基于傾向性評(píng)分估計(jì)因果效應(yīng)的方法研究及其在上市后藥品不良反應(yīng)評(píng)價(jià)中的應(yīng)用
發(fā)布時(shí)間：2018-08-07 20:58
【摘要】：一、研究背景和研究目的 藥品安全是一項(xiàng)關(guān)乎民生的基礎(chǔ)工程，對(duì)藥品安全性問題的研究一直是公共衛(wèi)生研究的重點(diǎn)工作。雖然藥品上市前已經(jīng)經(jīng)歷了大量的動(dòng)物實(shí)驗(yàn)和臨床試驗(yàn)，但是由于動(dòng)物實(shí)驗(yàn)的結(jié)果不足以預(yù)測(cè)人類應(yīng)用藥物的安全性；而臨床試驗(yàn)，由于試驗(yàn)時(shí)間短、樣本含量小、并且有嚴(yán)格的入選標(biāo)準(zhǔn)，其藥物應(yīng)用的條件與實(shí)際臨床實(shí)踐有一定的差異。因此，開展藥品上市后不良反應(yīng)監(jiān)測(cè)工作尤顯重要。在所有的藥物警戒工具中，自發(fā)呈報(bào)系統(tǒng)提供的數(shù)據(jù)最多，所花的代價(jià)也越小，是目前世界上藥品不良反應(yīng)監(jiān)測(cè)的最主要的手段。由于缺乏對(duì)整個(gè)人群中的事件背景發(fā)生率、暴露于研究藥品的病例數(shù)及漏報(bào)率等的了解，無法計(jì)算出可靠的藥品-不良事件期望發(fā)生數(shù)，目前常用的解決方案是進(jìn)行數(shù)據(jù)挖掘。 然而這些數(shù)據(jù)挖掘方法主要側(cè)重藥品與不良事件之間的關(guān)聯(lián)，沒有嘗試從因果方面解釋藥品與不良事件的關(guān)系。另一方面，沒有考慮混雜因素如年齡、性別、病種、體重、合并用藥等對(duì)檢測(cè)結(jié)果影響，得到的檢測(cè)結(jié)果存在一定程度的不準(zhǔn)確性，包括由混雜因素導(dǎo)致的假陽性結(jié)果，以及遺漏了被混雜因素所掩蓋的真實(shí)信息。由于自發(fā)呈報(bào)系統(tǒng)自身數(shù)據(jù)的特點(diǎn)，目前常用的控制混雜因素的方法，如分層分析法及Logistic回歸模型并不能很好地控制混雜因素。 因此，本研究期望引入一種新的方法，對(duì)自發(fā)呈報(bào)系統(tǒng)中藥品不良反應(yīng)進(jìn)行評(píng)價(jià)，既考慮到混雜因素對(duì)藥品和不良事件的影響，又從因果概念上來分析藥品和不良事件的關(guān)系，以期在發(fā)現(xiàn)信號(hào)后，進(jìn)行專家評(píng)價(jià)、藥物流行病學(xué)調(diào)查或?qū)ｎ}研究確證因果關(guān)系前，對(duì)發(fā)現(xiàn)的信號(hào)進(jìn)行進(jìn)一步的確證，為藥品風(fēng)險(xiǎn)管理、評(píng)價(jià)及決策提供依據(jù)。 二、研究方法 將Rubin因果模型框架引入自發(fā)呈報(bào)系統(tǒng)數(shù)據(jù)分析中，根據(jù)自發(fā)呈報(bào)系統(tǒng)中數(shù)據(jù)特點(diǎn)，構(gòu)建Rubin因果模型，明確分析總體及潛在結(jié)果的定義。綜合回顧利用傾向性評(píng)分估計(jì)因果效應(yīng)的各種方法的理論，并側(cè)重介紹當(dāng)結(jié)果變量為二分類變量時(shí)各種方法的性質(zhì)。 利用蒙特卡羅模擬方法，設(shè)置符合自發(fā)呈報(bào)系統(tǒng)數(shù)據(jù)特點(diǎn)的參數(shù)，考察利用分層、加權(quán)和匹配三大類傾向性評(píng)分估計(jì)因果效應(yīng)方法的性質(zhì)。分別構(gòu)建正確與錯(cuò)誤的傾向性評(píng)分模型，設(shè)置不同強(qiáng)度的協(xié)變量與分組變量之間的關(guān)系以及設(shè)置不同強(qiáng)度的協(xié)變量與結(jié)果變量之間的關(guān)系，計(jì)算各種設(shè)置下因果效應(yīng)估計(jì)值的偏倚率、標(biāo)準(zhǔn)誤和誤差均方，以考察各種方法在不同情況下估計(jì)值的準(zhǔn)確度和效率。另外，設(shè)置兩種樣本量相對(duì)較小的情況，模擬比較利用貝葉斯傾向性評(píng)分與傳統(tǒng)傾向性評(píng)分估計(jì)因果效應(yīng)值的估計(jì)值、標(biāo)準(zhǔn)誤及置信區(qū)間。 對(duì)美國FDA自發(fā)呈報(bào)系統(tǒng)（FAERS）2011年及2012年兩年的數(shù)據(jù)進(jìn)行規(guī)范整理，利用常規(guī)數(shù)據(jù)挖掘方法，結(jié)合報(bào)告數(shù)和數(shù)據(jù)挖掘方法檢測(cè)發(fā)現(xiàn)“可疑”的組合，確定目標(biāo)研究藥品。根據(jù)目標(biāo)研究藥品的適應(yīng)癥定義研究總體，選擇所有可能服用該藥品的人群，利用幾種傾向性評(píng)分方法進(jìn)行因果效應(yīng)估計(jì)，最終得到因果效應(yīng)估計(jì)值，以考察這些方法在實(shí)際數(shù)據(jù)中的可應(yīng)用性。 三、結(jié)果 建立了自發(fā)呈報(bào)系統(tǒng)數(shù)據(jù)中的不良反應(yīng)分析的因果模型，以所有可能服用目標(biāo)藥品的人群為研究總體；定義潛在結(jié)果為服用目標(biāo)藥品和不服用目標(biāo)藥品發(fā)生目標(biāo)不良事件的可能性；定義系統(tǒng)中可能與是否服用目標(biāo)藥品有關(guān)及可能與是否發(fā)生不良事件有關(guān)的變量為協(xié)變量；定義潛在結(jié)果差值的平均值為因果效應(yīng)。 模擬結(jié)果顯示，目前使用比較廣泛的基于對(duì)層內(nèi)均數(shù)差值進(jìn)行估算的傾向性評(píng)分分層法估計(jì)因果效應(yīng)值時(shí)會(huì)造成偏倚。