Method, System, Device and Storage Medium for Screening Treatment-Sensitive Populations Based on Test Data

Abstract

The present invention discloses a method, system, device and storage medium for screening treatment-sensitive populations based on trial data. The method includes: obtaining clinical trial data, and parsing alternative variables in the clinical trial data to obtain a variable parsing result; performing a full-variable combination traversal according to the variable parsing result, and screening the clinical trial data based on the traversal result to obtain multiple subgroups; determining the comprehensive score of each subgroup through a radar chart based on the effect size, statistical test value, conditional test power, pooled variance and number of alternative variables of each subgroup; and screening out treatment-sensitive subgroups from each subgroup according to the comprehensive score of each subgroup. Since the present invention can traverse all possible variable combinations and screen out treatment-sensitive subgroups from each subgroup based on the effect size, statistical test value, conditional test power, pooled variance and number of alternative variables of each subgroup, the efficiency and accuracy of screening treatment-sensitive populations are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of clinical trial data analysis, and particularly to a method, system, device and storage medium for screening treatment-sensitive populations based on trial data. Background Art

[0002] In clinical trials and individualized treatment research, the efficacy of the same treatment regimen may vary significantly among different patients. Therefore, in the process of trial data analysis, it is crucial to identify patient subgroups (i.e., treatment-sensitive populations) that are more sensitive to a specific treatment. However, traditional subgroup analysis methods are usually based on univariate or preset stratification criteria, and fail to fully utilize the multivariate information in the trial data, which may lead to the omission of effective subgroups or unstable analysis conclusions. In addition, the sample size of some subgroups is small, resulting in insufficient statistical power and affecting the reliability of the results.

[0003] Therefore, there is an urgent need for a method for screening treatment-sensitive populations based on trial data, which can improve the efficiency and accuracy of screening treatment-sensitive populations, and further optimize clinical trial data analysis. Summary of the Invention

[0004] The main object of the present invention is to provide a method, system, device and storage medium for screening treatment-sensitive populations based on trial data, aiming to solve the technical problem of low efficiency and accuracy in screening treatment-sensitive populations in clinical trial data analysis in the prior art.

[0005] To achieve the above object, the present invention provides a method for screening treatment-sensitive populations based on trial data, the method comprising the following steps:

[0006] Obtain clinical trial data, and parse alternative variables in the clinical trial data to obtain a variable parsing result;

[0007] Perform a full variable combination traversal according to the variable parsing result, and screen the clinical trial data based on the traversal result to obtain multiple subgroups;

[0008] Based on the effect size, statistical test value, conditional test power, combined variance and the number of alternative variables of each subgroup, determine the comprehensive score of each subgroup through a radar chart;

[0009] Screen treatment-sensitive subgroups from each subgroup according to the comprehensive scores of each subgroup.

[0010] Optionally, the step of obtaining clinical trial data, and parsing alternative variables in the clinical trial data to obtain a variable parsing result includes:

[0011] Obtain clinical trial data, identify the variable types of each alternative variable in the clinical trial data, and obtain an identification result;

[0012] If the variable type of the alternative variable in the identification result is a continuous variable, divide the corresponding alternative variable into a binary variable according to the median to obtain a first parsing result;

[0013] If the variable type of the alternative variable in the identification result is a categorical variable, determine the original value of the corresponding alternative variable to obtain a second parsing result;

[0014] Use the first parsing result and the second parsing result as the variable parsing result.

[0015] Optionally, the step of performing a full variable combination traversal according to the variable parsing result and screening the clinical trial data based on the traversal result to obtain multiple subgroups includes:

[0016] Perform a full variable combination traversal on each alternative variable based on the value range of each alternative variable in the variable parsing result to obtain a traversal result;

[0017] Match each variable-value combination in the traversal result in the clinical trial data, and determine the proportion information of the number of people of each variable-value combination in the clinical trial data;

[0018] Screen the clinical trial data based on the proportion information of the number of people to obtain multiple subgroups.

