General Relative Rate Models for the Analysis of Studies Using Case-Cohort Designs.
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ABSTRACT:
SUBMITTER: Richardson DB
PROVIDER: S-EPMC8045475 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Generalized case-cohort design has been proposed to assess the effects of exposures on survival outcomes when measuring exposures is expensive and events are not rare in the cohort. In such design, expensive exposure information is collected from both a (stratified) randomly selected subcohort and a subset of individuals with events. In this article, we consider extension of such design to study multiple types of survival events by selecting a proportion of cases for each type of event. We propose a general weighting scheme to analyze data. Furthermore, we examine the optimal choice of weights and show that this optimal weighting yields much improved efficiency gain both asymptotically and in simulation studies. Finally, we apply our proposed methods to data from the Atherosclerosis Risk in Communities study.
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Project description:IntroductionThe fractional concentration of exhaled nitric oxide (FeNO) is a biomarker of airway inflammation. Repeat FeNO maneuvers at multiple fixed exhalation flow rates (extended NO analysis) can be used to estimate parameters quantifying proximal and distal sources of NO in mathematical models of lower respiratory tract NO. A growing number of studies use extended NO analysis, but there is no official standard flow rate sampling protocol. In this paper, we provide information for study planning by deriving theoretically optimal flow rate sampling designs.MethodsFirst, we reviewed previously published designs. Then, under a nonlinear regression framework for estimating NO parameters in the steady-state two compartment model of NO, we identified unbiased optimal four flow rate designs (within the range of 10-400 ml s-1) using theoretical derivations and simulation studies. Optimality criteria included NO parameter standard errors (SEs). A simulation study was used to estimate sample sizes required to detect associations with NO parameters estimated from studies with different designs.ResultsMost designs (77%) were unbiased. NO parameter SEs were smaller for designs with: more target flows, more replicate maneuvers per target flow, and a larger range of target flows. High flows were most important for estimating alveolar NO concentration, while low flows were most important for the proximal NO parameters. The Southern California Children's Health Study design (30, 50, 100 and 300 ml s-1) had ≥1.8 fold larger SEs and required 1.1-3.2 fold more subjects to detect the association of a determinant with each NO parameter as compared to an optimal design of 10, 50, 100 and 400 ml s-1.ConclusionsThere is a class of reasonable flow rate sampling designs with good theoretical performance. In practice, designs should be selected to balance the tradeoffs between optimality and feasibility of the flow range and total number of maneuvers.
| S-EPMC5421556 | biostudies-literature
Project description:Two-phase sampling designs, including nested case-control and case-cohort designs, are frequently utilized in large cohort studies involving expensive biomarkers. To analyze data from two-phase designs with a binary outcome, parametric models such as logistic regression are often adopted. However, when the model assumptions are not valid, parametric models may lead to biased estimation and risk evaluation. In this paper, we propose a robust semiparametric regression model for binary outcomes and an easy-to-implement computational procedure that combines the pool-adjacent violators algorithm with inverse probability weighting. The asymptotic properties are established, including consistency and the convergence rate. Simulation studies show that the proposed method performs well and is more robust than logistic regression methods. We demonstrate the application of the proposed method to real data from the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial.
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Project description:ImportanceLow academic achievement (AA) in childhood and adolescence is associated with increased substance use. Empirical evidence, using longitudinal epidemiologic data, may provide support for interventions to improve AA as a means to reduce risk of drug abuse (DA).ObjectiveTo clarify the nature of the association between adolescent AA and risk of DA by using instrumental variable and co-relative analysis designs.Design, setting, and participantsThis study, assessing nationwide data from individuals born in Sweden between 1971 and 1982, used instrumental variable and co-relative analyses of the association between AA and DA. The instrument was month of birth. Co-relative analyses were conducted in pairs of cousins (263 222 pairs), full siblings (154 295), and monozygotic twins (1623) discordant for AA, with raw results fitted to a genetic model. The AA-DA association was modeled using Cox regression. Data analysis was conducted from October 2017 to January 2018.ExposuresAcademic achievement assessed at 16 years of age (for instrumental variable analyses), and estimated discordance in AA in pairs of monozygotic twins (for co-relative analyses).Main outcomes and measuresDrug abuse registration in national medical, criminal, or pharmacy registries.ResultsThis instrumental variable analysis included 934 462 participants (478 341 males and 456 121 females) with a mean (SD) age of 34.7 (4.3) years at a mean follow-up of 19 years. Earlier month of birth was associated with a linear effect on AA, with the regression coefficient per month equaling -0.0225 SDs (95% CI, -0.0231 to -0.0219). Controlling for AA, month of birth had no association with risk of DA (hazard ratio [HR], 1.000; 95% CI, 0.997-1.004). Lower AA had a significant association with risk of subsequent DA registration (HR per SD, 2.33; 95% CI, 2.30-2.35). Instrumental variable analysis produced a substantial but modestly attenuated association (HR, 2.04; 95% CI, 1.75-2.33). Controlling for modest associations between month of birth and parental educational status and DA risk reduced the association to a HR of 1.92 (95% CI, 1.67-2.22). The genetic model applied to the results of co-relative analyses fitted the observed data well and estimated the AA-DA association in monozygotic twins discordant for AA to equal a HR of 1.79 (95% CI, 1.64-1.92).Conclusions and relevanceTwo different methodological approaches with divergent assumptions both produced results consistent with the hypothesis that the significant association observed between AA at 16 years of age and risk of DA into middle adulthood may be causal. These results provide empirical support for efforts to improve AA as a means to reduce risk of DA.
