Highly active antiretroviral therapy (HAART) rapidly suppresses human immunodeficiency virus (HIV) viral replication and reduces circulating viral load, but the long-term effects of HAART on viral load remain unclear.
We evaluated HIV viral load trajectories over 8 years following HAART initiation in the Multicenter AIDS Cohort Study and the Women’s Interagency HIV Study. The study included 157 HIV-infected men and 199 HIV-infected women who were antiretroviral naïve and contributed 1311 and 1837 semiannual person-visits post-HAART, respectively. To account for within-subject correlation and the high proportion of left-censored viral loads, we used a segmental Bernoulli/lognormal random effects model.
Approximately 3 months (0.30 years for men and 0.22 years for women) after HAART initiation, HIV viral loads were optimally suppressed (ie, with very low HIV RNA) for 44% (95% confidence interval = 39%–49%) of men and 43% (38%–47%) of women, whereas the other 56% of men and 57% of women had on average 2.1 (1.5–2.6) and 3.0 (2.7–3.2) log10 copies/mL, respectively.
After 8 years on HAART, 75% of men and 80% of women had optimal suppression, whereas the rest of the men and women had suboptimal suppression with a median HIV RNA of 3.1 and 3.7 log10 copies/mL, respectively.
Melanoma cell lines and normal human melanocytes were assayed for p53-dependent G1 checkpoint response to ionizing radiation-induced DNA damage. Sixty six percent of melanoma cell lines displayed a defective G1 checkpoint. Checkpoint function was correlated with sensitivity to ionizing radiation with checkpoint-defective lines being radio-resistant. Microarray analysis identified 316 probes whose expression was correlated with G1 checkpoint function in melanoma lines (P≤0.007) including p53 transactivation targets CDKN1A, DDB2 and RRM2B. The 316 probe list predicted G1 checkpoint function of the melanoma lines with 86% accuracy using a binary analysis and 91% accuracy using a continuous analysis. When applied to microarray data from primary melanomas, the 316 probe list was prognostic of four year distant metastases-free survival. Thus, p53 function, radio-sensitivity and metastatic spread may be estimated in melanomas from a signature of gene expression.
gene; expression; signature; p53; function; checkpoint; melanoma
In retrospective studies, odds ratio is often used as the measure of association. Under independent beta prior assumption, the exact posterior distribution of odds ratio given a single 2 × 2 table has been derived in the literature. However, independence between risks within the same study may be an oversimplified assumption because cases and controls in the same study are likely to share some common factors and thus to be correlated. Furthermore, in a meta-analysis of case-control studies, investigators usually have multiple 2×2 tables. In this paper, we first extend the published results on a single 2×2 table to allow within study prior correlation while retaining the advantage of closed form posterior formula, and then extend the results to multiple 2 × 2 tables and regression setting. The hyperparameters, including within study correlation, are estimated via an empirical Bayes approach. The overall odds ratio and the exact posterior distribution of the study-specific odds ratio are inferred based on the estimated hyperparameters. We conduct simulation studies to verify our exact posterior distribution formulas and investigate the finite sample properties of the inference for the overall odds ratio. The results are illustrated through a twin study for genetic heritability and a meta-analysis for the association between the N-acetyltransferase 2 (NAT2) acetylation status and colorectal cancer.
