Designs and analysis of two-stage studies.

This paper concerns the design and analysis of two-stage studies, where, at the first stage, the response and the exposure variables are available among a large group of subjects. The other covariables, however, are available in only a subset of the large group, obtained in a second-stage sample. This paper introduces a class of twelve such two-stage designs, including two-stage case-control and case-cohort designs as special cases. In analysing such two-stage data, one objective is to extract information about the relationship between the exposure variable and the response after controlling for other covariables. We discuss three statistical methods to analyse the data and report results of Monte Carlo stimulation to study the efficiency of the three methods.     

WEIGHTED LIKELIHOOD,PSEUDO-LIKELIHOOD AND MAXIMUM LIKELIHOOD METHODS FOR LOGISTIC REGRESSION ANALYSIS OF TWO-STAGE DATA

General approaches to the fitting of binary response models to data collected in two-stage and other stratified sampling designs include weighted likelihood, pseudo-likelihood and full maximum likelihood. In previous work the authors developed the large sample theory and methodology for fitting of logistic regression models to two-stage case-control data using full maximum likelihood. The present paper describes computational algorithms that permit efficient estimation of regression coefficients using weighted, pseudo- and full maximum likelihood. It also presents results of a simulation study involving continuous covariables where maximum likelihood clearly outperformed the other two methods and discusses the analysis of data from three bona fide case-control studies that illustrate some important relationships among the three methods. A concluding section discusses the application of two-stage methods to case-control studies with validation subsampling for control of measurement error. © 1997 by John Wiley & Sons, Ltd.     

Occupational noise exposure and work accidents

The purpose of this study was to verify whether occupational noise exposure is a significant risk factor for work accidents in the city of Piracicaba, S?o Paulo State, Brazil. This hospital-based case-control study included 600 workers aged 15-60 who suffered typical occupational accidents between May and October 2004 and were seen at the Piracicaba Orthopedics and Trauma Center. The control group comprised 822 workers, aged 15-60, who were also seen at the Center, and either had a non-occupational accident or were accompanying someone who had suffered an accident. A multiple logistic regression model was adjusted with work accident as an independent variable, controlled by covariables of interest such as noise exposure. The risk of having a work accident was about twice as high among workers exposed to noise, after controlling for several covariables. Occupational noise exposure not only affected auditory health status but was also a risk factor for work accidents.     

Proper analysis of secondary phenotype data in case‐control association studies

Case‐control association studies often collect extensive information on secondary phenotypes, which are quantitative or qualitative traits other than the case‐control status. Exploring secondary phenotypes can yield valuable insights into biological pathways and identify genetic variants influencing phenotypes of direct interest. All publications on secondary phenotypes have used standard statistical methods, such as least‐squares regression for quantitative traits. Because of unequal selection probabilities between cases and controls, the case‐control sample is not a random sample from the general population. As a result, standard statistical analysis of secondary phenotype data can be extremely misleading. Although one may avoid the sampling bias by analyzing cases and controls separately or by including the case‐control status as a covariate in the model, the associations between a secondary phenotype and a genetic variant in the case and control groups can be quite different from the association in the general population. In this article, we present novel statistical methods that properly reflect the case‐control sampling in the analysis of secondary phenotype data. The new methods provide unbiased estimation of genetic effects and accurate control of false‐positive rates while maximizing statistical power. We demonstrate the pitfalls of the standard methods and the advantages of the new methods both analytically and numerically. The relevant software is available at our website. Genet. Epidemiol. 2009. © 2008 Wiley‐Liss, Inc.     