并且當(dāng)樣本量較大時(shí)，偏倚會(huì)隨之增大。另外一種分層法利用層內(nèi)回歸估計(jì)代替了直接計(jì)算處理差值，這一方法顯著地減少了估計(jì)偏倚，并且對(duì)傾向性評(píng)分模型多納入變量不敏感，與其他方法相比，估計(jì)的效率也比較高。但是當(dāng)結(jié)果變量與協(xié)變量呈非線性關(guān)系時(shí)，估計(jì)的方差將很難求得。利用傾向性評(píng)分加權(quán)法估計(jì)因果效應(yīng)通常能得到一個(gè)無偏估計(jì)，模擬結(jié)果也顯示，使用最廣泛的利用固定差值計(jì)算因果效應(yīng)的估計(jì)效率比較低，沒有充分利用到樣本的信息。另外，由于雙穩(wěn)健法的特殊性質(zhì)，可以使在回歸模型和傾向性評(píng)分模型任一構(gòu)建正確的情況下得到一個(gè)無偏的估計(jì)。在本研究模擬設(shè)置條件下，利用貝葉斯傾向傾向性評(píng)分估計(jì)二分類結(jié)果的因果效應(yīng)值與利用傳統(tǒng)傾向性評(píng)分估計(jì)因果效應(yīng)值區(qū)別不大，在小樣本設(shè)置下，固定分為5層進(jìn)行分析的兩種分層法估計(jì)偏倚較大，效率也較低。 分析2011年及2012年FAERS報(bào)告的數(shù)據(jù)顯示，使用雙膦酸鹽使骨折的發(fā)生率比不使用雙膦酸鹽骨折的發(fā)生率高，各種方法的效應(yīng)差估計(jì)值分為IPW1：0.1083(0.0028,0.2138)； IPW2：0.1086(0.0049,0.2123)； DR：0.1065(0.0028,0.2102)； S：0.0711(-0.0544,0.1966)； SR：0.1123(0.0068,0.2178)。 四、結(jié)論 在自發(fā)呈報(bào)系統(tǒng)數(shù)據(jù)中引入因果模型概念，可以使結(jié)果解釋更為直觀�；趦A向性評(píng)分估計(jì)因果效應(yīng)的方法，適用于自發(fā)呈報(bào)系統(tǒng)中藥品不良反應(yīng)的評(píng)價(jià)，可以克服以往的方法考慮報(bào)告數(shù)不考慮“混雜”的缺陷，使得到的結(jié)果更為可信，并為傳統(tǒng)單藥信號(hào)數(shù)據(jù)挖掘及確證最終因果關(guān)系之間提供了一個(gè)分析的方法。實(shí)例分析顯示，雙膦酸鹽對(duì)骨折的發(fā)生可能有因果關(guān)系，，提示我們有必要對(duì)這一組合進(jìn)行深入研究，如Meta分析、大規(guī)模的藥物流行病學(xué)調(diào)查、專題研究等。
[Abstract]:First, research background and research purpose
Drug safety is a basic project related to the livelihood of the people. Research on the safety of drugs has been the focus of public health research. Although a large number of animal experiments and clinical trials have been experienced before the drug listing, the results of animal experiments are not enough to predict the safety of human applications. The test time is short, the sample content is small, and there is a strict admission standard. There is a certain difference between the conditions of the drug application and the actual clinical practice. Therefore, it is very important to carry out the monitoring of adverse reactions after the drug listing. At present, the most important means of adverse drug reaction monitoring in the world. Due to the lack of understanding of the incidence of events in the whole population, the number of cases and the rate of missing reports, the number of reliable drug adverse event expectations can not be calculated, and the current solution is to carry out data mining.
However, these data mining methods mainly focus on the association between drugs and adverse events, and there is no attempt to explain the relationship between drugs and adverse events from the causal side. On the other hand, there is no consideration of the effects of confounding factors such as age, sex, disease species, weight, and combination of drugs on the results of the test, and the results obtained are inaccurate to a certain extent. Sex, including false positive results caused by confounding factors, and the omission of real information concealed by confounding factors. Due to the spontaneous reporting of the system's own data, the commonly used methods of controlling confounding, such as stratification analysis and Logistic regression model, do not control confounding factors well.
