Dataset Information

Improving External Validity of Epidemiologic Cohort Analyses: A Kernel Weighting Approach.

ABSTRACT: For various reasons, cohort studies generally forgo probability sampling required to obtain population representative samples. However, such cohorts lack population-representativeness, which invalidates estimates of population prevalences for novel health factors only available in cohorts. To improve external validity of estimates from cohorts, we propose a kernel weighting (KW) approach that uses survey data as a reference to create pseudo-weights for cohorts. A jackknife variance is proposed for the KW estimates. In simulations, the KW method outperformed two existing propensity-score-based weighting methods in mean-squared error while maintaining confidence interval coverage. We applied all methods to estimating US population mortality and prevalences of various diseases from the non-representative US NIH-AARP cohort, using the sample from US-representative National Health Interview Survey (NHIS) as the reference. Assuming that the NHIS estimates are correct, the KW approach yielded generally less biased estimates compared to the existing propensity-score-based weighting methods.

SUBMITTER: Wang L

PROVIDER: S-EPMC7566586 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Publications

Improving External Validity of Epidemiologic Cohort Analyses: A Kernel Weighting Approach.

Wang Lingxiao L Graubard Barry I BI Katki Hormuzd A HA Li Yan Y

Journal of the Royal Statistical Society. Series A, (Statistics in Society) 20200425 3

For various reasons, cohort studies generally forgo probability sampling required to obtain population representative samples. However, such cohorts lack population-representativeness, which invalidates estimates of population prevalences for novel health factors only available in cohorts. To improve external validity of estimates from cohorts, we propose a kernel weighting (KW) approach that uses survey data as a reference to create pseudo-weights for cohorts. A jackknife variance is proposed f ...[more]

PMID: 33071484

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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.

Project description:BackgroundSimilarity-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.MethodsWe 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.ResultsThe 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.ConclusionsOur 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.

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.

Dataset Information

Improving External Validity of Epidemiologic Cohort Analyses: A Kernel Weighting Approach.

Publications

Improving External Validity of Epidemiologic Cohort Analyses: A Kernel Weighting Approach.

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