Regressive logistic modeling of familial aggregation for asthma in 7,394 population-based nuclear families

The aim of this population-based study was to determine whether asthma aggregates in families, and if so, whether aggregation was consistent with environmental and/or genetic etiologies. Data were from 7,394 nuclear families (41,506 individuals) from the 1968 Tasmanian Asthma Survey, in which all Tasmanian schoolchildren born in 1961 were surveyed by respiratory questionnaire completed by their parents. Similar data were obtained for parents and siblings of probands. For a child, having ever had asthma was predicted by a parent or sibling having ever had asthma; odds ratio (OR) = 3.13 (95% confidence interval [CI] 2.82–3.48) for mother, 2.99 (2.69–3.32) for father, and 3.47 (3.23–3.72) for a sibling. Regressive logistic modeling showed that, in addition to parent-offspring effects, the data were consistent with the existence of an unmeasured factor shared by siblings, evident in 15% (SE 2%) of families and associated with a conditional OR of 9.68 (8.27–11.32). Familial aggregation was best described by a general oligogenic model with non-Mendelian transmission probabilities. Of the Mendelian models, a codominant model with an allele frequency of 16% (SE 0.3%) was preferred. Under a dominant model there was evidence for additional parent-offspring and sibling effects of similar magnitude. It is unlikely that there is one major loci influencing asthma susceptibility; the overall effects of asthma genes in the population are more likely to be inherited codominantly, at least for the majority of loci of major etiological importance. The role of environmental factors in explaining part of familial aggregation for asthma cannot be ruled out, as major triggers of asthma attacks are familial. Genet. Epidemiol. 14:317–332,1997. © 1997 Wiley-Liss, Inc.     

Empirical comparisons of proportional hazards,poisson, and logistic regression modeling of occupational cohort data

This research was conducted to examine the effect of model choice on the epidemiologic interpretation of occupational cohort data. Three multiplicative models commonly employed in the analysis of occupational cohort studies—proportional hazards, Poisson, and logistic regression—were used to analyze data from an historical cohort study of workers exposed to formaldehyde. Samples were taken from this dataset to create a number of predetermined scenarios for comparing the models, varying study size, outcome frequency, strength of risk factors, and follow-up length. The Poisson and proportional hazards models yielded nearly identical relative risk estimates and confidence intervals in all situations except when confounding by age could not be closely controlled in the Poisson analysis. Logistic regression findings were more variable, with risk estimates differing most from the proportional hazards results when there was a common outcome or strong relative risk. The logistic model also provided less precise estimates than the other two. Thus, although logistic was the easiest model to implement, it should be used only in occupational cohort studies when the outcome is rare (5% or less), and the relative risk is less than ∼2. Even then, the proportional hazards and Poisson models are better choices. Selecting between these two can be based on convenience in most circumstances. Am. J. Ind. Med. 33:33–47, 1998. © 1998 Wiley-Liss, Inc.     

Parametric models for accelerated and long-term survival: a comment on proportional hazards

The Cox proportional hazards model (CPH) is routinely used in clinical trials, but it may encounter serious difficulties with departures from the proportional hazards assumption, even when the departures are not readily detected by commonly used diagnostics. We consider the Gamel-Boag (GB) model, a log-normal model for accelerated failure in which a proportion of subjects are long-term survivors. When the CPH model is fit to simulated data generated from this model, the results can range from gross overstatement of the effect size, to a situation where increasing follow-up may cause a decline in power. We implement a fitting algorithm for the GB model that permits separate covariate effects on the rapidity of early failure and the fraction of long-term survivors. When effects are detected by both the CPH and GB methods, the attribution of the effect to long-term or short-term survival may change the interpretation of the data. We believe these examples motivate more frequent use of parametric survival models in conjunction with the semi-parametric Cox proportional hazards model.     

