scholarly journals Optimizing a Bayesian Hierarchical Adaptive Platform Trial Design for Stroke Patients

2021 ◽  
Author(s):  
Guangyi Gao ◽  
Byron J Gajewski ◽  
Jo Wick ◽  
Jonathan Beall ◽  
Jeffrey L Saver ◽  
...  
Keyword(s):  
Trial Design ◽  
Statistical Power ◽  
Binary Outcomes ◽  
Operating Characteristics ◽  
Patient Benefit ◽  
Stroke Patients ◽  
Bayesian Hierarchical ◽  
Cost Efficient ◽  
High Statistical Power ◽  
Over Time

Abstract Background: Platform trials are well-known for their ability to investigate multiple arms on heterogeneous patient populations and their flexibility to add/drop treatment arms due to efficacy/lack of efficacy. Because of their complexity, it is important to develop highly optimized, transparent, and rigorous designs that are cost-efficient, offer high statistical power, maximize patient benefit and are robust to changes over time. Methods: To address these needs, we present a Bayesian platform trial design based on a Beta-Binomial model for binary outcomes that uses three key strategies: 1) Hierarchical modelling of subgroups within treatment arms that allows for borrowing of information across subgroups, 2) utilization of response-adaptive randomization (RAR) schemes that seek a tradeoff between statistical power and patient benefit, and 3) adjustment for potential drift over time. Motivated by a proposed clinical trial that aims to find the appropriate treatment for different subgroup populations of ischemic stroke patients, extensive simulation studies were performed to validate the approach, compare different allocation rules and study the model operating characteristics.Results & Conclusions: Our proposed approach achieved high statistical power, good patient benefit and was also robust against population drift over time. Our design provided a nice balance between the strengths of both the traditional RAR scheme and fixed 1:1 allocation and may be a promising choice for dichotomous outcomes trials investigating multiple subgroups.

A point-based Bayesian hierarchical model to predict the outcome of tennis matches

2019 ◽  
Vol 15 (4) ◽  
pp. 313-325 ◽  
Author(s):  
Martin Ingram
Keyword(s):  
Random Walk ◽  
Hierarchical Model ◽  
Bayesian Hierarchical Model ◽  
Bayesian Hierarchical ◽  
Gaussian Random Walk ◽  
Over Time ◽  
Modelling Approach ◽  
Independent And Identically Distributed

Abstract A well-established assumption in tennis is that point outcomes on each player’s serve in a match are independent and identically distributed (iid). With this assumption, it is enough to specify the serve probabilities for both players to derive a wide variety of event distributions, such as the expected winner and number of sets, and number of games. However, models using this assumption, which we will refer to as “point-based”, have typically performed worse than other models in the literature at predicting the match winner. This paper presents a point-based Bayesian hierarchical model for predicting the outcome of tennis matches. The model predicts the probability of winning a point on serve given surface, tournament and match date. Each player is given a serve and return skill which is assumed to follow a Gaussian random walk over time. In addition, each player’s skill varies by surface, and tournaments are given tournament-specific intercepts. When evaluated on the ATP’s 2014 season, the model outperforms other point-based models, predicting match outcomes with greater accuracy (68.8% vs. 66.3%) and lower log loss (0.592 vs. 0.641). The results are competitive with approaches modelling the match outcome directly, demonstrating the forecasting potential of the point-based modelling approach.

scholarly journals The power of modelling pulsatile profiles

2021 ◽  
Author(s):  
Michiel J. van Esdonk ◽  
Jasper Stevens
Keyword(s):  
Growth Hormone ◽  
Statistical Power ◽  
Quantitative Description ◽  
Mixed Effects ◽  
Post Dose ◽  
Deconvolution Analysis ◽  
Linear Mixed Effects ◽  
Non Linear ◽  
Over Time ◽  
Modelling Approach

AbstractThe quantitative description of individual observations in non-linear mixed effects models over time is complicated when the studied biomarker has a pulsatile release (e.g. insulin, growth hormone, luteinizing hormone). Unfortunately, standard non-linear mixed effects population pharmacodynamic models such as turnover and precursor response models (with or without a cosinor component) are unable to quantify these complex secretion profiles over time. In this study, the statistical power of standard statistical methodology such as 6 post-dose measurements or the area under the curve from 0 to 12 h post-dose on simulated dense concentration–time profiles of growth hormone was compared to a deconvolution-analysis-informed modelling approach in different simulated scenarios. The statistical power of the deconvolution-analysis-informed approach was determined with a Monte-Carlo Mapped Power analysis. Due to the high level of intra- and inter-individual variability in growth hormone concentrations over time, regardless of the simulated effect size, only the deconvolution-analysis informed approach reached a statistical power of more than 80% with a sample size of less than 200 subjects per cohort. Furthermore, the use of this deconvolution-analysis-informed modelling approach improved the description of the observations on an individual level and enabled the quantification of a drug effect to be used for subsequent clinical trial simulations.

