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Abstract Objective Patients with diffuse cutaneous systemic sclerosis (dcSSc) frequently show spontaneous improvement of skin fibrosis. Our aim was to examine whether an improvement in skin fibrosis predicts lower likelihood of visceral organ progression and better survival. Methods Patients from the European Scleroderma Trials and Research (EUSTAR) cohort with dcSSc, baseline modified Rodnan skin score (mRSS) ≥7, and valid mRSS at 12±3 months follow up were included. Regression/progression of skin fibrosis was defined as a decrease/increase in mRSS >5 points and ≥25% from baseline to follow up. The outcomes included progression of lung, renal, cardiac and gastrointestinal manifestations using consensus derived definitions and all-cause death. Regressive, stable and progressive patients were compared by univariate, Kaplan-Meier survival curve and Cox regression analysis. Results Of 1257 included patients, 883 (70.2%) were stable, 282 (22.4%) regressive, and 92 (7.3%) progressive. Regressive patients, adjusted for baseline mRSS, baseline immunosuppression, baseline FVC, and disease duration, showed a significantly lower probability of FVC decline ≥10% than progressive patients (p=0.00003), lower probability of all-cause mortality during follow up (p=0.035) compared to progressive patients. .Improvement of skin fibrosis was not associated with progression of other organ manifestations. Conclusion We found that regression of skin fibrosis is associated with a lower probability of lung progression and better survival at follow up. The link between the disease course of skin and lung fibrosis in SSc can help to better stratify patients in clinical practice and enrich for ILD progressive patients in clinical trials.

Objectives: We investigated the validity of claims of the healthy vaccinee effect (HVE) in COVID-vaccine studies by analyzing associations between all-cause mortality (ACM) and COVID-19 vaccination status. Methods: Approximately 2.2 million individual records from two Czech health insurance companies were retrospectively analyzed. Each age group was stratified according to the vaccination status (unvaccinated vs. individuals less than 4 weeks vs. more than 4 weeks from Doses 1, 2, 3, and 4 or more doses of vaccine). ACMs in these groups were computed and compared. Results: Consistently over datasets and age categories, ACM was substantially lower in the vaccinated than unvaccinated groups regardless of the presence or absence of a wave of COVID-19 deaths. Moreover, the ACMs in groups more than 4 weeks from Doses 1, 2, or 3 were consistently several times higher than in those less than 4 weeks from the respective dose. HVE appears to be the only plausible explanation for this, which is further corroborated by a created mathematical model. Conclusions: In view of the presence of HVE, the baseline difference in the frailty of vaccinated and unvaccinated populations in periods without COVID-19 must be taken into account when estimating COVID-19 vaccine effectiveness from observational data.

Abstract Background The validity of observational studies and their meta-analyses is contested. Here, we aimed to appraise thousands of meta-analyses of observational studies using a pre-specified set of quantitative criteria that assess the significance, amount, consistency, and bias of the evidence. We also aimed to compare results from meta-analyses of observational studies against meta-analyses of randomized controlled trials (RCTs) and Mendelian randomization (MR) studies. Methods We retrieved from PubMed (last update, November 19, 2020) umbrella reviews including meta-analyses of observational studies assessing putative risk or protective factors, regardless of the nature of the exposure and health outcome. We extracted information on 7 quantitative criteria that reflect the level of statistical support, the amount of data, the consistency across different studies, and hints pointing to potential bias. These criteria were level of statistical significance (pre-categorized according to 10−6, 0.001, and 0.05 p-value thresholds), sample size, statistical significance for the largest study, 95% prediction intervals, between-study heterogeneity, and the results of tests for small study effects and for excess significance. Results 3744 associations (in 57 umbrella reviews) assessed by a median number of 7 (interquartile range 4 to 11) observational studies were eligible. Most associations were statistically significant at P < 0.05 (61.1%, 2289/3744). Only 2.6% of associations had P < 10−6, ≥1000 cases (or ≥20,000 participants for continuous factors), P < 0.05 in the largest study, 95% prediction interval excluding the null, and no large between-study heterogeneity, small study effects, or excess significance. Across the 57 topics, large heterogeneity was observed in the proportion of associations fulfilling various quantitative criteria. The quantitative criteria were mostly independent from one another. Across 62 associations assessed in both RCTs and in observational studies, 37.1% had effect estimates in opposite directions and 43.5% had effect estimates differing beyond chance in the two designs. Across 94 comparisons assessed in both MR and observational studies, such discrepancies occurred in 30.8% and 54.7%, respectively. Conclusions Acknowledging that no gold-standard exists to judge whether an observational association is genuine, statistically significant results are common in observational studies, but they are rarely convincing or corroborated by randomized evidence.

