Improving External Validity of Epidemiologic Cohort Analyses: A Kernel Weighting Approach.
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ABSTRACT:
SUBMITTER: Wang L
PROVIDER: S-EPMC7566586 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:: Regression modelling is one of the most widely utilized approaches in epidemiological analyses. It provides a method of identifying statistical associations, from which potential causal associations relevant to disease control may then be investigated. Multivariable regression - a single dependent variable (outcome, usually disease) with multiple independent variables (predictors) - has long been the standard model. Generalizing multivariable regression to multivariate regression - all variables potentially statistically dependent - offers a far richer modelling framework. Through a series of simple illustrative examples we compare and contrast these approaches. The technical methodology used to implement multivariate regression is well established - Bayesian network structure discovery - and while a relative newcomer to the epidemiological literature has a long history in computing science. Applications of multivariate analysis in epidemiological studies can provide a greater understanding of disease processes at the population level, leading to the design of better disease control and prevention programs.
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Project description:BackgroundThe external validity of randomised trials can be compromised when trial participants differ from real-world populations. In the Antenatal Late Preterm Steroids (ALPS) trial of antenatal corticosteroids at late preterm ages, participants had systematically younger gestational ages than those outside the trial setting. As risk of respiratory morbidity (the primary trial outcome) is higher at younger gestations, absolute benefits of corticosteroids calculated in the trial population may overestimate real-world treatment benefits.ObjectivesTo estimate the real-world absolute risk reduction and number-needed-to-treat (NNT) for antenatal corticosteroids at late preterm ages, accounting for gestational age differences between the ALPS and real-world populations.MethodsIndividual participant data from the ALPS trial (which recruited 2831 women with imminent preterm birth at 34+0 to 36+5 weeks') was appended to population-based data for 15,741 women admitted for delivery between 34+0 and 36+5 weeks' from British Columbia, Canada, 2000-2013. We used logistic regression to calculate inverse odds of sampling weights for each trial participant and re-estimated treatment effects of corticosteroids on neonatal respiratory morbidity in ALPS participants, weighted to reflect the gestational age distribution of the population-based (real-world) sample.ResultsThe real-world absolute risk reduction was estimated to be -2.2 (95% CI -4.6, 0.0) cases of respiratory morbidity per 100, compared with -2.8 (95% CI -5.3, -0.3) in original trial data. Corresponding NNTs were 46 in the real-world setting vs 35 in the trial. Our focus on absolute measures also highlighted that the benefits of antenatal corticosteroids may be meaningfully greater at 34 weeks vs. 36 weeks (e.g., risk reductions of -3.7 vs. -1.2 per 100 respectively).ConclusionsThe absolute risk reductions and NNTs associated with antenatal corticosteroid administration at late preterm ages estimated in our study may be more appropriate for patient counselling as they better reflect the anticipated benefits of treatment when used in a real-world situation.
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Project description:The compilation of comprehensive, worldwide epidemiologic data can inform hypotheses on cancer etiology and guide future drug development. These statistics are reported by a multitude of sources using varying methods; thus, compiling a complete database of these statistics is a challenge. To this end, this paper examined the usefulness of a novel, multisource approach-extracting data from the peer-reviewed literature, online reports, and query systems from cancer registries and health agencies and directly contacting cancer registry personnel-for building a comprehensive, multinational epidemiologic cancer database. The major B-cell malignancies were chosen as the cancer subtype to test this approach largely because their epidemiology has not been well characterized in the peer-reviewed literature. We found that a multisource approach yields a more comprehensive epidemiologic database than what would have been possible with the use of literature searches alone. In addition, our paper revealed that cancer registries vary considerably in their methodology, comprehensiveness, and ability to gather information on specific B-cell malignancy subtypes. Collectively, this paper demonstrates the feasibility and value of a multisource approach to gathering epidemiologic data.