Alternative models and randomization techniques for Bayesian response-adaptive randomization with binary outcomes

2021 ◽  
pp. 174077452110101
Author(s):  
Jennifer Proper ◽  
John Connett ◽  
Thomas Murray
Keyword(s):  
Logistic Regression ◽  
Sample Size ◽  
Error Rate ◽  
Adaptive Design ◽  
Type I Error ◽  
Probability Model ◽  
Binary Outcomes ◽  
Type I ◽  
Operating Characteristics ◽  
Type I Error Rate

Background: Bayesian response-adaptive designs, which data adaptively alter the allocation ratio in favor of the better performing treatment, are often criticized for engendering a non-trivial probability of a subject imbalance in favor of the inferior treatment, inflating type I error rate, and increasing sample size requirements. The implementation of these designs using the Thompson sampling methods has generally assumed a simple beta-binomial probability model in the literature; however, the effect of these choices on the resulting design operating characteristics relative to other reasonable alternatives has not been fully examined. Motivated by the Advanced R2 Eperfusion STrategies for Refractory Cardiac Arrest trial, we posit that a logistic probability model coupled with an urn or permuted block randomization method will alleviate some of the practical limitations engendered by the conventional implementation of a two-arm Bayesian response-adaptive design with binary outcomes. In this article, we discuss up to what extent this solution works and when it does not. Methods: A computer simulation study was performed to evaluate the relative merits of a Bayesian response-adaptive design for the Advanced R2 Eperfusion STrategies for Refractory Cardiac Arrest trial using the Thompson sampling methods based on a logistic regression probability model coupled with either an urn or permuted block randomization method that limits deviations from the evolving target allocation ratio. The different implementations of the response-adaptive design were evaluated for type I error rate control across various null response rates and power, among other performance metrics. Results: The logistic regression probability model engenders smaller average sample sizes with similar power, better control over type I error rate, and more favorable treatment arm sample size distributions than the conventional beta-binomial probability model, and designs using the alternative randomization methods have a negligible chance of a sample size imbalance in the wrong direction. Conclusion: Pairing the logistic regression probability model with either of the alternative randomization methods results in a much improved response-adaptive design in regard to important operating characteristics, including type I error rate control and the risk of a sample size imbalance in favor of the inferior treatment.

An oncology clinical trial design with randomization adaptive to both short- and long-term responses

2017 ◽  
Vol 28 (7) ◽  
pp. 2015-2031 ◽  
Author(s):  
Hao Liu ◽  
Xiao Lin ◽  
Xuelin Huang
Keyword(s):  
Trial Design ◽  
Clinical Trial Design ◽  
Therapeutic Benefit ◽  
Survival Outcome ◽  
Operating Characteristics ◽  
Short Term ◽  
Term Survival ◽  
Long Term Survival ◽  
Term Response

In oncology clinical trials, both short-term response and long-term survival are important. We propose an urn-based adaptive randomization design to incorporate both of these two outcomes. While short-term response can update the randomization probability quickly to benefit the trial participants, long-term survival outcome can also change the randomization to favor the treatment arm with definitive therapeutic benefit. Using generalized Friedman’s urn, we derive an explicit formula for the limiting distribution of the number of subjects assigned to each arm. With prior or hypothetical knowledge on treatment effects, this formula can be used to guide the selection of parameters for the proposed design to achieve desirable patient number ratios between different treatment arms, and thus optimize the operating characteristics of the trial design. Simulation studies show that the proposed design successfully assign more patients to the treatment arms with either better short-term tumor response or long-term survival outcome or both.

Abstract 2903: Nutritional Access of Dysphagic Patients 3 Months after Onset of Acute Stroke -From Database of 5,000 Patients in a Single Stroke Center-

Stroke ◽  
2012 ◽  
Vol 43 (suppl_1) ◽  
Author(s):  
Makoto Nakajima ◽  
Yuichiro Inatomi ◽  
Toshiro Yonehara ◽  
Yoichiro Hashimoto ◽  
Teruyuki Hirano
Keyword(s):  
Acute Stroke ◽  
Chronic Phase ◽  
Oral Intake ◽  
Continuous Variables ◽  
Operating Characteristics ◽  
Stroke Patients ◽  
Nihss Score ◽  
Swallowing Dysfunction ◽  
Stroke Center ◽  
Dysphagic Patients