Background: The validity of observational studies and their meta-analyses is contested. Here, we aimed to appraise thousands of meta-analyses of observational studies using a pre-specified set of quantitative criteria that assess the significance, amount, consistency, and bias of the evidence. We also aimed to compare results from meta-analyses of observational studies against meta-analyses of randomized controlled trials (RCTs) and Mendelian randomization (MR) studies. Methods: We retrieved from PubMed (last update, November 19, 2020) umbrella reviews including meta-analyses of observational studies assessing putative risk or protective factors, regardless of the nature of the exposure and health outcome. We extracted information on 7 quantitative criteria that reflect the level of statistical support, the amount of data, the consistency across different studies, and hints pointing to potential bias. These criteria were level of statistical significance (pre-categorized according to 10−6, 0.001, and 0.05 p-value thresholds), sample size, statistical significance for the largest study, 95% prediction intervals, between-study heterogeneity, and the results of tests for small study effects and for excess significance. Results: 3744 associations (in 57 umbrella reviews) assessed by a median number of 7 (interquartile range 4 to 11) observational studies were eligible. Most associations were statistically significant at P < 0.05 (61.1%, 2289/3744). Only 2.6% of associations had P < 10−6, ≥1000 cases (or ≥20,000 participants for continuous factors), P < 0.05 in the largest study, 95% prediction interval excluding the null, and no large between-study heterogeneity, small study effects, or excess significance. Across the 57 topics, large heterogeneity was observed in the proportion of associations fulfilling various quantitative criteria. The quantitative criteria were mostly independent from one another. Across 62 associations assessed in both RCTs and in observational studies, 37.1% had effect estimates in opposite directions and 43.5% had effect estimates differing beyond chance in the two designs. Across 94 comparisons assessed in both MR and observational studies, such discrepancies occurred in 30.8% and 54.7%, respectively. Conclusions: Acknowledging that no gold-standard exists to judge whether an observational association is genuine, statistically significant results are common in observational studies, but they are rarely convincing or corroborated by randomized evidence. [ABSTRACT FROM AUTHOR]

• Analysis based on two independent datasets, total population of approx. 2.2 million. • All-cause mortality (ACM) of vaccinated and unvaccinated against COVID-19. • ACM consistently much lower in freshly vaccinated groups even outside COVID waves. • Healthy vaccinee bias demonstrated on visualizations of the data. • This bias overestimates COVID-19 vaccine efficiency in observational studies. We investigated the validity of claims of the healthy vaccinee effect (HVE) in COVID-vaccine studies by analyzing associations between all-cause mortality (ACM) and COVID-19 vaccination status. Approximately 2.2 million individual records from two Czech health insurance companies were retrospectively analyzed. Each age group was stratified according to the vaccination status (unvaccinated vs. individuals less than 4 weeks vs. more than 4 weeks from Doses 1, 2, 3, and 4 or more doses of vaccine). ACMs in these groups were computed and compared. Consistently over datasets and age categories, ACM was substantially lower in the vaccinated than unvaccinated groups regardless of the presence or absence of a wave of COVID-19 deaths. Moreover, the ACMs in groups more than 4 weeks from Doses 1, 2, or 3 were consistently several times higher than in those less than 4 weeks from the respective dose. HVE appears to be the only plausible explanation for this, which is further corroborated by a created mathematical model. In view of the presence of HVE, the baseline difference in the frailty of vaccinated and unvaccinated populations in periods without COVID-19 must be taken into account when estimating COVID-19 vaccine effectiveness from observational data. [ABSTRACT FROM AUTHOR]

