Model misspecification and bias for inverse probability weighting estimators of average causal effects

Commonly used semiparametric estimators of causal effects specify parametric models for the propensity score (PS) and the conditional outcome. An example is an augmented inverse probability weighting (IPW) estimator, frequently referred to as a doubly robust estimator, because it is consistent if at least one of the two models is correctly specified. However, in many observational studies, the role of the parametric models is often not to provide a representation of the data-generating process but rather to facilitate the adjustment for confounding, making the assumption of at least one true model unlikely to hold. In this paper, we propose a crude analytical approach to study the large-sample bias of estimators when the models are assumed to be approximations of the data-generating process, namely, when all models are misspecified. We apply our approach to three prototypical estimators of the average causal effect, two IPW estimators, using a misspecified PS model, and an augmented IPW (AIPW) estimator, using misspecified models for the outcome regression (OR) and the PS. For the two IPW estimators, we show that normalization, in addition to having a smaller variance, also offers some protection against bias due to model misspecification. To analyze the question of when the use of two misspecified models is better than one we derive necessary and sufficient conditions for when the AIPW estimator has a smaller bias than a simple IPW estimator and when it has a smaller bias than an IPW estimator with normalized weights. If the misspecification of the outcome model is moderate, the comparisons of the biases of the IPW and AIPW estimators show that the AIPW estimator has a smaller bias than the IPW estimators. However, all biases include a scaling with the PS-model error and we suggest caution in modeling the PS whenever such a model is involved. For numerical and finite sample illustrations, we include three simulation studies and corresponding approximations of the large-sample biases. In a dataset from the National Health and Nutrition Examination Survey, we estimate the effect of smoking on blood lead levels.     

A latent class model to multiply impute missing treatment indicators in observational studies when inferences of the treatment effect are made using propensity score matching

Analysts often estimate treatment effects in observational studies using propensity score matching techniques. When there are missing covariate values, analysts can multiply impute the missing data to create m completed data sets. Analysts can then estimate propensity scores on each of the completed data sets, and use these to estimate treatment effects. However, there has been relatively little attention on developing imputation models to deal with the additional problem of missing treatment indicators, perhaps due to the consequences of generating implausible imputations. However, simply ignoring the missing treatment values, akin to a complete case analysis, could also lead to problems when estimating treatment effects. We propose a latent class model to multiply impute missing treatment indicators. We illustrate its performance through simulations and with data taken from a study on determinants of children's cognitive development. This approach is seen to obtain treatment effect estimates closer to the true treatment effect than when employing conventional imputation procedures as well as compared to a complete case analysis.     

Glutamine synthetase (GS) expression is reduced in senile dementia of the Alzheimer type

Glutamine synthetase (GS), a metabolic marker of the mature astrocyte, was investigated in the temporal neocortex of postmortem brain samples of 8 cases, either not demented or affected by senile dementia of the Alzheimer type. A negative correlation between the GS protein level and the density of both classical A4 deposits and senile plaques was evidenced. Such a correlation for GS underlies a dysfunction of the astroglial metabolism and particularly of the glutamate and ammonia neutralization. Since GS is sensitive to oxidative lesioning, the changes in GS level that were observed, occurring at the posttranslational stage, might reflect oxidative damage and have severe consequences on the pathological cascade of events.     

6_m2细胞转化与肌醇磷脂代谢相关性研究

肌醇磷脂代谢与V-mos癌基因转化细胞的相关性,迄今为止未见报导。本文用6m2细胞(Moloney鼠类肉瘤病毒(含V-mos)温度敏感突变株(MoMuSVts110)转化的NRK细胞)为模型,探讨了肌醇磷脂代谢与细胞转化的相关性。在33℃ (转化型温度)时,细胞内PIP(磷脂酰肌醇-4-磷酸)含量明显高于39℃(正常型温度),显示出转化型6m2细胞中存在一个提高的PI激酶活性。同时可见DG(二酰甘油)和IP_3(肌醇三磷酸)含量和蛋白激酶C(PKC)活性均明显高于正常型细胞。当细胞由39℃转至33℃10min,PIP、DG、IP_3含量和PKC活性均明显增加,并伴随有PKC活性由胞质向质膜上的转移。实验结果表明肌醇磷脂代谢参与了6m2细胞转化过程。文中对其作用机理进行了讨论。     