Bivariate beta-binomial model; Exact method; Hypergeometric function; Meta-analysis; Odds ratio; Sarmanov family
Bayesian posterior parameter distributions are often simulated using Markov chain Monte Carlo (MCMC) methods. However, MCMC methods are not always necessary and do not help the uninitiated understand Bayesian inference. As a bridge to understanding Bayesian inference, the authors illustrate a transparent rejection sampling method. In example 1, they illustrate rejection sampling using 36 cases and 198 controls from a case-control study (1976–1983) assessing the relation between residential exposure to magnetic fields and the development of childhood cancer. Results from rejection sampling (odds ratio (OR) = 1.69, 95% posterior interval (PI): 0.57, 5.00) were similar to MCMC results (OR = 1.69, 95% PI: 0.58, 4.95) and approximations from data-augmentation priors (OR = 1.74, 95% PI: 0.60, 5.06). In example 2, the authors apply rejection sampling to a cohort study of 315 human immunodeficiency virus seroconverters (1984–1998) to assess the relation between viral load after infection and 5-year incidence of acquired immunodeficiency syndrome, adjusting for (continuous) age at seroconversion and race. In this more complex example, rejection sampling required a notably longer run time than MCMC sampling but remained feasible and again yielded similar results. The transparency of the proposed approach comes at a price of being less broadly applicable than MCMC.
Bayes theorem; epidemiologic methods; inference; Monte Carlo method; posterior distribution; simulation
Following the outbreaks of 2009 pandemic H1N1 infection, rapid influenza diagnostic tests have been used to detect H1N1 infection. However, no meta-analysis has been undertaken to assess the diagnostic accuracy when this manuscript was drafted.
The literature was systematically searched to identify studies that reported the performance of rapid tests. Random effects meta-analyses were conducted to summarize the overall performance.
Seventeen studies were selected with 1879 cases and 3477 non-cases. The overall sensitivity and specificity estimates of the rapid tests were 0.51 (95%CI: 0.41, 0.60) and 0.98 (95%CI: 0.94, 0.99). Studies reported heterogeneous sensitivity estimates, ranging from 0.11 to 0.88. If the prevalence was 30%, the overall positive and negative predictive values were 0.94 (95%CI: 0.85, 0.98) and 0.82 (95%CI: 0.79, 0.85). The overall specificities from different manufacturers were comparable, while there were some differences for the overall sensitivity estimates. BinaxNOW had a lower overall sensitivity of 0.39 (95%CI: 0.24, 0.57) compared to all the others (p-value < 0.001), whereas QuickVue had a higher overall sensitivity of 0.57 (95%CI: 0.50, 0.63) compared to all the others (p-value = 0.005).
Rapid tests have high specificity but low sensitivity and thus limited usefulness.
meta analysis; H1N1; diagnostic tests; rapid tests; sensitivity and specificity
Characterize responses to a NNRTI-based antiretroviral treatment (ART) initiated during acute HIV infection (AHI).
This was a prospective, single-arm evaluation of once daily, co-formulated emtricitabine/tenofovir/efavirenz initiated during AHI.
The primary endpoint is the proportion of responders with HIV RNA <200 copies/mL by week 24. We examined time-to-viral-suppression and CD8 cell activation in relation to baseline participant characteristics. We compared time-to-viral-suppression and viral dynamics using linear mixed effects models between acutely infected participants and chronically-infected controls.
Between January 2005 and May 2009, 61 AHI participants were enrolled. Of participants whose enrollment date allowed 24 and 48 weeks of follow-up, 47 of 51 (92%) achieved viral suppression to <200 copies/mL by week 24, and 35 of 41 (85.4%) to <50 copies/mL by week 48. The median time from ART initiation to suppression <50 copies/mL was 93 days (range 14–337). Higher HIV RNA levels at ART initiation (p=0.02), but not time from estimated-date-of-infection to ART initiation (p=0.86), were associated with longer time-to-viral-suppression. The median baseline frequency of activated CD8+CD38+HLA-DR+ T-cells was 67% (range 40–95), and was not significantly associated with longer time to viral load suppression (p=0.15). Viremia declined to <50 copies/mL more rapidly in AHI than chronically-infected participants. Mixed model analysis demonstrated similar phase I HIV RNA decay rates between acute and chronically-infected participants, and more rapid viral decline in acutely-infected participants in phase II.
Once daily emtricitabine/tenofovir/efavirenz initiated during AHI achieves rapid and sustained HIV suppression during this highly infectious period.
