S. Krishna Padmanabhan

Bio: S. Krishna Padmanabhan is an academic researcher from Temple University. The author has contributed to research in topics: Data monitoring committee & Optimal design. The author has an hindex of 8, co-authored 8 publications receiving 224 citations.

A Simulation Study to Compare New Adaptive Dose–Ranging Designs

Abstract: The main goals in an adaptive dose-ranging study are to detect dose response, to determine if any doses(s) meets clinical relevance, to estimate the dose-response, and then to decide on the dose(s) (if any) to take into the confirmatory Phase III. Adaptive dose-ranging study designs may result in power gains to detect dose response and higher precision in estimating the target dose and the dose response curve. In this article, we complement the library of available methods with five new adaptive dose-ranging designs. Due to their inherent complexity, the operating characteristics can be assessed only through intensive simulations. We present here results of a comprehensive simulation study that compares and contrasts these designs for a variety of different scenarios.

Adaptive designs for dose-finding studies based on sigmoid Emax model.

Abstract: We propose an adaptive procedure for dose-finding in clinical trials when the primary efficacy endpoint is continuous. We model the mean of the efficacy endpoint, given the dose, as a four-parameter logistic function. The efficacy endpoint at each dose is distributed according to either a normal or a gamma distribution. We consider the cases of fixed variance and fixed coefficient of variation assuming them to be both known and unknown. The analytic formulae for the Fisher information matrix are obtained, which are used to build the locally and adaptive D-optimal designs.

Adaptive and Model-Based Dose-Ranging Trials: Quantitative Evaluation and Recommendations. White Paper of the PhRMA Working Group on Adaptive Dose-Ranging Studies

Abstract: Poor dose-regimen selection remains a key cause of the high attrition rate of investigational drugs in confirmatory trials, being directly related to the escalating costs of drug development. This article is a follow-up to the first white paper put forward by the PhRMA Working Group (WG) on Adaptive Dose-Ranging Studies (Bornkamp et al. 2007). It presents results and conclusions from a new round of simulation-based evaluations conducted by the WG, proposing a new set of recommendations to improve the accuracy and efficiency of dose-finding in clinical drug development.

Adaptive Dc‐optimal designs for dose finding based on a continuous efficacy endpoint

Abstract: We introduce a new optimal design for dose finding with a continuous efficacy endpoint. This design is studied in the context of a flexible model for the mean of the dose-response. The design incorporates aspects of both D- and c-optimality and can be used when the study goals under consideration include dose-response estimation, followed by identification of the target dose. Different optimality criteria are considered. Simulations are shown with results comparing our adaptive design to the fixed allocation (without adaptations). We show that both the estimation of dose-response and identification of the minimum effective dose are improved using our design.

Adaptive Penalized D-Optimal Designs for Dose Finding Based on Continuous Efficacy and Toxicity

Abstract: When a new drug is under development, a conventional dose-finding study involves learning about the dose–response curve in order to bring forward right doses of the drug to late-stage development. We propose an adaptive procedure for dose-finding in clinical trials in the presence of both efficacy and toxicity endpoints. We use the principles of optimal experimental designs for bivariate continuous endpoints.However, instead of using the traditional D-optimal design, which favors collective ethics but neglects the individual ethics, we consider the penalized D-optimal design that achieves an appropriate balance between the efficient treatment of patients in the trial (by penalizing allocation of patients to ineffective or toxic doses) and the precise estimation of the model parameters to be used in the identification of the target dose. This is compared with the traditional fixed allocation design in terms of allocation of subjects and precision of the identified dose–response curve and selection of the t...

The Hill equation: a review of its capabilities in pharmacological modelling

Abstract: The Hill equation was first introduced by A.V. Hill to describe the equilibrium relationship between oxygen tension and the saturation of haemoglobin. In pharmacology, the Hill equation has been extensively used to analyse quantitative drug-receptor relationships. Many pharmacokinetic-pharmacodynamic models have used the Hill equation to describe nonlinear drug dose-response relationships. Although the Hill equation is widely used, its many properties are not all well known. This article aims at reviewing the various properties of the Hill equation. The descriptive aspects of the Hill equation, in particular mathematical and graphical properties, are examined, and related to Hill's original work. The mechanistic aspect of the Hill equation, involving a strong connection with the Guldberg and Waage law of mass action, is also described. Finally, a probabilistic view of the Hill equation is examined. Here, we provide some new calculation results, such as Fisher information and Shannon entropy, and we introduce multivariate probabilistic Hill equations. The main features and potential applications of this probabilistic approach are also discussed. Thus, within the same formalism, the Hill equation has many different properties which can be of great interest for those interested in mathematical modelling in pharmacology and biosciences.