一个以前列腺特异抗原密度和穿刺评分为基础的中国人群中前列腺癌根治术后病理升级预测模型的建立

目的：分析前列腺癌根治术后病理得分较穿刺得分增加的原因，并建立一个可以预测中国人群中前列腺癌根治术后病理升 级的模型。方法：以2008 年8 月至2013 年12 月在我院泌尿科行前列腺癌根治性切除术的264例患者的临床资料为基础，根据 术前和术后患者病理得分的变化将其分为升级组和未升级组。运用单因素和多因素logistic 回归分析病理升级的原因，并通过多 因素回归系数建立预测病理升级的诺模图。结果：264 例患者中，共238 例最终纳入统计分析，多因素logistic 回归分析显示前列 腺特异抗原密度(0R=3.854，P=0.001 )和穿刺Gleason(≤ 6)评分是中国人群中前列腺癌根治术后病理升级的独立危险因素。前列腺 特异抗原密度和穿刺得分的ROC 最佳截断取值为0.37 ng/ml 2和8 分。运用上述两个变量建立了一个可用于预测病理升级的诺 模图。结论：前列腺特异抗原密度和穿刺Gleason 评分是预测中国人群中前列腺癌根治术后病理升级的独立危险因素，本研究所 得的诺模图可以很好地预测前列腺癌根治术后的病理升级。     

Sirtuin 5 regulates the proliferation,invasion and migration of prostate cancer cells through acetyl‐CoA acetyltransferase 1

Sirtuin 5 (SIRT5) is a NAD⁺‐dependent class III protein deacetylase, and its role in prostate cancer has not yet been reported. Therefore, to explore the diagnosis and treatment of prostate cancer, we investigated the effect of SIRT5 on prostate cancer. Sirtuin 5 was assessed by immunohistochemistry in 57 normal and cancerous prostate tissues. We found that the tissue expression levels of SIRT5 in patients with Gleason scores ≥7 were significantly different from those in patients with Gleason scores <7 (P < .05, R > 0). Further, mass spectrometry and pathway screening experiments showed that SIRT5 regulated the activity of the mitogen‐activated protein kinase (MAPK) pathway, which in turn modulated the expression of MMP9 and cyclin D1. Being a substrate of SIRT5, acetyl‐CoA acetyltransferase 1 (ACAT1) was regulated by SIRT5. SIRT5 also regulated MAPK pathway activity through ACAT1. These results revealed that SIRT5 promoted the activity of the MAPK pathway through ACAT1, increasing the ability of prostate cancer cells to proliferate, migrate and invade. Overall, these results indicate that SIRT5 expression is closely associated with prostate cancer progression. Understanding the underlying mechanism may provide new targets and methods for the diagnosis and treatment of the disease.     

Circulating exosome‐derived bona fide long non‐coding RNAs predicting the occurrence and metastasis of hepatocellular carcinoma

Although the diagnosis and therapy approach developed, techniques for the early diagnosis of HCC remain insufficient which results in poor prognosis of patients. The traditional biomarker AFP, however, has been proved with low specificity. Circulating exosomal ncRNAs revealed different profiles reflecting the characteristics of tumour. In this study, we mainly focused on circulating exosomal ncRNAs which might be the fingerprint for HCC, especially for the diagnosis or metastasis prediction. A high throughput lncRNA microarray in exosomes extracted from cell‐free plasma was applied. The risk score analysis was employed to screen the potential exosome‐derived lncRNAs in two independent sets based on different clinical parameters in 200 paired HCC patients. After a multi‐stage validation, we finally revealed three lncRNAs, ENSG00000248932.1, ENST00000440688.1 and ENST00000457302.2, increased in HCC comparing with the both chronic hepatitis (CH) patients and cancer‐free controls. ROC curve revealed a higher sensitivity and specificity in predicting the occurrence of HCC from cancer‐free controls and CH patients with the area under curve (AUC) of 0.905 and 0.879 by combining AFP. The three lncRNA panel combined with AFP also indicted a fingerprint function in predicting the metastasis of HCC with the AUC of 0.870. In conclusion, ENSG00000248932.1, ENST00000440688.1 and ENST00000457302.2 might be the potential biomarker for the tumorigenesis prediction from CH patients or healthy controls and may also be applied for dynamic monitoring the metastasis of HCC.     

房颤射频消融术后复发风险预测评分系统的建立及评价

目的:探讨心房颤动(房颤)患者射频消融术后复发的风险因素,并依此构建个性化的风险评分系统。方法:选取2017年1~8月行射频消融术的房颤患者154例作为研究对象,依据术后3个月的随访结果将患者分为复发组及未复发组,采用单因素分析和Logistic回归分析对各风险因素进行分析,构建其评分系统,采用Hosmer-Lemeshow拟合优度检验和ROC曲线下面积评价评分系统的准确度及区分度。结果:术后随访3个月的结果显示共37例(24.03%)房颤患者出现复发,房颤类型、病程、体质量指数(BMI)、左房前后径(LAD)、左房容积(LAV)及超敏C反应蛋白(hs-CRP)水平均是房颤复发的独立风险因素(P<0.05)。构建的风险评分系统得分为0~26分,Hosmer-Lemeshow拟合优度检验:x^2=7.520,P=0.482;ROC曲线下面积为0.864(95%CI:0.837~0.891),预测评分值为15分时,约登指数最大(0.605),此时的敏感度和特异度分别为77.3%和83.2%。结论:房颤患者射频消融术后的复发率较高,依据风险因素构建的风险评分系统具有较高的预测效率和区分能力,可作为房颤患者射频消融术后复发风险评估的参考工具。