Acute HIV infection; NNRTIs; antiretroviral therapy; immune activation; viral dynamics
The analysis of data subject to detection limits is becoming increasingly necessary in many environmental and laboratory studies. Covariates subject to detection limits are often left censored because of a measurement device having a minimal lower limit of detection. In this paper, we propose a Monte Carlo version of the expectation–maximization algorithm to handle large number of covariates subject to detection limits in generalized linear models. We model the covariate distribution via a sequence of one-dimensional conditional distributions, and sample the covariate values using an adaptive rejection metropolis algorithm. Parameter estimation is obtained by maximization via the Monte Carlo M-step. This procedure is applied to a real dataset from the National Health and Nutrition Examination Survey, in which values of urinary heavy metals are subject to a limit of detection. Through simulation studies, we show that the proposed approach can lead to a significant reduction in variance for parameter estimates in these models, improving the power of such studies.
EM algorithm; Gibbs sampling; logistic regression; maximum likelihood estimation; Monte Carlo EM; NHANES
Lagging exposure information is often undertaken to allow for a latency period in cumulative exposure-disease analyses. The authors first consider bias and confidence interval coverage when using the standard approaches of fitting models under several lag assumptions and selecting the lag that maximizes either the effect estimate or model goodness of fit. Next, they consider bias that occurs when the assumption that the latency period is a fixed constant does not hold. Expressions were derived for bias due to misspecification of lag assumptions, and simulations were conducted. Finally, the authors describe a method for joint estimation of parameters describing an exposure-response association and the latency distribution. Analyses of associations between cumulative asbestos exposure and lung cancer mortality among textile workers illustrate this approach. Selecting the lag that maximizes the effect estimate may lead to bias away from the null; selecting the lag that maximizes model goodness of fit may lead to confidence intervals that are too narrow. These problems tend to increase as the within-person exposure variation diminishes. Lagging exposure assignment by a constant will lead to bias toward the null if the distribution of latency periods is not a fixed constant. Direct estimation of latency periods can minimize bias and improve confidence interval coverage.
asbestos; cohort studies; latency; neoplasms; survival analysis
E1684 was the pivotal adjuvant melanoma trial for establishment of high-dose interferon (IFN) as effective therapy of high-risk melanoma patients. E1690 was an intriguing effort to corroborate E1684, and the differences between the outcomes of these trials have embroiled the field in controversy over the past several years. The analyses of E1684 and E1690 were carried out separately when the results were published, and there were no further analyses trying to perform a single analysis of the combined trials.
In this paper, we consider such a joint analysis by carrying out a Bayesian analysis of these two trials, thus providing us with a consistent and coherent methodology for combining the results from these two trials.
The Bayesian analysis using power priors provided a more coherent flexible and potentially more accurate analysis than a separate analysis of these data or a frequentist analysis of these data. The methodology provides a consistent framework for carrying out a single unified analysis by combining data from two or more studies.
Such Bayesian analyses can be crucial in situations where the results from two theoretically identical trials yield somewhat conflicting or inconsistent results.
Cure rate model; Historical data; Prior distribution; Posterior distribution
Owing to the rapid development of biomarkers in clinical trials, joint modeling of longitudinal and survival data has gained its popularity in the recent years because it reduces bias and provides improvements of efficiency in the assessment of treatment effects and other prognostic factors. Although much effort has been put into inferential methods in joint modeling, such as estimation and hypothesis testing, design aspects have not been formally considered. Statistical design, such as sample size and power calculations, is a crucial first step in clinical trials. In this paper, we derive a closed-form sample size formula for estimating the effect of the longitudinal process in joint modeling, and extend Schoenfeld’s sample size formula to the joint modeling setting for estimating the overall treatment effect. The sample size formula we develop is quite general, allowing for p-degree polynomial trajectories. The robustness of our model is demonstrated in simulation studies with linear and quadratic trajectories. We discuss the impact of the within-subject variability on power and data collection strategies, such as spacing and frequency of repeated measurements, in order to maximize the power. When the within-subject variability is large, different data collection strategies can influence the power of the study in a significant way. Optimal frequency of repeated measurements also depends on the nature of the trajectory with higher polynomial trajectories and larger measurement error requiring more frequent measurements.
