Correction methods for measurement error in epidemiologic research

Measurement error is common in epidemiologic research and may affect the validity of research results. It is therefore important to scrutinise the effects of measurement error in epidemiologic research. Even simple forms of measurement error, for instance random measurement error in an exposure, can introduce bias in exposure-outcome associations. And even though there are situations in which measurement error does not introduce bias in the exposure-outcome association, for instance in case of random measurement error in a continuous outcome, it nearly always affects the precision and power of a study. In addition, other forms of measurement error, for example systematic measurement error or differential measurement error in an exposure, covariate or outcome, can affect exposure-outcome associations in complex ways that may not easily be anticipated. Adjusting for measurement error using measurement error correction methods may thus be necessary to obtain reliable estimates of exposure-outcome associations.To facilitate measurement error correction, information about the underlying measurement error mechanism (i.e., model) and its parameters is needed. The measurement error model can sometimes be estimated from internal or external validation data, replicates data or calibration data. Collection and the use of such measurement error mechanism data will likely improve the quality of epidemiologic analyses in the presence of measurement error. This can be done through the application of measurement error correction methods, which adjust the analyses taking into account the information from the measurement error mechanism. Alternatively, in the absence of concrete data about the mechanisms or the parameters of measurement error, sensitivity analysis for measurement error can be used, in which the impact on the epidemiologic analyses of one or a range of hypothesised measurement error mechanisms or their parameters can be investigated. The studies described in the thesis were set out to improve the understanding of the impact of measurement error, to facilitate the application of measurement error correction methods, to improve the design of epidemiologic studies when measurement error in a variable is suspected and, to develop tools to quantitatively assess the impact of measurement error in epidemiologic research.LUMC / Geneeskund

Comparison of breast-conserving therapy with mastectomy for treatment of early breast cancer in community hospitals

Although the results of clinical trials support breast-conserving therapy as a replacement for mastectomy in early breast cancer, the question remains,whether these results apply in routine clinical practice. In the present analysis the breast cancer-specific survival and recurrence-free survival of 464 consecutive patients with breast tumours ≤ 3 cm across undergoing breast-conserving therapy were compared with a group of 459 patients with similar extent of disease and period of diagnosis undergoing mastectomy. All patients were treated in community hospitals in the south-eastern Netherlands. Median follow-up of both treatment groups was 6.2 years. After adjustment for the prognostic effects of age, tumour size, axillary nodal status and adjuvant systemic therapy, neither breast cancer-specific survival nor recurrence-free survival differed significantly between the breast-conserving therapy group and the mastectomy group. This finding indicates that in routine clinical practice breast-conserving therapy may be as effective as mastectomy

Approaches to addressing missing values, measurement error, and confounding in epidemiologic studies

Objectives: Epidemiologic studies often suffer from incomplete data, measurement error (or misclassification), and confounding. Each of these can cause bias and imprecision in estimates of exposure-outcome relations. We describe and compare statistical approaches that aim to control all three sources of bias simultaneously.Study Design and Setting: We illustrate four statistical approaches that address all three sources of bias, namely, multiple imputation for missing data and measurement error, multiple imputation combined with regression calibration, full information maximum likelihood within a structural equation modeling framework, and a Bayesian model. In a simulation study, we assess the performance of the four approaches compared with more commonly used approaches that do not account for measurement error, missing values, or confounding.Results: The results demonstrate that the four approaches consistently outperform the alternative approaches on all performance metrics (bias, mean squared error, and confidence interval coverage). Even in simulated data of 100 subjects, these approaches perform well.Conclusion: There can be a large benefit of addressing measurement error, missing values, and confounding to improve the estimation of exposure-outcome relations, even when the available sample size is relatively small. (C) 2020 The Authors. Published by Elsevier Inc.Clinical epidemiolog

Sampling strategies for internal validation samples for exposure measurement-error correction: a study of visceral adipose tissue measures replaced by waist circumference measures

