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1.
The Mendelian randomization is an epidemiologic method proposed to control for spurious associations in observational studies. These associations are commonly caused by confusion derived from social, environmental, and behavioral factors, which can be difficult to measure. Mendelian randomization is based on the selection of genetic variants that are used as instrumental variables that influence exposure patterns or are associated with an intermediate phenotype of the disease. The present work aims to discuss how to select the appropriate genetic variants as instrumental variables and to present methodological tools to deal with the limitations of this epidemiological method. The use of instrumental variables for modifiable exposures has the potential to mitigate the effects of common limitations, such as confusion, when robust genetic variants are chosen as instrumental variables.  相似文献   
2.

Purpose

To analyze trends in second primary cancer (SPC) incidence by using a case-mix approach to standardize on first cancer site distribution.

Methods

Cases registered by 13 French cancer registries between 1989 and 2010 and followed-up until June 2013 were included. The person-year approach was used to compute standardized incidence ratios (SIRs) of metachronous SPC. Usual SIRs and cancer site–specific weighted SIRs called “case-mix SIRs” (cmSIRs) were estimated by sex and calendar period of first cancer diagnosis. Calendar trends in SIRs and cmSIRs were compared.

Results

More than 2.9 million person-years at risk were included. Among males, SIRs dropped from 1.49 to 1.23 between 1989–1994 and 2005–2010, while cmSIRs decreased from 1.40 to 1.27. This difference seems mainly related to a stronger representation of prostate cancers (at lower risk of SPC) and a weaker contribution of bladder and head and neck cancers (at higher risk of SPC) in recent periods of diagnosis. Among females, both SIRs and cmSIRs have remained stable at around 1.22 and 1.21, respectively.

