|Use this URL to cite or link to this record in EThOS:||http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.288336|
|Title:||Identification of randomized trials for inclusion in meta-analyses of treatments for childhood acute lymphoblastic leukaemia, and investigation of factors leading to publication bias|
|Author:||Burrett, Julie Ann|
|Awarding Body:||Open University|
|Current Institution:||Open University|
|Date of Award:||2003|
|Availability of Full Text:||
Purpose: Some randomized trials are reported widely, while others remain unpublished. It is essential to systematic reviewers and meta-analysts that factors leading to publication bias in the form of delayed or non-publication of an eligible study are identified. This thesis is an attempt to do this.
Data: The set of randomized trials identified by the Childhood Acute Lymphoblastic Leukaemia (ALL) Collaborative Group was used. This consists of 149 trials comprising 243 randomized comparisons (randomizations), starting prior to 1 January 1988, reported in 257 articles, published prior to 1 January 2000. Each mention of a randomization in an article (irrespective of whether results are given) generates a publication record, of which there are 610.
Methods: The main focus is on identifying which trial characteristics lead to a delay in publication of a randomization. Time to the first mention of a randomization in an article (irrespective of whether any results are given) and to the first reporting of its results are both modelled using ordinary linear regression (the independence model). However, when these analyses are extended to include all mentions and all reportings of results respectively, non-independence necessitates the use of techniques for dealing with repeated measures. In such cases the independence model is the starting point, the residuals from which are used to form the covariance matrix, which in turn is used to suggest plausible correlation structures for repeated measures models. Generalised estimating equation (GEE) analysis is used to select an appropriate correlation structure, and a linear mixed effects model serves to confirm this. The conclusions are then discussed in the context of other studies identified. Finally logistic regression is used to identify trial characteristics associated with a randomization remaining unpublished, and Poisson and negative binomial models to identify those affecting frequency of reporting.
Results: Evidence was found of ‘pipeline bias’ in the reporting of first results since, although direction of effect was not found to be significant, highly statistically significant results are published faster than others. However this is not so for first mentions. Negative results (i.e. those in favour of the standard/control) arm were submitted for first publication faster than all others, although this did not effect time to publication. In addition, geographic location is an important predictor of whether a randomization is ever mentioned in an article, frequency of mentions and of time to first publication and results from single-centre trials are published more frequently than those with multi-centre participation.
Conclusions: Although ‘pipeline bias’ was identified in the analysis of time first reporting of results, it was not present in the analysis of time to first mention, and so not a problem for those wishing only to identify randomized trials for inclusion in meta-analyses. The importance of geographic location suggests that the practice of contacting known trialists is worthwhile in addition to the computerised literature searches and should be continued.
|Supervisor:||Not available||Sponsor:||Not available|
|Qualification Name:||Thesis (Ph.D.)||Qualification Level:||Doctoral|
|EThOS ID:||uk.bl.ethos.288336||DOI:||Not available|
|Keywords:||Clinical trials Medicine Mathematical statistics Operations research|