Clinical test allocation method, system and equipment based on adaptive random, and storage medium

Abstract

The invention discloses an adaptive random-based clinical test allocation method, system and device, and a storage medium, and the method comprises the steps: randomly allocating subjects to each treatment group according to a preset proportion, and obtaining the current curative effect data of each treatment group; determining the posterior probability of each treatment group based on the current curative effect data of each treatment group, and adjusting the random distribution probability according to the posterior probability; determining the condition test efficiency of each treatment group, and executing a preset resource optimization strategy on each treatment group according to the condition test efficiency to obtain a test result; and determining the current posterior probability of each treatment group according to the test result, and determining the optimal treatment group based on the current posterior probability. The random distribution probability of each treatment group is dynamically adjusted in the test process, the preset resource optimization strategy is executed on each treatment group according to the condition test efficiency, and the target treatment group is determined based on the test result, so that the subject distribution in the test is optimized, and the reliability and the accuracy of the test result are improved.

Description

Technical Field

[0001] The present invention relates to the field of clinical trial design and statistical technology, and in particular to a clinical trial allocation method, system, device and storage medium based on adaptive randomization. Background Art

[0002] In traditional clinical trial design, subjects are usually randomly assigned to different treatment groups using a fixed ratio (such as 1:1:1…) to ensure the fairness and scientific nature of the trial. However, this fixed randomization method may result in a large number of subjects receiving treatments with low efficacy or ineffectiveness, especially in multi-arm clinical trials, where some treatment groups may show poor efficacy in the early stages of the trial, but a large number of patients still need to be recruited, resulting in low utilization of trial resources and increasing the risk of patients receiving ineffective treatments.

[0003] To this end, the adaptive randomization (AR) method was proposed, in which the allocation probability of subjects is dynamically adjusted based on interim data, so that treatments with better efficacy are more likely to obtain new patients, thereby improving the efficiency of the trial and being more ethically reasonable. However, existing adaptive randomization methods (such as Bayesian adaptive randomization, "random winner rule", etc.) have the following problems: 1. Early data instability: Due to the small sample size, early trial data may have large random fluctuations, causing some methods (such as randomization based entirely on posterior probability) to "stick" to a certain treatment group, affecting the fairness of the trial. 2. Extreme allocation risk: Some adaptive randomization methods may cause certain treatment groups to be eliminated quickly, making the trial data overly biased towards certain treatment groups, affecting the reliability of the final statistical inference. 3. Resource utilization optimization problem: When certain treatment groups are facing elimination due to poor efficacy, whether their sample size should be reallocated and how to optimize the allocation of subjects to make the trial both efficient and fair is a problem that needs in-depth research.

[0004] Therefore, there is an urgent need for a clinical trial allocation method based on adaptive randomization that can enhance trial adaptability, optimize subject allocation, and thereby improve the reliability and accuracy of trial results. Summary of the invention

[0005] The main purpose of the present invention is to provide a clinical trial allocation method, system, device and storage medium based on adaptive randomization, aiming to solve the technical problem in the prior art that resource allocation is inflexible in the clinical trial stage, resulting in insufficient reliability and accuracy of trial results.

[0006] To achieve the above object, the present invention provides a clinical trial allocation method based on adaptive randomization, the method comprising the following steps: Randomly assigning subjects to treatment groups according to a preset ratio, and obtaining current efficacy data of each treatment group; Determine the posterior probability of each of the treatment groups based on the current efficacy data of each of the treatment groups, and adjust the random allocation probability according to the posterior probability; Determine the conditional test efficacy of each of the treatment groups, and execute a preset resource optimization strategy for each of the treatment groups according to the conditional test efficacy to obtain a test result; The current posterior probability of each treatment group is determined according to the test results, and the optimal treatment group of the target treatment group is determined based on the current posterior probability.

[0007] Optionally, the step of determining the conditional test efficacy of each of the treatment groups, and executing a preset resource optimization strategy for each of the treatment groups according to the conditional test efficacy to obtain the test results includes: Determining the conditional test efficacy of each treatment group according to a conditional test efficacy calculation formula; Comparing the conditional test efficacy of each of the treatment groups with a first preset threshold and a second preset threshold to obtain a comparison result; A preset resource optimization strategy is executed on each of the treatment groups according to the comparison results to obtain the test results, wherein the preset resource optimization strategy includes no redistribution of sample size and randomization probability compensation.