[0019] Optionally, before the step of determining the comprehensive score of each subgroup through a radar chart based on the effect size, statistical test value, conditional test power, combined variance, and number of alternative variables of each subgroup, it further includes:

[0020] Determine the effect size of each subgroup according to the difference in means between two groups, risk ratio, or survival analysis index between different treatment groups of each subgroup;

[0021] Determine the statistical test value of each subgroup according to the t-value, p-value, or confidence interval of each subgroup;

[0022] Determine the conditional test power of each subgroup based on the conditional test power calculation formula;

[0023] Determine the combined variance of the experimental group and the control group in each subgroup based on the combined variance calculation formula.

[0024] Optionally, the conditional test power calculation formula is:

[0025] ;

[0026] In the formula, represents the standardized test statistic for subgroup k, is the critical value for the significance test, represents the conditional test power for subgroup k, represents the currently collected trial data;

[0027] The formula for the combined variance is:

[0028] ;

[0029] In the formula, and respectively represent the sample sizes of the experimental group and the control group in each of the subgroups, and respectively represent the sample variances of the experimental group and the control group in each of the subgroups.

[0030] Optionally, the step of determining the comprehensive score of each subgroup through a radar chart based on the effect size, statistical test value, conditional test power, combined variance, and number of alternative variables of each subgroup includes:

[0031] Standardize the effect size, statistical test value, conditional test power, reciprocal of the combined variance, and reciprocal of the number of alternative variables of each subgroup to obtain standardized indicators;

[0032] Use the standardized indicators as the axes of the radar chart, generate the radar chart, and use the area enclosed by the radar chart as the comprehensive score of each subgroup.

[0033] Optionally, the step of screening out treatment-sensitive subgroups from each subgroup according to the comprehensive scores of each subgroup includes:

[0034] Arrange each subgroup in descending order according to the comprehensive score of each subgroup to obtain a sorting result;

[0035] Compare the comprehensive scores of each subgroup in the sorting result with a preset score threshold to obtain a comparison result;

[0036] Screen out treatment-sensitive subgroups from each subgroup based on the comparison result.

[0037] In addition, to achieve the above object, the present invention also proposes a system for screening treatment-sensitive populations based on trial data, and the system includes:

[0038] A variable analysis module, configured to obtain clinical trial data and analyze the alternative variables in the clinical trial data to obtain a variable analysis result;

[0039] A combined variable module, configured to perform a full variable combination traversal according to the variable parsing result, and screen the clinical trial data based on the traversal result to obtain multiple subgroups;

[0040] A comprehensive scoring module, configured to determine the comprehensive score of each subgroup through a radar chart based on the effect size, statistical test value, conditional test power, combined variance, and number of alternative variables of each subgroup;

[0041] A subgroup screening module, configured to screen out treatment-sensitive subgroups from each subgroup according to the comprehensive score of each subgroup.

[0042] In addition, to achieve the above object, the present invention further provides a device for screening treatment-sensitive populations based on trial data, the device includes: a memory, a processor, and a treatment-sensitive population screening program based on trial data stored on the memory and executable on the processor, and the treatment-sensitive population screening program based on trial data is configured to implement the steps of the method for screening treatment-sensitive populations based on trial data as described above.

[0043] In addition, to achieve the above object, the present invention further provides a storage medium, on which a treatment-sensitive population screening program based on trial data is stored, and when the treatment-sensitive population screening program based on trial data is executed by a processor, it implements the steps of the method for screening treatment-sensitive populations based on trial data as described above.

[0044] The present invention discloses obtaining clinical trial data, parsing alternative variables in the clinical trial data to obtain a variable parsing result; performing a full variable combination traversal according to the variable parsing result, and screening the clinical trial data based on the traversal result to obtain multiple subgroups; determining the comprehensive score of each subgroup through a radar chart based on the effect size, statistical test value, conditional test power, combined variance, and number of alternative variables of each subgroup; and screening out treatment-sensitive subgroups from each subgroup according to the comprehensive score of each subgroup. Since the present invention performs a full variable combination traversal according to the variable parsing result, screens the clinical trial data based on the traversal result, determines the comprehensive score of each subgroup through a radar chart based on the effect size, statistical test value, conditional test power, combined variance, and number of alternative variables of each subgroup, and then screens out treatment-sensitive subgroups, compared with the prior art, the present invention improves the efficiency and accuracy of screening treatment-sensitive populations, and thus optimizes the analysis of clinical trial data. Description of the Drawings

[0045] Figure 1 It is a schematic flowchart of the first embodiment of the method for screening treatment-sensitive populations based on trial data of the present invention;

[0046] Figure 2Schematic flowchart of the second embodiment of the method for screening treatment-sensitive populations based on experimental data according to the present invention;

[0047] Figure 3 Block diagram of the structure of the first embodiment of the system for screening treatment-sensitive populations based on experimental data according to the present invention;

[0048] Figure 4 Schematic diagram of the structure of the device for screening treatment-sensitive populations based on experimental data in the hardware operating environment related to the solution of the embodiment of the present invention.