Estimation of odds ratios of genetic variants for the secondary phenotypes associated with primary diseases

Genetic association studies for binary diseases are designed as case-control studies: the cases are those affected with the primary disease and the controls are free of the disease. At the time of case-control collection, information about secondary phenotypes is also collected. Association studies of secondary phenotype and genetic variants have received a great deal of interest recently. To study the secondary phenotypes, investigators use standard regression approaches, where individuals with secondary phenotypes are coded as cases and those without secondary phenotypes are coded as controls. However, using the secondary phenotype as an outcome variable in a case-control study might lead to a biased estimate of odds ratios (ORs) for genetic variants. The secondary phenotype is associated with the primary disease; therefore, individuals with and without the secondary phenotype are not sampled following the principles of a case-control study. In this article, we demonstrate that such analyses will lead to a biased estimate of OR and propose new approaches to provide more accurate OR estimates of genetic variants associated with the secondary phenotype for both unmatched and frequency-matched (with respect to the secondary phenotype) case-control studies. We also propose a bootstrapping method to estimate the empirical confidence intervals for the corrected ORs. Using simulation studies and analysis of lung cancer data for single-nucleotide polymorphism associated with smoking quantity, we compared our new approaches to standard logistic regression and to an extended version of the inverse-probability-of-sampling-weighted regression. The proposed approaches provide more accurate estimation of the true OR.     

Control for confounding in case-control studies using the stratification score, a retrospective balancing score

The stratification score for a case-control study is the probability of disease modeled as a function of potential confounders. The authors show that the stratification score is a retrospective balancing score and thus plays a similar role in case-control studies as the propensity score plays in prospective studies. The authors further show how standardization using the stratification score can be used to compare the distributions of exposures that would be found among case and control participants if both groups had the same distribution of confounding covariables. The authors illustrate these results using data from a genome-wide association study, the GAIN (Genetic Association Information Network) study of schizophrenia among African Americans (2006-2008).     

A polytomous conditional likelihood approach for combining matched and unmatched case–control studies

In genetic association studies it is becoming increasingly imperative to have large sample sizes to identify and replicate genetic effects. To achieve these sample sizes, many research initiatives are encouraging the collaboration and combination of several existing matched and unmatched case–control studies. Thus, it is becoming more common to compare multiple sets of controls with the same case group or multiple case groups to validate or confirm a positive or negative finding. Usually, a naive approach of fitting separate models for each case–control comparison is used to make inference about disease–exposure association. But, this approach does not make use of all the observed data and hence could lead to inconsistent results. The problem is compounded when a common case group is used in each case–control comparison. An alternative to fitting separate models is to use a polytomous logistic model but, this model does not combine matched and unmatched case–control data. Thus, we propose a polytomous logistic regression approach based on a latent group indicator and a conditional likelihood to do a combined analysis of matched and unmatched case–control data. We use simulation studies to evaluate the performance of the proposed method and a case–control study of multiple myeloma and Inter‐Leukin‐6 as an example. Our results indicate that the proposed method leads to a more efficient homogeneity test and a pooled estimate with smaller standard error. Copyright © 2010 John Wiley & Sons, Ltd.     

The effects of sensitivity and specificity of case selection on validity, sample size, precision, and power in hospital-based case-control studies

The consequences of imperfect sensitivity and specificity in disease diagnosis in epidemiologic studies have conventionally been assessed by models of misclassification which assume a fixed number of study participants. This assumption is not usually applicable to case-control studies in which disease diagnosis is part of the case selection process and sensitivity and specificity will, for a given time period and source of cases, affect the size of the case group. In this paper, a mathematical model that incorporates this is developed in the framework of a hospital-based case-control study. The separate and combined effects of imperfect sensitivity and specificity of case diagnosis on validity, sample size, precision, and power are assessed. The authors conclude that if several diagnostic procedures are available, specificity of case diagnosis should usually take precedence over sensitivity for the sake of validity. Although increasing specificity and sacrificing sensitivity may compromise precision to some extent, the latter can often be fully compensated for by an increased control:case ratio. Imperfect specificity also compromises power despite increased sample size. Since clinical diagnoses tend to focus on high sensitivity and sacrifice some specificity, their uncritical adoption for case recruitment in case-control studies may compromise their validity.     

The concept of residual confounding in regression models and some applications.

