Effects of work related confounders on the association between silica exposure and lung cancer: a nested case-control study among Chinese miners and pottery workers

Objective The role of silica in the causation of lung cancer is an ongoing debate. In order to explore whether observed association between silica exposure and lung cancer is confounded by exposure to other occupational carcinogens, we updated a previously nested case-control study among a cohort of male workers in 29 Chinese mines and factories on the basis of an extended follow-up. Methods Five hundred and eleven lung cancer cases and 1,879 matched controls were selected. Exposure to respirable silica as well as relevant occupational confounders were quantitatively assessed based on historical industrial hygiene data. The relationship between exposure to silica and lung cancer was analyzed by conditional logistic regression analysis adjusted for exposure to arsenic, polycyclic aromatic hydrocarbons (PAHs), radon, and smoking. Results In a crude analysis adjusted for smoking only, a significant trend of increasing risk of lung cancer with exposure to silica was found for tin, iron/copper miners, and pottery workers. But after adjustment for relevant occupational confounders, no relationship between silica and lung cancer can be observed. Instead, there is a significant association between lung cancer mortality and cumulative exposure to inorganic arsenic (OR = 1.86, 95% CI: 1.14, 3.04 for each mg/m³-year increase) and carcinogenic PAHs (OR = 1.35, 95% CI: 1.08, 1.69 for each 100 μg/m³-year increase). Conclusion This analysis does not provide any evidence to show that exposure to silica causes lung cancer in the absence of confounding factors.     

如何控制观察性疗效比较研究中的混杂因素：（二）未知或未测量混杂因素的统计学分析方法

观察性疗效比较研究作为随机对照研究的证据补充,其应用价值越来越受到关注。未测量混杂因素的统计学分析方法是观察性疗效比较研究中的重大挑战,本文对观察性疗效比较研究中未知或未测量的混杂因素控制的统计分析方法进行述评。未测量混杂因素的统计学方法包括工具变量法、本底事件率比校正法和双重差分模型及其衍生方法。工具变量法模型构造巧妙,但满足条件的工具变量在实际研究中并不易得;本底事件率比校正法和双重差分模型均要求研究数据有干预前信息,有些实际研究中往往无法满足。未测量混杂因素对统计学方法提出了新的要求、新的挑战,有待国内外统计学者的进一步完善和研究。     

Are Type 2 Diabetes Patients Who Self-Monitor Blood Glucose Special? The Role of Confounders in the Observational ROSSO Study

Background

In the German multicenter, retrospective cohort study (ROSSO), those patients with type 2 diabetes who performed self-monitoring of blood glucose (SMBG) had a better long-term clinical outcome. We analyzed whether confounders accounted for the lower rate of clinical events in the SMBG cohort.

Methods

ROSSO followed 3268 persons from diagnosis of type 2 diabetes for a mean of 6.5 years. Data were retrieved from patient files of randomly contacted primary care practices.

Results

In total, more than 60 potential confounders were documented, including nondisease-associated parameters such as patient''s health insurance, marital status, habitation, and characteristics of diabetes centers. There were only modest differences for these parameters between groups with versus without SMBG, and multiple adjustments did not weaken the association of SMBG use with better outcome (odds ratio 0.65, 95% confidence interval 0.53–0.81, p < .001). This was also true for subgroups of patients defined by type of antidiabetes treatment. Propensity score analysis confirmed the association of SMBG use with outcome. Using key baseline parameters, 813 matching pairs of patients were identified. The analysis again showed a better long-term outcome in the SMBG group (hazard ratio 0.67 p = .004).

Conclusion

An influence of nonrecognized confounders on better outcome in the SMBG group is rendered improbable by similar results obtained with adjustments for disease-associated or disease-independent parameters, by the analysis of patient subgroups, by propensity score analysis and by performing a matched-pair analysis. The higher flexibility in pharmacological antidiabetes treatment regimens in the SMBG cohort suggests a different attitude of treating physicians and patients in association with SMBG.     

知识图谱:一种系统性构建因果图的方法

因果推断(相对于相关性分析)是基于大数据的观察性研究的主要目标。因果图通过有向无环图(directed acyclic graph, DAG)整合大量先验知识将变量之间复杂的因果关系可视化,已成为合理制定因果推断策略的重要工具。然而目前因果图的构建主要依赖专家知识和经验,亟需从整个医学知识体系的角度进行系统化构建,从现有出版物中进行医学知识提取是系统构建DAG的基础,本文将系统介绍基于美国国立卫生研究院SemMedDB数据库研发的结构化医学知识体系平台。本文尝试从跨学科角度,将因果图定义为研究问题涉及概念(头概念和尾概念)及其所有第三方变量之间的复杂网络,提出系统化构建DAG提供新策略:一是将知识图谱修剪为因果图;二是将基于人群-干预/暴露-对照-结果(population-interventions/exposure-comparisons-outcomes,PI/ECO)框架的证据结论合成为因果图。     

On the use and misuse of scalar scores of confounders in design and analysis of observational studies

