Associations of coffee and tea consumption with lung cancer risk
Jingjing ZhuStephanie A. Smith‐WarnerDanxia YuXuehong ZhangWilliam J. BlotYong‐Bing XiangRashmi SinhaYikyung ParkShoichiro TsuganeEmily WhiteWoon‐Puay KohSue K. ParkNorie SawadaSeiki KanemuraYumi SugawaraIchiro TsujiKim RobienYasutake TomataKeun‐Young YooJeongseon KimJian‐Min YuanYu‐Tang GaoNathaniel RothmanDeAnn LazovichSarah Krull AbeMd. Shafiur RahmanErikka LoftfieldYumie TakataXin LiJung Eun LeeEiko SaitoNeal D. FreedmanManami InoueQing LanWalter C. WillettWei ZhengXiao‐Ou Shu
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Associations of coffee and tea consumption with lung cancer risk have been inconsistent, and most lung cancer cases investigated were smokers. Included in this study were over 1.1 million participants from 17 prospective cohorts. Cox regression analyses were conducted to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). Potential effect modifications by sex, smoking, race, cancer subtype and coffee type were assessed. After a median 8.6 years of follow-up, 20 280 incident lung cancer cases were identified. Compared with noncoffee and nontea consumption, HRs (95% CIs) associated with exclusive coffee drinkers (≥2 cups/d) among current, former and never smokers were 1.30 (1.15-1.47), 1.49 (1.27-1.74) and 1.35 (1.15-1.58), respectively. Corresponding HRs for exclusive tea drinkers (≥2 cups/d) were 1.16 (1.02-1.32), 1.10 (0.92-1.32) and 1.37 (1.17-1.61). In general, the coffee and tea associations did not differ significantly by sex, race or histologic subtype. Our findings suggest that higher consumption of coffee or tea is associated with increased lung cancer risk. However, these findings should not be assumed to be causal because of the likelihood of residual confounding by smoking, including passive smoking, and change of coffee and tea consumption after study enrolment.Keywords:
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Abstract This chapter begins with a discussion of definition and theoretical background of confounding. It then focuses on the quantification of potential confounding, evaluation of confounding, and integrated assessment of potential confounding.
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Effect modification
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Confounding may be present in nonrandomized etiological research involving human populations. It can result in erroneous conclusions about the effect of exposure on a disease outcome or about any form of causality between predictors and outcomes. Confounding can wholly or partially account for the apparent effect of the risk factor under consideration or mask the underlying, true association. Not controlling for the effects of confounding can lead to biased results, thus compromising the validity of study conclusions. The three goals of this article are: (1) to define a confounder or a confounding variable, (2) to discuss strategies for controlling the effects of confounding, and (3) to illustrate the perverse effects of confounding with the help of an example.
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Background Although most people with relapsing onset multiple sclerosis (R-MS) eventually transition to secondary progressive multiple sclerosis (SPMS), little is known about disability progression in SPMS. Methods All R-MS patients in the Cardiff MS registry were included. Cox proportional hazards regression was used to examine a) hazard of converting to SPMS and b) hazard of attaining EDSS 6.0 and 8.0 in SPMS. Results 1611 R-MS patients were included. Older age at MS onset (hazard ratio [HR] 1.02, 95%CI 1.01–1.03), male sex (HR 1.71, 95%CI 1.41–2.08), and residual disability after onset (HR 1.38, 95%CI 1.11–1.71) were asso- ciated with increased hazard of SPMS. Male sex (EDSS 6.0 HR 1.41 [1.04–1.90], EDSS 8.0 HR 1.75 [1.14–2.69]) and higher EDSS at SPMS onset (EDSS 6.0 HR 1.31 [1.17–1.46]; EDSS 8.0 HR 1.38 [1.19–1.61]) were associated with increased hazard of reaching disability milestones, while older age at SPMS was associated with a lower hazard of progression (EDSS 6.0 HR 0.94 [0.92–0.96]; EDSS 8.0: HR 0.92 [0.90–0.95]). Conclusions Different factors are associated with hazard of SPMS compared to hazard of disability progres- sion after SPMS onset. These data may be used to plan services, and provide a baseline for comparison for future interventional studies and has relevance for new treatments for SPMS RobertsonNP@cardiff.ac.uk
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Abstract The first part of this chapter discusses the conditions under which a factor can confound the association between exposure and disease, and the conditions under which this cannot occur. It also differentiates confounders from antecedents or mediators. The next part discusses methods devised to neutralize the effects of confounders. Two standard methods are presented: matching to prevent confounding in the data by equalizing the exposed and the unexposed on a potential confounder, and statistical adjustment to compensate for confounding in the data by separating the effects of the exposure from the effects of the confounder.
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Abstract Confounding biases study results when the effect of the exposure on the outcome mixes with the effects of other risk and protective factors for the outcome that are present differentially by exposure status. However, not all differences between the exposed and unexposed group cause confounding. Thus, sources of confounding must be identified before they can be addressed. Confounding is absent in an ideal study where all of the population of interest is exposed in one universe and is unexposed in a parallel universe. In an actual study, an observed unexposed population represents the unobserved parallel universe. Thinking about differences between this substitute population and the unexposed parallel universe helps identify sources of confounding. These differences can then be represented in a diagram that shows how risk and protective factors for the outcome are related to the exposure. Sources of confounding identified in the diagram should be addressed analytically and through study design. However, treating all factors that differ by exposure status as confounders without considering the structure of their relation to the exposure can introduce bias. For example, conditions affected by the exposure are not confounders. There are also special types of confounding, such as time‐varying confounding and unfixable confounding. It is important to evaluate carefully whether factors of interest contribute to confounding because bias can be introduced both by ignoring potential confounders and by adjusting for factors that are not confounders. The resulting bias can result in misleading conclusions about the effect of the exposure of interest on the outcome.
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In the last five years,production and sales of black tea developed very rapidly in China attracting more and more tea comsumers.Fermentation is a very important step in processing of black tea,and fermentation degree affected the quality of black tea.This paper discussed the effect of fectors including raw material,fermenting humidity,fermenting time,oxygen and leaf thickness on the black tea quality.Optical factors for producing a high quality black tea were suggested.
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The hazard ratio and median survival time are the routine indicators in survival analysis. We briefly introduced the relationship between hazard ratio and median survival time and the role of proportional hazard assumption. We compared 110 pairs of hazard ratio and median survival time ratio in 58 articles and demonstrated the reasons for the difference by examples. The results showed that the hazard ratio estimated by the Cox regression model is unreasonable and not equivalent to median survival time ratio when the proportional hazard assumption is not met. Therefore, before performing the Cox regression model, the proportional hazard assumption should be tested first. If proportional hazard assumption is met, Cox regression model can be used; if proportional hazard assumption is not met, restricted mean survival times is suggested.风险比（hazard ratio，HR）和中位生存时间是生存分析时的常规分析和报告指标。本文简要介绍了HR和中位生存时间的关系以及比例风险假定在这两者之间的作用，分析了检索出的58篇文献中的110对风险比和中位生存时间比的差异，并通过实例阐明了产生这种差异的原因。结果表明，在不满足比例风险假定时，Cox回归模型计算得到的风险比是不合理的，且与中位生存时间之比不等价。因此，在使用Cox回归模型前，应先进行比例风险假定的检验，只有符合比例风险假定时才能使用该模型；当不符合比例风险假定时，建议使用限制性平均生存时间。.
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