Abstract Background Analysis of competing risks is commonly achieved through a cause specific or a subdistribution framework using Cox or Fine & Gray models, respectively. The estimation of treatment effects in observational data is prone to unmeasured confounding which causes bias. There has been limited research into such biases in a competing risks framework. Methods We designed simulations to examine bias in the estimated treatment effect under Cox and Fine & Gray models with unmeasured confounding present. We varied the strength of the unmeasured confounding (i.e. the unmeasured variable’s effect on the probability of treatment and both outcome events) in different scenarios. Results In both the Cox and Fine & Gray models, correlation between the unmeasured confounder and the probability of treatment created biases in the same direction (upward/downward) as the effect of the unmeasured confounder on the event-of-interest. The association between correlation and bias is reversed if the unmeasured confounder affects the competing event. These effects are reversed for the bias on the treatment effect of the competing event and are amplified when there are uneven treatment arms. Conclusion The effect of unmeasured confounding on an event-of-interest or a competing event should not be overlooked in observational studies as strong correlations can lead to bias in treatment effect estimates and therefore cause inaccurate results to lead to false conclusions. This is true for cause specific perspective, but moreso for a subdistribution perspective. This can have ramifications if real-world treatment decisions rely on conclusions from these biased results. Graphical visualisation to aid in understanding the systems involved and potential confounders/events leading to sensitivity analyses that assumes unmeasured confounders exists should be performed to assess the robustness of results.

We present a robust generalization of the synthetic control method for comparative case studies. Like the classical method cf. Abadie and Gardeazabal (2003), we present an algorithm to estimate the unobservable counterfactual of a treatment unit. A distinguishing feature of our algorithm is that of de-noising the data matrix via singular value thresholding, which renders our approach robust in multiple facets: it automatically identifies a good subset of donors for the synthetic control, overcomes the challenges of missing data, and continues to work well in settings where covariate information may not be provided. We posit that the setting can be viewed as an instance of the Latent Variable Model and provide the first finite sample analysis (coupled with asymptotic results) for the estimation of the counterfactual. Our algorithm accurately imputes missing entries and filters corrupted observations in producing a consistent estimator of the underlying signal matrix, provided p = Ω (T-1+ζ) for some ζ > 0; here, p is the fraction of observed data and T is the time interval of interest. Under the same proportion of observations, we demonstrate that the mean-squared error in our counterfactual estimation scales as O(σ²=p + 1=√T), where σ² is the variance of the inherent noise. Additionally, we introduce a Bayesian framework to quantify the estimation uncertainty. Our experiments, using both synthetic and real-world datasets, demonstrate that our robust generalization yields an improvement over the classical synthetic control method. [ABSTRACT FROM AUTHOR]

Research on police decision-making and the use of discretion predominantly relies on official records or qualitative interview and participant observation data. Pioneered by Albert J. Reiss, Jr. in the 1960s, systematic social observation is a field research method simultaneously gathering quantitative and qualitative data in a natural setting. Data collection procedures are stipulated in advance reducing bias and allowing for scientific inference, replication, and measurable process outcomes as the observations recorded are independent of the observer. Although three large scale studies have been conducted in the United States to understand police behaviour, this 'gold standard' is used infrequently due to methodological challenges in an applied setting. The current research is the first sole observer and non-U.S. study conducted with a Canadian regional police service. Discussion of methodological innovation, challenges, and process are based on the experience of collecting data on 406 police-citizen encounters involving 568 citizens over 637 observational hours. To assist future researchers, the method and data collection using modified instruments as well as the challenges of resources, police cooperation, bias, reactivity, field role, and dynamics of conducting research as a female in an insular, male-dominated setting are explored. [ABSTRACT FROM AUTHOR]

This paper describes our general framework for the investigation of how human gestures can be used to facilitate the interaction and communication between humans and robots. Two studies were carried out to reveal which “naturally occurring” gestures can be observed in a scenario where users had to explain to a robot how to perform a home task. Both studies followed a within-subjects design: participants had to demonstrate how to lay a table to a robot using two different methods — utilizing only gestures or gestures and speech. The first study enabled the validation of the COGNIRON coding scheme for human gestures in Human–Robot Interaction (HRI). Based on the data collected in both studies, an annotated video corpus was produced and characteristics such as frequency and duration of the different gestural classes have been gathered to help capture requirements for the designers of HRI systems. The results from the first study regarding the frequencies of the gestural types suggest an interaction between the order of presentation of the two methods and the actual type of gestures produced. However, the analysis of the speech produced along with the gestures did not reveal differences due to ordering of the experimental conditions. The second study expands the issues addressed by the first study: we aimed at extending the role of the interaction partner (the robot) by introducing some positive acknowledgement of the participants’ activity. The results show no significant differences in the distribution of gestures (frequency and duration) between the two explanation methods, in contrast to the previous study. Implications for HRI are discussed focusing on issues relevant for the design of the robot’s communication skills to support the interaction loop with humans in home scenarios. [ABSTRACT FROM AUTHOR]