[0049] The realization, functional features, and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. Detailed implementation manners

[0050] 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.

[0051] The embodiments of the present invention provide a method for screening treatment-sensitive populations based on experimental data. Refer to Figure 1 , Figure 1 Schematic flowchart of the first embodiment of the method for screening treatment-sensitive populations based on experimental data according to the present invention.

[0052] In this embodiment, the method for screening treatment-sensitive populations based on experimental data includes steps S10 to S40:

[0053] Step S10: Obtain clinical trial data, and analyze the alternative variables in the clinical trial data to obtain a variable analysis result.

[0054] 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 applied to the scenario of clinical trial data analysis, such as a server, a tablet computer, a personal computer, etc., or an electronic device, a device for screening treatment-sensitive populations based on experimental data, etc. that can implement the above functions. Hereinafter, the device for screening treatment-sensitive populations based on experimental data will be taken as an example to illustrate this embodiment and the following embodiments.

[0055] It should be understood that clinical trial data is structured information collected through systematic medical research (such as testing drugs, devices, or treatment regimens) to evaluate the safety, effectiveness, and applicability of intervention measures.

[0056] It should be explained that alternative variables refer to a set of potential variables pre-selected in clinical trial data for defining and analyzing patient subgroups that may respond differently to a specific treatment regimen.

[0057] It should be noted that the alternative variables are derived from various patient characteristics recorded in clinical trial data, and the variable types of the alternative variables include, but are not limited to: continuous variables (such as age, weight, biomarker concentration, disease score, etc.); categorical variables (such as gender, gene mutation status, disease stage, treatment history, etc.).

[0058] In specific implementation, potential patient subgroup division schemes can be generated by combining the values of different alternative variables and used to evaluate the differences in treatment effects (such as efficacy, safety, etc.) among different subgroups.

[0059] It should be noted that subgroup division needs to be based on clear combinations of variable values (such as "age > 50 years and gene mutation positive"), and the infinite values of continuous variables (such as age, blood glucose level) will lead to a combinatorial explosion and cannot be exhausted. Therefore, continuous variables can be dichotomized (such as "high / low") according to the median, quantiles or clinical thresholds to be converted into discrete variables, so that the subgroup definition is clear and the number of combinations is controllable. For example, if the trial includes the continuous variable "age", it is divided into "≤ 50 years" and "> 50 years" according to the median age of 50 years, and then can be combined with other categorical variables (such as gender) to generate finite subgroups (such as "male and age > 50 years"). The direct analysis of continuous variables may lead to a reduction in statistical power due to insufficient sample size or skewed distribution (such as the t-test being not robust in small samples). Therefore, the subgroup sample sizes can be balanced (such as median division to ensure similar numbers of people in both groups) to reduce the influence of extreme values.

[0060] In specific implementation, to improve the feasibility, efficiency and interpretability of subgroup screening, step S10 includes steps S101 to S104:

[0061] Step S101: Obtain clinical trial data, identify the variable types of each alternative variable in the clinical trial data, and obtain the identification result.

[0062] Step S102: If the variable type of the alternative variable in the identification result is a continuous variable, divide the corresponding alternative variable into a dichotomous variable according to the median to obtain the first analysis result.

[0063] Step S103: If the variable type of the alternative variable in the identification result is a categorical variable, determine the original value of the corresponding alternative variable to obtain the second analysis result.

[0064] Step S104: Use the first analysis result and the second analysis result as the variable analysis result.

[0065] It should be noted that since the effect sizes of continuous variables (such as regression coefficients) are not intuitive enough for clinicians and are difficult to directly guide treatment decisions. Therefore, in this embodiment, the alternative variables are divided into binary variables according to the median. Binary variables provide clear clinical cutoffs (such as "hypertension: yes / no"), which is convenient for formulating personalized treatment strategies.

