that ignores the imperfectness of the reference test. Conditional on the selected sample size and prior distributions, the simulation study results indicate satisfactory performance of the model-based estimates. In particular, the obtained average estimates for all parameters are close to the true values. For the real-data application, AUC estimates for the proposed model are substantially higher than those from the 'traditional' logistic-regression model.