Meta-analysis of silicosis and lung cancer

OBJECTIVES: This study examined the association between silicosis and lung cancer in a systematic review (and meta-analysis) of the epidemiologic literature, with special reference to the methodological quality of observational studies. METHODS: We searched Medline, Toxline, BIOSIS and Embase (1966-May 2004) for original articles published in any language and systematically reviewed the bibliographies of the retrieved articles. Observational studies (cohort and case-control studies) were selected if they reported a measure of association [standardized mortality ratio (SMR), relative risk or odds ratio] relating lung cancer to silicosis. RESULTS: Thirty-one studies (27 cohort studies, 4 case-control studies) met the inclusion criteria of the meta-analysis. Without any adjustment for smoking, the meta-analysis of the cohort studies indicated that the common SMR was 2.45 [95% confidence interval (95% CI) 1.63-3.66; homogeneity P<0.0001]. When the results of the cohorts for which mortality data were adjusted for smoking were pooled, the common SMR was 1.60 (95% CI 1.33-1.93; homogeneity P=0.52). In a dose-response analysis, the profusion of small and large opacities found in chest X-rays correlated with the risk of death from lung cancer. Overall, the case-control studies were more conservative in their conclusions. CONCLUSIONS: Because of biases inherent to observational studies, it is likely that the risk of lung cancer among silicosis patients is overestimated in the current literature. There is nevertheless evidence, from data restricted to never-smokers and from a dose-response analysis, that silicosis and lung cancer are associated.     

Estimating cancer prevalence using mixture models for cancer survival

Knowledge of cancer prevalence is useful for estimating the ongoing level of resources utilized in the treatment of disease and is of some public health interest. Cancer prevalence is estimated first as the proportion of persons previously diagnosed (PD) with cancer that are still alive; and second as the proportion of individuals in the population who were previously diagnosed with cancer and who have not been cured (NC). The proportion of cases that are cured is estimated by assuming that the cured and uncured cases have distinct survival patterns. The hazard for cured cases is assumed to be a multiple of the hazard from causes other than cancer in the general population. The hazard for uncured cases is assumed to have two independent components: one corresponding to the disease-specific hazard, and the other a multiple of the population hazard from 'other causes'. Future prevalence estimates are obtained by projecting the survival of current prevalent cases as well as the survival of future incident cases.     

矽尘、矽肺与肺癌关系的Meta分析

目的对矽尘暴露、矽肺与肺癌间的关系进行综合评价，为制定科学的防制措施提供依据。方法本文收集了1997年以来关于矽尘、矽肺与肺癌关系的原始文献，按纳入与排除标准分类整理，采用Meta分析，分别分析矽尘、矽肺与肺癌间的关系，并进行敏感性分析。结果分别综合所选资料，得出各自合并后的标化死亡比（SMR）值及95％的可信区间（CI），分别为SMR矽尘=1．30（CI：1、07～1.57，P=0．007），SMR矽肺=1.65（CI：1．21～2．25，P=0．002），矽尘暴露和矽肺与肺癌间的关联均有统计学意义。结论本文显示矽尘对人类有微弱致癌作用，矽肺与肺癌有中度关联，但最终定论还需深入研究。     

Meta-analysis of residential exposure to radon gas and lung cancer

OBJECTIVES: To investigate the relation between residential exposure to radon and lung cancer. METHODS: A literature search was performed using Medline and other sources. The quality of studies was assessed. Adjusted odds ratios with 95% confidence intervals (CI) for the risk of lung cancer among categories of levels of exposure to radon were extracted. For each study, a weighted log-linear regression analysis of the adjusted odds ratios was performed according to radon concentration. The random effect model was used to combine values from single studies. Separate meta-analyses were performed on results from studies grouped with similar characteristics or with quality scores above or equal to the median. FINDINGS: Seventeen case-control studies were included in the meta-analysis. Quality scoring for individual studies ranged from 0.45 to 0.77 (median, 0.64). Meta-analysis based on exposure at 150 Bq/m3 gave a pooled odds ratio estimate of 1.24 (95% CI, 1.11-1.38), which indicated a potential effect of residential exposure to radon on the risk of lung cancer. Pooled estimates of fitted odds ratios at several levels of randon exposure were all significantly different from unity--ranging from 1.07 at 50 Bq/m3 to 1.43 at 250 Bq/m3. No remarkable differences from the baseline analysis were found for odds ratios from sensitivity analyses of studies in which > 75% of eligible cases were recruited (1.12, 1.00-1.25) and studies that included only women (1.29, 1.04-1.60). CONCLUSION: Although no definitive conclusions may be drawn, our results suggest a dose-response relation between residential exposure to radon and the risk of lung cancer. They support the need to develop strategies to reduce human exposure to radon.     