[0066] In addition, if continuous variables are retained, the computational complexity of combinatorial traversal grows exponentially (for example, when dividing intervals for 3 continuous variables, the number of combinations cannot be exhausted). Therefore, dividing the alternative variables into binary variables according to the median limits the possible values of each variable to 2 (such as "high / low"), changing the number of combinations from infinite to finite (for example, 5 variables → = 32 combinations), ensuring that the system can efficiently complete the traversal and avoid missing valid subgroups.

[0067] Step S20: Perform a full-variable combination traversal based on the variable parsing result, and screen the clinical trial data based on the traversal result to obtain multiple subgroups.

[0068] It needs to be explained that full-variable combination traversal means that in the analysis of clinical trial data, all possible combinations of alternative variable values are systematically enumerated and evaluated to identify patient subgroups with significant response differences to specific treatment regimens. This process aims to ensure that no potential effective subgroup division schemes are missed.

[0069] In a specific implementation, for all possible value levels of all alternative variables, a full-variable combination traversal is performed to ensure coverage of all possible subgroup division schemes. The full-variable combination traversal provides a comprehensive, efficient, and interpretable solution for subgroup screening in clinical trials by exhausting all variable combinations and combining multi-dimensional scoring (such as radar charts).

[0070] Step S30: Determine the comprehensive scores of each subgroup through radar charts based on the effect size, statistical test value, conditional test power, pooled variance, and the number of alternative variables of each subgroup.

[0071] In a specific implementation, before step S30, it further includes: determining the effect size of each subgroup according to the difference in means between two groups, risk ratio, or survival analysis index between different treatment groups of each subgroup; determining the statistical test value of each subgroup according to the t-value, p-value, or confidence interval of each subgroup; determining the conditional test power of each subgroup based on the conditional test power calculation formula; and determining the pooled variance of the experimental group and the control group in each subgroup based on the pooled variance calculation formula.

[0072] It should be noted that for each subgroup corresponding to a variable combination, calculating its effect size (such as the difference in means between two groups, risk ratio, or survival analysis index) between different treatment groups can evaluate the difference in treatment response.

[0073] It should be noted that the statistical test values (such as t-value, p-value or confidence interval) of each subgroup can be calculated under the hypothetical condition that the sample size of the subgroup population is enlarged to that of the test population (i.e., all the samples in this test come from this subgroup) to evaluate the statistical significance of the subgroup.

[0074] The formula for calculating the conditional test power is as follows:

[0075] ;

[0076] In the formula, represents the standardized test statistic of subgroup k, is the critical value of the significance test, represents the conditional test power of subgroup k, represents the test data that has been collected currently;

[0077] The formula for calculating the pooled variance is as follows:

[0078] ;

[0079] In the formula, and respectively represent the sample sizes of the experimental group and the control group in each of the subgroups, and respectively represent the sample variances of the experimental group and the control group in each of the subgroups.

[0080] It should be explained that the reciprocal of the pooled variance can be used to measure the stability of the data within this subgroup.

[0081] It should be understood that the radar chart (Radar Chart), also known as the spider web chart or star chart, is a multi-dimensional data visualization tool. By mapping multiple indicators to different positions on concentric axes, it intuitively displays the comprehensive performance of each indicator. Its features include: multi-axis layout: each axis represents an independent indicator (such as effect size, statistical significance, etc.); closed polygon: a closed figure is formed by connecting the numerical points on each axis, and the larger the area, the better the comprehensive performance; comparative analysis: radar charts of multiple subgroups can be displayed simultaneously for easy horizontal comparison.

[0082] In specific implementation, the effect size, statistical test value, conditional test power, reciprocal of the pooled variance, and reciprocal of the number of alternative variables of each of the subgroups can be standardized to obtain the standardized indicators; the standardized indicators are used as the radar chart axes to generate a radar chart, and the area enclosed by the radar chart is used as the comprehensive score of each of the subgroups.

[0083] For example, the five axes of the radar chart can respectively represent: Axis 1: effect size (such as mean difference, risk ratio); Axis 2: statistical test value (such as t-value); Axis 3: conditional test power; Axis 4: reciprocal of the combined variance; Axis 5: reciprocal of the number of variables (used to encourage more concise subgroup definitions).