In this paper the concept of residual confounding is generalized to various types of regression models such as logistic regression or Cox regression. Residual confounding and a newly suggested parameter, the relative residual confounding, are defined on the regression parameters of the models. The estimator gives the proportion of confounding which has been removed by incomplete adjustment. The concept quantifies the effects of categorizing continuous covariables and of model misspecification. These are investigated by a simulation study and with data from an epidemiological investigation. A case-control study of laryngeal cancer is used to illustrate the residual confounding effect of arbitrary transformation of a continuous confounder, smoking, on the effect of alcohol consumption on laryngeal cancer risk. The data also showed that categorization into two levels can yield high residual confounding. The parameters described in this paper are of some use in quantifying the effect of inadequate adjustment for confounding variables.     

Efficiency of two-phase methods with focus on a planned population-based case-control study on air pollution and stroke

Background  

We plan to conduct a case-control study to investigate whether exposure to nitrogen dioxide (NO₂) increases the risk of stroke. In case-control studies, selective participation can lead to bias and loss of efficiency. A two-phase design can reduce bias and improve efficiency by combining information on the non-participating subjects with information from the participating subjects. In our planned study, we will have access to individual disease status and data on NO₂ exposure on group (area) level for a large population sample of Scania, southern Sweden. A smaller sub-sample will be selected to the second phase for individual-level assessment on exposure and covariables. In this paper, we simulate a case-control study based on our planned study. We develop a two-phase method for this study and compare the performance of our method with the performance of other two-phase methods.     

流行性红斑性肢痛症的病例对照研究

本文对红斑性肢痛症的高发人群(中学生)采用分层整群抽样共调查1 633名对象,其中病例组462例,对照组1 191例,以病例对照研究进行Mental-Haenszal法和多元回归分析。结果表明,本病与住读、寒潮时保暖差、膳食营养不良以及植物神经系统功能紊乱等综合因素的作用有关。预防本病应重点对14~20岁青少年,尤其对女学生和住读生采取适当的措施。     

Bias correction to secondary trait analysis with case–control design

In genetic association studies with densely typed genetic markers, it is often of substantial interest to examine not only the primary phenotype but also the secondary traits for their association with the genetic markers. For more efficient sample ascertainment of the primary phenotype, a case–control design or its variants, such as the extreme‐value sampling design for a quantitative trait, are often adopted. The secondary trait analysis without correcting for the sample ascertainment may yield a biased association estimator. We propose a new method aiming at correcting the potential bias due to the inadequate adjustment of the sample ascertainment. The method yields explicit correction formulas that can be used to both screen the genetic markers and rapidly evaluate the sensitivity of the results to the assumed baseline case‐prevalence rate in the population. Simulation studies demonstrate good performance of the proposed approach in comparison with the more computationally intensive approaches, such as the compensator approaches and the maximum prospective likelihood approach. We illustrate the application of the approach by analysis of the genetic association of prostate specific antigen in a case–control study of prostate cancer in the African American population. Copyright © 2012 John Wiley & Sons, Ltd.     

Likelihood-based methods for regression analysis with binary exposure status assessed by pooling

The need for resource-intensive laboratory assays to assess exposures in many epidemiologic studies provides ample motivation to consider study designs that incorporate pooled samples. In this paper, we consider the case in which specimens are combined for the purpose of determining the presence or absence of a pool-wise exposure, in lieu of assessing the actual binary exposure status for each member of the pool. We presume a primary logistic regression model for an observed binary outcome, together with a secondary regression model for exposure. We facilitate maximum likelihood analysis by complete enumeration of the possible implications of a positive pool, and we discuss the applicability of this approach under both cross-sectional and case-control sampling. We also provide a maximum likelihood approach for longitudinal or repeated measures studies where the binary outcome and exposure are assessed on multiple occasions and within-subject pooling is conducted for exposure assessment. Simulation studies illustrate the performance of the proposed approaches along with their computational feasibility using widely available software. We apply the methods to investigate gene-disease association in a population-based case-control study of colorectal cancer. Copyright ? 2012 John Wiley & Sons, Ltd.     

