Visualizing the (Causal) Effect of a Continuous Variable on a Time-To-Event Outcome

Visualization is a key aspect of communicating the results of any study aiming to estimate causal effects. In studies with time-to-event outcomes, the most popular visualization approach is depicting survival curves stratified by the variable of interest. This approach cannot be used when the variable of interest is continuous. Simple workarounds, such as categorizing the continuous covariate and plotting survival curves for each category, can result in misleading depictions of the main effects. Instead, we propose a new graphic, the survival area plot, to directly depict the survival probability over time and as a function of a continuous covariate simultaneously. This plot utilizes g-computation based on a suitable time-to-event model to obtain the relevant estimates. Through the use of g-computation, those estimates can be adjusted for confounding without additional effort, allowing a causal interpretation under the standard causal identifiability assumptions. If those assumptions are not met, the proposed plot may still be used to depict noncausal associations. We illustrate and compare the proposed graphics to simpler alternatives using data from a large German observational study investigating the effect of the Ankle Brachial Index on survival. To facilitate the usage of these plots, we additionally developed the contsurvplot R-package which includes all methods discussed in this paper.Comment: currently under review in "Epidemiology

Impact of Record-Linkage Errors in Covid-19 Vaccine-Safety Analyses using German Health-Care Data: A Simulation Study

With unprecedented speed, 192,248,678 doses of Covid-19 vaccines were administered in Germany by July 11, 2023 to combat the pandemic. Limitations of clinical trials imply that the safety profile of these vaccines is not fully known before marketing. However, routine health-care data can help address these issues. Despite the high proportion of insured people, the analysis of vaccination-related data is challenging in Germany. Generally, the Covid-19 vaccination status and other health-care data are stored in separate databases, without persistent and database-independent person identifiers. Error-prone record-linkage techniques must be used to merge these databases. Our aim was to quantify the impact of record-linkage errors on the power and bias of different analysis methods designed to assess Covid-19 vaccine safety when using German health-care data with a Monte-Carlo simulation study. We used a discrete-time simulation and empirical data to generate realistic data with varying amounts of record-linkage errors. Afterwards, we analysed this data using a Cox model and the self-controlled case series (SCCS) method. Realistic proportions of random linkage errors only had little effect on the power of either method. The SCCS method produced unbiased results even with a high percentage of linkage errors, while the Cox model underestimated the true effect

A Comparison of Different Methods to Adjust Survival Curves for Confounders

Treatment specific survival curves are an important tool to illustrate the treatment effect in studies with time-to-event outcomes. In non-randomized studies, unadjusted estimates can lead to biased depictions due to confounding. Multiple methods to adjust survival curves for confounders exist. However, it is currently unclear which method is the most appropriate in which situation. Our goal is to compare these methods in different scenarios with a focus on their bias and goodness-of-fit. We provide a short review of all methods and illustrate their usage by contrasting the survival of smokers and non-smokers, using data from the German Epidemiological Trial on Ankle Brachial Index. Subsequently, we compare the methods using a Monte-Carlo simulation. We consider scenarios in which correctly or incorrectly specified covariate sets for describing the treatment assignment and the time-to-event outcome are used with varying sample sizes. The bias and goodness-of-fit is determined by summary statistics which take into account the entire survival curve. When used properly, all methods showed no systematic bias in medium to large samples. Cox-Regression based methods, however, showed systematic bias in small samples. The goodness-of-fit varied greatly between different methods and scenarios. Methods utilizing an outcome model were more efficient than other techniques, while augmented estimators using an additional treatment assignment model were unbiased when either model was correct with a goodness-of-fit comparable to other methods. These "doubly-robust" methods have important advantages in every considered scenario. Pseudo-Value based methods, coupled with isotonic regression to correct for non-monotonicity, are viable alternatives to traditional methods.Comment: 26 pages, 5 figures, submitted to "Statistics in Medicine" as research article, accepted for oral presentation at the International Biometric Conference 202

Correction to: Amyloid-β misfolding as a plasma biomarker indicates risk for future clinical Alzheimer’s disease in individuals with subjective cognitive decline (Alzheimer's Research & Therapy, (2020), 12, 1, (169), 10.1186/s13195-020-00738-8): Amyloid-β misfolding as a plasma biomarker indicates risk for future clinical Alzheimer’s disease in individuals with subjective cognitive decline (Alzheimer's Research & Therapy, (2020), 12, 1, (169), 10.1186/s13195-020-00738-8)

Following publication of the original article [1], the authors noticed that the published figures have errors which was occurred during processing of the figures in production team. 1) fonts are shifted (Figure 1) 2) colors are not displayed (Figure 2, open circles should be colored) 3) labelling is incorrect (Figure 4, "Afl" should be "Aß", Supplementary Figures "D Absorbance" should be "? absorbance" (Δ = delta)) The correct Figures 1, 2 and 4 are shown below. The original article [1] has been updated. (Figure presented.)