Adaptive designs in clinical trials: why use them, and how to run and report them

Abstract: Adaptive designs can make clinical trials more flexible by utilising results accumulating in the trial to modify the trial’s course in accordance with pre-specified rules. Trials with an adaptive design are often more efficient, informative and ethical than trials with a traditional fixed design since they often make better use of resources such as time and money, and might require fewer participants. Adaptive designs can be applied across all phases of clinical research, from early-phase dose escalation to confirmatory trials. The pace of the uptake of adaptive designs in clinical research, however, has remained well behind that of the statistical literature introducing new methods and highlighting their potential advantages. We speculate that one factor contributing to this is that the full range of adaptations available to trial designs, as well as their goals, advantages and limitations, remains unfamiliar to many parts of the clinical community. Additionally, the term adaptive design has been misleadingly used as an all-encompassing label to refer to certain methods that could be deemed controversial or that have been inadequately implemented. We believe that even if the planning and analysis of a trial is undertaken by an expert statistician, it is essential that the investigators understand the implications of using an adaptive design, for example, what the practical challenges are, what can (and cannot) be inferred from the results of such a trial, and how to report and communicate the results. This tutorial paper provides guidance on key aspects of adaptive designs that are relevant to clinical triallists. We explain the basic rationale behind adaptive designs, clarify ambiguous terminology and summarise the utility and pitfalls of adaptive designs. We discuss practical aspects around funding, ethical approval, treatment supply and communication with stakeholders and trial participants. Our focus, however, is on the interpretation and reporting of results from adaptive design trials, which we consider vital for anyone involved in medical research. We emphasise the general principles of transparency and reproducibility and suggest how best to put them into practice.

Adaptive trial designs: a review of barriers and opportunities

Abstract: Adaptive designs allow planned modifications based on data accumulating within a study. The promise of greater flexibility and efficiency stimulates increasing interest in adaptive designs from clinical, academic, and regulatory parties. When adaptive designs are used properly, efficiencies can include a smaller sample size, a more efficient treatment development process, and an increased chance of correctly answering the clinical question of interest. However, improper adaptations can lead to biased studies. A broad definition of adaptive designs allows for countless variations, which creates confusion as to the statistical validity and practical feasibility of many designs. Determining properties of a particular adaptive design requires careful consideration of the scientific context and statistical assumptions. We first review several adaptive designs that garner the most current interest. We focus on the design principles and research issues that lead to particular designs being appealing or unappealing in particular applications. We separately discuss exploratory and confirmatory stage designs in order to account for the differences in regulatory concerns. We include adaptive seamless designs, which combine stages in a unified approach. We also highlight a number of applied areas, such as comparative effectiveness research, that would benefit from the use of adaptive designs. Finally, we describe a number of current barriers and provide initial suggestions for overcoming them in order to promote wider use of appropriate adaptive designs. Given the breadth of the coverage all mathematical and most implementation details are omitted for the sake of brevity. However, the interested reader will find that we provide current references to focused reviews and original theoretical sources which lead to details of the current state of the art in theory and practice.

Adaptive Designs for Randomized Trials in Public Health

Abstract: In this article, we present a discussion of two general ways in which the traditional randomized trial can be modified or adapted in response to the data being collected. We use the term adaptive design to refer to a trial in which characteristics of the study itself, such as the proportion assigned to active intervention versus control, change during the trial in response to data being collected. The term adaptive sequence of trials refers to a decision-making process that fundamentally informs the conceptualization and conduct of each new trial with the results of previous trials. Our discussion below investigates the utility of these two types of adaptations for public health evaluations. Examples are provided to illustrate how adaptation can be used in practice. From these case studies, we discuss whether such evaluations can or should be analyzed as if they were formal randomized trials, and we discuss practical as well as ethical issues arising in the conduct of these new-generation trials.