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Project description:Similarity-based retrieval of Electronic Health Records (EHRs) from large clinical information systems provides physicians the evidence support in making diagnoses or referring examinations for the suspected cases. Clinical Terms in EHRs represent high-level conceptual information and the similarity measure established based on these terms reflects the chance of inter-patient disease co-occurrence. The assumption that clinical terms are equally relevant to a disease is unrealistic, reducing the prediction accuracy. Here we propose a term weighting approach supported by PubMed search engine to address this issue.We collected and studied 112 abdominal computed tomography imaging examination reports from four hospitals in Hong Kong. Clinical terms, which are the image findings related to hepatocellular carcinoma (HCC), were extracted from the reports. Through two systematic PubMed search methods, the generic and specific term weightings were established by estimating the conditional probabilities of clinical terms given HCC. Each report was characterized by an ontological feature vector and there were totally 6216 vector pairs. We optimized the modified direction cosine (mDC) with respect to a regularization constant embedded into the feature vector. Equal, generic and specific term weighting approaches were applied to measure the similarity of each pair and their performances for predicting inter-patient co-occurrence of HCC diagnoses were compared by using Receiver Operating Characteristics (ROC) analysis.The Areas under the curves (AUROCs) of similarity scores based on equal, generic and specific term weighting approaches were 0.735, 0.728 and 0.743 respectively (p?<?0.01). In comparison with equal term weighting, the performance was significantly improved by specific term weighting (p?<?0.01) but not by generic term weighting. The clinical terms "Dysplastic nodule", "nodule of liver" and "equal density (isodense) lesion" were found the top three image findings associated with HCC in PubMed.Our findings suggest that the optimized similarity measure with specific term weighting to EHRs can improve significantly the accuracy for predicting the inter-patient co-occurrence of diagnosis when compared with equal and generic term weighting approaches.
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Project description:BackgroundAutopsy studies have provided valuable insights into the pathophysiology of COVID-19. Controversies remain about whether the clinical presentation is due to direct organ damage by SARS-CoV-2 or secondary effects, such as overshooting immune response. SARS-CoV-2 detection in tissues by RT-qPCR and immunohistochemistry (IHC) or electron microscopy (EM) can help answer these questions, but a comprehensive evaluation of these applications is missing.MethodsWe assessed publications using IHC and EM for SARS-CoV-2 detection in autopsy tissues. We systematically evaluated commercially available antibodies against the SARS-CoV-2 proteins in cultured cell lines and COVID-19 autopsy tissues. In a multicentre study, we evaluated specificity, reproducibility, and inter-observer variability of SARS-CoV-2 IHC. We correlated RT-qPCR viral tissue loads with semiquantitative IHC scoring. We used qualitative and quantitative EM analyses to refine criteria for ultrastructural identification of SARS-CoV-2.FindingsPublications show high variability in detection and interpretation of SARS-CoV-2 abundance in autopsy tissues by IHC or EM. We show that IHC using antibodies against SARS-CoV-2 nucleocapsid yields the highest sensitivity and specificity. We found a positive correlation between presence of viral proteins by IHC and RT-qPCR-determined SARS-CoV-2 viral RNA load (N= 35; r=-0.83, p-value <0.0001). For EM, we refined criteria for virus identification and provide recommendations for optimized sampling and analysis. 135 of 144 publications misinterpret cellular structures as virus using EM or show only insufficient data. We provide publicly accessible digitized EM sections as a reference and for training purposes.InterpretationSince detection of SARS-CoV-2 in human autopsy tissues by IHC and EM is difficult and frequently incorrect, we propose criteria for a re-evaluation of available data and guidance for further investigations of direct organ effects by SARS-CoV-2.FundingGerman Federal Ministry of Health, German Federal Ministry of Education and Research, Berlin University Alliance, German Research Foundation, German Center for Infectious Research.
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Project description:BackgroundVariable selection is an important step in building a multivariate regression model for which several methods and statistical packages are available. A comprehensive approach for variable selection in complex multivariate regression analyses within HIV cohorts is explored by utilizing both epidemiological and biostatistical procedures.MethodsThree different methods for variable selection were illustrated in a study comparing survival time between subjects in the Department of Defense's National History Study and the Atlanta Veterans Affairs Medical Center's HIV Atlanta VA Cohort Study. The first two methods were stepwise selection procedures, based either on significance tests (Score test), or on information theory (Akaike Information Criterion), while the third method employed a Bayesian argument (Bayesian Model Averaging).ResultsAll three methods resulted in a similar parsimonious survival model. Three of the covariates previously used in the multivariate model were not included in the final model suggested by the three approaches. When comparing the parsimonious model to the previously published model, there was evidence of less variance in the main survival estimates.ConclusionsThe variable selection approaches considered in this study allowed building a model based on significance tests, on an information criterion, and on averaging models using their posterior probabilities. A parsimonious model that balanced these three approaches was found to provide a better fit than the previously reported model.
