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The International Stroke Trial (IST): a randomized trial of aspirin, subcutaneous heparin, both, or neither among 19435 patients with acute ischaemic stroke
No full textBACKGROUND:
Only a few small trials have compared antithrombotic therapy (antiplatelet or anticoagulant agents) versus control in acute ischaemic stroke, and none has been large enough to provide reliable evidence on safety or efficacy.
METHODS:
The International Stroke Trial (IST) was a large, randomised, open trial of up to 14 days of antithrombotic therapy started as soon as possible after stroke onset. The aim was to provide reliable evidence on the safety and efficacy of aspirin and of subcutaneous heparin. Half the patients were allocated unfractionated heparin (5000 or 12,500 IU bd [twice daily]), and half were allocated "avoid heparin"; and, in a factorial design, half were allocated aspirin 300 mg daily and half "avoid aspirin". The primary outcomes were death within 14 days and death or dependency at 6 months. 19,435 patients with suspected acute ischaemic stroke entering 467 hospitals in 36 countries were randomised within 48 hours of symptom onset.
RESULTS:
Among heparin-allocated patients, there were non-significantly fewer deaths within 14 days (876 [9.0%] heparin vs 905 [9.3%] no heparin), corresponding to 3 (SD 4) fewer deaths per 1000 patients. At 6 months the percentage dead or dependent was identical in both groups (62.9%). Patients allocated to heparin had significantly fewer recurrent ischaemic strokes within 14 days (2.9% vs 3.8%) but this was offset by a similar-sized increase in haemorrhagic strokes (1.2% vs 0.4%), so the difference in death or non-fatal recurrent stroke (11.7% vs 12.0%) was not significant. Heparin was associated with a significant excess of 9 (SD 1) transfused or fatal extracranial bleeds per 1000. Compared with 5000 IU bd heparin, 12,500 IU bd heparin was associated with significantly more transfused or fatal extracranial bleeds, more haemorrhagic strokes, and more deaths or non-fatal strokes within 14 days (12.6% vs 10.8%). Among aspirin-allocated patients there were non-significantly fewer deaths within 14 days (872 [9.0%] vs 909 [9.4%]), corresponding to 4 (SD 4) fewer deaths per 1000 patients. At 6 months there was a non-significant trend towards a smaller percentage of the aspirin group being dead or dependent (62.2% vs 63.5%, 2p = 0.07), a difference of 13 (SD 7) per 1000; after adjustment for baseline prognosis the benefit from aspirin was significant (14 [SD 6] per 1000, 2p = 0.03). Aspirin-allocated patients had significantly fewer recurrent ischaemic strokes within 14 days (2.8% vs 3.9%) with no significant excess of haemorrhagic strokes (0.9% vs 0.8%), so the reduction in death or non-fatal recurrent stroke with aspirin (11.3% vs 12.4%) was significant. Aspirin was associated with a significant excess of 5 (SD 1) transfused or fatal extracranial bleeds per 1000; in the absence of heparin the excess was 2 (SD 1) and was not significant. There was no interaction between aspirin and heparin in the main outcomes.
