Instrumental Variables in Influenza Vaccination Studies:Mission Impossible?!

AbstractObjectivesUnobserved confounding has been suggested to explain the effect of influenza vaccination on mortality reported in several observational studies. An instrumental variable (IV) is strongly related to the exposure under study, but not directly or indirectly (through other variables) with the outcome. Theoretically, analyses using IVs to control for both observed and unobserved confounding may provide unbiased estimates of influenza vaccine effects. We assessed the usefulness of IV analysis in influenza vaccination studies.MethodsInformation on patients aged 65 years and older from the computerized Utrecht General Practitioner (GP) research database over seven influenza epidemic periods was pooled to estimate the association between influenza vaccination and all-cause mortality among community-dwelling elderly. Potential IVs included in the analysis were a history of gout, a history of orthopaedic morbidity, a history of antacid medication use, and GP-specific vaccination rates.ResultsUsing linear regression analyses, all possible IVs were associated with vaccination status: risk difference (RD) 7.8% (95% confidence interval [CI] 3.6%; 12.0%), RD 2.8% (95% CI 1.7%; 3.9%), RD 8.1% (95% CI 6.1%; 10.1%), and RD 100.0% (95% CI 89.0%; 111.0%) for gout, orthopaedic morbidity, antacid medication use, and GP-specific vaccination rates, respectively. Each potential IV, however, also appeared to be related to mortality through other observed confounding variables (notably age, sex, and comorbidity).ConclusionsThe potential IVs studied did not meet the necessary criteria, because they were (indirectly) associated with the outcome. These variables may, therefore, not be suited to assess unconfounded influenza vaccine effects through IV analysis

Impact of predictor measurement heterogeneity across settings on performance of prediction models: a measurement error perspective

It is widely acknowledged that the predictive performance of clinical prediction models should be studied in patients that were not part of the data in which the model was derived. Out-of-sample performance can be hampered when predictors are measured differently at derivation and external validation. This may occur, for instance, when predictors are measured using different measurement protocols or when tests are produced by different manufacturers. Although such heterogeneity in predictor measurement between deriviation and validation data is common, the impact on the out-of-sample performance is not well studied. Using analytical and simulation approaches, we examined out-of-sample performance of prediction models under various scenarios of heterogeneous predictor measurement. These scenarios were defined and clarified using an established taxonomy of measurement error models. The results of our simulations indicate that predictor measurement heterogeneity can induce miscalibration of prediction and affects discrimination and overall predictive accuracy, to extents that the prediction model may no longer be considered clinically useful. The measurement error taxonomy was found to be helpful in identifying and predicting effects of heterogeneous predictor measurements between settings of prediction model derivation and validation. Our work indicates that homogeneity of measurement strategies across settings is of paramount importance in prediction research.Comment: 32 pages, 4 figure

Estimating incidence of venous thromboembolism in COVID-19:Methodological considerations

Background Coagulation abnormalities and coagulopathy are recognized as consequences of severe acute respiratory syndrome coronavirus 2 infection and the resulting coronavirus disease 2019 (COVID-19). Specifically, venous thromboembolism (VTE) has been reported as a frequent complication. By May 27, 2021, at least 93 original studies and 25 meta-analyses investigating VTE incidence in patients with COVID-19 had been published, showing large heterogeneity in reported VTE incidence ranging from 0% to 85%. This large variation complicates interpretation of individual study results as well as comparisons across studies, for example, to investigate changes in incidence over time, compare subgroups, and perform meta-analyses. Objectives This study sets out to provide an overview of sources of heterogeneity in VTE incidence studies in patients with COVID-19, illustrated using examples. Methods The original studies of three meta-analyses were screened and a list of sources of heterogeneity that may explain observed heterogeneity across studies was composed. Results The sources of heterogeneity in VTE incidence were classified as clinical sources and methodologic sources. Clinical sources of heterogeneity include differences between studies regarding patient characteristics that affect baseline VTE risk and protocols used for VTE testing. Methodologic sources of heterogeneity include differences in VTE inclusion types, data quality, and the methods used for data analysis. Conclusions To appreciate reported estimates of VTE incidence in patients with COVID-19 in relation to its etiology, prevention, and treatment, researchers should unambiguously report about possible clinical and methodological sources of heterogeneity in those estimates. This article provides suggestions for that.Thrombosis and Hemostasi

Коректність задачі Коші для нескінченної системи нелінійних осциляторів, розміщених на двовимірній решітці

Стаття присвячена вивченню нескінченної системи диференціальних рівнянь, яка описує нескінченний ланцюг лінійно зв’язаних нелінійних осциляторів. Отримано результати про існування та єдиність локального та глобального розв’язків задачі Коші.The article deals with infinite systems of differential equations that describe infinite system of nonlinear oscillators on 2D–lattice. It is obtained results on existence and uniqueness of local and global solutions to the Cauchy problem

№ 121. Протокол допиту Володимира Чехівського від 17 липня 1929 р.

