Spatially explicit survival modeling for small area cancer data

In this paper we propose a novel Bayesian statistical methodology for spatial survival data. Our methodology broadens the definition of the survival, density and hazard functions by explicitly modeling the spatial dependency using direct derivations of these functions and their marginals and conditionals. We also derive spatially dependent likelihood functions. Finally we examine the applications of these derivations with geographically augmented survival distributions in the context of the Louisiana Surveillance, Epidemiology, and End Results registry prostate cancer data.     

Aggregating Disparate Epidemiological Evidence: Comparing Two Seminal EMF Reviews

Two seminal reviews ( IARC, 2002 ; CDHS, 2002 ) of possible health effects from power-frequency EMFs reached partly different conclusions from similar epidemiological evidence. These differences have an impact on precautionary policy. We examine the statistical aggregation of results from individual disparate studies. Without consistent exposure metrics, the advantage of meta-analysis to estimate magnitude of effect is lost. However, counting positive and statistically significant results yields important information. This is not a substitute for meta-analysis, but a fall-back when meaningful meta-analysis is not available. Representative results from 33 independent adult leukemia studies tabled by IARC yielded 23.5 positives ( p ≈ 0.01) and 9 significant-positives ( p  < 10⁻⁷). From 43 representative results from CDHS, there were 32 positive ( p  < 0.001) and 14 significant-positives ( p  < 10⁻¹²). There were no significant-negative results in either list. Results for adult brain cancer gave a similar, but less clear, message. Childhood leukemia EMF studies have been sufficiently comparable to allow selective pooled analysis, which was important in classifying carcinogenicity. Aggregating all the studies suggests that results for childhood leukemia are not stronger, numerically, than those for adult leukemia. CDHS did not note the number of significant-positives, but noted the meta-analytic summary and the number of positives, forming a view about the strength of these findings. IARC shows no evidence of considering the aggregation of results other than subjectively. It considered individual studies but this led to a tendency to fragment and dismiss evidence that is intrinsically highly significant. We make recommendations for future reviews.     

Individual differences in numerical representations of risk in health decision making: A fuzzy-trace theory approach

Fuzzy-trace theory predicts that decisionmakers process numerical information about risk at multiple levels in parallel: the simplest level, nominal (categorical some-none) gist, and at more fine-grained levels, involving relative comparison (ordinal less-more gist) and exact quantities (verbatim representations). However, little is known about how individual differences in these numerical representations relate to judgments and decisions, especially involving health tradeoffs and relative risks. To investigate these differences, we administered measures of categorical and ordinal gist representations of number, objective numeracy, and intelligence in two studies (Ns = 978 and 956). In both studies, categorical and ordinal gist representations of number predicted risk judgments and decisions beyond objective numeracy and intelligence. Participants with higher scores in categorical gist were more likely to choose options to avoid cancer recurrence risks; those who were higher in ordinal gist of numbers were more likely to discriminate relative risk of skin cancer; and those with higher scores in objective numeracy were more likely to choose options that were numerically superior overall in terms of relative risk of skin cancer and of genetic risks of breast cancer (e.g., lower numerical probability of cancer). Results support parallel-processing models that assume multiple representations of numerical information about risk, which vary in precision, and illustrate how individual differences in numerical representations are relevant to tradeoffs and risk comparisons in health decisions. These representations cannot be reduced to one another and explain psychological variations in risk processing that go beyond low versus high levels of objective numeracy.     

TWIST基因和蛋白与宫颈癌发生发展关系的研究

目的:检测TWIST基因在宫颈癌组织和宫颈正常组织中的表达,探讨其与宫颈癌发生?发展的相关性及在宫颈癌诊疗中的意义?方法:收集56例宫颈癌组织和35例宫颈正常组织,用实时定量PCR检测TWIST mRNA的表达;用Western blot检测TWIST蛋白的表达,并分析TWIST mRNA在宫颈癌中的表达水平与临床病理因素及高危型HPV病毒载量相关性?结果:实时定量PCR结果显示宫颈癌组织中TWIST mRNA表达高于宫颈正常组织,差异具有统计学意义(P < 0.01);Western blot在蛋白质水平上证实了这一点(P < 0.01)?宫颈癌组织中TWIST的表达与淋巴结转移相关(P = 0.022),但与年龄?FIGO分期?肿瘤大小?病理类型?肿瘤分化程度无关(P均> 0.05),Logistic回归分析显示,高水平的TWIST mRNA表达是淋巴结转移的独立危险因子(P < 0.05)?而TWIST基因的相对表达水平与高危型HPV病毒载量无相关性(r = 0.205,P > 0.05)?结论:宫颈癌组织中TWIST的表达上调,且与是否有淋巴结转移相关,TWIST的表达可能对宫颈癌的发生?发展及转移有重要作用?     

单中心食管胃结合部腺癌发病情况11年变化趋势

目的:研究食管胃结合部腺癌(adenocarcinoma of the esophagogastric junction,AEG)发病情况的变化趋势?方法:回顾分析2001年1月~2011年12月南京医科大学第一附属医院收治的4 685例胃癌病例,其中AEG1 824例,非食管胃结合部腺癌(non-esophagogastric junction adenocarcinoma,NEGA)2 861例?研究AEG在胃癌中所占比例变化趋势?早期食管胃结合部腺癌(early adenocarcinoma of esophagogastric junction,EAEG)检出率变化趋势?AEG 性别年龄分布及比较AEG和NEGA患者发病年龄和性别比的差异?结果:该中心11年来AEG在胃癌中所占比例无明显变化;2007~2011年EAEG的检出率为12.23%(135/1 104),2001~2006年为8.47%(61/720),前者显著高于后者(P = 0.013);AEG患者平均发病年龄以及男女性别比均显著高于NEGA患者[(62.7 ± 9.5)岁 vs (59.1 ± 12.7)岁,P < 0.001;1 434∶390 vs 1 990∶871,P < 0.001)]?结论:AEG在胃癌中所占比例无明显增高;近5年EAEG检出率显著增高;AEG好发于男性老年患者?     

Statistical inference for Cox proportional hazards models with a diverging number of covariates

For statistical inference on regression models with a diverging number of covariates, the existing literature typically makes sparsity assumptions on the inverse of the Fisher information matrix. Such assumptions, however, are often violated under Cox proportion hazards models, leading to biased estimates with under-coverage confidence intervals. We propose a modified debiased lasso method, which solves a series of quadratic programming problems to approximate the inverse information matrix without posing sparse matrix assumptions. We establish asymptotic results for the estimated regression coefficients when the dimension of covariates diverges with the sample size. As demonstrated by extensive simulations, our proposed method provides consistent estimates and confidence intervals with nominal coverage probabilities. The utility of the method is further demonstrated by assessing the effects of genetic markers on patients' overall survival with the Boston Lung Cancer Survival Cohort, a large-scale epidemiology study investigating mechanisms underlying the lung cancer.