[0008] Optionally, the step of executing a preset resource optimization strategy on each of the treatment groups according to the comparison result to obtain a test result includes: If the comparison result indicates that the conditional test power of the treatment group is greater than the first preset threshold or the conditional test power of the treatment group is less than the second preset threshold, then terminating the recruitment of subjects for the treatment group, and the first preset threshold is greater than the second preset threshold; The treatment group corresponding to the terminated subject recruitment is taken as the terminated group, the sample size of the terminated group is no longer reallocated, and the corresponding test results are obtained.

[0009] Optionally, the step of executing a preset resource optimization strategy on each of the treatment groups according to the comparison result to obtain a test result includes: If the comparison result indicates that the conditional test efficacy of the treatment group is less than the first preset threshold and greater than the second preset threshold, determining whether the conditional test efficacy of the treatment group is in a preset invalid interval; If the conditional test efficacy of the treatment group is in a preset invalid interval, then within the preset invalid interval, randomization probability compensation is performed on the treatment group with the worst efficacy, and the corresponding test results are obtained.

[0010] Optionally, the conditional test efficiency calculation formula is: ; In the formula, Treatment group k The standardized test statistic, is the critical value of the significance test, Treatment group k The conditional test efficiency of Represents the experimental data currently collected.

[0011] Optionally, the step of determining the posterior probability of each treatment group based on the current efficacy data of each treatment group, and adjusting the random allocation probability according to the posterior probability includes: Determine the posterior probability of each of the treatment groups based on the current efficacy data of each of the treatment groups and a posterior probability calculation formula; Determine the random assignment probability of each treatment group according to the posterior probability and the random assignment probability calculation formula; Adjusting the random allocation probability of each treatment group based on the preset upper and lower limits of the random allocation probability to obtain the adjusted random allocation probability of each treatment group; The posterior probability calculation formula is: ; In the formula, represents the efficacy parameter of treatment group k, Represents the test data currently collected. It is a marker variable indicating that the efficacy of treatment group k is better than that of the remaining treatment groups; The random allocation probability calculation formula is: ; In the formula, the index c is used to control the degree of randomization. is a marker variable indicating that the efficacy of treatment group j is better than that of the remaining treatment groups, represents the probability of random assignment to treatment group k.

[0012] Optionally, the step of randomly allocating the subjects to treatment groups according to a preset ratio and obtaining current efficacy data of each treatment group includes: Determine the sample size of subjects in the initial stage of the trial based on the trial scale and the pre-set differences between the groups; Based on the sample size of the subjects, the corresponding subjects are randomly assigned to each treatment group according to a preset ratio, and the current efficacy data of each treatment group is obtained.

[0013] In addition, to achieve the above-mentioned purpose, the present invention also proposes a clinical trial allocation system based on adaptive randomization, the system comprising: A trial allocation module, used to randomly allocate subjects to treatment groups according to a preset ratio and obtain current efficacy data of each treatment group; A probability adjustment module, configured to determine the posterior probability of each of the treatment groups based on the current efficacy data of each of the treatment groups, and adjust the random allocation probability according to the posterior probability; A resource optimization module, used to determine the conditional test efficacy of each of the treatment groups, and execute a preset resource optimization strategy for each of the treatment groups according to the conditional test efficacy to obtain a test result; The treatment recommendation module is used to determine the current posterior probability of each treatment group according to the test results, and determine the best treatment group based on the current posterior probability.

[0014] In addition, to achieve the above-mentioned objectives, the present invention also proposes a clinical trial allocation device based on adaptive randomization, the device comprising: a memory, a processor, and a clinical trial allocation program based on adaptive randomization stored on the memory and executable on the processor, the clinical trial allocation program based on adaptive randomization being configured to implement the steps of the clinical trial allocation method based on adaptive randomization as described above.

[0015] In addition, to achieve the above-mentioned purpose, the present invention also proposes a storage medium, on which a clinical trial allocation program based on adaptive randomization is stored. When the clinical trial allocation program based on adaptive randomization is executed by a processor, the steps of the clinical trial allocation method based on adaptive randomization as described above are implemented.