sample size; power determination; joint modeling; survival analysis; longitudinal data; repeated measurements
Multivariate meta-analysis is increasingly utilized in biomedical research to combine data of multiple comparative clinical studies for evaluating drug efficacy and safety profile. When the probability of the event of interest is rare or when the individual study sample sizes are small, a substantial proportion of studies may not have any event of interest. Conventional meta-analysis methods either exclude such studies or include them through ad-hoc continuality correction by adding an arbitrary positive value to each cell of the corresponding 2 by 2 tables, which may result in less accurate conclusions. Furthermore, different continuity corrections may result in inconsistent conclusions. In this article, we discuss a bivariate Beta-binomial model derived from Sarmanov family of bivariate distributions and a bivariate generalized linear mixed effects model for binary clustered data to make valid inferences. These bivariate random effects models use all available data without ad hoc continuity corrections, and accounts for the potential correlation between treatment (or exposure) and control groups within studies naturally. We then utilize the bivariate random effects models to reanalyze two recent meta-analysis data sets.
clustered binary data; bivariate random effects models; Beta-binomial distribution; meta-analysis; bivariate generalized linear mixed models
DNA methylation, an epigenetic alteration typically occurring early in cancer development, could aid in the molecular diagnosis of melanoma. We determined technical feasibility for high-throughput DNA-methylation array-based profiling using formalin-fixed paraffin-embedded tissues for selection of candidate DNA-methylation differences between melanomas and nevi. Promoter methylation was evaluated in 27 common benign nevi and 22 primary invasive melanomas using a 1505 CpG-site microarray. Unsupervised hierarchical clustering distinguished melanomas from nevi; and 26 CpG sites in 22 genes were identified with significantly different methylation levels between melanomas and nevi after adjustment for age, sex, and multiple comparisons and with β-value differences of ≥ 0.2. Prediction Analysis for Microarrays identified 12 CpG loci that were highly predictive of melanoma, with area under the receiver operating characteristic curves of greater than 0.95. Of our panel of 22 genes, 14 were statistically significant in an independent sample set of 29 nevi (including dysplastic nevi) and 25 primary invasive melanomas after adjustment for age, sex, and multiple comparisons. This first report of a DNA-methylation signature discriminating melanomas from nevi indicates that DNA methylation appears promising as an additional tool for enhancing melanoma diagnosis.
melanoma; nevi; methylation profiling; diagnostic markers
Linear regression with a left-censored independent variable X due to limit of detection (LOD) was recently considered by 2 groups of researchers: Richardson and Ciampi, and Schisterman and colleagues.
Both groups obtained consistent estimators for the regression slopes by replacing left-censored X with a constant, that is, the expectation of X given X below LOD E(X|X
Schisterman and colleagues argued that their approach would be a better choice because the sample mean of X given X above LOD is available, whereas E(X|X
Recommendations are given based on theoretical and simulation results. These recommendations are illustrated with 1 case study.
Motivation: The Illumina BeadArray is a popular platform for profiling DNA methylation, an important epigenetic event associated with gene silencing and chromosomal instability. However, current approaches rely on an arbitrary detection P-value cutoff for excluding probes and samples from subsequent analysis as a quality control step, which results in missing observations and information loss. It is desirable to have an approach that incorporates the whole data, but accounts for the different quality of individual observations.