Therefore, this study expects to introduce a new method to evaluate the adverse drug reactions in the spontaneous reporting system, taking into account the effects of confounding factors on drugs and adverse events, and from the causal concept of the relationship between drugs and adverse events, with a view to conducting expert evaluation, epidemiological investigation, or special topics after the discovery of the signal. Before confirming the causal relationship, further confirmation of the detected signals was carried out to provide evidence for drug risk management, evaluation and decision-making.
Two, research methods
The Rubin causality model framework is introduced into the data analysis of the spontaneous reporting system. According to the characteristics of the data in the spontaneous reporting system, the Rubin causality model is constructed, and the definition of the overall and potential results is clearly analyzed. The theory of various methods of estimating the effect effect by the tendency score is reviewed, and the two classification variables are introduced when the result variable is the result variable. The nature of various methods.
Using the Monte Carlo simulation method, we set up the parameters that conform to the characteristics of the data of the spontaneous reporting system, and examine the properties of the method of estimating causality effect by three major categories of layering, weighting and matching, and constructing the correct and wrong tendency grading model respectively, setting up the relationship between the covariate and the grouping variables of different intensity and setting up the relationship and setting up. The relationship between the covariate and the result variable of different intensities is used to calculate the bias rate of the estimated value of the causality effect under various settings, the standard error and the error mean square, in order to investigate the accuracy and efficiency of the estimated values of various methods under different circumstances. In addition, a relatively small sample of two samples is set up and the Bayesian tendency score is used for simulation and comparison. Estimation of the causal effect value, standard error and confidence interval with traditional tendentious score.