[0016] The present invention discloses randomly allocating subjects to treatment groups according to a preset ratio, and obtaining the current efficacy data of each treatment group; determining the posterior probability of each treatment group based on the current efficacy data of each treatment group, and adjusting the random allocation probability according to the posterior probability; determining the conditional test efficacy of each treatment group, and executing a preset resource optimization strategy for each treatment group according to the conditional test efficacy to obtain test results; determining the current posterior probability of each treatment group according to the test results, and determining the best treatment group based on the current posterior probability. Since the present invention dynamically adjusts the random allocation probability of each treatment group during the test, and executes a preset resource optimization strategy for each treatment group according to the conditional test efficacy, and finally determines the current posterior probability of each treatment group according to the test results, and determines the best treatment group based on the current posterior probability, compared with the prior art, the present invention optimizes the allocation of subjects during the test, thereby improving the reliability and accuracy of the test results. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a flow chart of a first embodiment of a clinical trial allocation method based on adaptive randomization of the present invention; Figure 2 It is a flow chart of a second embodiment of a clinical trial allocation method based on adaptive randomization according to the present invention; Figure 3 It is a flow chart of a third embodiment of a clinical trial allocation method based on adaptive randomization according to the present invention; Figure 4 It is a structural block diagram of a first embodiment of a clinical trial allocation system based on adaptive randomization of the present invention; Figure 5 It is a structural diagram of a clinical trial allocation device based on adaptive randomization in a hardware operating environment involved in an embodiment of the present invention.

[0018] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0019] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0020] The embodiment of the present invention provides a clinical trial allocation method based on adaptive randomization, referring to Figure 1 , Figure 1 It is a flowchart of the first embodiment of the clinical trial allocation method based on adaptive randomization of the present invention.

[0021] In this embodiment, the clinical trial allocation method based on adaptive randomization includes steps S10 to S40: Step S10: Randomly assign the subjects to treatment groups according to a preset ratio, and obtain the current efficacy data of each treatment group.

[0022] It should be noted that the execution subject of this embodiment can be a computer server device with data processing, network communication and program running functions used in clinical trial design scenarios, such as a server, a tablet computer, a personal computer, etc., or an electronic device capable of realizing the above functions, a clinical trial allocation device based on adaptive randomization, etc. The following takes the clinical trial allocation device based on adaptive randomization as an example to illustrate this embodiment and the following embodiments.

[0023] It should be understood that the above preset ratio can be a custom setting, which is not limited in this embodiment. For example, the preset ratio of 1:1:1... is evenly distributed to each treatment group to ensure that the initial data of the experiment has good comparability and stability.

[0024] It is understandable that the sample size in the initial stage of the trial can be set based on the scale of the trial and the expected size of the difference between the groups, for example, the first 50 patients (i.e. subjects) are uniformly randomly assigned.

[0025] In a specific implementation, in a clinical experiment, the sample size of subjects in the initial stage of the trial can be determined based on the trial scale and the preset inter-group differences; based on the sample size of subjects, the corresponding subjects are randomly assigned to each treatment group according to a preset ratio, and the current efficacy data of each treatment group is obtained.

[0026] It needs to be explained that the current efficacy data may include but are not limited to binary response rate, continuous effect value, survival analysis data, etc.

[0027] Step S20: determining the posterior probability of each treatment group based on the current efficacy data of each treatment group, and adjusting the random allocation probability according to the posterior probability.

[0028] In a specific implementation, the posterior probability of each treatment group may be determined based on the current efficacy data of each treatment group and a posterior probability calculation formula.

[0029] The posterior probability calculation formula is: ; In the formula, represents the efficacy parameter of treatment group k, Represents the test data currently collected. is a marker variable indicating that the efficacy of treatment group k is better than that of the remaining treatment groups, and Pr represents the probability function.

[0030] It should be explained that the above random allocation probability refers to the dynamic probability of newly enrolled subjects being assigned to different treatment groups in clinical trials. Different from the traditional fixed ratio (such as 1:1:1), this embodiment dynamically adjusts the allocation probability of each group through the current efficacy data, so that the treatment group with better efficacy obtains a higher allocation probability, while avoiding extreme allocation.

[0031] In the specific implementation, the random allocation probability can be adjusted according to the random allocation probability calculation formula and the posterior probability: The random allocation probability calculation formula is: ; In the formula, the index c is used to control the degree of randomization. is a marker variable indicating that the efficacy of treatment group j is better than that of the remaining treatment groups, represents the probability of random assignment to treatment group k.

[0032] Step S30: determining the conditional test efficacy of each of the treatment groups, and executing a preset resource optimization strategy for each of the treatment groups according to the conditional test efficacy to obtain a test result.