Background: The results from studies on adverse drug effects in electronic health care databases may vary due to multiple reasons, one of them being differences in (left and right) censoring mechanisms between databases. Such censoring mechanisms can be features of the database and are therefore hard to avoid by the researcher. Objectives: To assess the impact of left and right censoring on estimates of adverse effects of drugs. Methods: We used simulation studies to assess the impact of left and right censoring (differential or nondifferential) on bias of estimates of adverse drug effects. We studied three types of adverse drug effects: (1) a constant exposure effect; (2) a first-time exposure effect (e.g. anaphylactic reaction); and (3) a cumulative exposure effect. Effects were expressed as incidence rate ratios and estimated using Poisson regression. Results: Non-random censoring biased all three types of adverse drug effects. Random right censoring did not result in a bias. Random left-censoring resulted in an overestimation of the drug effect in case of a cumulative exposure effect and an underestimation of the drug effect in case of a first-time exposure effect. For example, when 50% of the observation time was left censored, the observed first-time exposure effect was RR 1.4 instead of the true RR 3.0 and a cumulative exposure effect of RR 1.15 per unit time exposure was observed instead of the true RR 1.1 per unit time exposure. The impact of censoring depended on exposure prevalence, outcome incidence, and duration of the time-interval that was censored. Conclusions: Censoring may differentially impact estimates of exposure effect in studies of constant, firsttime, and cumulative exposure effects. Researchers should be aware of this when combining data from multiple databases or when comparing drug effects across databases

Mecor: An R package for measurement error correction in linear regression models with a continuous outcome

Measurement error in a covariate or the outcome of regression models is common, but is often ignored, even though measurement error can lead to substantial bias in the estimated covariate-outcome association. While several texts on measurement error correction methods are available, these methods remain seldomly applied. To improve the use of measurement error correction methodology, we developed mecor, an R package that implements measurement error correction methods for regression models with a continuous outcome. Measurement error correction requires information about the measurement error model and its parameters. This information can be obtained from four types of studies, used to estimate the parameters of the measurement error model: an internal validation study, a replicates study, a calibration study and an external validation study. In the package mecor, regression calibration methods and a maximum likelihood method are implemented to correct for measurement error in a continuous covariate in regression analyses. Additionally, methods of moments methods are implemented to correct for measurement error in the continuous outcome in regression analyses. Variance estimation of the corrected estimators is provided in closed form and using the bootstrap

Tell me what you want, what you really really want: Estimands in observational pharmacoepidemiologic comparative effectiveness and safety studies

PURPOSE: Ideally, the objectives of a pharmacoepidemiologic comparative effectiveness or safety study should dictate its design and data analysis. This paper discusses how defining an estimand is instrumental to this process. METHODS: We applied the ICH-E9 (Statistical Principles for Clinical Trials) R1 addendum on estimands - which originally focused on randomized trials - to three examples of observational pharmacoepidemiologic comparative effectiveness and safety studies. Five key elements specify the estimand: the population, contrasted treatments, endpoint, intercurrent events, and population-level summary measure. RESULTS: Different estimands were defined for case studies representing three types of pharmacological treatments: (1) single-dose treatments using a case study about the effect of influenza vaccination versus no vaccination on mortality risk in an adult population of ≥60 years of age; (2) sustained-treatments using a case study about the effect of dipeptidyl peptidase 4 inhibitor versus glucagon-like peptide-1 agonist on hypoglycemia risk in treatment of uncontrolled diabetes; and (3) as needed treatments using a case study on the effect of nitroglycerin spray as-needed versus no nitroglycerin on syncope risk in treatment of stabile angina pectoris. CONCLUSIONS: The case studies illustrated that a seemingly clear research question can still be open to multiple interpretations. Defining an estimand ensures that the study targets a treatment effect that aligns with the treatment decision the study aims to inform. Estimand definitions further help to inform choices regarding study design and data-analysis and clarify how to interpret study findings

Selection of confounding variables should not be based on observed associations with exposure

In observational studies, selection of confounding variables for adjustment is often based on observed baseline incomparability. The aim of this study was to evaluate this selection strategy. We used clinical data on the effects of inhaled long-acting beta-agonist (LABA) use on the risk of mortality among patients with obstructive pulmonary disease to illustrate the impact of selection of confounding variables for adjustment based on baseline comparisons. Among 2,394 asthma and COPD patients included in the analyses, the LABA ever-users were considerably older than never-users, but cardiovascular co-morbidity was equally prevalent (19.9% vs. 19.9%). Adjustment for cardiovascular co-morbidity status did not affect the crude risk ratio (RR) for mortality: crude RR 1.19 (95% CI 0.93–1.51) versus RR 1.19 (95% CI 0.94–1.50) after adjustment for cardiovascular co-morbidity. However, after adjustment for age (RR 0.95, 95% CI 0.76–1.19), additional adjustment for cardiovascular co-morbidity status did affect the association between LABA use and mortality (RR 1.01, 95% CI 0.80–1.26). Confounding variables should not be discarded based on balanced distributions among exposure groups, because residual confounding due to the omission of confounding variables from the adjustment model can be relevant