饮酒行为与癌症发生关系的Meta分析——以胰腺癌和肺癌为例

目的 癌症是目前最重要的公共卫生问题之一,饮酒行为已经被证实是乳腺癌、结肠癌等癌症发病的重要影响因素。然而,以胰腺癌和肺癌为代表的多种其他癌症与饮酒之间的关系并未得到证实。本文以胰腺癌与肺癌为例,探究饮酒行为与癌症的发生是否相关。方法 本研究通过主题检索,根据“饮酒”“肺癌”“胰腺癌”“危险因素”等关键词,搜索了中国知网、维普、万方、CBM数据库,收集并筛选了2002年1月至2019年4月国内已发表的关于饮酒与胰腺癌和肺癌的病例对照研究,并对目标文献进行Meta分析。结果 纳入12篇研究饮酒与胰腺癌发病关系的中文文献,均为来自中文数据库的病例对照研究,研究地区均在中国,共纳入13240名研究对象。随机效应模型显示饮酒与胰腺癌的发病存在相关性（OR=1.35,95% CI:1.07~ 1.72）;纳入12篇研究饮酒与肺癌发病关系的中文文献,均为来自中文数据库的病例对照研究,研究地区均在中国,共纳入315862名研究对象。随机效应模型结果显示饮酒与肺癌的发生存在相关性（OR=1.47,95% CI:1.17~ 1.86）。结论 在中国人群中,饮酒行为与肺癌和胰腺癌的发生均具有显著的正相关性,是两种癌症的危险因素。     

饮酒与肺癌发病关联的Meta分析

目的探讨饮酒与肺癌的关系。方法全面检索相关文献,应用Meta分析方法对各研究进行数据合并与分析。结果纳入合并分析的文章共21篇,其中队列研究6篇,随访人数累计122288例,病例3053例;病例对照研究15篇,累计病例8838例,对照21591例。Meta分析饮酒与肺癌合并OR值为1.17(95%CI:0.96～1.42);男、女饮酒合并OR值分别为1.67(95%CI:0.61～4.59)和0.93(95%CI:0.51～1.68);男性饮啤酒合并OR值为1.46(95%CI:1.28～1.67);饮烈酒合并OR值为1.34(95%CI:1.02～1.74);饮酒≥7次/周与肺癌呈正相关(P<0.05)。结论饮用啤酒、烈酒和经常饮酒与肺癌有统计学关联。     

Meta-analysis of summary survival curve data

The use of standard univariate fixed- and random-effects models in meta-analysis has become well known in the last 20 years. However, these models are unsuitable for meta-analysis of clinical trials that present multiple survival estimates (usually illustrated by a survival curve) during a follow-up period. Therefore, special methods are needed to combine the survival curve data from different trials in a meta-analysis. For this purpose, only fixed-effects models have been suggested in the literature. In this paper, we propose a multivariate random-effects model for joint analysis of survival proportions reported at multiple time points and in different studies, to be combined in a meta-analysis. The model could be seen as a generalization of the fixed-effects model of Dear (Biometrics 1994; 50:989-1002). We illustrate the method by using a simulated data example as well as using a clinical data example of meta-analysis with aggregated survival curve data. All analyses can be carried out with standard general linear MIXED model software. Copyright (c) 2008 John Wiley & Sons, Ltd.     

中国非吸烟女性肺癌危险因素的Meta分析

目的 探讨中国非吸烟女性肺癌发病的主要危险因素,为制定肺癌防控措施提供科学依据。方法 收集国内外1995年1月至2014年11月公开发表的关于中国非吸烟女性肺癌发病危险因素的病例对照研究文献,采用Meta分析方法计算中国非吸烟女性肺癌发病危险因素的合并OR值及其95% CI,辅以敏感性分析和发表偏倚检测。结果 共纳入文献24篇,累计病例11 946例,对照12 596例。非吸烟女性肺癌发病危险因素的合并OR值及其95% CI分别为:总肺部疾病史1.89(1.57~2.27)、肺结核病史1.86(1.53~2.27)、慢性支气管炎病史1.51(1.04~2.19)、肿瘤家族史2.02(1.67~2.44)、肺癌家族史2.45(1.80~3.34)、成年期被动吸入香烟烟雾[工作场所1.47(1.28~1.69)、家庭环境1.22(1.09~1.36)]、终生被动吸入香烟烟雾1.52(1.29~1.79)、烹饪烟雾量2.21(1.27~2.96)、厨房位置1.76(1.48~2.09)、每周油炸频次2.24(1.61~3.12)。结论 中国非吸烟女性肺癌发病的主要危险因素为肺部疾病史、肿瘤家族史、被动吸烟(香烟烟雾、烹饪烟雾),其中肺癌家族史和烹饪烟雾量的合并OR值具有更为强烈的关联效应,提示遗传因素和环境因素在肺癌发病中的重要作用。     

