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Background: Regarding the analysis of RCT data there is a debate going on whether an adjustment for the baseline value of the outcome variable should be made. When an adjustment is made, there is a lot of misunderstanding regarding the way this should be done. Therefore, the aims of this educational paper are: 1) to explain different methods used to estimate treatment effects in RCTs, 2) to illustrate the different methods with a real life example and 3) to give an advise on how to analyse RCT data. Methods: Longitudinal analysis of covariance, repeated measures analysis in which also the baseline value is used as outcome and the analysis of changes were theoretically explained and applied to an example dataset investigating a systolic blood pressure lowering treatment. Results: It was shown that differences at baseline should be taken into account and that regular repeated measures analysis and regular analysis of changes did not adjust for the baseline differences between the groups and therefore lead to biased estimates of the treatment effect. In the real life example, due to the differences at baseline between the treatment and control group, the different methods lead to different estimates of the treatment effect. Conclusion: Regarding the analysis of RCT data, it is advised to use longitudinal analysis of covariance or a repeated measures analysis without the treatment variable, but with the interaction between treatment and time in the model. Keywords: Randomised controlled trials, Longitudinal of covariance, Repeated measures, Analysis of changes, Regression to the mean

Purpose: Osteoarthritis (OA) has been shown to be associated with decreased physical function, which may impact upon a person's self-rated health (SRH). Only a few studies have examined the association between OA and SRH in the general population, but to date none have used a clinical definition of OA. The objectives are: (1) To examine the cross-sectional association between clinical OA and fair-to-poor SRH in the general population; (2) To examine whether this association differs between countries; (3) To examine whether physical function is a mediator in the association between clinical OA and SRH.Methods: Baseline data of the European Project on OSteoArthritis (EPOSA) were used, which includes pre-harmonized data from six European cohort studies (n = 2709). Clinical OA was defined according to the American College of Rheumatology criteria. SRH was assessed using one question: How is your health in general? Physical function was assessed using the Western Ontario and McMaster Universities OA Index and Australian/Canadian OA Hand Index.Results: The prevalence of fair-to-poor SRH ranged from 19.8 % in the United Kingdom to 63.5 % in Italy. Although country differences in the strength of the associations were observed, clinical OA of the hip, knee and hand were significantly associated with fair-to-poor SRH in five out of six European countries. In most countries and at most sites, the association between clinical OA and fair-to-poor SRH was partly or fully mediated by physical function.Conclusions: Clinical OA at different sites was related to fair-to-poor SRH in the general population. Most associations were (partly) mediated by physical functioning, indicating that deteriorating physical function in patients with OA should be a point of attention in patient care. [ABSTRACT FROM AUTHOR]