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Project description:For genetic association studies in designs of unrelated individuals, current statistical methodology typically models the phenotype of interest as a function of the genotype and assumes a known statistical model for the phenotype. In the analysis of complex phenotypes, especially in the presence of ascertainment conditions, the specification of such model assumptions is not straight-forward and is error-prone, potentially causing misleading results.In this paper, we propose an alternative approach that treats the genotype as the random variable and conditions upon the phenotype. Thereby, the validity of the approach does not depend on the correctness of assumptions about the phenotypic model. Misspecification of the phenotypic model may lead to reduced statistical power. Theoretical derivations and simulation studies demonstrate both the validity and the advantages of the approach over existing methodology. In the COPDGene study (a GWAS for Chronic Obstructive Pulmonary Disease (COPD)), we apply the approach to a secondary, quantitative phenotype, the Fagerstrom nicotine dependence score, that is correlated with COPD affection status. The software package that implements this method is available.The flexibility of this approach enables the straight-forward application to quantitative phenotypes and binary traits in ascertained and unascertained samples. In addition to its robustness features, our method provides the platform for the construction of complex statistical models for longitudinal data, multivariate data, multi-marker tests, rare-variant analysis, and others.
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Project description:Case-cohort studies are increasingly used to quantify the association of novel factors with disease risk. Conventional measures of predictive ability need modification for this design. We show how Harrell's C-index, Royston's D, and the category-based and continuous versions of the net reclassification index (NRI) can be adapted.We simulated full cohort and case-cohort data, with sampling fractions ranging from 1% to 90%, using covariates from a cohort study of coronary heart disease, and two incidence rates. We then compared the accuracy and precision of the proposed risk prediction metrics.The C-index and D must be weighted in order to obtain unbiased results. The NRI does not need modification, provided that the relevant non-subcohort cases are excluded from the calculation. The empirical standard errors across simulations were consistent with analytical standard errors for the C-index and D but not for the NRI. Good relative efficiency of the prediction metrics was observed in our examples, provided the sampling fraction was above 40% for the C-index, 60% for D, or 30% for the NRI. Stata code is made available.Case-cohort designs can be used to provide unbiased estimates of the C-index, D measure and NRI.
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Project description:BackgroundThe rapid growth of high-throughput sequencing-based microbiome profiling has yielded tremendous insights into human health and physiology. Data generated from high-throughput sequencing of 16S rRNA gene amplicons are often preprocessed into composition or relative abundance. However, reproducibility has been lacking due to the myriad of different experimental and computational approaches taken in these studies. Microbiome studies may report varying results on the same topic, therefore, meta-analyses examining different microbiome studies to provide consistent and robust results are important. So far, there is still a lack of implemented methods to properly examine differential relative abundances of microbial taxonomies and to perform meta-analysis examining the heterogeneity and overall effects across microbiome studies.ResultsWe developed an R package 'metamicrobiomeR' that applies Generalized Additive Models for Location, Scale and Shape (GAMLSS) with a zero-inflated beta (BEZI) family (GAMLSS-BEZI) for analysis of microbiome relative abundance datasets. Both simulation studies and application to real microbiome data demonstrate that GAMLSS-BEZI well performs in testing differential relative abundances of microbial taxonomies. Importantly, the estimates from GAMLSS-BEZI are log (odds ratio) of relative abundances between comparison groups and thus are analogous between microbiome studies. As such, we also apply random effects meta-analysis models to pool estimates and their standard errors across microbiome studies. We demonstrate the meta-analysis examples and highlight the utility of our package on four studies comparing gut microbiomes between male and female infants in the first six months of life.ConclusionsGAMLSS-BEZI allows proper examination of microbiome relative abundance data. Random effects meta-analysis models can be directly applied to pool comparable estimates and their standard errors to evaluate the overall effects and heterogeneity across microbiome studies. The examples and workflow using our 'metamicrobiomeR' package are reproducible and applicable for the analyses and meta-analyses of other microbiome studies.
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Project description:Infants are therapeutic orphans. Many drugs used in infants are used "off-label", increasing the risk of drug toxicity and suboptimal efficacy in this vulnerable population. This knowledge gap in clinical pharmacology is partly attributed to challenges associated with conducting clinical trials in infants. Consequently, there is a need for novel and efficient study designs more suited to this population. Available literature describing the use of minimal-risk approaches to characterize the pharmacokinetics (PK) of drugs in infants was critically reviewed. Population PK approach with sparse sampling was found to be well established. Other, more recent alternatives, like dried blood spots sampling, opportunistic design, and the use of non-blood matrices are promising strategies with an increasing number of applications in infants. Physiologically based pharmacokinetic modeling provides valuable insight in drug disposition but still needs more prospective validation. Increasing experience with these methods provides understanding of their strengths and limitations and will help improve the design of future PK studies in infants.
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