Results: We first investigate and propose a statistical framework for removing the source of biases in Illumina Methylation BeadArray based on several positive control samples. We then introduce a weighted model-based clustering called LumiWCluster for Illumina BeadArray that weights each observation according to the detection P-values systematically and avoids discarding subsets of the data. LumiWCluster allows for discovery of distinct methylation patterns and automatic selection of informative CpG loci. We demonstrate the advantages of LumiWCluster on two publicly available Illumina GoldenGate Methylation datasets (ovarian cancer and hepatocellular carcinoma).
Availability: R package LumiWCluster can be downloaded from http://www.unc.edu/~pfkuan/LumiWCluster
Supplementary information: Supplementary data are available at Bioinformatics online.
Treatment effect is traditionally assessed through either superiority or non-inferiority clinical trials. Investigators may find that because of safety concerns and/or wide variability across strata of the superiority margin of active controls over placebo, neither a superiority nor a non-inferiority trial design is ethical or practical in some disease populations. Prior knowledge may allow and drive study designers to consider more sophisticated designs for a clinical trial.
In this paper, the authors propose hybrid designs which may combine a superiority design in one subgroup with a non-inferiority design in another subgroup or combine designs with different control regimens in different subgroups in one trial when a uniform design is unethical or impractical. The authors show how the hybrid design can be planned and how inferences can be made. Through two examples, the authors illustrate the scenarios where hybrid designs are useful while the conventional designs are not preferable.
The hybrid design is a useful alternative to current superiority and non-inferiority designs.
We propose hybrid designs for the trials when neither a superiority nor a non-inferiority trial design is ethical and practical.
The hybrid design is practical, flexible and feasible.
We expect it to become a major alternative to the superiority and non-inferiority designs.
Strengths and limitations of this study
Hybrid design provides a powerful and relatively simple solution to the difficult problem of active controls with varying efficacy and/or safety concern. The problem is becoming more common as more drugs become available.
The design and analysis are moderately complex compared with the superiority and non-inferiority designs.
Bivariate random effect models are currently one of the main methods recommended to synthesize diagnostic test accuracy studies. However, only the logit-transformation on sensitivity and specificity has been previously considered in the literature. In this paper, we consider a bivariate generalized linear mixed model to jointly model the sensitivities and specificities, and discuss the estimation of the summary receiver operating characteristic curve (ROC) and the area under the ROC curve (AUC). As the special cases of this model, we discuss the commonly used logit, probit and complementary log-log transformations. To evaluate the impact of misspecification of the link functions on the estimation, we present two case studies and a set of simulation studies. Our study suggests that point estimation of the median sensitivity and specificity, and AUC is relatively robust to the misspecification of the link functions. However, the misspecification of link functions has a noticeable impact on the standard error estimation and the 95% confidence interval coverage, which emphasizes the importance of choosing an appropriate link function to make statistical inference.
meta-analysis; bivariate random effect models; sensitivity; specificity; receiver operating characteristic curve; area under the ROC curve
NF-κB is an antiapoptotic transcription factor that has been shown to be a mediator of treatment resistance. Bcl-3 is a regulator of NF-κB that may play a role in oncogenesis. The goal of this study was to correlate the activation status of NF-κB and Bcl-3 with clinical outcome in a group of patients with metastatic colorectal cancer (CRC).
A retrospective study of 23 patients who underwent surgical resection of CRC at the University of North Carolina (UNC). Activation of NF-κB was defined by nuclear expression of select components of NF-κB (p50, p52, p65) and Bcl-3. Tissue microarrays were created from cores of normal mucosa, primary tumor, lymph node metastases and liver metastases in triplicate from disparate areas of the blocks, and an intensity score was generated by multiplying intensity (0–3+) by percent of positive tumor cells. Generalized estimating equations were used to note differences in intensity scores among normal mucosa and nonnormal tissues. Cox regression models were fit to see if scores were significantly associated with overall survival.
p65 NE was significantly higher in primary tumor and liver metastases than normal mucosa (both p < 0.01). p50 nuclear expression was significantly higher for all tumor sites than for normal mucosa (primary tumor and lymph node metastases p < 0.0001, liver metastases p < 0.01). Bcl-3 nuclear expression did not differ significantly between normal mucosa and tumor; however, nuclear expression in primary tumor for each of these components was strongly associated with survival: the increase in hazard for each 50-point increase in nuclear expression was 91% for Bcl-3, 66% for p65, and 52% for p50 (all p < 0.05).