Amyloid-β misfolding as a plasma biomarker indicates risk for future clinical Alzheimer’s disease in individuals with subjective cognitive decline

Background: We evaluated Aβ misfolding in combination with Aβ42/40 ratio as a prognostic tool for future clinical progression to mild cognitive impairment (MCI) or dementia due to Alzheimer’s disease (AD) in individuals with subjective cognitive decline (SCD). Methods: Baseline plasma samples (n = 203) from SCD subjects in the SCIENCe project and Amsterdam Dementia Cohort (age 61 ± 9 years; 57% male, mean follow-up time 2.7 years) were analyzed using immuno-infrared-sensor technology. Within 6 years of follow-up, 22 (11%) individuals progressed to MCI or dementia due to AD. Sensor readout values > 1646 cm− 1 reflected normal Aβ folding; readouts at ≤ 1646 cm− 1 reflected low and at < 1644 cm− 1 high misfolding. We used Cox proportional hazard models to quantify Aβ misfolding as a prognostic biomarker for progression to MCI and dementia due to AD. The accuracy of the predicted development of MCI/AD was determined by time-dependent receiver operating characteristic (t-ROC) curve analyses that take individual follow-up and conversion times into account. Statistical models were adjusted for age, sex, and APOEε4 status. Additionally, plasma Aβ42/40 data measured by SIMOA were statistically analyzed and compared. Results: All 22 patients who converted to MCI or AD-dementia within 6 years exhibited Aβ misfolding at baseline. Cox analyses revealed a hazard ratio (HR) of 19 (95% confidence interval [CI] 2.2–157.8) for future conversion of SCD subjects with high misfolding and of 11 (95% CI 1.0–110.1) for those with low misfolding. T-ROC curve analyses yielded an area under the curve (AUC) of 0.94 (95% CI 0.86–1.00; 6-year follow-up) for Aβ misfolding in an age, sex, and APOEε4 model. A similar model with plasma Aβ42/40 ratio yielded an AUC of 0.92 (95% CI, 0.82–1.00). The AUC increased to 0.99 (95% CI, 0.99–1.00) after inclusion of both Aβ misfolding and the Aβ42/40 ratio. Conclusions: A panel of structure- and concentration-based plasma amyloid biomarkers may predict conversion to clinical MCI and dementia due to AD in cognitively unimpaired subjects. These plasma biomarkers provide a noninvasive and cost-effective alternative for screening early AD pathological changes. Follow-up studies and external validation in larger cohorts are in progress for further validation of our findings

Amyloid-β\beta misfolding as a plasma biomarker indicates risk for future clinical Alzheimer's disease in individuals with subjective cognitive decline

$\bf Background$ We evaluated A$\beta$ misfolding in combination with A$\beta_{42/40}$ ratio as a prognostic tool for future clinical progression to mild cognitive impairment (MCI) or dementia due to Alzheimer's disease (AD) in individuals with subjective cognitive decline (SCD). $\bf Methods$ Baseline plasma samples ($\it n$ = 203) from SCD subjects in the SCIENCe project and Amsterdam Dementia Cohort (age 61 $\pm$ 9 years; 57% male, mean follow-up time 2.7 years) were analyzed using immuno-infrared-sensor technology. Within 6 years of follow-up, 22 (11%) individuals progressed to MCI or dementia due to AD. Sensor readout values > 1646 $cm^{− 1}$ reflected normal A$\beta$ folding; readouts at ≤ 1646 $cm^{− 1}$ reflected low and at < 1644 $cm^{− 1}$ high misfolding. We used Cox proportional hazard models to quantify Aβ$\beta$ misfolding as a prognostic biomarker for progression to MCI and dementia due to AD. The accuracy of the predicted development of MCI/AD was determined by time-dependent receiver operating characteristic (t-ROC) curve analyses that take individual follow-up and conversion times into account. Statistical models were adjusted for age, sex, and $APOE_{\epsilon}$4 status. Additionally, plasma A$\beta_{42/40}$ data measured by SIMOA were statistically analyzed and compared. $\bf Results$ All 22 patients who converted to MCI or AD-dementia within 6 years exhibited A$\beta$ misfolding at baseline. Cox analyses revealed a hazard ratio (HR) of 19 (95% confidence interval [CI] 2.2–157.8) for future conversion of SCD subjects with high misfolding and of 11 (95% CI 1.0–110.1) for those with low misfolding. T-ROC curve analyses yielded an area under the curve (AUC) of 0.94 (95% CI 0.86–1.00; 6-year follow-up) for A$\beta$ misfolding in an age, sex, and $APOE_{\epsilon}$4 model. A similar model with plasma A$\beta_{42/40}$ ratio yielded an AUC of 0.92 (95% CI, 0.82–1.00). The AUC increased to 0.99 (95% CI, 0.99–1.00) after inclusion of both A$\beta$ misfolding and the A$\beta_{42/40}$ ratio. $\bf Conclusions$ A panel of structure- and concentration-based plasma amyloid biomarkers may predict conversion to clinical MCI and dementia due to AD in cognitively unimpaired subjects. These plasma biomarkers provide a noninvasive and cost-effective alternative for screening early AD pathological changes. Follow-up studies and external validation in larger cohorts are in progress for further validation of our findings

Dancing amidst the flames: Imagination and self-organization in a minor key

Drawing from Deleuze and Guattari's (1986) formulation of the concept of a 'minor literature' and Nick Thoburn's extension of this into a 'minor politics' (2003a) this paper examines the relation between the workings of the imagination and forms of self-organization found within anticapitalist organizing of the Industrial Workers of the World and related movements. This paper explores the modulations of the social imaginary found within these particular examples as indicative of a more general process of minor composition. Rather than affirming an already existing and known subjective position (of the people, the workers), it will be argued that rather such campaigns have playfully and strategically redirected and appropriated the social energies found within pop culture to articulate their demands. Copyright © 2008 SAGE Publications