CYP1B1基因多态性与异常体液型乳腺癌易感性

目的 探讨细胞色素氧化酶p450(cytochrome,CYP)1B1基因外显子3密码子432亮氨酸(Leu)-缬氨酸(Val)位点多态性与乳腺癌(BC)及其异常体液型乳腺癌(BCAH)的相关性.方法 按维吾尔医学将乳腺癌病例组分为4种体液型,应用聚合酶链反应-限制性片断长度多态性(PCR-RFLP)技术检测84例BC患者和131例对照组的CYP1B1基因Leu432Val位点多态性的分布频率.结果 CYP1 B1各基因型及等位基因分布频率在BC、BCAH与对照组之间差异均无统计学意义(P＞0.05);在BC病例组中Leu/Val和Val/Val合并后其发生BC的风险是野生型Leu/Leu个体的2.137倍(95%CI=0.969 ～4.717,P=0.056),在BCABH病例组中其发生BC的风险是野生型Leu/Leu个体的3.636倍(95% CI =0.996～13.157,P=0.062).结论 CYP1B1突变基因型(Leu/Val+ Val/Val)可能与新疆汉族人群BC和BCABH易感性有关.     

队列研究资料分析中Cox比例风险回归模型的时间尺度选择和应用

Cox比例风险回归模型（Cox模型）是时间-事件数据分析中常用的多因素分析方法,拟合Cox模型时一个关键问题是如何选择合适的与结局事件发生相关的时间尺度。目前国内开展的队列研究在资料分析中较少关注Cox模型的时间尺度选择问题。本研究对文献报道中常见的几种时间尺度选择策略进行简要介绍和比较;并利用上海女性健康队列资料,以中心性肥胖与肝癌发病风险的关联为例,说明选择不同时间尺度的Cox模型对数据分析结果的影响;在此基础上提出几点Cox模型时间尺度选择上的建议,以期为队列研究资料的分析提供参考。     

Generating survival times to simulate Cox proportional hazards models with time‐varying covariates

Simulations and Monte Carlo methods serve an important role in modern statistical research. They allow for an examination of the performance of statistical procedures in settings in which analytic and mathematical derivations may not be feasible. A key element in any statistical simulation is the existence of an appropriate data‐generating process: one must be able to simulate data from a specified statistical model. We describe data‐generating processes for the Cox proportional hazards model with time‐varying covariates when event times follow an exponential, Weibull, or Gompertz distribution. We consider three types of time‐varying covariates: first, a dichotomous time‐varying covariate that can change at most once from untreated to treated (e.g., organ transplant); second, a continuous time‐varying covariate such as cumulative exposure at a constant dose to radiation or to a pharmaceutical agent used for a chronic condition; third, a dichotomous time‐varying covariate with a subject being able to move repeatedly between treatment states (e.g., current compliance or use of a medication). In each setting, we derive closed‐form expressions that allow one to simulate survival times so that survival times are related to a vector of fixed or time‐invariant covariates and to a single time‐varying covariate. We illustrate the utility of our closed‐form expressions for simulating event times by using Monte Carlo simulations to estimate the statistical power to detect as statistically significant the effect of different types of binary time‐varying covariates. This is compared with the statistical power to detect as statistically significant a binary time‐invariant covariate. Copyright © 2012 John Wiley & Sons, Ltd.     

Data generation for the Cox proportional hazards model with time‐dependent covariates: a method for medical researchers

The proliferation of longitudinal studies has increased the importance of statistical methods for time‐to‐event data that can incorporate time‐dependent covariates. The Cox proportional hazards model is one such method that is widely used. As more extensions of the Cox model with time‐dependent covariates are developed, simulations studies will grow in importance as well. An essential starting point for simulation studies of time‐to‐event models is the ability to produce simulated survival times from a known data generating process. This paper develops a method for the generation of survival times that follow a Cox proportional hazards model with time‐dependent covariates. The method presented relies on a simple transformation of random variables generated according to a truncated piecewise exponential distribution and allows practitioners great flexibility and control over both the number of time‐dependent covariates and the number of time periods in the duration of follow‐up measurement. Within this framework, an additional argument is suggested that allows researchers to generate time‐to‐event data in which covariates change at integer‐valued steps of the time scale. The purpose of this approach is to produce data for simulation experiments that mimic the types of data structures applied that researchers encounter when using longitudinal biomedical data. Validity is assessed in a set of simulation experiments, and results indicate that the proposed procedure performs well in producing data that conform to the assumptions of the Cox proportional hazards model. Copyright © 2013 John Wiley & Sons, Ltd.     

