The prognostic value of the hamstring outcome score to predict the risk of hamstring injuries

OBJECTIVES: Hamstring injuries are common among soccer players. The hamstring outcome score (HaOS) might be useful to identify amateur players at risk of hamstring injury. Therefore the aims of this study were: To determine the association between the HaOS and prior and new hamstring injuries in amateur soccer players, and to determine the prognostic value of the HaOS for identifying players with or without previous hamstring injuries at risk of future injury. DESIGN: Cohort study. METHODS: HaOS scores and information about previous injuries were collected at baseline and new injuries were prospectively registered during a cluster-randomized controlled trial involving 400 amateur soccer players. Analysis of variance and t-tests were used to determine the association between the HaOS and previous and new hamstring injury, respectively. Logistic regression analysis indicated the prognostic value of the HaOS for predicting new hamstring injuries. RESULTS: Analysis of data of 356 players indicated that lower HaOS scores were associated with more previous hamstring injuries (F=17.4; p=0.000) and that players with lower HaOS scores sustained more new hamstring injuries (T=3.59, df=67.23, p=0.001). With a conventional HaOS score cut-off of 80%, logistic regression models yielded a probability of hamstring injuries of 11%, 18%, and 28% for players with 0,1, or 2 hamstring injuries in the previous season, respectively. CONCLUSIONS: The HaOS is associated with previous and future hamstring injury and might be a useful tool to provide players with insight into their risk of sustaining a new hamstring injury risk when used in combination with previous injuries

Prognostic factors for adverse outcomes in patients with COVID-19: a field-wide systematic review and meta-analysis

INTRODUCTION: The individual prognostic factors for COVID-19 are unclear. For this reason, we aimed to present a state-of-the-art systematic review and meta-analysis on the prognostic factors for adverse outcomes in COVID-19 patients. METHODS: We systematically reviewed PubMed from January 1, 2020 to July 26, 2020 to identify non-overlapping studies examining the association of any prognostic factor with any adverse outcome in patients with COVID-19. Random-effects meta-analysis was performed, and between-study heterogeneity was quantified using I2 metric. Presence of small-study effects was assessed by applying the Egger's regression test. RESULTS: We identified 428 eligible articles, which were used in a total of 263 meta-analyses examining the association of 91 unique prognostic factors with 11 outcomes. Angiotensin-converting enzyme inhibitors, obstructive sleep apnea, pharyngalgia, history of venous thromboembolism, sex, coronary heart disease, cancer, chronic liver disease, chronic obstructive pulmonary disease, dementia, any immunosuppressive medication, peripheral arterial disease, rheumatological disease and smoking were associated with at least one outcome and had >1000 events, p-value <0.005, I2 <50%, 95% prediction interval excluding the null value, and absence of small-study effects in the respective meta-analysis. The risk of bias assessment using the Quality In Prognosis Studies tool indicated high risk of bias in 302 of 428 articles for study participation, 389 articles for adjustment for other prognostic factors, and 396 articles for statistical analysis and reporting. CONCLUSIONS: Our findings could be used for prognostic model building and guide patients' selection for randomised clinical trials

Reflection on modern methods: five myths about measurement error in epidemiological research

Epidemiologists are often confronted with datasets to analyse which contain measurement error due to, for instance, mistaken data entries, inaccurate recordings and measurement instrument or procedural errors. If the effect of measurement error is misjudged, the data analyses are hampered and the validity of the study's inferences may be affected. In this paper, we describe five myths that contribute to misjudgments about measurement error, regarding expected structure, impact and solutions to mitigate the problems resulting from mismeasurements. The aim is to clarify these measurement error misconceptions. We show that the influence of measurement error in an epidemiological data analysis can play out in ways that go beyond simple heuristics, such as heuristics about whether or not to expect attenuation of the effect estimates. Whereas we encourage epidemiologists to deliberate about the structure and potential impact of measurement error in their analyses, we also recommend exercising restraint when making claims about the magnitude or even direction of effect of measurement error if not accompanied by statistical measurement error corrections or quantitative bias analysis. Suggestions for alleviating the problems or investigating the structure and magnitude of measurement error are given.Clinical epidemiolog

Comment on Williamson et al. (OpenSAFELY): The Table 2 Fallacy in a Study of COVID-19 Mortality Risk Factors