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Project description:BackgroundMany important applications in bioinformatics, including sequence alignment and protein family profiling, employ sequence weighting schemes to mitigate the effects of non-independence of homologous sequences and under- or over-representation of certain taxa in a dataset. These schemes aim to assign high weights to sequences that are 'novel' compared to the others in the same dataset, and low weights to sequences that are over-represented.ResultsWe formalise this principle by rigorously defining the evolutionary 'novelty' of a sequence within an alignment. This results in new sequence weights that we call 'phylogenetic novelty scores'. These scores have various desirable properties, and we showcase their use by considering, as an example application, the inference of character frequencies at an alignment column-important, for example, in protein family profiling. We give computationally efficient algorithms for calculating our scores and, using simulations, show that they are versatile and can improve the accuracy of character frequency estimation compared to existing sequence weighting schemes.ConclusionsOur phylogenetic novelty scores can be useful when an evolutionarily meaningful system for adjusting for uneven taxon sampling is desired. They have numerous possible applications, including estimation of evolutionary conservation scores and sequence logos, identification of targets in conservation biology, and improving and measuring sequence alignment accuracy.
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Project description:BackgroundChromatin immunoprecipitation followed by high throughput sequencing (ChIP-seq), initially introduced more than a decade ago, is widely used by the scientific community to detect protein/DNA binding and histone modifications across the genome. Every experiment is prone to noise and bias, and ChIP-seq experiments are no exception. To alleviate bias, the incorporation of control datasets in ChIP-seq analysis is an essential step. The controls are used to account for the background signal, while the remainder of the ChIP-seq signal captures true binding or histone modification. However, a recurrent issue is different types of bias in different ChIP-seq experiments. Depending on which controls are used, different aspects of ChIP-seq bias are better or worse accounted for, and peak calling can produce different results for the same ChIP-seq experiment. Consequently, generating "smart" controls, which model the non-signal effect for a specific ChIP-seq experiment, could enhance contrast and increase the reliability and reproducibility of the results.ResultWe propose a peak calling algorithm, Weighted Analysis of ChIP-seq (WACS), which is an extension of the well-known peak caller MACS2. There are two main steps in WACS: First, weights are estimated for each control using non-negative least squares regression. The goal is to customize controls to model the noise distribution for each ChIP-seq experiment. This is then followed by peak calling. We demonstrate that WACS significantly outperforms MACS2 and AIControl, another recent algorithm for generating smart controls, in the detection of enriched regions along the genome, in terms of motif enrichment and reproducibility analyses.ConclusionsThis ultimately improves our understanding of ChIP-seq controls and their biases, and shows that WACS results in a better approximation of the noise distribution in controls.
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Project description: Not available
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Project description:This article presents data on the external validity of an alcohol administration study of sexual decision-making in men who have sex with men (MSM) ages 21-50. Men (N = 135) randomized to alcohol (blood alcohol concentration [BAC] = .075%) or water control conditions reported intentions to engage in condomless anal intercourse (CAI) in response to video vignettes. Following the experiment participants provided 6 weeks of experience sampling method (ESM) data assessing intoxication, sexual arousal, partner relationship, and sexual behavior. Laboratory CAI intentions were hypothesized to predict future CAI behavior, and associations were hypothesized to be conditional upon sexual arousal and intoxication contextual factors as well as laboratory beverage condition. The hypotheses were partially supported. CAI intentions were correlated with subject proportions of days engaging in CAI (r = .29). A multilevel analysis indicated, on average, CAI intention predicted increased probability of CAI versus anal intercourse with a condom (relative risk ratio [RRR] = 1.43). There was mixed evidence of CAI intentions effects being conditional upon laboratory condition as well as arousal and intoxication contextual factors. Graphs of conditional marginal effects identified regions of significance. Effects of CAI intention for men in the alcohol condition on the CAI versus No Sex contrast were significant when sexual arousal was elevated. CAI intentions for men in the water control condition predicted a higher probability of CAI versus anal intercourse with a condom when intoxication was moderately elevated and/or arousal moderately low. The results support the external validity of alcohol administration experiments of sexual decision-making among MSM and, reciprocally, provide support for the validity of ESM assessment of sexual behavior. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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