Utilization of Diagnostic Procedures After Lung Cancer Screening in the National Lung Screening Trial.

OBJECTIVE: To examine utilization patterns of diagnostic procedures after lung cancer screening among participants enrolled in the National Lung Screening Trial. METHODS: Using a sample of National Lung Screening Trial participants with abstracted medical records, we assessed utilization of imaging, invasive, and surgical procedures after lung cancer screening. Missing data were imputed using multiple imputation by chained equations. For each procedure type, we examined utilization within a year after the screening or until the next screen, whichever came first, across arms (low-dose CT [LDCT] versus chest X-ray [CXR]) and by screening results. We also explored factors associated with having these procedures using multivariable negative binomial regressions. RESULTS: After baseline screening, our sample had 176.5 and 46.7 procedures per 100 person-years for those with a false-positive and negative result, respectively. Invasive and surgical procedures were relatively infrequent. Among those who screened positive, follow-up imaging and invasive procedures were 25% and 34% less frequent in those screened with LDCT, compared with CXR. Postscreening utilization of invasive and surgical procedures was 37% and 34% lower at the first incidence screen compared with baseline. Participants with positive results at baseline were six times more likely to undergo additional imaging than those with normal findings. DISCUSSION: Use of imaging and invasive procedures to evaluate abnormal findings varied by screening modality, with a lower rate for LDCT than CXR. Invasive and surgical workup were less prevalent after subsequent screening examinations compared with baseline screening. Utilization was associated with older age but not gender, race or ethnicity, insurance status, or income.

A mediation analysis for a nonrare dichotomous outcome with sequentially ordered multiple mediators.

Mediation analyses can help us to understand the biological mechanism in which an exposure or treatment affects an outcome. Single mediator analyses have been used in various applications, but may not be appropriate for analyzing intricate mechanisms involving multiple mediators that affect each other. Thus, in this article, we studied multiple sequentially ordered mediators for a dichotomous outcome and presented the identifiability assumptions for the path-specific effects on the outcome, that is, the effect of an exposure on the outcome mediated by a specific set of mediators. We proposed a closed-form estimator for the path-specific effects by modeling the dichotomous outcome using a probit model. Asymptotic variance of the proposed estimator is derived and can be approximated via delta method or bootstrapping. Simulations under a finite sample showed the validity of our method in capturing the path-specific effects when the probability of each potential counterfactual outcome is not small and demonstrated the utility of a computationally efficient alternative to bootstrapping for calculating variance. The method is applied to investigate the effects of polycystic ovarian syndrome on live birth rates mediated by estradiol levels and the number of oocytes retrieved in a large electronic in vitro fertilization database. We implemented the method into an R package SOMM, which is available at https://github.com/roqe/SOMM.

A multiple mediator analysis approach to quantify the effects of the ADH1B and ALDH2 genes on hepatocellular carcinoma risk.

Previous work suggested a genetic component affecting the risk of hepatocellular carcinoma (HCC) and mediation analyses have elucidated potential indirect pathways of these genetic effects. Specifically, the effects of alcohol dehydrogenase (ADH1B) and aldehyde dehydrogenase (ALDH2) genes on HCC risk vary based on alcohol consumption habits. However, alcohol consumption may not be the only mediator in the identified pathway: factors related to alcohol consumption may contribute to the same indirect pathway. Thus, we developed a multimediator model to quantify the genetic effects on HCC risk through sequential dichotomous mediators under the counterfactual framework. Our method provided a closed form formula for the mediation effects through different indirect paths, which requires no assumption for the rarity of outcome. In simulation studies of a finite sample, we presented the utility of the method with the variance of the effects estimated using the delta method and bootstrapping. We applied our method to data from participants in Taiwan (580 cases and 3,207 controls) and quantified the mediation effects of single nucleotide polymorphisms (SNPs) in the ADH1B and ALDH2 genes on HCC through alcohol consumption (yes/no) and high alanine transaminase (ALT) levels (greater than or equal to 45 U/L or below 45 U/L). Assuming a dominant risk model, we identified that the SNPs' effects through alcohol consumption is more significant than through ALT levels on HCC risk. This new method provides insight to the magnitude of various casual mechanisms as a closed form solution and can be readily applied in other genomic studies.

Genomic tests of variation in inbreeding among individuals and among chromosomes.

We examine the distribution of heterozygous sites in nine European and nine Yoruban individuals whose genomic sequences were made publicly available by Complete Genomics. We show that it is possible to obtain detailed information about inbreeding when a relatively small set of whole-genome sequences is available. Rather than focus on testing for deviations from Hardy-Weinberg genotype frequencies at each site, we analyze the entire distribution of heterozygotes conditioned on the number of copies of the derived (non-chimpanzee) allele. Using Levene's exact test, we reject Hardy-Weinberg in both populations. We generalized Levene's distribution to obtain the exact distribution of the number of heterozygous individuals given that every individual has the same inbreeding coefficient, F. We estimated F to be 0.0026 in Europeans and 0.0005 in Yorubans, but we could also reject the hypothesis that F was the same in each individual. We used a composite-likelihood method to estimate F in each individual and within each chromosome. Variation in F across chromosomes within individuals was too large to be consistent with sampling effects alone. Furthermore, estimates of F for each chromosome in different populations were not correlated. Our results show how detailed comparisons of population genomic data can be made to theoretical predictions. The application of methods to the Complete Genomics data set shows that the extent of apparent inbreeding varies across chromosomes and across individuals, and estimates of inbreeding coefficients are subject to unexpected levels of variation, which might be partly accounted for by selection.