Integrative genomic testing of cancer survival using semiparametric linear transformation models

The wide availability of multi‐dimensional genomic data has spurred increasing interests in integrating multi‐platform genomic data. Integrative analysis of cancer genome landscape can potentially lead to deeper understanding of the biological process of cancer. We integrate epigenetics (DNA methylation and microRNA expression) and gene expression data in tumor genome to delineate the association between different aspects of the biological processes and brain tumor survival. To model the association, we employ a flexible semiparametric linear transformation model that incorporates both the main effects of these genomic measures as well as the possible interactions among them. We develop variance component tests to examine different coordinated effects by testing various subsets of model coefficients for the genomic markers. A Monte Carlo perturbation procedure is constructed to approximate the null distribution of the proposed test statistics. We further propose omnibus testing procedures to synthesize information from fitting various parsimonious sub‐models to improve power. Simulation results suggest that our proposed testing procedures maintain proper size under the null and outperform standard score tests. We further illustrate the utility of our procedure in two genomic analyses for survival of glioblastoma multiforme patients. Copyright © 2016 John Wiley & Sons, Ltd.     

中国人群饮茶与肺癌关系的Meta分析

目的 探讨中国人群饮茶与肺癌发生的关系。方法 检索中英文文献,选择在中国人群中进行的饮茶与肺癌相关研究,运用Meta分析方法,计算合并OR值及其95%CI,探讨饮茶与肺癌的联系及剂量反应关系。结果 共纳入12篇文献,饮茶者相对不饮茶者患肺癌危险性有所下降（OR=0.66,95%CI：0.49~0.89）。结论 饮茶可能是中国人群肺癌的保护性因素。     

Baseline FACT-G score is a predictor of survival for advanced lung cancer

The objective of this study is to evaluate whether patient-reported baseline health-related quality of life (HRQL) measured by the Functional Assessment of Cancer Therapy-General (FACT-G) instrument is predictive of survival for patients with advanced lung cancer. METHODS: Consecutive patients with advanced lung cancer planning to undergo palliative chemotherapy in the outpatient clinics of a Canadian tertiary care cancer centre were enrolled on study. FACT-G total scores and clinical predictors of survival (age, sex, histology, stage of disease, previous weight loss, presence of liver metastases and performance status) were prospectively collected at baseline. Survival data was subsequently collected retrospectively from the Alberta Cancer Registry. Stratified Cox Proportional Hazards analysis was done examining the influence of baseline total FACT-G scores on survival, controlling for potential clinical confounders. RESULTS: Median survival of the 42 patient cohort was 9.9 months with a 2-year survival of 16.7%. Multivariate analysis indicated that baseline FACT-G total score is significantly associated with survival (p = 0.004). CONCLUSION: Baseline HRQL is a statistically significant predictor of survival for patients with advanced lung cancer. When used along with traditional clinical factors, patient-reported baseline HRQL assessment using the FACT-G provides additional prognostic information to the patient and clinician.     

Meta-analysis of studies of passive smoking and lung cancer: effects of study type and continent

BACKGROUND: To calculate a pooled estimate of relative risk (RR) of lung cancer associated with exposure to passive smoking in never smoking women exposed to smoking spouses. This study is an updated meta-analysis that also assesses the differences between estimated risks according to continent and study type using meta-regression. METHODS: From a total of 101 primary studies, 55 studies are included in this meta-analysis, of which, 7 are cohort studies, 25 population-based case-control and 23 non-population-based case-control studies. Twenty previously published meta-analyses are also reviewed. Fixed and random effect models and meta-regression are used to obtain pooled estimates of RR and P-value functions are used to demonstrate consistency of results. RESULTS: The pooled RR for never-smoking women exposed to passive smoking from spouses is 1.27 (95% CI 1.17-1.37). The RR for North America is 1.15 (95% CI 1.03-1.28), Asia, 1.31 (95% CI 1.16-1.48) and Europe, 1.31 (1.24-1.52). Sequential cumulative meta-analysis shows no trend. There is no strong evidence of publication bias. CONCLUSIONS: The abundance of evidence, consistency of finding across continent and study type, dose-response relationship and biological plausibility, overwhelmingly support the existence of a causal relationship between passive smoking and lung cancer.     

Application of kriging models for a drug combination experiment on lung cancer

Combinatorial drugs have been widely applied in disease treatment, especially chemotherapy for cancer, due to its improved efficacy and reduced toxicity compared with individual drugs. The study of combinatorial drugs requires efficient experimental designs and proper follow-up statistical modeling techniques. Linear and nonlinear models are often used in the response surface modeling for such experiments. We propose the use of kriging models to better depict the response surfaces of combinatorial drugs. We illustrate our method via a drug combination experiment on lung cancer and further show how proper experimental designs can reduce the necessary run size. We demonstrate that only 27 runs are needed to predict all 512 runs in the original experiment and achieve better precision than existing analyses.     