Flexible parametric proportional-hazards and proportional-odds models for censored survival data,with application to prognostic modelling and estimation of treatment effects

Modelling of censored survival data is almost always done by Cox proportional-hazards regression. However, use of parametric models for such data may have some advantages. For example, non-proportional hazards, a potential difficulty with Cox models, may sometimes be handled in a simple way, and visualization of the hazard function is much easier. Extensions of the Weibull and log-logistic models are proposed in which natural cubic splines are used to smooth the baseline log cumulative hazard and log cumulative odds of failure functions. Further extensions to allow non-proportional effects of some or all of the covariates are introduced. A hypothesis test of the appropriateness of the scale chosen for covariate effects (such as of treatment) is proposed. The new models are applied to two data sets in cancer. The results throw interesting light on the behaviour of both the hazard function and the hazard ratio over time. The tools described here may be a step towards providing greater insight into the natural history of the disease and into possible underlying causes of clinical events. We illustrate these aspects by using the two examples in cancer.     

Method for calculating risk associated with family history of a disease

A method is described for estimating excess relative risks of a disease from familial factors. Beginning with population-based series of cases and controls, a cohort of each subject's relatives is formed and checked for disease against a population based registry. The disease experience of the cohort formed from each subject's relatives is summarized as a kinship-weighted familial standardized incidence ratio (FSIR). The FSIR's are used as exposure estimates in conditional linear excess relative risk models, which may be used not only to screen for significant familial disease aggregations, but also to estimate relative risks, population attributable risks, and gene-environment interactions. The method is demonstrated on 4083 breast cancer cases from Utah and a set of matched controls. ©1995 Wiley-Liss, Inc.     

Sample size and power for a logrank test and Cox proportional hazards model with multiple groups and strata,or a quantitative covariate with multiple strata

I describe general expressions for the evaluation of sample size and power for the K group Mantel‐logrank test or the Cox proportional hazards (PH) model score test. Under an exponential model, the method of Lachin and Foulkes for the 2 group case is extended to the group case using the non‐centrality parameter of the K ? 1 df chi‐square test. I also show similar results to apply to the K group score test in a Cox PH model. Lachin and Foulkes employed a truncated exponential distribution to provide for a non‐linear rate of enrollment. I present expressions for the mean time of enrollment and the expected follow‐up time in the presence of exponential losses to follow‐up. When used with the expression for the noncentrality parameter for the test, equations are derived for the evaluation of sample size and power under specific designs with r years of recruitment and T years total duration. I also describe sample size and power for a stratified‐adjusted K group test and for the assessment of a group by stratum interaction. Similarly, I describe computations for a stratified‐adjusted analysis of a quantitative covariate and a test of a stratum by covariate interaction in the Cox PH model. Copyright © 2013 John Wiley & Sons, Ltd.     

Bivariate survival models for analysis of genetic and environmental effects in twins

Classic methods in genetics for the analysis of binary attributes, based on an assumption of a "threshold" on a normally distributed latent variable called "liability," estimate the strength of genetic and environmental effects from differences in correlations between relatives of differing genetic relatedness. Two problems that are not easily addressed by these methods are the need to take the age of onset into account (particularly in chronic diseases in which incidence rates vary considerably with age and the lengths of time at risk can vary between individuals) and the desirability of incorporating measured covariates (genetic or environmental). The standard methods of cohort analysis used in epidemiology allow for both of these features, but until recently have been restricted to independent individuals. Recent developments in survival analysis have extended the widely used "proportional hazards" model of Cox by the addition of latent variable, epsilon, reflecting the shared susceptibility of related subjects because of their shared genes or shared environment. We show how this approach can be combined with more traditional models of gene-environment interaction to allow the main effects of measured genetic markers and environmental variables to be estimated, as well as the residual variance of genetic and environment and their interactions. The approaches are applied to a cohort of female twin births in Sweden from 1886 to 1958, linked with the Swedish cancer registry from 1961 to 1982.     