[0033] It should be noted that whether to terminate the recruitment of subjects for each treatment group in advance can be determined based on the conditional test efficacy of each treatment group. For example, if the conditional test efficacy of a treatment group is higher than a preset threshold (such as 0.90) or lower than a preset threshold (such as 0.10), the recruitment of subjects for this treatment group is stopped.

[0034] It should be explained that the above-mentioned preset resource optimization strategy includes no redistribution of sample size and randomization probability compensation.

[0035] In specific implementation, if the efficacy of a treatment group k is significantly lower than that of the control group (or all other treatment groups), and it is impossible to achieve the established efficacy standards in the remaining trials, the recruitment of subjects in this treatment group will be terminated:

[0036] In the formula, The efficacy of the control group was δ For the pre-specified clinically significant differences between the groups, α is the statistical confidence level (e.g. 0.01).

[0037] Step S40: determining the current posterior probability of each treatment group according to the test results, and determining the best treatment group based on the current posterior probability.

[0038] It should be noted that at the end of the trial, the best treatment group is recommended based on the current posterior probability of each treatment group: Here, γ is the treatment selection threshold (e.g., 0.95).

[0039] It is understandable that the significance of the optimal treatment group is to ensure that the selected treatment plan is effective and clinically meaningful through scientific and statistical methods, thereby providing a reliable basis for the final decision of the clinical trial, optimizing resource allocation, and improving patient treatment outcomes and safety.

[0040] This embodiment discloses randomly allocating subjects to treatment groups according to a preset ratio, and obtaining the current efficacy data of each treatment group; determining the posterior probability of each treatment group based on the current efficacy data of each treatment group, and adjusting the random allocation probability according to the posterior probability; determining the conditional test efficacy of each treatment group, and executing a preset resource optimization strategy on each treatment group according to the conditional test efficacy to obtain the test results; determining the current posterior probability of each treatment group according to the test results, and determining the best treatment group based on the current posterior probability. Since this embodiment dynamically adjusts the random allocation probability of each treatment group during the test, and executes a preset resource optimization strategy on each treatment group according to the conditional test efficacy, and finally determines the current posterior probability of each treatment group according to the test results, and determines the best treatment group based on the current posterior probability, compared with the prior art, this embodiment optimizes the allocation of subjects during the test, thereby improving the reliability and accuracy of the test results.

[0041] refer to Figure 2 , Figure 2 It is a flow chart of the second embodiment of the clinical trial allocation method based on adaptive randomization of the present invention.

[0042] Based on the above first embodiment, in this embodiment, step S30 includes steps S301 to S303: Step S301: Determine the conditional test efficacy of each treatment group according to the conditional test efficacy calculation formula.

[0043] It should be explained that the conditional test efficiency calculation formula is: ; In the formula, Treatment group k The standardized test statistic, is the critical value of the significance test, Treatment group k The conditional test efficiency of Represents the experimental data currently collected.

[0044] Step S302: Compare the conditional test efficacy of each treatment group with the first preset threshold and the second preset threshold to obtain a comparison result.

[0045] It should be understood that the conditional test efficacy of each treatment group can be used to determine whether to terminate the recruitment of subjects for each treatment group in advance. For example, if the conditional test efficacy of a treatment group is higher than a first preset threshold (such as 0.90) or lower than a second preset threshold (such as 0.10), the recruitment of subjects for this treatment group is stopped, i.e., high-efficiency termination and low-efficiency termination. The following is an example with the first preset threshold of 0.90 and the second preset threshold of 0.10 as examples.

[0046] Early Success Stopping: If , indicating that the treatment group is very likely to be significantly better than the control group, recruitment can be terminated early, and priority should be given to promoting this treatment group.

[0047] Futility Stopping: If , indicating that it is almost impossible for this treatment group to achieve significant effects in the remaining trials and recruitment should be terminated to save resources.

[0048] Step S303: Execute a preset resource optimization strategy on each of the treatment groups according to the comparison results to obtain a test result, wherein the preset resource optimization strategy includes no redistribution of sample size and randomization probability compensation.