Background and purpose: Prediction of swallowing function in dysphagic patients with acute stroke is indispensable for discussing percutaneous endoscopic gastrostomy (PEG) placement. We performed a retrospective study using database of a large number of acute ischemic stroke patients to clarify predictors for acquisition of oral intake in chronic phase. Methods: A total 4,972 consecutive acute stroke patients were admitted to our stroke center during 8.5 years; a questionnaire was sent to all the survivors after 3 months of onset. We investigated nutritional access after 3 months of onset in 588 patients who could not eat orally 10 days after admission, and analyzed predictive factors for their acquisition of oral intake. Continuous variables were dichotomized to identify the most sensitive predictors; the cutoff values were investigated by receiver operating characteristics curve analysis. Results: Out of 588 dysphagic patients, 75 died during the 3 months, and 143 (28%) of the residual 513 achieved oral intake after 3 months. In logistic-regression models, age ≤80 years, absence of hyperlipidemia, absence of atrial fibrillation, modified Rankin Scale score 0 before onset, and low National Institutes of Health Stroke Scale (NIHSS) score independently predicted oral intake 3 months after onset. From two different model analyses, NIHSS score ≤17 on day 10 (OR 3.63, 95% CI 2.37-5.56) was found to be a stronger predictor for oral intake than NIHSS score ≤17 on admission (OR 2.34, 95% CI 1.52-3.59). At 3 months, 17/143 (12%) patients with oral intake were living at home, while only 1/370 (0.3%) patients without oral intake were. Conclusion: A quarter of dysphagic patients with acute stroke obtained oral intake 3 months after onset. Clinicians should be cautious about PEG placement for stroke patients with severe dysphagia who were independent prior to the stroke, aged ≤80 years, and show NIHSS score ≤17 on day 10, because their swallowing dysfunction may improve in a few months.

WEB-BASED SOFTWARE FOR REAL-TIME SIMULATION-ASSISTED TRIAL DESIGN IN ALZHEIMER’S DISEASE

2015 ◽  
pp. 1-4
Author(s):  
D. Polhamus ◽  
J. Kang ◽  
J. Rogers ◽  
M. Gastonguay
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Statistical Power ◽  
Operating Characteristics ◽  
Longitudinal Models ◽  
Exclusion Criteria ◽  
Web Based ◽  
Correct Dose ◽  
Time Simulation ◽  
Simulation Based

Clinical trials for Alzheimer’s Disease (AD) are necessarily designed in the presence of substantial quantitative uncertainty. Certain important aspects of this uncertainty can be mitigated by developing longitudinal models for AD progression and by using these models to simulate virtual trials and estimate operating characteristics (such as statistical power, the probability of stopping at an interim analysis, the probability of identifying the correct dose, etc.) as a function of candidate design features, such as inclusion / exclusion criteria. In this brief report we describe the development and deployment of a customized software solution that allows such simulation-based results to be generated “on the fly” in the context of a drug development team meeting. This solution leverages a number of recent practical advances in statistical and scientific computing that could be much more broadly leveraged to assure more quantitatively grounded trial designs in Alzheimer’s Disease.

scholarly journals Automatic Grading of Stroke Symptoms for Rapid Assessment Using Optimized Machine Learning and 4-Limb Kinematics: Clinical Validation Study (Preprint)

2020 ◽  
Author(s):  
Eunjeong Park ◽  
Kijeong Lee ◽  
Taehwa Han ◽  
Hyo Suk Nam
Keyword(s):  
Machine Learning ◽  
Medical Staff ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Support Vector ◽  
Grading System ◽  
Operating Characteristics ◽  
Stroke Patients ◽  
Motor Weakness ◽  
Automatic Grading