[0084] In a specific implementation, the area enclosed by the radar chart can be determined according to the radar chart area calculation formula, and the area enclosed by the radar chart can be used as the comprehensive score of each of the subgroups. The radar chart area calculation formula is:

[0085] ;

[0086] In the formula, represents the standardized value of each axis, is the angle between the axes of the radar chart.

[0087] Step S40: Screen out the treatment-sensitive subgroups from each of the subgroups according to the comprehensive scores of each of the subgroups.

[0088] It should be noted that all subgroups can be sorted in descending order according to the comprehensive score, and the subgroups with higher scores are preferably selected to ensure that the subgroups screened out are at the optimal level in terms of efficacy, statistical significance, stability, statistical power and operability.

[0089] In a specific implementation, each of the subgroups can be sorted in descending order according to the comprehensive score of each of the subgroups to obtain a sorting result; the comprehensive scores of each of the subgroups in the sorting result are compared with a preset score threshold to obtain a comparison result; and treatment-sensitive subgroups are screened out from each of the subgroups based on the comparison result.

[0090] This embodiment discloses obtaining clinical trial data, parsing alternative variables in the clinical trial data to obtain a variable parsing result; performing a full variable combination traversal according to the variable parsing result, and screening the clinical trial data based on the traversal result to obtain multiple subgroups; determining the comprehensive score of each of the subgroups through a radar chart based on the effect size, statistical test value, conditional test power, combined variance and number of alternative variables of each of the subgroups; and screening out the treatment-sensitive subgroups from each of the subgroups according to the comprehensive scores of each of the subgroups. Since this embodiment performs a full variable combination traversal according to the variable parsing result, screens the clinical trial data based on the traversal result, determines the comprehensive score of each subgroup through a radar chart based on the effect size, statistical test value, conditional test power, combined variance and number of alternative variables of each subgroup, and then screens out the treatment-sensitive subgroups, compared with the prior art, this embodiment improves the efficiency and accuracy of screening treatment-sensitive populations, and further optimizes the analysis of clinical trial data.

[0091] Reference Figure 2 ,Figure 2 This is a schematic flowchart of the second embodiment of the method for screening treatment-sensitive populations based on experimental data according to the present invention.

[0092] Based on the above first embodiment, in this embodiment, step S20 includes steps S201 to S203:

[0093] Step S201: Perform a full-variable combination traversal on each of the alternative variables based on the value ranges of the alternative variables in the variable parsing result to obtain a traversal result.

[0094] Step S202: Match each variable-value combination in the traversal result in the clinical trial data and determine the proportion information of the number of people of each variable-value combination in the clinical trial data.

[0095] Step S203: Screen the clinical trial data based on the proportion information of the number of people to obtain multiple subgroups.

[0096] It should be noted that when matching each variable-value combination in the traversal result in the clinical trial data and determining the proportion information of the number of people of each variable-value combination in the clinical trial data, subgroups with a proportion of the number of people lower than the threshold (such as <5%) can be excluded to avoid insufficient statistical power (because small samples are likely to lead to unstable statistical tests (such as large fluctuations in p-values)).

[0097] It should be noted that in this embodiment, through full-variable combination traversal, data matching, and screening based on the proportion of the number of people, statistically significant subgroups are systematically extracted from the clinical trial data. This method combines computational optimization and scientific threshold setting to ensure that the results are both comprehensive (covering all potential combinations) and reliable (excluding the interference of small samples), providing a data-driven subgroup analysis basis for precision medicine.

[0098] This embodiment discloses performing a full-variable combination traversal on each of the alternative variables based on the value ranges of the alternative variables in the variable parsing result to obtain a traversal result; matching each variable-value combination in the traversal result in the clinical trial data and determining the proportion information of the number of people of each variable-value combination in the clinical trial data; screening the clinical trial data based on the proportion information of the number of people to obtain multiple subgroups. Compared with the prior art, since in this embodiment, each variable-value combination in the traversal result is matched in the clinical trial data, the proportion information of the number of people of each variable-value combination in the clinical trial data is determined, and the clinical trial data is screened based on the proportion information of the number of people, statistically significant subgroups are extracted from the clinical trial data, ensuring the reliability of subsequent clinical trial data analysis.