To the Editor: We write with respect to the recently published work by Williamson et al. “OpenSAFELY: factors associated with COVID-19 death in 17 million patients.” We have serious concerns about both the way these results are presented, and how they are likely to be interpreted. Our specific concerns revolve around whether the work is intended by the authors to estimate causal effects, or not—and how, regardless of their intent, it seems likely to us that their work will be interpreted as causal

A weighting method for simultaneous adjustment for confounding and joint exposure-outcome misclassifications

Joint misclassification of exposure and outcome variables can lead to considerable bias in epidemiological studies of causal exposure-outcome effects. In this paper, we present a new maximum likelihood based estimator for marginal causal effects that simultaneously adjusts for confounding and several forms of joint misclassification of the exposure and outcome variables. The proposed method relies on validation data for the construction of weights that account for both sources of bias. The weighting estimator, which is an extension of the outcome misclassification weighting estimator proposed by Gravel and Platt (Weighted estimation for confounded binary outcomes subject to misclassification. Stat Med 2018; 37: 425-436), is applied to reinfarction data. Simulation studies were carried out to study its finite sample properties and compare it with methods that do not account for confounding or misclassification. The new estimator showed favourable large sample properties in the simulations. Further research is needed to study the sensitivity of the proposed method and that of alternatives to violations of their assumptions. The implementation of the estimator is facilitated by a new R function (ipwm) in an existing R package (mecor).Clinical epidemiolog

Regression shrinkage methods for clinical prediction models do not guarantee improved performance: simulation study

When developing risk prediction models on datasets with limited sample size, shrinkage methods are recommended. Earlier studies showed that shrinkage results in better predictive performance on average. This simulation study aimed to investigate the variability of regression shrinkage on predictive performance for a binary outcome. We compared standard maximum likelihood with the following shrinkage methods: uniform shrinkage (likelihood-based and bootstrap-based), penalized maximum likelihood (ridge) methods, LASSO logistic regression, adaptive LASSO, and Firth's correction. In the simulation study, we varied the number of predictors and their strength, the correlation between predictors, the event rate of the outcome, and the events per variable. In terms of results, we focused on the calibration slope. The slope indicates whether risk predictions are too extreme (slope 1). The results can be summarized into three main findings. First, shrinkage improved calibration slopes on average. Second, the between-sample variability of calibration slopes was often increased relative to maximum likelihood. In contrast to other shrinkage approaches, Firth's correction had a small shrinkage effect but showed low variability. Third, the correlation between the estimated shrinkage and the optimal shrinkage to remove overfitting was typically negative, with Firth's correction as the exception. We conclude that, despite improved performance on average, shrinkage often worked poorly in individual datasets, in particular when it was most needed. The results imply that shrinkage methods do not solve problems associated with small sample size or low number of events per variable.Development and application of statistical models for medical scientific researc

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

Title, abstract, and keyword searching resulted in poor recovery of articles in systematic reviews of epidemiologic practice

Objective: Article full texts are often inaccessible via the standard search engines of biomedical literature, such as PubMed and Embase, which are commonly used for systematic reviews. Excluding the full-text bodies from a literature search may result in a small or selective subset of articles being included in the review because of the limited information that is available in only title, abstract, and keywords. This article describes a comparison of search strategies based on a systematic literature review of all articles published in 5 topranked epidemiology journals between 2000 and 2017. Study Design and Setting: Based on a text-mining approach, we studied how nine different methodological topics were mentioned across text fields (title, abstract, keywords, and text body). The following methodological topics were studied: propensity score methods, inverse probability weighting, marginal structural modeling, multiple imputation, Kaplan-Meier estimation, number needed to treat, measurement error, randomized controlled trial, and latent class analysis. Results: In total, 31,641 Hypertext Markup Language (HTML) files were downloaded from the journals' websites. For all methodological topics and journals, at most 50% of articles with a mention of a topic in the text body also mentioned the topic in the title, abstract, or keywords. For several topics, a gradual decrease over calendar time was observed of reporting in the title, abstract, or keywords. Conclusion: Literature searches based on title, abstract, and keywords alone may not be sufficiently sensitive for studies of epidemiological research practice. This study also illustrates the potential value of full-text literature searches, provided there is accessibility of fulltext bodies for literature searches. (C) 2020 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).Clinical epidemiolog