INTERPRETATION:
Neither heparin regimen offered any clinical advantage at 6 months. The results suggest that if heparin is given in routine clinical practice, the dose should not exceed 5000 IU subcutaneously twice daily. For aspirin, the IST suggests a small but worthwhile improvement at 6 months. Taking the IST together with the comparably large Chinese Acute Stroke Trial, aspirin produces a small but real reduction of about 10 deaths or recurrent strokes per 1000 during the first few weeks. Both trials suggest that aspirin should be started as soon as possible after the onset of ischaemic stroke; previous trials have already shown that continuation of low-dose aspirin gives protection in the longer term
Indirect comparisons of competing interventions:In collaboration with the International Stroke Trial Collaborative Group
Get PDFOBJECTIVES: To survey the frequency of use of indirect comparisons in systematic reviews and evaluate the methods used in their analysis and interpretation. Also to identify alternative statistical approaches for the analysis of indirect comparisons, to assess the properties of different statistical methods used for performing indirect comparisons and to compare direct and indirect estimates of the same effects within reviews. DATA SOURCES: Electronic databases. REVIEW METHODS: The Database of Abstracts of Reviews of Effects (DARE) was searched for systematic reviews involving meta-analysis of randomised controlled trials (RCTs) that reported both direct and indirect comparisons, or indirect comparisons alone. A systematic review of MEDLINE and other databases was carried out to identify published methods for analysing indirect comparisons. Study designs were created using data from the International Stroke Trial. Random samples of patients receiving aspirin, heparin or placebo in 16 centres were used to create meta-analyses, with half of the trials comparing aspirin and placebo and half heparin and placebo. Methods for indirect comparisons were used to estimate the contrast between aspirin and heparin. The whole process was repeated 1000 times and the results were compared with direct comparisons and also theoretical results. Further detailed case studies comparing the results from both direct and indirect comparisons of the same effects were undertaken. RESULTS: Of the reviews identified through DARE, 31/327 (9.5%) included indirect comparisons. A further five reviews including indirect comparisons were identified through electronic searching. Few reviews carried out a formal analysis and some based analysis on the naive addition of data from the treatment arms of interest. Few methodological papers were identified. Some valid approaches for aggregate data that could be applied using standard software were found: the adjusted indirect comparison, meta-regression and, for binary data only, multiple logistic regression (fixed effect models only). Simulation studies showed that the naive method is liable to bias and also produces over-precise answers. Several methods provide correct answers if strong but unverifiable assumptions are fulfilled. Four times as many similarly sized trials are needed for the indirect approach to have the same power as directly randomised comparisons. Detailed case studies comparing direct and indirect comparisons of the same effect show considerable statistical discrepancies, but the direction of such discrepancy is unpredictable. CONCLUSIONS: Direct evidence from good-quality RCTs should be used wherever possible. Without this evidence, it may be necessary to look for indirect comparisons from RCTs. However, the results may be susceptible to bias. When making indirect comparisons within a systematic review, an adjusted indirect comparison method should ideally be used employing the random effects model. If both direct and indirect comparisons are possible within a review, it is recommended that these be done separately before considering whether to pool data. There is a need to evaluate methods for the analysis of indirect comparisons for continuous data and for empirical research into how different methods of indirect comparison perform in cases where there is a large treatment effect. Further study is needed into when it is appropriate to look at indirect comparisons and when to combine both direct and indirect comparisons. Research into how evidence from indirect comparisons compares to that from non-randomised studies may also be warranted. Investigations using individual patient data from a meta-analysis of several RCTs using different protocols and an evaluation of the impact of choosing different binary effect measures for the inverse variance method would also be useful
Risk of early death and recurrent stroke and effect of heparin in 3169 patients with acute ischemic stroke and atrial fibrillation in the Intenational Stroke Trial
No full textEvaluating non-randomised intervention studies
Get PDFBackground
In the absence of randomised controlled trials (RCTs), healthcare practitioners and policy-makers rely on non-randomised studies to provide evidence of the effectiveness of healthcare interventions. However, there is controversy over the validity of non-randomised evidence, related to the existence and magnitude of selection bias.
Objectives
To consider methods and related evidence for evaluating bias in non-randomised intervention studies.
Methods
1. Three reviews were conducted to consider:
empirical evidence of bias associated with non-randomised studies
the content of quality assessment tools for non-randomised studies
the use of quality assessment in systematic reviews of non-randomised studies.
These reviews were conducted systematically, identifying relevant literature through comprehensive searches across electronic databases, handsearches and contact with experts.
2. New empirical investigations were conducted generating non-randomised studies from two large, multicentre RCTs by selectively resampling trial participants according to allocated treatment, centre and period. These were used to examine:
systematic bias introduced by the use of historical and non-randomised concurrent controls
whether results of non-randomised studies are more variable than results of RCTs
the ability of case-mix adjustment methods to correct for selection bias introduced by non-random allocation.
The resampling design overcame particular problems of meta-confounding and variability of direction and magnitude of bias that hinder the interpretation of previous reviews.
Results
Empirical comparisons of randomised and non-randomised evidence
Eight studies compared results of randomised and non-randomised studies across multiple interventions using meta-epidemiological techniques. The studies reached conflicting conclusions, explicable by differences in:
whether data were sourced from primary studies or systematic reviews
consideration of meta-confounding
inclusion of studies of varying quality
criterion for classifying discrepancies in results.
