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Data processing method and system based on clinical test data

A clinical trial and data processing technology, applied in the field of data processing methods and systems based on clinical trial data, can solve problems such as insufficient elimination of population heterogeneity inference accuracy, and achieve the effect of eliminating bias

Active Publication Date: 2021-04-30
PEKING UNIV +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] In view of the above analysis, the embodiment of the present invention aims to provide a data processing method and system based on clinical trial data to solve the problem that the existing technology does not fully eliminate the bias caused by the heterogeneity of the population, and does not make full use of the data to improve the inference problem of precision

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  • Data processing method and system based on clinical test data
  • Data processing method and system based on clinical test data
  • Data processing method and system based on clinical test data

Examples

Experimental program
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Embodiment 1

[0086] A specific embodiment of the present invention discloses a data processing method based on clinical trial data, such as figure 1 shown, including the following steps:

[0087] S1. Obtain data samples of the treatment group and control group in the clinical trial.

[0088]Among them, the data sample size of the clinical trial is 2n, and the data sample sizes of the treatment group and the control group are n respectively. The data samples of the treatment group are data samples corresponding to a certain treatment plan (including drugs or treatment means or treatment process, marked as z), and the data samples of the control group are data samples corresponding to only placebo or control measures.

[0089] S2. Based on the above-mentioned data samples of the treatment group and the control group, determine a sample survival estimation model under potential treatment outcomes. The sample survival estimation model includes a potential outcome model for individuals with a...

Embodiment 2

[0096] On the basis of the method in Example 1, the data samples of the treatment group and the control group include covariate set W, treatment status Z, survival status S, and quality of life rating Y related to the treatment effect.

[0097] The treatment status Z is 0 means no treatment, the individual is in the control group, 1 means the treatment of treatment plan z is implemented, and the individual is in the treatment group.

[0098] The survival status S is 0 for dead and 1 for alive.

[0099] Treatment effect Y or quality of life rating Y can be set according to needs, for example, 0 means no impact, 1 means slight impact, and 2 means severe impact.

[0100] Let Y(Z) and S(Z) denote potential outcome and potential survival status at treatment treatment status Z. In fact, in the experiment, only one of S(0) and S(1) can be observed, because only one treatment can be applied to the individual; only when the observed S(Z) is equal to 1, we can Y(Z) of the response is ...

Embodiment 3

[0186] The present invention also provides a kind of data processing system corresponding to embodiment 1, 2, comprises the data acquisition module, processing module, result module connected in sequence, such as figure 2 shown.

[0187] The data collection module is used to obtain the data samples of the treatment group and the control group in the clinical trial.

[0188] The processing module is used to determine the sample survival estimation model under the potential treatment result based on the data samples of the treatment group and the control group; and obtain the covariates related to the treatment effect in the sample survival estimation model, and according to the sample survival estimation The model calculation obtains the potential outcome estimate of the surviving individual under the treatment plan and the probability of the individual being in the surviving state; and, according to the above-mentioned potential outcome estimate of the surviving individual un...

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Abstract

The invention relates to a data processing method based on clinical test data, belongs to the technical field of clinical test evaluation, and solves the problems that the deviation caused by crowd heterogeneity is not fully eliminated and the data is not fully utilized to improve the inference precision in the prior art. The method comprises the following steps: acquiring data samples of a treatment group and a control group in a clinical test; determining a sample survival estimation model under a potential treatment result based on the data samples of the treatment group and the control group; acquiring a covariable related to a treatment effect in the sample survival estimation model, and calculating according to the sample survival estimation model to obtain potential result estimation of a survival individual under a treatment scheme and the probability that the individual is in a survival state; according to the above-mentioned potential outcome estimates of the surviving individuals under the treatment plan and the probability of the individuals being in a survival state, determining the confidence interval of the average causal effect SACE of the survival group. The confidence interval of the SACE obtained by the method can be used for judging whether a clinical test is effective or not, and the inference conclusion is accurate.

Description

technical field [0001] The invention relates to the technical field of clinical trial evaluation, in particular to a data processing method and system based on clinical trial data. Background technique [0002] In a randomized causal trial, subjects are randomly assigned to a treatment or control group. For some time-consuming trials, failure of participants to complete follow-up is a common source of missing data, while another source of 'missing' is due to trial design. [0003] It should be noted that missing data and death truncation are two different concepts. Missing data refers to the existence of outcomes that have not been observed. In contrast, the outcome of death truncation individuals is not defined, because the outcome variables are only for those Surviving individuals are defined. At present, the existing technology has not accurately measured missing data and death cut-off, and cannot eliminate the bias caused by population heterogeneity, and cannot obtain ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16H10/40G16H50/30G06F16/21
CPCG16H10/40G16H50/30G06F16/212Y02A90/10
Inventor 周晓华邓宇昊陆芳赵阳
Owner PEKING UNIV
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