[0099] In addition, an embodiment of the present invention further provides a storage medium, on which a screening program for treatment-sensitive populations based on trial data is stored. When the screening program for treatment-sensitive populations based on trial data is executed by a processor, the steps of the screening method for treatment-sensitive populations based on trial data as described above are implemented.

[0100] Referring to Figure 3 , Figure 3 FIG. is a structural block diagram of the first embodiment of the screening system for treatment-sensitive populations based on trial data of the present invention.

[0101] As Figure 3 shown, the screening system for treatment-sensitive populations based on trial data proposed in the embodiment of the present invention includes: a variable analysis module 501, a combined variable module 502, a comprehensive scoring module 503, and a subgroup screening module 504.

[0102] The variable analysis module 501 is configured to obtain clinical trial data and analyze alternative variables in the clinical trial data to obtain a variable analysis result.

[0103] The combined variable module 502 is configured to perform a full-variable combination traversal according to the variable analysis result and screen the clinical trial data based on the traversal result to obtain multiple subgroups.

[0104] The comprehensive scoring module 503 is configured to determine a comprehensive score for each subgroup through a radar chart based on the effect size, statistical test value, conditional test power, combined variance, and the number of alternative variables of each subgroup.

[0105] The subgroup screening module 504 is configured to screen out treatment-sensitive subgroups from each subgroup according to the comprehensive scores of each subgroup.

[0106] The variable analysis module 501 is further configured to obtain clinical trial data, identify the variable types of each alternative variable in the clinical trial data to obtain an identification result; if the variable type of the alternative variable in the identification result is a continuous variable, the corresponding alternative variable is segmented into a binary variable according to the median to obtain a first analysis result; if the variable type of the alternative variable in the identification result is a categorical variable, the original value of the corresponding alternative variable is determined to obtain a second analysis result; the first analysis result and the second analysis result are used as the variable analysis result.

[0107] The comprehensive scoring module 503 is further configured to determine the effect size of each subgroup according to the difference between the two means, risk ratio or survival analysis index of each subgroup between different treatment groups; determine the statistical test value of each subgroup according to the t-value, p-value or confidence interval of each subgroup; determine the conditional test power of each subgroup based on the conditional test power calculation formula; and determine the combined variance of the experimental group and the control group in each subgroup based on the combined variance calculation formula.

[0108] The comprehensive scoring module 503 is further configured to perform standardization processing on the reciprocal of the effect size, statistical test value, conditional test power, combined variance of each subgroup and the reciprocal of the number of alternative variables to obtain standardized indicators; use the standardized indicators as the radar chart axes to generate a radar chart, and use the area enclosed by the radar chart as the comprehensive score of each subgroup.

[0109] The subgroup screening module 504 is further configured to sort each subgroup in descending order according to the comprehensive score of each subgroup to obtain a sorting result; compare the comprehensive score of each subgroup in the sorting result with a preset score threshold to obtain a comparison result; and screen out treatment-sensitive subgroups from each subgroup based on the comparison result.

[0110] This system embodiment discloses obtaining clinical trial data, parsing alternative variables in the clinical trial data to obtain a variable parsing result; performing a full variable combination traversal according to the variable parsing result, and screening the clinical trial data based on the traversal result to obtain multiple subgroups; determining the comprehensive score of each subgroup through a radar chart based on the effect size, statistical test value, conditional test power, combined variance and number of alternative variables of each subgroup; and screening out treatment-sensitive subgroups from each subgroup according to the comprehensive score of each subgroup. Since this system embodiment performs a full variable combination traversal according to the variable parsing result, screens the clinical trial data based on the traversal result, determines the comprehensive score of each subgroup through a radar chart based on the effect size, statistical test value, conditional test power, combined variance and number of alternative variables of each subgroup, and then screens out treatment-sensitive subgroups, compared with the prior art, this system embodiment improves the efficiency and accuracy of screening treatment-sensitive populations, and thus optimizes the analysis of clinical trial data.

[0111] Based on the first embodiment of the treatment-sensitive population screening system based on experimental data of the present invention, a second embodiment of the treatment-sensitive population screening system based on experimental data of the present invention is proposed.