The only deducible conclusions were (a) results of randomised and non-randomised studies sometimes, but not always, differ and (b) both similarities and differences may often be explicable by other confounding factors.
Quality assessment tools for evaluating non-randomised studies
We identified 194 tools that could be or had been used to assess non-randomised studies. Around half were scales and half checklists, most were published within systematic reviews and most were poorly developed with scant attention paid to principles of scale development.
Sixty tools covered at least five of six pre-specified internal validity domains (creation of groups, blinding, soundness of information, follow-up, analysis of comparability, analysis of outcome), although the degree of coverage varied. Fourteen tools covered three of four core items of particular importance for non-randomised studies (How allocation occurred? Was the study designed to generate comparable groups? Were prognostic factors identified? Was case-mix adjustment used?). Six tools were thought suitable for use in systematic reviews.
Use of quality assessment in systematic reviews of non-randomised studies
Of 511 systematic reviews that included non-randomised studies, only 169 (33%) assessed study quality. Many used quality assessment tools designed for RCTs or developed by the authors themselves, and did not include key quality criteria relevant to non-randomised studies. Sixty-nine reviews investigated the impact of quality on study results in a quantitative manner.
Empirical estimates of bias associated with non-random allocation
The bias introduced by non-random allocation was noted to have two components. First, the bias could lead to consistent over- or underestimations of treatment effects. This occurred for historical controls, the direction of bias depending on time trends in the case-mix of participants recruited to the study. Second, the bias increased variation in results for both historical and concurrent controls, owing to haphazard differences in case-mix between groups. The biases were large enough to lead studies falsely to conclude significant findings of benefit or harm.
Empirical evaluation of case-mix adjustment methods
Four strategies for case-mix adjustment were evaluated: none adequately adjusted for bias in historically and concurrently controlled studies. Logistic regression on average increased bias. Propensity score methods performed better, but were not satisfactory in most situations. Detailed investigation revealed that adequate adjustment can only be achieved in the unrealistic situation when selection depends on a single factor. Omission of important confounding factors can explain underadjustment. Correlated misclassifications and measurement error in confounding variables may explain the observed increase in bias with logistic regression, as may differences between conditional and unconditional odds ratio estimates of treatment effects.
Conclusions
Results of non-randomised studies sometimes, but not always, differ from results of randomised studies of the same intervention. Non-randomised studies may still give seriously misleading results when treated and control groups appear similar in key prognostic factors. Standard methods of case-mix adjustment do not guarantee removal of bias. Residual confounding may be high even when good prognostic data are available, and in some situations adjusted results may appear more biased than unadjusted results.
Although many quality assessment tools exist and have been used for appraising non-randomised studies, most omit key quality domains. Six tools were considered potentially suitable for use in systematic reviews, but each requires revision to cover all relevant quality domains.
Healthcare policies based upon non-randomised studies or systematic reviews of non-randomised studies may need re-evaluation if the uncertainty in the true evidence base was not fully appreciated when policies were made.
The inability of case-mix adjustment methods to compensate for selection bias and our inability to identify non-randomised studies which are free of selection bias indicate that non-randomised studies should only be undertaken when RCTs are infeasible or unethical
International Stroke Trial database (version 2)
No full textThe International Stroke Trial (IST) was one of the biggest randomised trials in acute stroke. Methods: Available data on variables assessed at randomisation, at the early outcome point (14-days after randomisation or prior discharge) and at 6-months were extracted and made publically available. Results and Conclusions: The IST provides an excellent source of primary data easy-to-use for sample size calculations and preliminary analysis necessary for planning a good quality trial.
# Associated publications #
* The erratum paper explains the difference between this version ie version 2, and the previous version: Sandercock, P.A.G., Niewada, M., Członkowska, A. et al. Erratum to: The International Stroke Trial database. Trials 13, 24 (2012). https://doi.org/10.1186/1745-6215-13-24 .
* Main results paper for the trial: International Stroke Trial Collaborative Group 1997, 'The International Stroke Trial (IST): A randomised trial of aspirin, subcutaneous heparin, both, or neither among 19 435 patients with acute ischaemic stroke', The Lancet, vol. 349, no. 9065, pp. 1569-1581. https://doi.org/10.1016/S0140-6736(97)04011-7 .International Stroke Trial database unicode data file
International Stroke Trial database csv data fil
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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