Statistical correction for measurement error in epidemiologic studies is possible, provided that information about the measurement error model and its parameters are available. Such information is commonly obtained from a randomly sampled internal validation sample. It is however unknown whether randomly sampling the internal validation sample is the optimal sampling strategy. We conducted a simulation study to investigate various internal validation sampling strategies in conjunction with regression calibration. Our simulation study showed that for an internal validation study sample of 40% of the main study's sample size, stratified random and extremes sampling had a small efficiency gain over random sampling (10% and 12% decrease on average over all scenarios, respectively). The efficiency gain was more pronounced in smaller validation samples of 10% of the main study's sample size (i.e., a 31% and 36% decrease on average over all scenarios, for stratified random and extremes sampling, respectively). To mitigate the bias due to measurement error in epidemiologic studies, small efficiency gains can be achieved for internal validation sampling strategies other than random, but only when measurement error is nondifferential. For regression calibration, the gain in efficiency is, however, at the cost of a higher percentage bias and lower coverage.Clinical epidemiolog

Patchiness of ion-exchanged mica revealed by DNA binding dynamics at short length scales

The binding of double-stranded (ds) DNA to mica can be controlled through ion-exchanging the mica with divalent cations. Measurements of the end-to-end distance of linear DNA molecules discriminate whether the binding mechanism occurs through 2D surface equilibration or kinetic trapping. A range of linear dsDNA fragments have been used to investigate length dependences of binding. Mica, ion-exchanged with Ni(II) usually gives rise to kinetically trapped DNA molecules, however, short linear fragments (<800 bp) are seen to deviate from the expected behaviour. This indicates that ion-exchanged mica is heterogeneous, and contains patches or domains, separating different ionic species. These results correlate with imaging of dsDNA under aqueous buffer on Ni(II)-mica and indicate that binding domains are of the order of 100 nm in diameter. Shorter DNA fragments behave intermediate to the two extreme cases of 2D equilibration and kinetic trapping. Increasing the incubation time of Ni(II) on mica, from minutes to hours, brings the conformations of the shorter DNA fragments closer to the theoretical value for kinetic trapping, indicating that long timescale kinetics play a role in ion-exchange. X-ray photoelectron spectroscopy (XPS) was used to confirm that the relative abundance of Ni(II) ions on the mica surface increases with time. These findings can be used to enhance spatial control of binding of DNA to inorganic surfaces with a view to patterning high densities arrays

Maternal Iodine Status and Associations with Birth Outcomes in Three Major Cities in the United Kingdom

Severe iodine deficiency in mothers is known to impair foetal development. Pregnant women in the UK may be iodine insufficient, but recent assessments of iodine status are limited. This study assessed maternal urinary iodine concentrations (UIC) and birth outcomes in three UK cities. Spot urines were collected from 541 women in London, Manchester and Leeds from 2004–2008 as part of the Screening for Pregnancy End points (SCOPE) study. UIC at 15 and 20 weeks’ gestation was estimated using inductively coupled plasma-mass spectrometry (ICP-MS). Associations were estimated between iodine status (UIC and iodine-to-creatinine ratio) and birth weight, birth weight centile (primary outcome), small for gestational age (SGA) and spontaneous preterm birth. Median UIC was highest in Manchester (139 μg/L, 95% confidence intervals (CI): 126, 158) and London (130 μg/L, 95% CI: 114, 177) and lowest in Leeds (116 μg/L, 95% CI: 99, 135), but the proportion with UIC <50 µg/L was <20% in all three cities. No evidence of an association was observed between UIC and birth weight centile (−0.2% per 50 μg/L increase in UIC, 95% CI: −1.3, 0.8), nor with odds of spontaneous preterm birth (odds ratio = 1.00, 95% CI: 0.84, 1.20). Given the finding of iodine concentrations being insufficient according to World Health Organization (WHO) guidelines amongst pregnant women across all three cities, further studies may be needed to explore implications for maternal thyroid function and longer-term child health outcomes

Improved breast cancer survival following introduction of an organized mammography screening program among both screened and unscreened women: a population-based cohort study