Objectives: To investigate the contribution of behavioural, social and psychological factors to inequalities in mortality by educational level between birth cohorts., Design: Cohort-sequential design., Setting: Two population-based studies in the Netherlands: the Longitudinal Aging Study Amsterdam (LASA) and the Doetinchem Cohort Study (DCS)., Participants: Data from the LASA included 1990 individuals with birth years 1928-1937 (cohort 1) and 1938-1947 (cohort 2) and, for replication, data from the DCS included 2732 individuals with birth years 1929-1941 (cohort 1) and 1939-1951 (cohort 2)., Methods: Years of education, 15-year mortality, lifestyle factors, social factors and psychological factors were modelled using multiple-group accelerated failure time models based on structural equation modelling to compare indirect effects between cohorts., Results: Both studies showed similar educational inequalities, with higher mortality among those with lower education. The indirect effects of education via smoking (LASA: difference in survival time ratio (TR)=1.0018, 95% CI 1.0000 to 1.0155, DCS: TR=1.0051, 95% CI 1.0000 to 1.0183), physical activity (LASA: TR=1.0056, 95% CI 1.00009 to 1.0132) and alcohol use (LASA: TR=1.0275, 95% CI 1.0033 to 1.0194) on mortality were stronger in cohort 2 than in cohort 1. In contrast to the other effects, alcohol use was the only factor that was associated positively with education and survival time, which effect increased in the most recent cohort. Emotional support, network size and cognitive functioning showed no difference between cohorts., Conclusions: Smoking, physical activity and alcohol use contributed more to educational inequalities in mortality in recent cohorts. Hence, in addition to tackling fundamental social causes of inequality, policies focusing on intermediary mechanisms such as lifestyle need to adapt their targets to those that prove to be most important within a given time frame., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Importance: Mediation analyses of randomized trials and observational studies can generate evidence about the mechanisms by which interventions and exposures may influence health outcomes. Publications of mediation analyses are increasing, but the quality of their reporting is suboptimal., Objective: To develop international, consensus-based guidance for the reporting of mediation analyses of randomized trials and observational studies (A Guideline for Reporting Mediation Analyses; AGReMA)., Design, Setting, and Participants: The AGReMA statement was developed using the Enhancing Quality and Transparency of Health Research (EQUATOR) methodological framework for developing reporting guidelines. The guideline development process included (1) an overview of systematic reviews to assess the need for a reporting guideline; (2) review of systematic reviews of relevant evidence on reporting mediation analyses; (3) conducting a Delphi survey with panel members that included methodologists, statisticians, clinical trialists, epidemiologists, psychologists, applied clinical researchers, clinicians, implementation scientists, evidence synthesis experts, representatives from the EQUATOR Network, and journal editors (n = 19; June-November 2019); (4) having a consensus meeting (n = 15; April 28-29, 2020); and (5) conducting a 4-week external review and pilot test that included methodologists and potential users of AGReMA (n = 21; November 2020)., Results: A previously reported overview of 54 systematic reviews of mediation studies demonstrated the need for a reporting guideline. Thirty-three potential reporting items were identified from 3 systematic reviews of mediation studies. Over 3 rounds, the Delphi panelists ranked the importance of these items, provided 60 qualitative comments for item refinement and prioritization, and suggested new items for consideration. All items were reviewed during a 2-day consensus meeting and participants agreed on a 25-item AGReMA statement for studies in which mediation analyses are the primary focus and a 9-item short-form AGReMA statement for studies in which mediation analyses are a secondary focus. These checklists were externally reviewed and pilot tested by 21 expert methodologists and potential users, which led to minor adjustments and consolidation of the checklists., Conclusions and Relevance: The AGReMA statement provides recommendations for reporting primary and secondary mediation analyses of randomized trials and observational studies. Improved reporting of studies that use mediation analyses could facilitate peer review and help produce publications that are complete, accurate, transparent, and reproducible.

Objective: Several studies have found an increased fall risk in persons with osteoarthritis (OA). However, most prospective studies did not use a clinical definition of OA. In addition, it is not clear which factors explain this risk. Our objectives were: (1) to confirm the prospective association between clinical OA of the hip and knee and falls; (2) to examine the modifying effect of sex; and (3) to examine whether low physical performance, low physical activity and use of pain medication are mediating these relationships., Methods: Baseline and 1-year follow-up data from the European Project on OSteoArthritis (EPOSA) were used involving pre-harmonized data from five European population-based cohort studies (ages 65-85, n = 2535). Clinical OA was defined according to American College of Rheumatology (ACR) criteria. Falls were assessed using self-report., Results: Over the follow-up period, 27.7% of the participants fell once or more (defined as faller), and 9.8% fell twice or more (recurrent faller). After adjustment for confounding, clinical knee OA was associated with the risk of becoming a recurrent faller (relative risk=1.55; 95% confidence interval: 1.10-2.18), but not with the risk of becoming a faller. No associations between clinical hip OA and (recurrent) falls were observed after adjustment for confounding. Use of opioids and analgesics mediated the associations between clinical OA and (recurrent) falls, while physical performance and physical activity did not., Conclusion: Individuals with clinical knee OA were at increased risk for recurrent falls. This relationship was mediated by pain medication, particularly opioids. The fall risk needs to be considered when discussing the risk benefit ratio of prescribing these medications., Competing Interests: Declarations of Competing Interest Professor Cooper reports personal fees from Alliance for Better Bone Health, Amgen, Eli Lilly, GSK, Medtronic, Merck, Novartis, Pfizer, Roche, Servier, Takeda and UCB. Professor Dennison reports payment for lecture by Pfizer and UCB. Dr. Edwards reports travel/accommodation/meeting expenses paid by Eli Lilly, Pfizer, UCB, Chugai and Abbvie. All other authors: none., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)