[0049] It should be noted that the preset resource optimization strategy includes no reallocation of sample size and randomization probability compensation. No reallocation of sample size means that after a treatment group is terminated, its originally planned sample size will not be reallocated to maintain the independence and interpretability of data analysis. Randomization probability compensation means setting a conditional efficacy invalid interval. When the conditional test efficacy of a treatment group is Being in the ineffective interval (such as 0.30 - 0.70) means that the therapeutic effect of the treatment group is neither significantly better than that of the control group nor significantly lower than that of the control group, and belongs to the uncertain area; then within the ineffective interval, except for the control group, a randomization probability compensation is set for the treatment group with the worst efficacy, that is, the probability of random allocation of subjects in the treatment group is increased to help it reach the decision criteria (such as termination or optimization) as soon as possible.

[0050] In a specific implementation, if the comparison result indicates that the conditional test efficacy of the treatment group is greater than the first preset threshold or the conditional test efficacy of the treatment group is less than the second preset threshold, the recruitment of subjects for the treatment group is terminated, and the first preset threshold is greater than the second preset threshold; the treatment group corresponding to the terminated subject recruitment is used as the termination group, the sample size of the termination group is no longer reallocated, and the corresponding test results are obtained.

[0051] Furthermore, if the comparison result indicates that the conditional test efficacy of the treatment group is less than the first preset threshold and greater than the second preset threshold, it is determined whether the conditional test efficacy of the treatment group is in a preset invalid interval; if the conditional test efficacy of the treatment group is in the preset invalid interval, then within the preset invalid interval, randomization probability compensation is performed on the treatment group with the worst efficacy, and the corresponding test results are obtained.

[0052] It should be noted that the above-mentioned preset invalid interval, the first preset threshold and the second preset threshold can be custom settings, wherein the first preset threshold is greater than the second preset threshold, and the preset invalid interval is between the first preset threshold and the second preset threshold. The specific values ​​are not limited in this embodiment.

[0053] This embodiment discloses that the subjects are randomly assigned to each treatment group according to a preset ratio, and the current efficacy data of each treatment group is obtained; the posterior probability of each treatment group is determined based on the current efficacy data of each treatment group, and the random assignment probability is adjusted according to the posterior probability; the conditional test efficacy of each treatment group is determined according to the conditional test efficacy calculation formula; the conditional test efficacy of each treatment group is compared with the first preset threshold and the second preset threshold to obtain a comparison result; according to the comparison result, a preset resource optimization strategy is executed on each treatment group to obtain a test result, and the preset resource optimization strategy includes no more sample size reallocation and randomization probability compensation; the current posterior probability of each treatment group is determined according to the test result, and the best treatment group is determined based on the current posterior probability. Since this embodiment determines whether to terminate the recruitment of subjects in each treatment group in advance based on the conditional test efficacy of each treatment group, and executes a preset resource optimization strategy on each treatment group according to the comparison result, including no more sample size reallocation and randomization probability compensation, compared with the prior art, this embodiment effectively improves the test efficiency and saves test resources.

[0054] refer to Figure 3 , Figure 3 It is a flowchart of the third embodiment of the clinical trial allocation method based on adaptive randomization of the present invention.

[0055] Based on the above embodiments, in this embodiment, step S20 includes steps S201 to S203: Step S201: Determine the posterior probability of each treatment group based on the current efficacy data of each treatment group and the posterior probability calculation formula.

[0056] The posterior probability calculation formula is: ; In the formula, represents the efficacy parameter of treatment group k, Represents the test data currently collected. It is a marker variable indicating that the efficacy of treatment group k is better than that of the remaining treatment groups.

[0057] Step S202: Determine the random allocation probability of each treatment group according to the posterior probability and the random allocation probability calculation formula.

[0058] It should be added that in cases where there is a fixed control group (such as a placebo group), the control group is mandatory. . Accordingly, the random allocation probability calculation formula is: ; In the formula, the index c is used to control the degree of randomization. is a marker variable indicating that the efficacy of treatment group j is better than that of the remaining treatment groups, represents the probability of random assignment to treatment group k.

[0059] It should be noted that the smaller c is, the more consistent the allocation probabilities of the groups are (e.g. c=0.5 can reduce extreme randomization). Step S203: Adjust the random allocation probabilities of the treatment groups based on the preset upper and lower limits of the random allocation probability to obtain the adjusted random allocation probabilities of the treatment groups.

[0060] It should be understood that the above-mentioned adjusted random allocation probability may be the final random allocation probability obtained after the random allocation probability is adjusted based on the upper and lower limits of the preset random allocation probability.