[0112] In this embodiment, the combined variable module 502 is further configured to perform a full variable combination traversal on each of the alternative variables based on the value ranges of the alternative variables in the variable parsing result to obtain a traversal result; match each variable-value combination in the traversal result in the clinical trial data, and determine the proportion information of the number of people of each variable-value combination in the clinical trial data; and screen the clinical trial data based on the proportion information of the number of people to obtain a plurality of subgroups.

[0113] For other embodiments or specific implementation manners of the treatment-sensitive population screening system based on trial data of the present invention, reference may be made to the above method embodiments, which will not be elaborated here.

[0114] The present application provides a treatment-sensitive population screening device based on trial data. The treatment-sensitive population screening device based on trial data 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 treatment-sensitive population screening method based on trial data in Embodiment 1 above.

[0115] Next, referring to Figure 4 , which shows a schematic structural diagram of a treatment-sensitive population screening device suitable for implementing the embodiments of the present application. The treatment-sensitive population screening device in the embodiments 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), in-vehicle terminals (such as in-vehicle navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 4 The treatment-sensitive population screening device shown is only an example and should not impose any limitation on the functions and usage scope of the embodiments of the present application.

[0116] As Figure 4As shown, the treatment-sensitive population screening device based on trial data may include a processing device 1001 (such as a central processing unit, a graphics processing unit, etc.), which may 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 into the random access memory 1004. In the random access memory 1004, various programs and data required for the operation of the treatment-sensitive population screening device based on trial data 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. An input / output interface 1006 is also connected to the bus. Generally, the following systems may 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 may allow the treatment-sensitive population screening device based on trial data to communicate with other devices wirelessly or wiredly to exchange data. Although the figure shows a treatment-sensitive population screening device having various systems, it should be understood that it is not required to implement or have all the shown systems. More or fewer systems may be implemented or had alternatively.

[0117] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart may 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 contains program codes for executing the method shown in the flowchart. In such an embodiment, the computer program may be downloaded and installed from the network through the communication device, or installed from the storage device 1003, or installed from the 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 embodiments disclosed in the present application are executed.

[0118] The treatment-sensitive population screening device based on trial data provided by the present application adopts the treatment-sensitive population screening method in the above-mentioned embodiment, and can solve the technical problems of low efficiency and accuracy in screening treatment-sensitive populations in clinical trial data analysis in the prior art. Compared with the prior art, the beneficial effects of the treatment-sensitive population screening device based on trial data provided by the present application are the same as those of the treatment-sensitive population screening method provided by the above-mentioned embodiment, and other technical features in the treatment-sensitive population screening device are the same as those disclosed in the method of the previous embodiment, and will not be elaborated here.

[0119] It should be understood that each part 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 a suitable manner in any one or more embodiments or examples.

[0120] As mentioned above, the above are only specific embodiments of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in this application, and all such changes or substitutions should be covered within the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

[0121] It should be noted that in this text, the term "comprising", "including", or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article, or system comprising a series of elements not only includes those elements but also includes other elements not explicitly listed, or further includes elements inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising a..." does not exclude the existence of additional identical elements in the process, method, article, or system comprising such element.

[0122] The serial numbers of the above embodiments of the present invention are only for description and do not represent the superiority or inferiority of the embodiments.

[0123] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-described embodiment methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented 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, in essence, or the part that contributes to the prior art can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as a read-only memory / random access memory, magnetic disk, optical disk), and includes several instructions for causing a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in various embodiments of the present invention.

[0124] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present invention.

Claims

1. A method for screening treatment-sensitive populations based on experimental data, characterized in that: The method comprises: Acquire clinical trial data, and parse candidate variables in the clinical trial data to obtain variable parsing results; Performing a full variable combination traversal according to the variable analysis results, and screening the clinical trial data based on the traversal results to obtain multiple subgroups; Based on the effect size, statistical test value, conditional test power, combined variance and number of alternative variables of each subgroup, a comprehensive score of each subgroup is determined by a radar chart; According to the comprehensive scores of each of the subgroups, a treatment-sensitive subgroup is screened out from each of the subgroups.

2. The method for screening treatment-sensitive populations based on test data according to claim 1, characterized in that: The step of acquiring clinical trial data, parsing candidate variables in the clinical trial data, and obtaining variable parsing results includes: Acquire clinical trial data, identify the variable type of each candidate variable in the clinical trial data, and obtain an identification result; If the variable type of the candidate variable in the identification result is a continuous variable, the corresponding candidate variable is divided into two-category variables according to the median to obtain a first analysis result; If the variable type of the candidate variable in the recognition result is a categorical variable, determining the original value of the corresponding candidate variable to obtain a second parsing result; The first analysis result and the second analysis result are used as variable analysis results.