Conclusions

The cmSIR is an indicator that is not influenced by changes in first cancer site distribution. Its use should be encouraged to assess second cancer incidence control.  相似文献   
3.
Randomized controlled trials (RCTs) emphasize the average or overall effect of a treatment (ATE) on the primary endpoint. Even though the ATE provides the best summary of treatment efficacy, it is of critical importance to know whether the treatment is similarly efficacious in important, predefined subgroups. This is why the RCTs, in addition to the ATE, also present the results of subgroup analysis for preestablished subgroups. Typically, these are marginal subgroup analysis in the sense that treatment effects are estimated in mutually exclusive subgroups defined by only one baseline characteristic at a time (e.g., men versus women, young versus old). Forest plot is a popular graphical approach for displaying the results of subgroup analysis. These plots were originally used in meta-analysis for displaying the treatment effects from independent studies. Treatment effect estimates of different marginal subgroups are, however, not independent. Correlation between the subgrouping variables should be addressed for proper interpretation of forest plots, especially in large effectiveness trials where one of the goals is to address concerns about the generalizability of findings to various populations. Failure to account for the correlation between the subgrouping variables can result in misleading (confounded) interpretations of subgroup effects. Here we present an approach called standardization, a commonly used technique in epidemiology, that allows for valid comparison of subgroup effects depicted in a forest plot. We present simulations results and a subgroup analysis from parallel-group, placebo-controlled randomized trials of antibiotics for acute otitis media.  相似文献   
4.
《Vaccine》2017,35(1):118-124
Because the real-world impact of new vaccines cannot be known before they are implemented in national programs, post-implementation studies at the population level are critical. Studies based on analysis of hospitalization rates of vaccine-preventable outcomes are typically used for this purpose. However, estimates of vaccine impact based on hospitalization data are particularly prone to confounding, as hospitalization rates are tightly linked to changes in the quality, access and use of the healthcare system, which often occur simultaneously with introduction of new vaccines. Here we illustrate how changes in healthcare delivery coincident with vaccine introduction can influence estimates of vaccine impact, using as an example reductions in infant pneumonia hospitalizations after introduction of the 10-valent pneumococcal conjugate vaccine (PCV10) in Brazil. To this end, we explore the effect of changes in several metrics of quality and access to public healthcare on trends in hospitalization rates before (2008–09) and after (2011−12) PCV10 introduction in 2010. Changes in infant pneumonia hospitalization rates following vaccine introduction were significantly associated with concomitant changes in hospital capacity and the fraction of the population using public hospitals. Importantly, reduction of pneumonia hospitalization rates after PCV10 were also associated with the expansion of outpatient services in several Brazilian states, falling more sharply where primary care coverage and the number of health units offering basic and emergency care increased more. We show that adjustments for unrelated (non-vaccine) trends commonly employed by impact studies, such as use of single control outcomes, are not always sufficient for accurate impact assessment. We discuss several ways to identify and overcome such biases, including sensitivity analyses using different denominators to calculate hospitalizations rates and methods that track changes in the outpatient setting. Employing these practices can improve the accuracy of vaccine impact estimates, particularly in evolving healthcare settings typical of low- and middle-income countries.  相似文献   
5.
研究设计时混杂控制策略的结构分类   总被引:2,自引:2,他引:0       下载免费PDF全文
混杂影响着人群因果关系的发生。依据混杂因素是否已知、可测量及已测量,可将其分为4类情形。基于有向无环图,对混杂的控制策略分为两类:①混杂路径打断法,又可分为单路径和双路径打断法,分别对应于暴露完全干预法、限制法和分层法;②混杂路径保留法,分别对应于暴露不完全干预法(工具变量设计或不完美的随机对照试验)、中间变量法和匹配法。其中,随机对照试验、工具变量设计或孟德尔随机化设计、中间变量分析可满足4类混杂的控制,而限制法、分层法和匹配法仅适用于已知、可测量并已测量的混杂。识别不同类型混杂的控制机制,有助于在研究设计阶段提出应对措施,是获得正确因果效应估计的前提。  相似文献   
6.
Biomarker validation, like any other confirmatory process based on statistical methodology, must discern associations that occur by chance from those reflecting true biological relationships. Validity of a biomarker is established by authenticating its correlation with clinical outcome. Validated biomarkers can lead to targeted therapy, improve clinical diagnosis, and serve as useful prognostic and predictive factors of clinical outcome. Statistical concerns such as confounding and multiplicity are common in biomarker validation studies. This article discusses four major areas of concern in the biomarker validation process and some of the proposed solutions. Because present‐day statistical packages enable the researcher to address these common concerns, the purpose of this discussion is to raise awareness of these statistical issues in the hope of improving the reproducibility of validation study findings.  相似文献   
7.
The association between the exposure to oral disease and the outcomes of oesophageal and gastric cancer was examined in a Swedish nationwide inpatient register-based nested case-control study in 1964-2008. The study included 6,156 oesophageal squamous-cell carcinoma cases that were compared with 29,993 controls, 2684 oesophageal adenocarcinoma cases that were compared with 15,036 controls and 38,308 gastric cancer cases that were compared with 99,991 controls. For oesophageal squamous cell carcinoma, the age and sex adjusted odds ratio (OR) among patients with a history of oral disease was 1.3 (95% confidence interval (95% CI): 0.9,−1.9), and 1.1 (95% CI 0.8,−1.7) after adjustment for diseases related to alcohol consumption or tobacco smoking. For oesophageal adenocarcinoma, the age and sex adjusted OR was increased (OR 1.7, 95% CI 1.1-2.6), and remained increased (OR 1.6, 95% CI 1.0-2.4) after adjustment for diseases related to smoking or alcohol consumption, gastroesophageal reflux, obesity and ulcer disease. For gastric cancer, no statistically significantly increased risk was observed (age and sex adjusted OR 0.9, 95% CI 0.7-1.1, and fully adjusted OR 0.9, 95% CI 0.7-1.1). In conclusion, this study supports the hypothesis that oral disease increases the risk of oesophageal adenocarcinoma, but not for oesophageal squamous cell carcinoma or gastric cancer. Further investigations are warranted.  相似文献   
8.
9.
皮质醇是下丘脑-垂体-肾上腺系统的终端产物,它可能是将心理压力转化为神经症的生理中介,因而在消极情绪、情绪障碍等相关研究中占有重要地位。皮质醇的分泌以天为周期,其浓度在早晨睡醒后的1个小时内达到峰值,之后由快到慢地降低,到午夜时达到当天的最低点。文章着重于皮质醇在心理学研究中的具体应用,重点介绍皮质醇测量的样本选择、测量介质、混淆因素以及取样中的注意事项,最后则说明了常用的皮质醇参数。  相似文献   
10.
基于倾向性评分的中医复杂干预临床疗效评价   总被引:5,自引:0,他引:5  
临床研究的成功与否,与是否有效控制偏倚有关。在非随机化的观察性研究中,倾向性评分能减少对比组间的差异,缩小偏倚。与试验设计阶段进行配比控制偏倚相比,倾向性分析法不受试验设计(随机对照试验设计)方法的限制,无须在试验设计阶段进行随机,其结果更接近"真实世界"的实际干预效果而非临床试验的效果。因此,倾向性分析应用于中医个性化治疗的复杂干预疗效评价是适合的,具有重要的研究前景和使用价值。  相似文献   
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