A5031033007- Statistical Methods in Clinical Trials
- Advanced Queuing Theory Analysis
- Probability and Risk Models
- Simulation Techniques and Applications
- Health Systems, Economic Evaluations, Quality of Life
- Stochastic processes and financial applications
- Statistical Methods and Inference
- Stochastic processes and statistical mechanics
- Advanced Causal Inference Techniques
- advanced mathematical theories
- Reliability and Maintenance Optimization
- Statistical Methods and Bayesian Inference
- Network Traffic and Congestion Control
- Mathematical Control Systems and Analysis
- Pharmaceutical Economics and Policy
- Mathematical Biology Tumor Growth
- Markov Chains and Monte Carlo Methods
- Financial Risk and Volatility Modeling
- Biomedical Ethics and Regulation
- Health and Medical Research Impacts
- Advanced Mathematical Modeling in Engineering
- Biosimilars and Bioanalytical Methods
- Random Matrices and Applications
- Microwave Engineering and Waveguides
- Computational Drug Discovery Methods
Amgen (United Kingdom)
2019-2024
Amgen (United States)
2020
University of Glasgow
2014-2017
IQVIA (United Kingdom)
2012-2015
GlaxoSmithKline (United Kingdom)
2002-2011
Moscow State University of Civil Engineering
2009
GlaxoSmithKline (Netherlands)
2008
GlaxoSmithKline (United States)
2007
Providence College
2007
Pskov State University
1989-2005
Abstract This paper is focused on statistical modelling, prediction and adaptive adjustment of patient recruitment in multicentre clinical trials. We consider a model, where patients arrive at different centres according to Poisson processes, with rates viewed as sample from gamma distribution. A analysis completed studies provided properties few types parameter estimators are investigated analytically using simulation. The model has been validated many real technique for predictive...
Abstract Background The design of a multi-center randomized controlled trial (RCT) involves multiple considerations, such as the choice sample size, number centers and their geographic location, strategy for recruitment study participants, amongst others. There are plenty methods to sequentially randomize patients in RCT, with or without considering stratification factors. goal this paper is perform systematic assessment randomization 1:1 RCT assuming competitive policy patient process....
This article is devoted to developing further a statistical technique for modeling patient recruitment together with randomization process in multicentre clinical trials. The analytic predicting the number of patients recruited different centers/regions ongoing trials accounting possible delays and closure some centers developed. asymptotic properties particular regions are investigated analysis performance provided. approximations predictive confidence bounds randomized region using...
Some general points regarding efficiency in clinical trials are made. Reasons as to why fitting many covariates adjust the estimate of treatment effect may be less problematic than commonly supposed given. Two methods dynamic allocation patients based on covariates, minimization and Atkinson's approach, compared contrasted for particular case where all binary. The results Monte Carlo simulations also presented. It is concluded that cases considered, approach slightly more efficient although...
A new analytic statistical technique for predictive event modeling in ongoing multicenter clinical trials with waiting time to response is developed. It allows the mean and bounds number of events be constructed over time, accounting newly recruited patients already at risk trial, different recruitment scenarios. For patient recruitment, an advanced Poisson-gamma model used, which accounts variation rates between centers opening or closing some future. few models appearance allowing...
Abstract This paper deals with the analysis of randomization effects in multi‐centre clinical trials. The two schemes most often used trials are considered: unstratified and centre‐stratified block‐permuted randomization. prediction number patients randomized to different treatment arms regions during recruitment period accounting for stochastic nature multiple centres is investigated. A new analytic approach using a Poisson‐gamma patient model (patients arrive at according Poisson processes...
Modelling and simulation has been used in many ways when developing new treatments. To be useful credible, it is generally agreed that modelling should undertaken according to some kind of best practice. A number authors have suggested elements required for practice simulation. Elements include the pre-specification goals, assumptions, methods, outputs. However, a project involves could simple or complex relatively low high importance project. It argued level detail strictness allowed vary,...
Abstract Background When running a randomized controlled trial (RCT), clinical site may face situation when an eligible participant is to be the treatment that not available at site. In this case, there are two options: enroll participant, or, without disclosing site, allocate arm with drug using built-in feature of interactive response technology (IRT). latter one has employed “forced randomization” (FR). There seems industry-wide consensus FR can acceptable in confirmatory trials provided...
A common problem seen in the ineffective execution of global multicenter trials is frequent inability to recruit a sufficient number patients. myriad barriers recruiting enough patients timely exist and may include practical limits on sites imposed by various countries, as well administrative, cost unanticipated issues. The Poisson-gamma recruitment model widely accepted statistical tool used predict track at different levels trial make inference. An optimal plan can be designed using...
At the design of clinical trial operation, a question paramount interest is how long it takes to recruit given number patients. Modelling recruitment dynamics necessary step answer this question. Poisson–gamma model provides very convenient, flexible and realistic approach. This allows predicting duration using data collected at an interim time with good accuracy. A natural arises: evaluate parameters before begins? The harder handle as there are no available for trial. However, if exist...