In a Phase II dose-finding study with a placebo control, a new drug with several dose levels is compared with a placebo to test for the effectiveness of the new drug. The main focus of such studies often lies in the characterization of the dose-response relationship followed by the estimation of a target dose that leads to a clinically relevant effect over the placebo. This target dose is known as the minimum effective dose (MED) in a drug development study. Several approaches exist that combine multiple comparison procedures with modeling techniques to efficiently estimate the dose-response model and thereafter select the target dose. Despite the flexibility of the existing approaches, they cannot completely address the model uncertainty in the model-selection step and may lead to target dose estimates that are biased. In this article, we propose two new MED estimation approaches based on weighted regression modeling that are robust against deviations from the dose-response model assumptions. These approaches are compared with existing approaches with regard to their accuracy in point and interval estimation of the MED. We illustrate by a simulation study that by integrating one of the new dose estimation approaches with the existing dose-response profile estimation approaches one can take into account the uncertainty of the model selection step.
We consider a Bayesian framework based on "probability of decision" for dose-finding trial designs. The proposed PoD-BIN design evaluates the posterior predictive probabilities of up-and-down decisions. In PoD-BIN, multiple grades of toxicity, categorized as the mild toxicity (MT) and dose-limiting toxicity (DLT), are modeled simultaneously, and the primary outcome of interests is time-to-toxicity for both MT and DLT. This allows the possibility of enrolling new patients when previously enrolled patients are still being followed for toxicity, thus potentially shortening trial length. The Bayes…
We study the problem of learning individualized dose intervals using observational data. There are very few previous works for policy learning with continuous treatment, and all of them focused on recommending an optimal dose rather than an optimal dose interval. In this paper, we propose a new method to estimate such an optimal dose interval, named probability dose interval (PDI). The potential outcomes for doses in the PDI are guaranteed better than a pre-specified threshold with a given probability (e.g., 50%). The associated nonconvex optimization problem can be efficiently solved by the D…
Recently, the strategy for dose optimization in oncology has shifted to conduct Phase 2 randomized controlled trials with multiple doses. Optimal biologic dose selection from Phase 1 trial data to determine candidate doses for Phase 2 trials has been gaining attention. This study proposes a novel adaptive cohort size determination method for optimal biologic dose-finding to accelerate trials. The cohort size expansion is determined adaptively depending on the toxicity and efficacy data of the ongoing trial. In a simulation, the proposed method shortened the trial duration and maintained accura…
Network meta-analysis (NMA) has been used to answer a range of clinical questions about the preferable intervention for a given condition. Although the effectiveness and safety of pharmacological agents depend on the dose administered, NMA applications typically ignore the role that drugs dosage play on the results. This leads to more heterogeneity in the network. In this paper we present a suite of network meta-analysis models that incorporates the dose-effect relationship (DE-NMA) using restricted cubic splines (RCS). We extend the model into a dose-effect network meta-regression to account …
Cohort-based enrollment can slow down dose-finding trials since the outcomes of the previous cohort must be fully evaluated before the next cohort can be enrolled. This results in frequent suspension of patient enrollment. The issue is exacerbated in recent immune-oncology trials where toxicity outcomes can take a long time to observe. We propose a novel phase I design, the probability-of-decision toxicity probability interval (PoD-TPI) design, to accelerate phase I trials. PoD-TPI enables dose assignment in real-time in the presence of pending toxicity outcomes. With uncertain outcomes, the d…
Efficient frequentist fractional polynomials for skewed dose-response and survival data: a variance-reducing alternative to OLS-FP
Cherkasy State Business College（切尔卡瑟国立商业学院）；State Scientific Research Institute of Armament and Military Equipment Testing and Certification（国家武器和军事装备测试与认证科学研究院）；Uzhhorod National University（乌日霍罗德国立大学）
A new method for the analysis of time to ankylosis complication on a dataset of replanted teeth is proposed. In this context of left-censored, interval-censored and right-censored data, a Cox model with piecewise constant baseline hazard is introduced. Estimation is carried out with the EM algorithm by treating the true event times as unobserved variables. This estimation procedure is shown to produce a block diagonal Hessian matrix of the baseline parameters. Taking advantage of this interesting feature of the estimation method a L0 penalised likelihood method is implemented in order to autom…
On a penalised likelihood approach for joint modelling of longitudinal covariates and partly interval-censored data -- an application to the Anti-PD1 brain collaboration trial
Consider a set of categorical variables $\mathcal{P}$ where at least one, denoted by $Y$, is binary. The log-linear model that describes the counts in the resulting contingency table implies a specific logistic regression model, with the binary variable as the outcome. Extending results in Christensen (1997), by also considering the case where factors present in the contingency table disappear from the logistic regression model, we prove that the Maximum Likelihood Estimate (MLE) for the parameters of the logistic regression equals the MLE for the corresponding parameters of the log-linear mod…
In Oncology, trials evaluating drug combinations are becoming more common. While combination therapies bring the potential for greater efficacy, they also create unique challenges for ensuring drug safety. In Phase-I dose escalation trials of drug combinations, model-based approaches enable efficient use of information gathered, but the models need to account for trial complexities: appropriate modeling of interactions becomes increasingly important with growing numbers of drugs being tested simultaneously in a given trial. In principle, we can use data from multiple arms testing varying combi…
Multistate models can be used to describe transitions over time across states. In the presence of interval-censored times for transitions, the likelihood is constructed using transition probabilities. Models are specified using proportional hazards model for the transitions. Time-dependency is usually defined by parametric models, which can be too restrictive. Nonparametric hazards specification with splines allow for flexible modelling of time-dependency without making strong model assumptions. Penalised maximum likelihood is used to estimate the models. Selecting the optimal amount of smooth…
PKComb-BOIN12: A Pharmacokinetically Guided Bayesian Design for Dose Optimisation in Oncology Drug-Combination Trials
Global Statistical Sciences, Eli Lilly and Company（礼来公司全球统计科学部）；Department of Biostatistics, The University of Texas MD Anderson Cancer Center（德克萨斯大学安德森癌症中心生物统计学系）；Population Health Sciences Institute, Newcastle University（纽卡斯尔大学人口健康科学研究所）