Double-robust estimation of an exposure-outcome odds ratio adjusting for confounding in cohort and case-control studies

Modern epidemiologic studies often aim to evaluate the causal effect of a point exposure on the risk of a disease from cohort or case-control observational data. Because confounding bias is of serious concern in such non-experimental studies, investigators routinely adjust for a large number of potential confounders in a logistic regression analysis of the effect of exposure on disease outcome. Unfortunately, when confounders are not correctly modeled, standard logistic regression is likely biased in its estimate of the effect of exposure, potentially leading to erroneous conclusions. We partially resolve this serious limitation of standard logistic regression analysis with a new iterative approach that we call ProRetroSpective estimation, which carefully combines standard logistic regression with a logistic regression analysis in which exposure is the dependent variable and the outcome and confounders are the independent variables. As a result, we obtain a correct estimate of the exposure-outcome odds ratio, if either thestandard logistic regression of the outcome given exposure and confounding factors is correct, or the regression model of exposure given the outcome and confounding factors is correct but not necessarily both, that is, it is double-robust. In fact, it also has certain advantadgeous efficiency properties. The approach is general in that it applies to both cohort and case-control studies whether the design of the study is matched or unmatched on a subset of covariates. Finally, an application illustrates the methods using data from the National Cancer Institute's Black/White Cancer Survival Study.     

Meta‐analysis with missing study‐level sample variance data

We consider a study‐level meta‐analysis with a normally distributed outcome variable and possibly unequal study‐level variances, where the object of inference is the difference in means between a treatment and control group. A common complication in such an analysis is missing sample variances for some studies. A frequently used approach is to impute the weighted (by sample size) mean of the observed variances (mean imputation). Another approach is to include only those studies with variances reported (complete case analysis). Both mean imputation and complete case analysis are only valid under the missing‐completely‐at‐random assumption, and even then the inverse variance weights produced are not necessarily optimal. We propose a multiple imputation method employing gamma meta‐regression to impute the missing sample variances. Our method takes advantage of study‐level covariates that may be used to provide information about the missing data. Through simulation studies, we show that multiple imputation, when the imputation model is correctly specified, is superior to competing methods in terms of confidence interval coverage probability and type I error probability when testing a specified group difference. Finally, we describe a similar approach to handling missing variances in cross‐over studies. Copyright © 2016 John Wiley & Sons, Ltd.     

Control of confounding through secondary samples

The control of confounding is essential in many statistical problems, especially in those that attempt to estimate exposure effects. In some cases, in addition to the 'primary' sample, there is another 'secondary' sample which, though having no direct information about the exposure effect, contains information about the confounding factors. The purpose of this article is to study the influence of the secondary sample on likelihood inference for the exposure effect. In particular, we investigate the interplay between the efficiency improvement and the possible bias introduced by the secondary sample as a function of the degree of confounding in the primary sample and the sizes of the primary and secondary samples. In the case of weak confounding, the secondary sample can only little improve estimation of the exposure effect, whereas with strong confounding the secondary sample can be much more useful. On the other hand, it will be more important to consider possible biasing effects in the latter case. For illustration, we use a formal example of a generalized linear model and a real example with sparse data from a case-control study of the association between gastric cancer and HM-CAP/Band 120.     

Using sign score regression models to select variables in case-control studies

This paper evaluates the performance of four variable selection methods suitable for case-control studies. Two of the methods are logistic regression and the rank transformed version of it which uses the ranks of the explanatory variables in place of the original observations. The third method is based on Kendall's τ_b correlations. I propose a fourth method, a sign score regression model to select variables. To evaluate these four methods, I generate many data sets for a case group and a control group with the use of several different distributions and covariance matrices. I evaluate the methods on their ability to select correctly the variables related to case-control status while not selecting the unrelated variables. Using this criterion, the sign score regression method and the τ_b method are more effective than the other two methods with uncorrelated or weakly correlated variables. The sign score regression method is more effective than the τ_b method for all simulations that use normal variables and for some that use log-normal variables. Overall, the sign score regression method is the most effective variable selection method for data sets that have low or moderate correlations between variables.     