Introduction: Mammography screening reduces breast cancer mortality through earlier diagnosis but may convey further benefit if screening is associated with optimized treatment through multidisciplinary medical care. In Norway, a national mammography screening program was introduced among women aged 50 to 69 years during 1995/6 to 2004. Also during this time, multidisciplinary breast cancer care units were implemented. Methods: We constructed three cohorts of breast cancer patients: 1) the pre-program group comprising women diagnosed and treated before mammography screening began in their county of residence, 2) the post-program group comprising women diagnosed and treated through multidisciplinary breast cancer care units in their county but before they had been invited to mammography screening; and 3) the screening group comprising women diagnosed and treated after invitation to screening. We calculated Kaplan-Meier plots and multivariable Cox proportional hazard models. Results: We studied 41,833 women with breast cancer. The nine-year breast cancer-specific survival rate was 0.66 (95%CI: 0.65 to 0.67) in the pre-program group; 0.72 (95%CI: 0.70 to 0.74) in the post-program group; and 0.84 (95%CI: 0.80 to 0.88) in the screening group. In multivariable analyses, the risk of death from breast cancer was 14% lower in the post-program group than in the pre-program group (hazard ratio 0.86; (95%CI: 0.78 to 0.95, P = 0.003)). Conclusions: After nine years follow-up, at least 33% of the improved survival is attributable to improved breast cancer management through multidisciplinary medical care

Prior infection with SARS-CoV-2 boosts and broadens Ad26.COV2.S immunogenicity in a variant-dependent manner

The Johnson and Johnson Ad26.COV2.S single-dose vaccine represents an attractive option for coronavirus disease 2019 (COVID-19) vaccination in countries with limited resources. We examined the effect of prior infection with different SARS-CoV-2 variants on Ad26.COV2.S immunogenicity. We compared participants who were SARS-CoV-2 naive with those either infected with the ancestral D614G virus or infected in the second wave when Beta predominated. Prior infection significantly boosts spike-binding antibodies, antibody-dependent cellular cytotoxicity, and neutralizing antibodies against D614G, Beta, and Delta; however, neutralization cross-reactivity varied by wave. Robust CD4 and CD8 T cell responses are induced after vaccination, regardless of prior infection. T cell recognition of variants is largely preserved, apart from some reduction in CD8 recognition of Delta. Thus, Ad26.COV2.S vaccination after infection could result in enhanced protection against COVID-19. The impact of the infecting variant on neutralization breadth after vaccination has implications for the design of second-generation vaccines based on variants of concern

Outcomes of laboratory-confirmed SARS-CoV-2 infection during resurgence driven by Omicron lineages BA.4 and BA.5 compared with previous waves in the Western Cape Province, South Africa

OBJECTIVE: We aimed to compare clinical severity of Omicron BA.4/BA.5 infection with BA.1 and earlier variant infections among laboratory-confirmed SARS-CoV-2 cases in the Western Cape, South Africa, using timing of infection to infer the lineage/variant causing infection. METHODS: We included public sector patients aged ≥20 years with laboratory-confirmed COVID-19 between 1-21 May 2022 (BA.4/BA.5 wave) and equivalent prior wave periods. We compared the risk between waves of (i) death and (ii) severe hospitalization/death (all within 21 days of diagnosis) using Cox regression adjusted for demographics, comorbidities, admission pressure, vaccination and prior infection. RESULTS: Among 3,793 patients from the BA.4/BA.5 wave and 190,836 patients from previous waves the risk of severe hospitalization/death was similar in the BA.4/BA.5 and BA.1 waves (adjusted hazard ratio (aHR) 1.12; 95% confidence interval (CI) 0.93; 1.34). Both Omicron waves had lower risk of severe outcomes than previous waves. Prior infection (aHR 0.29, 95% CI 0.24; 0.36) and vaccination (aHR 0.17; 95% CI 0.07; 0.40 for at least 3 doses vs. no vaccine) were protective. CONCLUSION: Disease severity was similar amongst diagnosed COVID-19 cases in the BA.4/BA.5 and BA.1 periods in the context of growing immunity against SARS-CoV-2 due to prior infection and vaccination, both of which were strongly protective