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Accounting for centre-effects in multicentre trials with a binary outcome – when, why, and how?
Open Access Research article
Accounting for centre-effects in multicentre trials with a binary outcome – when, why, and how?
Brennan C Kahan
Correspondence: Brennan C Kahan [email protected]
Author Affiliations
Pragmatic Clinical Trials Unit, Queen Mary University of London, 58 Turner Street, London E1 2AB, UK
MRC Clinical Trials Unit at UCL, 125 Kingsway, London WC2B 6NH, UK
BMC Medical Research Methodology 2014, 14:20 doi:10.1186/1471-2288-14-20
The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1471-2288/14/20
Received: 5 July 2013
Accepted: 3 February 2014
Published: 10 February 2014
© 2014 Kahan; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.
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Abstract
Background
It is often desirable to account for centre-effects in the analysis of multicentre randomised trials, however it is unclear which analysis methods are best in trials with a binary outcome.
Methods
We compared the performance of four methods of analysis (fixed-effects models, random-effects models, generalised estimating equations (GEE), and Mantel-Haenszel) using a re-analysis of a previously reported randomised trial (MIST2) and a large simulation study.
Results
The re-analysis of MIST2 found that fixed-effects and Mantel-Haenszel led to many patients being dropped from the analysis due to over-stratification (up to 69% dropped for Mantel-Haenszel, and up to 33% dropped for fixed-effects). Conversely, random-effects and GEE included all patients in the analysis, however GEE did not reach convergence. Estimated treatment effects and p-values were highly variable across different analysis methods.
The simulation study found that most methods of analysis performed well with a small number of centres. With a large number of centres, fixed-effects led to biased estimates and inflated type I error rates in many situations, and Mantel-Haenszel lost power compared to other analysis methods in some situations. Conversely, both random-effects and GEE gave nominal type I error rates and good power across all scenarios, and were usually as good as or better than either fixed-effects or Mantel-Haenszel. However, this was only true for GEEs with non-robust standard errors (SEs); using a robust ‘sandwich’ estimator led to inflated type I error rates across most scenarios.
Conclusions
With a small number of centres, we recommend the use of fixed-effects, random-effects, or GEE with non-robust SEs. Random-effects and GEE with non-robust SEs should be used with a moderate or large number of centres
Efficiency of Health Care Sector at Sub-State Level in India: A Case of Punjab
In recent years, WHO and other individual researchers have advocated estimation of health system performance through stochastic frontier models. It provides an idealized yardstick to evaluate economic performance of health system. So far attempts in India have remained focused at state level analysis. This paper attempts a sub-state level analysis for an affluent Indian state, namely Punjab, by using stochastic frontier technique. Our results provide pertinent insight into state health system and facilitate health facility planning at the sub-state level. Carried out in two stages of estimation, our results suggest that life expectancy in the Indian state could be enhanced considerably by correcting the factors that are adversely influencing the sub-state level health system efficiency. A higher budgetary allocation for health manpower is recommended by us to improve efficiency in poorly performing districts. This may be supported by policy initiatives outside the health system by empowering women through better education and work participatio
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Despite government-led good practice initiatives aimed to improve competitiveness in the U.K. construction sector, fluctuations in growth-driven demand, investment and constant regulatory revisions make it very difficult for an enterprise to plan strategically and remain competitive over a timescale exceeding 2 to 3 years. Research has been carried out to understand the historical evolution and changing face of the construction sector and the dynamic capabilities needed for an enterprise to secure a more sustainable competitive future. A dynamic model of a typical contracting firm has been created based upon extensive knowledge capture arising from fieldwork in collaborating firms together with a detailed review of the literature. A construct called the competitive index is used to model contract allocation in a stylised market. The simulations presented enable contracting enterprises to reflect strategically with a view to remaining competitive over a much longer time horizon of between 15 and 20 years. The rehearsal of strategy through simulated scenarios helps to minimise unexpected behaviour and offers insights about how endogenous behaviour can shape the future of the enterprise. To date, work on construction competitiveness has been either of a static nature or set predominantly at the level of the project. This study offers a new perspective by providing a dynamic tool to analyse competitiveness. It creates a new paradigm to support enhanced construction sector performance
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