[0061] It should be noted that in order to prevent a treatment group from being eliminated prematurely or over-concentrated, the upper and lower limits of the preset random allocation probability are set. , effectively ensuring the reliability and scientificity of the test results:

[0062] Here, ϵ is a threshold value less than 0.5 (such as 0.10), ensuring that all treatment groups have reasonable probability of subject allocation during the trial. For the newly added intervention group, the initial allocation probability can be set to .

[0063] This embodiment discloses that the subjects are randomly assigned to each treatment group according to a preset ratio, and the current efficacy data of each treatment group is obtained; the posterior probability of each treatment group is determined based on the current efficacy data of each treatment group and the posterior probability calculation formula; the random allocation probability of each treatment group is determined according to the posterior probability and the random allocation probability calculation formula; the random allocation probability of each treatment group is adjusted based on the upper and lower limits of the preset random allocation probability to obtain the adjusted random allocation probability of each treatment group; the conditional test efficiency of each treatment group is determined, and the preset resource optimization strategy is executed on each treatment group according to the conditional test efficiency to obtain the test results; the current posterior probability of each treatment group is determined according to the test results, and the best treatment group is determined based on the current posterior probability. Since this embodiment determines the random allocation probability of each treatment group according to the posterior probability and the random allocation probability calculation formula, and adjusts the random allocation probability of each treatment group based on the upper and lower limits of the preset random allocation probability to obtain the adjusted random allocation probability of each treatment group, compared with the prior art, this embodiment effectively prevents a treatment group from being eliminated prematurely or over-concentrated, thereby improving the reliability and scientificity of the test results.

[0064] In addition, an embodiment of the present invention further proposes a storage medium, on which a clinical trial allocation program based on adaptive randomization is stored. When the clinical trial allocation program based on adaptive randomization is executed by a processor, the steps of the clinical trial allocation method based on adaptive randomization as described above are implemented.

[0065] Reference Figure 4 , Figure 4 It is a structural block diagram of the first embodiment of the clinical trial allocation system based on adaptive randomization of the present invention.

[0066] like Figure 4 As shown, the clinical trial allocation system based on adaptive randomization proposed in the embodiment of the present invention includes: a trial allocation module 501, a probability adjustment module 502, a resource optimization module 503 and a treatment recommendation module 504.

[0067] The trial allocation module 501 is used to randomly allocate subjects to treatment groups according to a preset ratio and obtain current efficacy data of each treatment group.

[0068] The probability adjustment module 502 is used to determine the posterior probability of each treatment group based on the current efficacy data of each treatment group, and adjust the random allocation probability according to the posterior probability.

[0069] The resource optimization module 503 is used to determine the conditional test efficacy of each of the treatment groups, and execute a preset resource optimization strategy on each of the treatment groups according to the conditional test efficacy to obtain a test result.

[0070] The treatment recommendation module 504 is used to determine the current posterior probability of each treatment group according to the test results, and determine the best treatment group based on the current posterior probability.

[0071] The trial allocation module 501 is also used to determine the subject sample size at the initial stage of the trial based on the trial scale and preset inter-group differences; based on the subject sample size, the corresponding subjects are randomly allocated to each treatment group according to a preset ratio, and the current efficacy data of each treatment group is obtained.

[0072] The present system embodiment discloses randomly allocating subjects to treatment groups according to a preset ratio, and obtaining the current efficacy data of each treatment group; determining the posterior probability of each treatment group based on the current efficacy data of each treatment group, and adjusting the random allocation probability according to the posterior probability; determining the conditional test efficacy of each treatment group, and executing a preset resource optimization strategy on each treatment group according to the conditional test efficacy to obtain the test results; determining the current posterior probability of each treatment group according to the test results, and determining the best treatment group based on the current posterior probability. Since the present system embodiment dynamically adjusts the random allocation probability of each treatment group during the test, and executes the preset resource optimization strategy on each treatment group according to the conditional test efficacy, and finally determines the current posterior probability of each treatment group according to the test results, and determines the best treatment group based on the current posterior probability, compared with the prior art, the present system embodiment optimizes the allocation of subjects during the test, thereby improving the reliability and accuracy of the test results.

[0073] Based on the above-mentioned first embodiment of the clinical trial allocation system based on adaptive randomization of the present invention, a second embodiment of the clinical trial allocation system based on adaptive randomization of the present invention is proposed.

