2021
DOI: 10.1186/s13063-021-05266-w
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Including random centre effects in design, analysis and presentation of multi-centre trials

Abstract: Background In large multicentre trials in diverse settings, there is uncertainty about the need to adjust for centre variation in design and analysis. A key distinction is the difference between variation in outcome (independent of treatment) and variation in treatment effect. Through re-analysis of the CRASH-2 trial (2010), this study clarifies when and how to use multi-level models for multicentre studies with binary outcomes. Methods CRASH-2 ran… Show more

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Cited by 6 publications
(5 citation statements)
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“…It is widely recognized that centers can differ in terms of their patient characteristics, clinical practices, and available resources, which often lead to variations in success proportions. 16 However, in our study, we observed a homogeneous efficacy of HC across centers, indicating that there was no significant interaction between the center and the effect of HC treatment.…”
Section: Discussioncontrasting
confidence: 50%
“…It is widely recognized that centers can differ in terms of their patient characteristics, clinical practices, and available resources, which often lead to variations in success proportions. 16 However, in our study, we observed a homogeneous efficacy of HC across centers, indicating that there was no significant interaction between the center and the effect of HC treatment.…”
Section: Discussioncontrasting
confidence: 50%
“…The randomised controlled trial (RCT) and meta-analysis literature have also introduced methods for combining estimates from different populations. Randomised controlled trials which have recurred people from different (sub-)populations, for example a multi-centre trial like the CRASH-II trial [16,17], generally account for population differences by controlling for retirement centre in the analysis [18,19]. The analogue for metaanalyses is a multi-level meta-analysis in which known population differences between trials are modelled by adding a random effect to the analysis model [20].…”
Section: Introductionmentioning
confidence: 99%
“…In South Africa, food insecurity is a major cause of malnutrition, alongside with chronic substance abuse which often lead to being underweight as the outcomes. Several studies have shown that nutrition plays a major role in establishing a strong immune system, under-nourishment, including significant loss of fat and muscle mass, is one of the risk factors of TB (34)(35)(36). Similarly, another study have indicated that where there is malnutrition, stress, drug addiction, alcoholism, and abject poverty, the presence of TB disease is often seen among vulnerable and high-risk populations (32,33).…”
Section: Discussionmentioning
confidence: 99%
“…In this study, factors at the individual-and contextual-level were considered. These factors were considered because they had a statistically significant association with older persons who had HIV and TB in earlier studies (34)(35)(36). The individual-level factors included age (50-54, 55-59, 60-64, 65-69, 70-74, 75+), population group (African/Black, Colored, Indian/Asian, White), marital status (married, never married, divorced, widowed), gender (male, female), educational level (no education, primary, secondary/higher), and business activities (yes, no).…”
Section: Independent Variablesmentioning
confidence: 99%