Construction and validation of a prognostic model across several studies, with an application in superficial bladder cancer

Many models for clinical prediction (prognosis or diagnosis) are published in the medical literature every year but few such models find their way into clinical practice. The reason may be that since in most cases models have not been validated in independent data, they lack generality and/or credibility. In this paper we consider the situation in which several compatible, independent data sets relating to a given disease with a time-to-event endpoint are available for analysis. The aim is to construct and evaluate a single prognostic model. Building a multivariable model from the available prognostic factors is accomplished within the Cox proportional hazards framework, stratifying by study. Non-linear relationships with continuous predictors are modelled by using fractional polynomials. To assess the discrimination or separation of a survival model, we use the D statistic of Royston and Sauerbrei. D may be interpreted as the separation (log hazard ratio) between the survival distributions for two independent prognostic groups. To evaluate the generality of a prognostic model across the data sets, we propose 'internal-external cross-validation' on D: each study is omitted in turn, the model parameters are estimated from the remaining studies and D is evaluated in the omitted study. Because the linear predictor of a survival model tells only part of the story, we also suggest a method for investigating heterogeneity in the baseline distribution function across studies which involves fitting completely specified, flexible parametric survival models (Royston and Parmar). Our final models combine the prognostic index (obtained with stratification by study) with the pooled baseline survival distribution (estimated parametrically). By applying this methodology, we construct two prognostic scores in superficial bladder cancer. The simpler of the two scores is more suited to clinical application. We show that a three-group prognostic classification scheme based on either score produces well-separated survival curves for each of the data sets, despite identifiable heterogeneity among the baseline distribution functions and to a lesser extent among the prognostic indexes for the individual studies.     

Interactions between genetic and reproductive factors in breast cancer risk in a population-based sample of African-American families

Incidence of breast cancer (BC) varies among ethnic groups, with higher rates in white than in African-American women. Until now, most epidemiological and genetic studies have been carried out in white women. To investigate whether interactions between genetic and reproductive risk factors may explain part of the ethnic disparity in BC incidence, a genetic epidemiology study was conducted, between 1989 and 1994, at the Howard University Cancer Center (Washington, DC), which led to the recruitment of 245 African-American families. Segregation analysis of BC was performed by use of the class D regressive logistic model that allows for censored data to account for a variable age of onset of disease, as implemented in the REGRESS program. Segregation analysis of BC was consistent with a putative dominant gene effect (P < 0.000001) and residual sister-dependence (P < 0.0001). This putative gene was found to interact significantly with age at menarche (P = 0.048), and an interaction with a history of spontaneous abortions was suggested (P = 0.08). A late age at menarche increased BC risk in gene carriers but had a protective effect in non-gene carriers. A history of spontaneous abortions had a protective effect in gene carriers and increased BC risk in non-gene carriers. Our findings agree partially with a similar analysis of French families showing a significant gene x parity interaction and a suggestive gene x age at menarche interaction. Investigating gene x risk factor interactions in different populations may have important implications for further biological investigations and for BC risk assessment.     

3166例已婚妇女乳腺癌、宫颈癌为重点的妇科疾病筛查结果分析

目的通过农八师133团20～60岁本地户籍已婚妇女进行以＂乳腺癌、宫颈癌＂为重点的妇科病普查工作（以下简称＂两癌＂）,了解农八师133团妇女20～60岁生殖健康状况和妇科疾病发生率,分析发病因素,掌握发病原因,提高＂两癌＂的早诊、早治率,降低死亡率。方法对3166例用统一固定的体检表格进行基本情况及病史的询问填写,常规妇科检查、乳腺检查及辅助检查。结果乳腺癌2例,宫颈癌1例。妇科疾病患病总人数2350人,患病率74.2%,其中宫颈炎56.6%,乳腺疾病30%,阴道炎21.29%。结论妇女常见病以宫颈炎为主,乳腺增生有上升趋势。加强妇女保健宣传力度,增强妇女自我保健意识,坚持定期进行妇科病查治,提高生存质量。     

Joint modeling longitudinal semi‐continuous data and survival,with application to longitudinal medical cost data

It has been increasingly common to analyze simultaneously repeated measures and time to failure data. In this paper we propose a joint model when the repeated measures are semi‐continuous, characterized by the presence of a large portion of zero values, as well as right skewness of non zero (positive) values. Examples include monthly medical costs, car insurance annual claims, or annual number of hospitalization days. A random effects two‐part model is used to describe respectively the odds of being positive and the level of positive values. The random effects from the two‐part model are then incorporated in the hazard of the failure time to form the joint model. The estimation can be carried out by Gaussian quadrature techniques conveniently implemented in SAS Proc NLMIXED. Our model is applied to longitudinal (monthly) medical costs of 1455 chronic heart‐failure patients from the clinical data repository at the University of Virginia. Copyright © 2008 John Wiley & Sons, Ltd.     