Genome‐wide association scans for secondary traits using case‐control samples

Genome‐wide association studies (GWAS) require considerable investment, so researchers often study multiple traits collected on the same set of subjects to maximize return. However, many GWAS have adopted a case‐control design; improperly accounting for case‐control ascertainment can lead to biased estimates of association between markers and secondary traits. We show that under the null hypothesis of no marker‐secondary trait association, naïve analyses that ignore ascertainment or stratify on case‐control status have proper Type I error rates except when both the marker and secondary trait are independently associated with disease risk. Under the alternative hypothesis, these methods are unbiased when the secondary trait is not associated with disease risk. We also show that inverse‐probability‐of‐sampling‐weighted (IPW) regression provides unbiased estimates of marker‐secondary trait association. We use simulation to quantify the Type I error, power and bias of naïve and IPW methods. IPW regression has appropriate Type I error in all situations we consider, but has lower power than naïve analyses. The bias for naïve analyses is small provided the marker is independent of disease risk. Considering the majority of tested markers in a GWAS are not associated with disease risk, naïve analyses provide valid tests of and nearly unbiased estimates of marker‐secondary trait association. Care must be taken when there is evidence that both the secondary trait and tested marker are associated with the primary disease, a situation we illustrate using an analysis of the relationship between a marker in FGFR2 and mammographic density in a breast cancer case‐control sample. Genet. Epidemiol. 33:717–728, 2009. © 2009 Wiley‐Liss, Inc.     

三氯乙烯作业工人潜在职业伤害的社会因素分析

目的分析影响三氯乙烯(trichloroethylene,TCE)职业伤害的社会因素,为制定正确有效的TCE防护措施提供依据。方法采用病例对照研究的方法,以是否出现DNA损伤作为判断TCE潜在伤害发生的依据,以发生DNA损伤的工人为病例组,没有DNA损伤的工人为对照组,按1∶3的方式进行匹配。结果单因素Logistic回归分析显示,病例组与对照组在文化程度、接触时间、是否有岗前培训、是否进行在岗期间职业健康检查,以及个人防护用品的使用情况方面差异具有统计学意义(P<0.05)。多因素Logistic回归分析显示,文化程度小学及以下、工龄大于1.5年、未进行岗前培训以及偶尔使用个人防护用品的职工发生TCE潜在伤害的危险性会增加,调整后的OR值分别为:1.75,4.21,1.95,3.67。结论在实际工作中,应以低文化程度和工龄相对较长的工人为重点防护人群,并加强上岗前体检和培训,同时提高个人防护用品的使用情况,以控制TCE潜在职业伤害的发生。     

Statistical analysis of MMR vaccine adverse events on aseptic meningitis using the case cross-over design

Vaccination is quite effective in reducing the incidence of disease. However, it may cause some adverse events. For example, one of the adverse events of measles-mumps-rubella(MMR) vaccination is the occurrence of aseptic meningitis. Since the vaccination rate is usually quite high, it is not plausible to use popular study designs such as cohort or case-control studies. We considered a case cross-over design to investigate the association between MMR vaccination and aseptic meningitis in Korean children. We used the Cochran-Mantel-Haenszel(CMH) approach, and obtained a Mantel-Haenszel odds ratio estimator as a measure of association. However, the validity of case cross-over design or the CMH approach in vaccine adverse studies has not been fully investigated. In this paper, through Monte Carlo simulation studies, we show the appropriateness of the case cross-over design and the CMH approach. We also discuss alternative approaches such as Poisson regression using offset and a simple uniformity test. In conclusion, the case cross-over design seems useful to investigate the association between vaccination and occurrence of acute adverse events.