3. The method for screening treatment-sensitive populations based on test data according to claim 1, characterized in that: The step of performing a full variable combination traversal according to the variable analysis result, and screening the clinical trial data based on the traversal result to obtain multiple subgroups includes: Performing a full variable combination traversal on each candidate variable based on the value range of each candidate variable in the variable analysis result to obtain a traversal result; Matching each variable-value combination in the traversal result in the clinical trial data, and determining the proportion of the number of people in each variable-value combination in the clinical trial data; The clinical trial data is screened based on the population proportion information to obtain multiple subgroups.

4. The method for screening treatment-sensitive populations based on test data according to claim 1, characterized in that: Before the step of determining the comprehensive score of each subgroup through a radar chart based on the effect size, statistical test value, conditional test efficiency, combined variance and number of candidate variables of each subgroup, the step further includes: Determine the effect size of each subgroup according to the mean difference between two groups, risk ratio or survival analysis index between different treatment groups; Determine the statistical test value of each subgroup according to the t value, p value or confidence interval of each subgroup; Determine the conditional test efficacy of each of the subgroups based on a conditional test efficacy calculation formula; The combined variance of the experimental group and the control group in each of the subgroups was determined based on the combined variance calculation formula.

5. The method for screening treatment-sensitive populations based on test data according to claim 4, characterized in that: The conditional test efficiency calculation formula is: ； In the formula, represents the standardized test statistic for subgroup k, is the critical value of the significance test, represents the conditional test power for subgroup k, Represents the test data currently collected; The combined variance calculation formula is: ； In the formula, and Respectively represent the sample size of the experimental group and the control group in each subgroup, and Represent the sample variance of the experimental group and the control group in each subgroup, respectively.

6. The method for screening treatment-sensitive populations based on test data according to claim 1, characterized in that: The step of determining the comprehensive score of each subgroup through a radar chart based on the effect size, statistical test value, conditional test power, combined variance and number of candidate variables of each subgroup comprises: The effect size, statistical test value, conditional test power, reciprocal of the combined variance and reciprocal of the number of alternative variables of each subgroup are standardized to obtain standardized indicators; The standardized index is used as the radar chart axis to generate a radar chart, and the area enclosed by the radar chart is used as the comprehensive score of each subgroup.

7. The method for screening treatment-sensitive populations based on test data according to claim 1, characterized in that: The step of screening out treatment-sensitive subgroups from each of the subgroups according to the comprehensive scores of each of the subgroups comprises: Arrange the subgroups in descending order according to their comprehensive scores to obtain a ranking result; Comparing the comprehensive score of each of the subgroups in the sorting result with a preset score threshold to obtain a comparison result; Based on the comparison results, a treatment-sensitive subgroup is screened out from each of the subgroups.

8. A treatment-sensitive population screening system based on test data, characterized in that: The system comprises: A variable analysis module, used to obtain clinical trial data, and analyze candidate variables in the clinical trial data to obtain variable analysis results; A combination variable module, used for performing a full variable combination traversal according to the variable analysis results, and screening the clinical trial data based on the traversal results to obtain multiple subgroups; A comprehensive scoring module, used to determine the comprehensive score of each of the subgroups through a radar chart based on the effect size, statistical test value, conditional test power, combined variance and number of alternative variables of each of the subgroups; The subgroup screening module is used to screen out treatment-sensitive subgroups from each of the subgroups based on the comprehensive scores of each of the subgroups.

9. A device for screening treatment-sensitive populations based on test data, characterized in that: The device includes: a memory, a processor, and a treatment sensitive population screening program based on test data stored in the memory and executable on the processor, wherein the treatment sensitive population screening program based on test data is configured to implement the steps of the treatment sensitive population screening method based on test data as described in any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium stores a treatment sensitive population screening program based on test data, and when the treatment sensitive population screening program based on test data is executed by the processor, the steps of the treatment sensitive population screening method based on test data as described in any one of claims 1 to 7 are implemented.

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