Systems, methods and processes for dynamic data monitoring and real-time optimization of ongoing clinical research trials

A technology for clinical trials and dynamic monitoring, applied in the input/output process of data processing, electronic clinical trials, electronic digital data processing, etc.

Pending Publication Date: 2021-05-25
BRIGHT CLINICAL RES LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Phase 2 trials vary in size by treatment area and population, with some trials being large and potentially containing hundreds of subjects
Therefore, in general, up-front estimated information or specific sample sizes may not provide the required statistical power

Method used

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  • Systems, methods and processes for dynamic data monitoring and real-time optimization of ongoing clinical research trials
  • Systems, methods and processes for dynamic data monitoring and real-time optimization of ongoing clinical research trials
  • Systems, methods and processes for dynamic data monitoring and real-time optimization of ongoing clinical research trials

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0214] initial design

[0215] Assuming that θ is the experimental treatment effect, depending on the type of research data, its value may be the difference in means, odds ratio, risk ratio, etc. In the initial design of the experiment, the number of samples in each group is N 0 , the significance level is α and the expected statistical test power, the hypothesis test is carried out, the null hypothesis is that the treatment is ineffective, and the opposite hypothesis is that the treatment is effective (H 0 :θ=0versusH A :θ>0). Considering that the experiment is randomly assigned and the main indicators obey the assumption of normal distribution, the effect of the experimental group is X E subject to mean value μ E , the variance is The normal distribution of , then the effect of the control group is The experimental power is the difference between the two means θ = μ E -μ C . Estimates for other indicators can be obtained using the assumption of approaching norma...

Embodiment 2

[0244] Considering DAD / DDM of SSR: When the sample size is re-estimated

[0245] Condition test power is being calculated Useful at times, but not very useful in determining the timing of an SSR at an interim analysis. when Approaches , the equation (1) in by Bringing in, that is, when the cumulative number of people is as expected as the number of samples, there are two probabilities for the conditional test power, one is close to 0 (when close to C, but less than C), or close to 1 (when Approaching C, but greater than C)). When deciding on an SSR, Stability also needs to be considered. because when when increasing will be more stable. When the observed value equal When, the test verification force can be provided as additional information for , and when It will be more stable as it increases. However, if an adjustment is required, the later the SSR is performed, the less willing and feasible it is to adjust the sample size. Because "operating ...

Embodiment 3

[0266] DAD / DDM Considering Early Efficacy and Type I Error Rate Control

[0267] DAD / DDM is a method based on the seminal theory proposed by Lan, Rosenberger and Lachin (1993), aiming at the use of continuous monitoring in the early stage of the trial, and then seeing significant efficacy. DAD / DDM use alpha continuous cost function 0 will reject the null hypothesis.

[0268] Perform SSR after early efficacy monitoring with cohort sequence boundaries in the design and a final boundary value of C g , the second part of Example 1 discusses the formula for adjusting the final test threshold. For DAD / DDM with continuous monitoring, C g is 2.24.

[0269] On the other hand, if after performing SSR (either or CP mTR ) for continuous monitoring of efficacy, then the above alpha costs the z of the function α(t) 1-α / 2 The quantile should be adjusted to C of equation (3) 1 . Therefore, the bounds of the Z value will be adjusted to The information score t will be based on the ...

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Abstract

A method and process which dynamically monitors data from an on-going randomized clinical trial associated with a drug, device, or treatment automatically and continuously unblinds the study data without human involvement. In one embodiment, a complete trace of statistical parameters such as treatment effect, trend ratio, maximum trend ratio, mean trend ratio, minimum sample size ratio, confidence interval and conditional power are calculated continuously at all points along the information time. In one embodiment, a method early concludes a decision, i.e., futile, promising, sample size re-estimate, for an on-going clinical trial. In one embodiment, exact type I error rate control, median unbiased estimate of treatment effect, and exact two-sided confidence interval can be continuously calculated.

Description

[0001] related application [0002] This application claims priority to U.S. Provisional Application No. 62 / 713,565, filed August 2, 2018, and U.S. Provisional Application No. 62 / 807,584, filed February 19, 2019. The entire contents of these prior applications are incorporated by reference into this application. [0003] This application also cites various publications, the entire contents of which are incorporated by reference into this application to more fully describe the state of the art to which this invention pertains. technical field [0004] This research invention is aimed at the dynamic data monitoring and data optimization system of ongoing clinical trial research, as well as the description of its method and process. [0005] Through the use of electronic patient data management systems (such as EDC systems), treatment allocation systems (such as IWRS systems), and customized statistical software packages, the present invention is a tool for dynamically monitorin...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F7/00
CPCG16H10/20G06F3/0482G06F17/18
Inventor 谢泰亮高平
Owner BRIGHT CLINICAL RES LTD
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