Kernel Cox partially linear regression: Building predictive models for cancer patients' survival

Wide heterogeneity exists in cancer patients' survival, ranging from a few months to several decades. To accurately predict clinical outcomes, it is vital to build an accurate predictive model that relates the patients' molecular profiles with the patients' survival. With complex relationships between survival and high-dimensional molecular predictors, it is challenging to conduct nonparametric modeling and irrelevant predictors removing simultaneously. In this article, we build a kernel Cox proportional hazards semi-parametric model and propose a novel regularized garrotized kernel machine (RegGKM) method to fit the model. We use the kernel machine method to describe the complex relationship between survival and predictors, while automatically removing irrelevant parametric and nonparametric predictors through a LASSO penalty. An efficient high-dimensional algorithm is developed for the proposed method. Comparison with other competing methods in simulation shows that the proposed method always has better predictive accuracy. We apply this method to analyze a multiple myeloma dataset and predict the patients' death burden based on their gene expressions. Our results can help classify patients into groups with different death risks, facilitating treatment for better clinical outcomes.     

Mixture models for cancer survival analysis: application to population-based data with covariates

The interest in estimating the probability of cure has been increasing in cancer survival analysis as the curability of many cancer diseases is becoming a reality. Mixture survival models provide a way of modelling time to death when cure is possible, simultaneously estimating death hazard of fatal cases and the proportion of cured case. In this paper we propose an application of a parametric mixture model to relative survival rates of colon cancer patients from the Finnish population-based cancer registry, and including major survival determinants as explicative covariates. Disentangling survival into two different components greatly facilitates the analysis and the interpretation of the role of prognostic factors on survival patterns. For example, age plays a different role in determining, from one side, the probability of cure, and, from the other side, the life expectancy of fatal cases. The results support the hypothesis that observed survival trends are really due to a real prognostic gain for more recently diagnosed patients.     

肥胖与前列腺癌关系的Meta分析

目的探讨肥胖致前列腺癌的因果关系,为前列腺癌的预防和病因研究提供参考依据。方法分别用"肥胖"、"前列腺癌"、"Meta分析"中的英文检索词检索中国期刊网、Science Direct、Springer Link、等数据库,检索近10年公开发表的有关肥胖与前列腺癌的研究文献,分别采用RevMan 5.0软件中的分类数据和一般反向方差法进行Meta分析。结果纳入该次分析的文献共有6篇,累计病例3 328例,对照535 724例。单项研究的OR值范围为0.78～1.31,合并OR值为1.11,95%可信区间为1.01～1.23。结论病例对照文献提示前列腺癌与肥胖存在联系。     

Regression models for relative survival

Four approaches to estimating a regression model for relative survival using the method of maximum likelihood are described and compared. The underlying model is an additive hazards model where the total hazard is written as the sum of the known baseline hazard and the excess hazard associated with a diagnosis of cancer. The excess hazards are assumed to be constant within pre-specified bands of follow-up. The likelihood can be maximized directly or in the framework of generalized linear models. Minor differences exist due to, for example, the way the data are presented (individual, aggregated or grouped), and in some assumptions (e.g. distributional assumptions). The four approaches are applied to two real data sets and produce very similar estimates even when the assumption of proportional excess hazards is violated. The choice of approach to use in practice can, therefore, be guided by ease of use and availability of software. We recommend using a generalized linear model with a Poisson error structure based on collapsed data using exact survival times. The model can be estimated in any software package that estimates GLMs with user-defined link functions (including SAS, Stata, S-plus, and R) and utilizes the theory of generalized linear models for assessing goodness-of-fit and studying regression diagnostics.     

Spatio-temporal models with errors in covariates: mapping Ohio lung cancer mortality

In estimating spatial disease patterns, as well as in related assessments of environmental equity, regional morbidity and mortality rate maps are widely used. Hierarchical Bayes methods are increasingly popular tools for creating such maps, since they permit smoothing of the fitted rates toward spatially local mean values, with more unreliable estimates (those arising in low-population regions) receiving more smoothing. In this paper we blend methods for spatial-temporal mapping with those for handling errors in covariates in a single hierarchical model framework. Estimated posterior distributions for the resulting highly-parameterized models are obtained via Markov chain Monte Carlo (MCMC) methods, which also play a key role in our approach to model evaluation and selection. We apply our approach to a data set of county-specific lung cancer rates in the state of Ohio during the period 1968–1988. Our model uses age-adjusted death rates, and incorporates recent information regarding smoking prevalence, population density, and the socio-economic status of the counties. This information is critical to understanding the role played by a certain depleted uranium fuel processing facility on the elevated lung cancer rates in the counties that neighbour it. © 1998 John Wiley & Sons, Ltd.