BACKGROUND Subtle abnormal motor signs are indications of serious neurological diseases. Although neurological deficits require fast initiation of treatment in a restricted time, it is difficult for nonspecialists to detect and objectively assess the symptoms. In the clinical environment, diagnoses and decisions are based on clinical grading methods, including the National Institutes of Health Stroke Scale (NIHSS) score or the Medical Research Council (MRC) score, which have been used to measure motor weakness. Objective grading in various environments is necessitated for consistent agreement among patients, caregivers, paramedics, and medical staff to facilitate rapid diagnoses and dispatches to appropriate medical centers. OBJECTIVE In this study, we aimed to develop an autonomous grading system for stroke patients. We investigated the feasibility of our new system to assess motor weakness and grade NIHSS and MRC scores of 4 limbs, similar to the clinical examinations performed by medical staff. METHODS We implemented an automatic grading system composed of a measuring unit with wearable sensors and a grading unit with optimized machine learning. Inertial sensors were attached to measure subtle weaknesses caused by paralysis of upper and lower limbs. We collected 60 instances of data with kinematic features of motor disorders from neurological examination and demographic information of stroke patients with NIHSS 0 or 1 and MRC 7, 8, or 9 grades in a stroke unit. Training data with 240 instances were generated using a synthetic minority oversampling technique to complement the imbalanced number of data between classes and low number of training data. We trained 2 representative machine learning algorithms, an ensemble and a support vector machine (SVM), to implement auto-NIHSS and auto-MRC grading. The optimized algorithms performed a 5-fold cross-validation and were searched by Bayes optimization in 30 trials. The trained model was tested with the 60 original hold-out instances for performance evaluation in accuracy, sensitivity, specificity, and area under the receiver operating characteristics curve (AUC). RESULTS The proposed system can grade NIHSS scores with an accuracy of 83.3% and an AUC of 0.912 using an optimized ensemble algorithm, and it can grade with an accuracy of 80.0% and an AUC of 0.860 using an optimized SVM algorithm. The auto-MRC grading achieved an accuracy of 76.7% and a mean AUC of 0.870 in SVM classification and an accuracy of 78.3% and a mean AUC of 0.877 in ensemble classification. CONCLUSIONS The automatic grading system quantifies proximal weakness in real time and assesses symptoms through automatic grading. The pilot outcomes demonstrated the feasibility of remote monitoring of motor weakness caused by stroke. The system can facilitate consistent grading with instant assessment and expedite dispatches to appropriate hospitals and treatment initiation by sharing auto-MRC and auto-NIHSS scores between prehospital and hospital responses as an objective observation.

scholarly journals Changes in Pharyngeal Width Over Time as an Indicator of Dysphagia in Stroke Patients

2020 ◽  
Vol 44 (3) ◽  
pp. 203-209
Author(s):  
Seungki Baek ◽  
Il Hwan Jung ◽  
Ho Young Lee ◽  
Jimin Song ◽  
Eunsil Cha ◽  
...  
Keyword(s):  
X Rays ◽  
Lateral Neck ◽  
Spearman Correlation ◽  
Severity Scale ◽  
Stroke Patients ◽  
Average Value ◽  
Scale Scores ◽  
Vertebral Bodies ◽  
Serial Measurements ◽  
Over Time

Objective To verify the pharyngeal width at rest as a measurement that could be used to assess changes in the degree of dysphagia over time in stroke patients.Methods In a cohort of stroke patients, we performed serial measurements of the pharyngeal width at the midpoints of the second (C2) and third (C3) cervical vertebral bodies using lateral neck X-rays while the patients were at rest. The JOSCYL width, a parameter named after the first initial of each developers’ surname and defined as the average value of the upper and lower pharyngeal widths, was used to formulate the JOSCYL scale, which was calculated as the JOSCYL width × 100/neck circumference. All patients also underwent serial videofluoroscopic swallowing studies (VFSSs). The Spearman correlation analysis was used to detect correlations between the serial VFSS results, JOSCYL widths, and JOSCYL scale values.Results Over time, we observed significant positive and negative correlations of change in the JOSCYL width and scale with changes in the Penetration-Aspiration Scale and the Dysphagia Outcome and Severity Scale scores, respectively.Conclusion The JOSCYL width and JOSCYL scale clearly reflected changes in dysphagia in stroke patients over time. These parameters may provide an easier method for evaluating whether post-stroke dysphagia has been alleviated.

scholarly journals LACK OF EMPHASIS ON PSYCHOLOGICAL REHABILITATION FOR STROKE PATIENTS

2012 ◽  
Vol 1 (1) ◽  
pp. 2
Author(s):  
Samina Masood Haider
Keyword(s):  
Emotional Difficulties ◽  
Stroke Survivors ◽  
Stroke Patients ◽  
Post Stroke ◽  
Post Stroke Depression ◽  
The Many ◽  
Over Time

It has been observed that most of the patients are not aware of the dilapidating affects of post stroke depression on their recovery, survival and a return to normal activities of life. The lack of emphasis on psychological rehabilitation for stroke patients is a source of concern for me and I would like to bring to your attention about the facts regarding the implications of proper psychological rehabilitation is not undertaken. Stroke survivors report a range of emotional difficulties, most common being fear, anxiety, frustration, anger, sadness and a sense of grief for their physical and mental losses. Usually these feelings may fade over time however, some patients may struggle with adjusting to the many changes following stroke. When this happens these feelings can develop into depression. It is estimated that approximately one-third of stroke1 survivors develop post-stroke depression (PSD)

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