We assess the asymptotic bias of estimates of exposure effects conditional on covariates when summary scores of confounders, instead of the confounders themselves, are used to analyze observational data. First, we study regression models for cohort data that are adjusted for summary scores. Second, we derive the asymptotic bias for case‐control studies when cases and controls are matched on a summary score, and then analyzed either using conditional logistic regression or by unconditional logistic regression adjusted for the summary score. Two scores, the propensity score (PS) and the disease risk score (DRS) are studied in detail. For cohort analysis, when regression models are adjusted for the PS, the estimated conditional treatment effect is unbiased only for linear models, or at the null for non‐linear models. Adjustment of cohort data for DRS yields unbiased estimates only for linear regression; all other estimates of exposure effects are biased. Matching cases and controls on DRS and analyzing them using conditional logistic regression yields unbiased estimates of exposure effect, whereas adjusting for the DRS in unconditional logistic regression yields biased estimates, even under the null hypothesis of no association. Matching cases and controls on the PS yield unbiased estimates only under the null for both conditional and unconditional logistic regression, adjusted for the PS. We study the bias for various confounding scenarios and compare our asymptotic results with those from simulations with limited sample sizes. To create realistic correlations among multiple confounders, we also based simulations on a real dataset. Copyright © 2015 John Wiley & Sons, Ltd.     

Comparative effectiveness of high dose versus adjuvanted influenza vaccine: A retrospective cohort study

BackgroundAdults 65 years and older (seniors) experience more complications following influenza infection than younger adults. We estimated the relative vaccine effectiveness (rVE) of a trivalent high dose (HD-IIV3) versus an adjuvanted trivalent influenza vaccine (aIIV3) in seniors for respiratory-related hospitalizations.MethodsWe conducted a retrospective cohort study using claims data from Optum’s Clinformatics® Data Mart to compare outcome rates between seniors who received HD-IIV3 versus aIIV3 during the 2016/17 and 2017/18, predominantly A/H3N2 respiratory seasons. Rates were adjusted for demographic characteristics, comorbid conditions, previous influenza vaccination, and geography. We used the previous event rate ratio (PERR) approach to address bias by time-fixed unmeasured confounders.ResultsWe identified 842,282 HD-IIV3 and 34,157 aIIV3 recipients for the 2016/17 season and 1,058,638 HD-IIV3 and 189,636 aIIV3 recipients for the 2017/18 season. The pooled rVE of HD-IIV3 versus aIIV3 for respiratory-related hospitalizations over both seasons was 12% (95% confidence interval: 3.3%–20%); 13% (−6.4% to 32%) for the 2016/17 season and 12% (2.1%–21%) for the 2017/18 season.ConclusionsPooled over two predominantly A/H3N2 respiratory seasons, HD-IIV3 was associated with fewer respiratory hospital admissions than aIIV3 in senior members of large national managed health care company in the U.S.     

Confounder adjustment in vaccine safety studies: Comparing three offset terms for case-centered approach

The case-centered approach (CCA) can be useful in vaccine safety studies to adjust for time varying confounders, especially seasonality. The method compares the observed odds of vaccination in the time interval prior to the event with the expected odds of vaccination during this interval. The log of the expected odds is included in the analysis model as an offset term and it is critical to correctly estimate vaccine effects. In a recent Zostavax^® safety study, we used three alternative sources of reasonable expectations for the offset term. We conducted a simulation study to compare the performance of the three offset terms with different types of seasonal confounders and at different degrees of confounding. The offset term calculated by modeling the timing of vaccination based on predictors such as age, sex and site resulted in the least biased estimate of relative risk (RR).     

The effect of salvage therapy on survival in a longitudinal study with treatment by indication

We consider using observational data to estimate the effect of a treatment on disease recurrence, when the decision to initiate treatment is based on longitudinal factors associated with the risk of recurrence. The effect of salvage androgen deprivation therapy (SADT) on the risk of recurrence of prostate cancer is inadequately described by the existing literature. Furthermore, standard Cox regression yields biased estimates of the effect of SADT, since it is necessary to adjust for prostate‐specific antigen (PSA), which is a time‐dependent confounder and an intermediate variable. In this paper, we describe and compare two methods which appropriately adjust for PSA in estimating the effect of SADT. The first method is a two‐stage method which jointly estimates the effect of SADT and the hazard of recurrence in the absence of treatment by SADT. In the first stage, PSA is predicted in the absence of SADT, and in the second stage, a time‐dependent Cox model is used to estimate the benefit of SADT, adjusting for PSA. The second method, called sequential stratification, reorganizes the data to resemble a sequence of experiments in which treatment is conditionally randomized given the time‐dependent covariates. Strata are formed, each consisting of a patient undergoing SADT and a set of appropriately matched controls, and analysis proceeds via stratified Cox regression. Both methods are applied to data from patients initially treated with radiation therapy for prostate cancer and give similar SADT effect estimates. Copyright © 2010 John Wiley & Sons, Ltd.