Activation of canonical NF-κB subunits p50 and p65 as measured by nuclear expression is strongly associated with survival suggesting NF-κB as a prognostic factor in this disease. Primary tumor nuclear expression appears to be as good as, or better than, metastatic sites at predicting prognosis. Bcl-3 nuclear expression is also negatively associated with survival and deserves further study in CRC.
NF-κB; P65; P50; Colorectal carcinoma
To evaluate the probabilities of a disease state, ideally all subjects in a study should be diagnosed by a definitive diagnostic or gold standard test. However, since definitive diagnostic tests are often invasive and expensive, it is generally unethical to apply them to subjects whose screening tests are negative. In this article, we consider latent class models for screening studies with two imperfect binary diagnostic tests and a definitive categorical disease status measured only for those with at least one positive screening test. Specifically, we discuss a conditional independent and three homogeneous conditional dependent latent class models and assess the impact of misspecification of the dependence structure on the estimation of disease category probabilities using frequentist and Bayesian approaches. Interestingly, the three homogeneous dependent models can provide identical goodness-of-fit but substantively different estimates for a given study. However, the parametric form of the assumed dependence structure itself is not “testable” from the data, and thus the dependence structure modeling considered here can only be viewed as a sensitivity analysis concerning a more complicated non-identifiable model potentially involving heterogeneous dependence structure. Furthermore, we discuss Bayesian model averaging together with its limitations as an alternative way to partially address this particularly challenging problem. The methods are applied to two cancer screening studies, and simulations are conducted to evaluate the performance of these methods. In summary, further research is needed to reduce the impact of model misspecification on the estimation of disease prevalence in such settings.
maximum likelihood; Bayesian inference; diagnostic test; dependence; screening; latent class models
That conditioning on a common effect of exposure and outcome may cause selection, or collider-stratification, bias is not intuitive. We provide two hypothetical examples to convey concepts underlying bias due to conditioning on a collider. In the first example, fever is a common effect of influenza and consumption of a tainted egg-salad sandwich. In the second example, case-status is a common effect of a genotype and an environmental factor. In both examples, conditioning on the common effect imparts an association between two otherwise independent variables; we call this selection bias.
Bias; selection; methods; epidemiologic
In the survival analysis context, when an intervention either reduces a harmful exposure or introduces a beneficial treatment, it seems useful to quantify the gain in survival attributable to the intervention as an alternative to the reduction in risk. To accomplish this we introduce two new concepts, the attributable survival and attributable survival time, and study their properties. Our analysis includes comparison with the attributable risk function as well as hazard-based alternatives. We also extend the setting to the case where the intervention takes place at discrete points in time, and may either eliminate exposure or introduce a beneficial treatment in only a proportion of the available group. This generalization accommodates the more realistic situation where the treatment or exposure is dynamic. We apply these methods to assess the effect of introducing highly active antiretroviral therapy for the treatment of clinical AIDS at the population level.
attributable risk function; survival analysis; parametric models; generalized gamma distribution; product limit estimate
Neighborhood socioeconomic environment may be a determinant of injection drug use cessation. The authors used data from a prospective cohort study of Baltimore City, Maryland, injection drug users assessed between 1990 and 2006. The study examined the relation between living in a poorer neighborhood and the probability of injection cessation among active injectors, independent of individual characteristics and while respecting the temporality of potential confounders, exposure, and outcome. Participants’ residences were geocoded, and the crude, adjusted, and inverse probability of exposure weighted associations between neighborhood poverty and injection drug use cessation were estimated. Weighted models showed a strong association between neighborhood poverty and injection drug use cessation; living in a neighborhood with fewer than 10%, compared with more than 30%, of residents in poverty was associated with a 44% increased odds of not injecting in the prior 6 months (odds ratio = 1.44, 95% confidence interval: 1.14, 1.82). Results show that neighborhood environment may be an important determinant of drug injection behavior independent of individual-level characteristics.