The data of the American FDA voluntary reporting system (FAERS) in 2011 and 2012 were standardized. The combination of the conventional data mining method, the number of reports and the data mining methods were used to detect the combination of "suspicious" and determine the target medicine. In order to investigate the applicability of these methods in the actual data, the population of the products is estimated by several tendencies.
Three, the result
A causal model for the analysis of adverse reactions in the spontaneous reporting system was established, and the population of all those who were likely to take the target drug was studied as a whole; the potential results were defined as the possibility of taking the target drugs and not taking the target drugs; the definition system could be related to the use of the target drugs and the possibility of taking the target drugs. The variables associated with adverse events are covariates, and the average value of the difference between potential outcomes is causal effect.
The simulation results show that biased rating stratification based on a relatively wide range of intra - level difference values estimates the bias when estimating the cause and effect value. And when the sample size is large, the bias will increase. Another method of stratification is used to replace the direct calculation of the difference. This method is significant. The estimation bias is reduced, and the tendency score model is insensitive to many variables. Compared with other methods, the estimation efficiency is also higher. However, when the result variable is nonlinear with the covariate, the estimated variance will be difficult. The simulation results also show that the estimation efficiency of using the most extensive use of fixed difference to calculate the causal effect is low and does not make full use of the information of the sample. In addition, due to the special properties of the dual robust method, an unbiased estimate can be obtained in the correct case of any construction of the regression model and the tendency scoring model. Under the condition of simulation setting, the causality effect value of the two classification results is estimated to be different from the traditional tendency score. Under the setting of small sample, the two stratification methods, which are divided into 5 layers, are more biased and less efficient.
Data from the 2011 and 2012 FAERS reports showed that bisphosphonates were used to make the incidence of fractures higher than those without bisphosphonates, and the estimated effects of various methods were divided into IPW1:0.1083 (0.0028,0.2138); IPW2:0.1086 (0.0049,0.2123); DR:0.1065 (0.0028,0.2102); S:0.0711 (-0.0544,0.1966); S. R:0.1123 (0.0068,0.2178).
Four. Conclusion
The introduction of the concept of causality model in the spontaneous reporting system can make the result interpretation more intuitive. The method of estimating the effect effect based on the tendency score is applicable to the evaluation of adverse drug reactions in the spontaneous reporting system, and can overcome the previous method of considering the number of reports without considering the defects of "confounding" and make the results more credible. The analysis shows that bisphosphonates may have causality in the occurrence of fractures, suggesting that it is necessary for us to study this combination, such as Meta analysis, large-scale pharmaco epidemiological investigations, and thematic studies.
【學(xué)位授予單位】：第二軍醫(yī)大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類號(hào)】：R95
本文編號(hào)：2171324
[Abstract]:First, research background and research purpose
Drug safety is a basic project related to the livelihood of the people. Research on the safety of drugs has been the focus of public health research. Although a large number of animal experiments and clinical trials have been experienced before the drug listing, the results of animal experiments are not enough to predict the safety of human applications. The test time is short, the sample content is small, and there is a strict admission standard. There is a certain difference between the conditions of the drug application and the actual clinical practice. Therefore, it is very important to carry out the monitoring of adverse reactions after the drug listing. At present, the most important means of adverse drug reaction monitoring in the world. Due to the lack of understanding of the incidence of events in the whole population, the number of cases and the rate of missing reports, the number of reliable drug adverse event expectations can not be calculated, and the current solution is to carry out data mining.
However, these data mining methods mainly focus on the association between drugs and adverse events, and there is no attempt to explain the relationship between drugs and adverse events from the causal side. On the other hand, there is no consideration of the effects of confounding factors such as age, sex, disease species, weight, and combination of drugs on the results of the test, and the results obtained are inaccurate to a certain extent. Sex, including false positive results caused by confounding factors, and the omission of real information concealed by confounding factors. Due to the spontaneous reporting of the system's own data, the commonly used methods of controlling confounding, such as stratification analysis and Logistic regression model, do not control confounding factors well.
Therefore, this study expects to introduce a new method to evaluate the adverse drug reactions in the spontaneous reporting system, taking into account the effects of confounding factors on drugs and adverse events, and from the causal concept of the relationship between drugs and adverse events, with a view to conducting expert evaluation, epidemiological investigation, or special topics after the discovery of the signal. Before confirming the causal relationship, further confirmation of the detected signals was carried out to provide evidence for drug risk management, evaluation and decision-making.