Development of the WPC approach and application to linkage analysis of breast cancer

We develop the weighted pairwise correlation approach in several directions: we use a method based on martingale residuals rather than rank residuals; we give a measure of the strength of the linkage and we propose a method for multipoint analysis by combining the weights used in the statistics for different markers. These tools are applied to 214 breast cancer families. The values of the test statistics for the different markers are very similar to those obtained with the lod-score approach; the multipoint analysis increases the value of the test statistic and is also in agreement with previous results. © 1995 Wiley-Liss, Inc.     

Uncertainty and the value of diagnostic information, with application to axillary lymph node dissection in breast cancer

In clinical decision making, it is common to ask whether, and how much, a diagnostic procedure is contributing to subsequent treatment decisions. Statistically, quantification of the value of the information provided by a diagnostic procedure can be carried out using decision trees with multiple decision points, representing both the diagnostic test and the subsequent treatments that may depend on the test's results. This article investigates probabilistic sensitivity analysis approaches for exploring and communicating parameter uncertainty in such decision trees. Complexities arise because uncertainty about a model's inputs determines uncertainty about optimal decisions at all decision nodes of a tree. We present the expected utility solution strategy for multistage decision problems in the presence of uncertainty on input parameters, propose a set of graphical displays and summarization tools for probabilistic sensitivity analysis in multistage decision trees, and provide an application to axillary lymph node dissection in breast cancer.     

Parametric mixture models to evaluate and summarize hazard ratios in the presence of competing risks with time-dependent hazards and delayed entry

In the analysis of survival data, there are often competing events that preclude an event of interest from occurring. Regression analysis with competing risks is typically undertaken using a cause-specific proportional hazards model. However, modern alternative methods exist for the analysis of the subdistribution hazard with a corresponding subdistribution proportional hazards model. In this paper, we introduce a flexible parametric mixture model as a unifying method to obtain estimates of the cause-specific and subdistribution hazards and hazard-ratio functions. We describe how these estimates can be summarized over time to give a single number comparable to the hazard ratio that is obtained from a corresponding cause-specific or subdistribution proportional hazards model. An application to the Women's Interagency HIV Study is provided to investigate injection drug use and the time to either the initiation of effective antiretroviral therapy, or clinical disease progression as a competing event.     

Joint analysis of multi-level repeated measures data and survival: an application to the end stage renal disease (ESRD) data

Shared random effects models have been increasingly common in the joint analyses of repeated measures (e.g. CD4 counts, hemoglobin levels) and a correlated failure time such as death. In this paper we study several shared random effects models in the multi-level repeated measures data setting with dependent failure times. Distinct random effects are used to characterize heterogeneity in repeated measures at different levels. The hazard of death may be dependent on random effects from various levels. To simplify the estimation procedure, we adopt the Gaussian quadrature technique with a piecewise log-linear baseline hazard for the death process, which can be conveniently implemented in the freely available software aML. As an example, we analyze repeated measures of hematocrit level and survival for end stage renal disease patients clustered within a randomly selected 126 dialysis centers in the U.S. renal data system data set. Our model is very comprehensive yet easy to implement, making it appealing to general statistical practitioners.     

Estimating disease risks for individuals with a given family history in different populations with an application to breast cancer

For many chronic diseases, including most of the common cancers, a family history is known to be a strong independent risk factor. For breast cancer, estimation of risk as a function of family history is considered to provide useful risk assessment for women with a family history of breast cancer. Detailed tables that predict the cumulative risk of breast cancer at specific ages based on various combinations of family history have been constructed for the American white population. Most chronic diseases, however, have a multiple etiology, with multiple genetic and environmental factors. Family history can thus be a result of different susceptibility loci and the aggregation of sporadic cases by chance. Since the relative contribution of the genetic factor may differ in different populations, these tables may not be directly applicable to other populations. We present a method to decompose available estimates of risk based on family history for an arbitrary disease into probabilities for genotype frequency given a particular family history and for disease probabilities by genotype carrier status. These can be reconstructed to obtain risk estimates for different populations. Implicit assumptions are made in the estimation process. These are based on the current state of knowledge and can be updated as further knowledge accumulates. © 1996 Wiley-Liss, Inc.