[0074] In this embodiment, the resource optimization module 503 is also used to determine the conditional test efficacy of each of the treatment groups according to the conditional test efficacy calculation formula; compare the conditional test efficacy of each of the treatment groups with the first preset threshold and the second preset threshold to obtain a comparison result; execute a preset resource optimization strategy on each of the treatment groups according to the comparison result to obtain a test result, and the preset resource optimization strategy includes no longer reallocating the sample size and randomization probability compensation.

[0075] The resource optimization module 503 is also used to terminate the recruitment of subjects to the treatment group if the comparison result indicates that the conditional test efficacy of the treatment group is greater than the first preset threshold or the conditional test efficacy of the treatment group is less than the second preset threshold, and the first preset threshold is greater than the second preset threshold; the treatment group corresponding to the terminated subject recruitment is used as the termination group, the sample size of the termination group is no longer reallocated, and the corresponding test results are obtained.

[0076] The resource optimization module 503 is also used to determine whether the conditional test efficacy of the treatment group is in a preset invalid interval if the comparison result indicates that the conditional test efficacy of the treatment group is less than the first preset threshold and greater than the second preset threshold; if the conditional test efficacy of the treatment group is in the preset invalid interval, then within the preset invalid interval, randomization probability compensation is performed on the treatment group with the worst efficacy, and the corresponding test results are obtained.

[0077] Other embodiments or specific implementations of the clinical trial allocation system based on adaptive randomization of the present invention can refer to the above-mentioned method embodiments, which will not be described in detail here.

[0078] The present application provides a clinical trial allocation device based on adaptive randomization, and the clinical trial allocation device based on adaptive randomization includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the clinical trial allocation method based on adaptive randomization in the above-mentioned embodiment one.

[0079] Reference below Figure 5 , which shows a schematic diagram of the structure of a clinical trial allocation device based on adaptive randomization suitable for implementing the embodiment of the present application. The clinical trial allocation device based on adaptive randomization in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 5 The clinical trial allocation device based on adaptive randomization is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0080] like Figure 5As shown, the clinical trial allocation device based on adaptive randomization may include a processing device 1001 (such as a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to the program stored in the read-only memory 1002 or the program loaded from the storage device 1003 to the random access memory 1004. In the random access memory 1004, various programs and data required for the operation of the clinical trial allocation device based on adaptive randomization are also stored. The processing device 1001, the read-only memory 1002 and the random access memory 1004 are connected to each other through a bus 1005. The input / output interface 1006 is also connected to the bus. Generally, the following systems can be connected to the input / output interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the clinical trial allocation device based on adaptive randomization to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows a clinical trial allocation device based on adaptive randomization with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have alternatively.

[0081] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a read-only memory 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0082] The clinical trial allocation device based on adaptive randomization provided by the present application adopts the clinical trial allocation method based on adaptive randomization in the above-mentioned embodiment, which can solve the technical problem that the resource allocation in the prior art is inflexible during the clinical trial stage, resulting in insufficient reliability and accuracy of the test results. Compared with the prior art, the beneficial effects of the clinical trial allocation device based on adaptive randomization provided by the present application are the same as the beneficial effects of the clinical trial allocation method based on adaptive randomization provided by the above-mentioned embodiment, and the other technical features of the clinical trial allocation device based on adaptive randomization are the same as the features disclosed in the method of the previous embodiment, which will not be described in detail here.

[0083] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0084] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0085] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.

[0086] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0087] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as a read-only memory / random access memory, a magnetic disk, or an optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0088] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A clinical trial allocation method based on adaptive randomization, characterized in that: The method comprises: Randomly assigning subjects to treatment groups according to a preset ratio, and obtaining current efficacy data of each treatment group; Determine the posterior probability of each of the treatment groups based on the current efficacy data of each of the treatment groups, and adjust the random allocation probability according to the posterior probability; Determine the conditional test efficacy of each of the treatment groups, and execute a preset resource optimization strategy for each of the treatment groups according to the conditional test efficacy to obtain a test result; The current posterior probability of each treatment group is determined according to the test results, and the best treatment group is determined based on the current posterior probability.

2. The clinical trial allocation method based on adaptive randomization according to claim 1, characterized in that: The step of determining the conditional test efficacy of each treatment group, and executing a preset resource optimization strategy for each treatment group according to the conditional test efficacy to obtain the test results includes: Determining the conditional test efficacy of each treatment group according to a conditional test efficacy calculation formula; Comparing the conditional test efficacy of each of the treatment groups with a first preset threshold and a second preset threshold to obtain a comparison result; A preset resource optimization strategy is executed on each of the treatment groups according to the comparison results to obtain the test results, wherein the preset resource optimization strategy includes no redistribution of sample size and randomization probability compensation.