drug users; epidemiologic methods; heroin; poverty; residence characteristics; social environment; substance-related disorders
Background In epidemiologic research, little emphasis has been placed on methods to account for left-hand censoring of ‘exposures’ due to a limit of detection (LOD).
Methods We calculate the odds of anti-HIV therapy naiveté in 45 HIV-infected men as a function of measured log10 plasma HIV RNA viral load using five approaches including ad hoc methods as well as a maximum likelihood estimate (MLE). We also generated simulations of a binary outcome with 10% incidence and a 1.5-fold increased odds per log increase in a log-normally distributed exposure with 25, 50 and 75% of exposure data below LOD. Simulated data were analysed using the same five methods, as well as the full data.
Results In the example, the estimated odds ratio (OR) varied by 1.22-fold across methods, from 1.45 to 1.77 per log10 copies of viral load and the standard error for the log OR varied by 1.52-fold across methods, from 0.31 to 0.47. In the simulations, use of full data or the MLE was unbiased with appropriate confidence interval (CI) coverage. However, as the proportion of exposure below LOD increased, substituting LOD, LOD/√2 or LOD/2 was increasingly biased with increasingly inappropriate CI coverage. Finally, exclusion of values below LOD was unbiased but imprecise.
Conclusions In this example and the settings explored by simulation, and among methods readily available to investigators (i.e. sans full data), the MLE provided an unbiased and appropriately precise estimate of the exposure–outcome OR.
Biomarkers; epidemiologic methods; limit of detection; statistical method
In occupational case–control studies, work-related exposure assessments are often fallible measures of the true underlying exposure. In lieu of a gold standard, often more than 2 imperfect measurements (e.g. triads) are used to assess exposure. While methods exist to assess the diagnostic accuracy in the absence of a gold standard, these methods are infrequently used to correct for measurement error in exposure–disease associations in occupational case–control studies. Here, we present a likelihood-based approach that (a) provides evidence regarding whether the misclassification of tests is differential or nondifferential; (b) provides evidence whether the misclassification of tests is independent or dependent conditional on latent exposure status, and (c) estimates the measurement error–corrected exposure–disease association. These approaches use information from all imperfect assessments simultaneously in a unified manner, which in turn can provide a more accurate estimate of exposure–disease association than that based on individual assessments. The performance of this method is investigated through simulation studies and applied to the National Occupational Hazard Survey, a case–control study assessing the association between asbestos exposure and mesothelioma.
Case–control study; Gold standard; Missing data; Occupational exposure assessment
To estimate the association of rear seat safety belt use with death in a traffic crash.
Matched cohort study.
The US during 2000 through 2004.
Drivers (10 427) and rear seat passengers (15 922) in passenger vehicles that crashed and had at least one driver or rear passenger death. Data from the Fatality Analysis Reporting System.
Main outcome measures
The adjusted relative risk (aRR) of death for a belted rear seat passenger compared with an otherwise similar unbelted rear passenger.
Safety belt use was associated with a reduced risk of death for rear car occupants: outboard rear seat aRR 0.42 (95% CI 0.38 to 0.46), and center rear seat aRR 0.30 (95% CI 0.20 to 0.44). For rear occupants of light trucks, vans, and utility vehicles, the estimates were: outboard aRR 0.25 (95% CI 0.21 to 0.29), center aRR 0.34 (95% CI 0.24 to 0.48).
If the authors' estimates are causal, traffic crash mortality can be reduced for rear occupants by approximately 55–75% if they use safety belts.
Results 1-25 (30)
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