Two, research methods
The Rubin causality model framework is introduced into the data analysis of the spontaneous reporting system. According to the characteristics of the data in the spontaneous reporting system, the Rubin causality model is constructed, and the definition of the overall and potential results is clearly analyzed. The theory of various methods of estimating the effect effect by the tendency score is reviewed, and the two classification variables are introduced when the result variable is the result variable. The nature of various methods.
Using the Monte Carlo simulation method, we set up the parameters that conform to the characteristics of the data of the spontaneous reporting system, and examine the properties of the method of estimating causality effect by three major categories of layering, weighting and matching, and constructing the correct and wrong tendency grading model respectively, setting up the relationship between the covariate and the grouping variables of different intensity and setting up the relationship and setting up. The relationship between the covariate and the result variable of different intensities is used to calculate the bias rate of the estimated value of the causality effect under various settings, the standard error and the error mean square, in order to investigate the accuracy and efficiency of the estimated values of various methods under different circumstances. In addition, a relatively small sample of two samples is set up and the Bayesian tendency score is used for simulation and comparison. Estimation of the causal effect value, standard error and confidence interval with traditional tendentious score.
The data of the American FDA voluntary reporting system (FAERS) in 2011 and 2012 were standardized. The combination of the conventional data mining method, the number of reports and the data mining methods were used to detect the combination of "suspicious" and determine the target medicine. In order to investigate the applicability of these methods in the actual data, the population of the products is estimated by several tendencies.
Three, the result
A causal model for the analysis of adverse reactions in the spontaneous reporting system was established, and the population of all those who were likely to take the target drug was studied as a whole; the potential results were defined as the possibility of taking the target drugs and not taking the target drugs; the definition system could be related to the use of the target drugs and the possibility of taking the target drugs. The variables associated with adverse events are covariates, and the average value of the difference between potential outcomes is causal effect.
The simulation results show that biased rating stratification based on a relatively wide range of intra - level difference values estimates the bias when estimating the cause and effect value. And when the sample size is large, the bias will increase. Another method of stratification is used to replace the direct calculation of the difference. This method is significant. The estimation bias is reduced, and the tendency score model is insensitive to many variables. Compared with other methods, the estimation efficiency is also higher. However, when the result variable is nonlinear with the covariate, the estimated variance will be difficult. The simulation results also show that the estimation efficiency of using the most extensive use of fixed difference to calculate the causal effect is low and does not make full use of the information of the sample. In addition, due to the special properties of the dual robust method, an unbiased estimate can be obtained in the correct case of any construction of the regression model and the tendency scoring model. Under the condition of simulation setting, the causality effect value of the two classification results is estimated to be different from the traditional tendency score. Under the setting of small sample, the two stratification methods, which are divided into 5 layers, are more biased and less efficient.
Data from the 2011 and 2012 FAERS reports showed that bisphosphonates were used to make the incidence of fractures higher than those without bisphosphonates, and the estimated effects of various methods were divided into IPW1:0.1083 (0.0028,0.2138); IPW2:0.1086 (0.0049,0.2123); DR:0.1065 (0.0028,0.2102); S:0.0711 (-0.0544,0.1966); S. R:0.1123 (0.0068,0.2178).
Four. Conclusion
The introduction of the concept of causality model in the spontaneous reporting system can make the result interpretation more intuitive. The method of estimating the effect effect based on the tendency score is applicable to the evaluation of adverse drug reactions in the spontaneous reporting system, and can overcome the previous method of considering the number of reports without considering the defects of "confounding" and make the results more credible. The analysis shows that bisphosphonates may have causality in the occurrence of fractures, suggesting that it is necessary for us to study this combination, such as Meta analysis, large-scale pharmaco epidemiological investigations, and thematic studies.
【學(xué)位授予單位】：第二軍醫(yī)大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類號(hào)】：R95
【參考文獻(xiàn)】
相關(guān)期刊論文 前1條
1 周曉楓;劉青;蔡兵;Andrew Bate;;全球上市后藥品主動(dòng)監(jiān)測(cè)系統(tǒng)概況[J];藥物流行病學(xué)雜志;2012年07期
本文編號(hào)：2171324
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