3. The clinical trial allocation method based on adaptive randomization according to claim 2, characterized in that: The step of executing a preset resource optimization strategy on each of the treatment groups according to the comparison results to obtain the test results comprises: If the comparison result indicates that the conditional test power of the treatment group is greater than the first preset threshold or the conditional test power of the treatment group is less than the second preset threshold, then terminating the recruitment of subjects for the treatment group, and the first preset threshold is greater than the second preset threshold; The treatment group corresponding to the terminated subject recruitment is taken as the terminated group, the sample size of the terminated group is no longer reallocated, and the corresponding test results are obtained.

4. The clinical trial allocation method based on adaptive randomization according to claim 2, characterized in that: The step of executing a preset resource optimization strategy on each of the treatment groups according to the comparison results to obtain the test results comprises: If the comparison result indicates that the conditional test efficacy of the treatment group is less than the first preset threshold and greater than the second preset threshold, determining whether the conditional test efficacy of the treatment group is in a preset invalid interval; If the conditional test efficacy of the treatment group is in a preset invalid interval, then within the preset invalid interval, randomization probability compensation is performed on the treatment group with the worst efficacy, and the corresponding test results are obtained.

5. The clinical trial allocation method based on adaptive randomization according to claim 2, characterized in that: The conditional test efficiency calculation formula is: ； In the formula, Treatment group k The standardized test statistic, is the critical value of the significance test, Treatment group k The conditional test efficiency of Represents the experimental data currently collected.

6. The clinical trial allocation method based on adaptive randomization according to claim 1, characterized in that: The step of determining the posterior probability of each treatment group based on the current efficacy data of each treatment group, and adjusting the random allocation probability according to the posterior probability, comprises: Determine the posterior probability of each of the treatment groups based on the current efficacy data of each of the treatment groups and a posterior probability calculation formula; Determine the random assignment probability of each treatment group according to the posterior probability and the random assignment probability calculation formula; Adjusting the random allocation probability of each treatment group based on the preset upper and lower limits of the random allocation probability to obtain the adjusted random allocation probability of each treatment group; The posterior probability calculation formula is: ； In the formula, represents the efficacy parameter of treatment group k, Represents the test data currently collected. It is a marker variable indicating that the efficacy of treatment group k is better than that of the remaining treatment groups; The random allocation probability calculation formula is: ； In the formula, the index c is used to control the degree of randomization. is a marker variable indicating that the efficacy of treatment group j is better than that of the remaining treatment groups, represents the probability of random assignment to treatment group k.

7. The clinical trial allocation method based on adaptive randomization according to claim 1, characterized in that: The step of randomly allocating the subjects to each treatment group according to a preset ratio and obtaining the current efficacy data of each treatment group includes: Determine the sample size of subjects in the initial stage of the trial based on the trial scale and the pre-set differences between the groups; Based on the sample size of the subjects, the corresponding subjects are randomly assigned to each treatment group according to a preset ratio, and the current efficacy data of each treatment group is obtained.

8. A clinical trial allocation system based on adaptive randomization, characterized in that: The system comprises: A trial allocation module, used to randomly allocate subjects to treatment groups according to a preset ratio and obtain current efficacy data of each treatment group; A probability adjustment module, configured to determine the posterior probability of each of the treatment groups based on the current efficacy data of each of the treatment groups, and adjust the random allocation probability according to the posterior probability; A resource optimization module, used to determine the conditional test efficacy of each of the treatment groups, and execute a preset resource optimization strategy for each of the treatment groups according to the conditional test efficacy to obtain a test result; The treatment recommendation module is used to determine the current posterior probability of each treatment group according to the test results, and determine the best treatment group based on the current posterior probability.

9. A clinical trial allocation device based on adaptive randomization, characterized in that: The device includes: a memory, a processor, and a clinical trial allocation program based on adaptive randomization stored in the memory and executable on the processor, wherein the clinical trial allocation program based on adaptive randomization is configured to implement the steps of the clinical trial allocation method based on adaptive randomization as described in any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium stores a clinical trial allocation program based on adaptive randomization, and when the clinical trial allocation program based on adaptive randomization is executed by a processor, the steps of the clinical trial allocation method based on adaptive randomization as described in any one of claims 1 to 7 are implemented.

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