Biomedical subjects

International Stroke Trial Collaborative Group

Publications and source records attributed to International Stroke Trial Collaborative Group.

3 recordsLinked to original sources

PubMed2005

Indirect comparisons of competing interventions.

OBJECTIVES: 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.

Bias↗

PubMed2004

Influence of gender on baseline features and clinical outcomes among 17,370 patients with confirmed ischaemic stroke in the international stroke trial.

AIM: We sought to determine whether there were differences between men and women with acute stroke in their baseline characteristics and outcome in a large cohort of patients randomized in the International Stroke Trial (IST). METHODS: Of the 19,435 patients randomized in the IST, 17,370 had an ischemic stroke confirmed by CT scan or autopsy (8,003 female and 9,367 male). In males and females, we compared baseline characteristics (age, frequency of atrial fibrillation, pre-stroke administration of aspirin and systolic blood pressure, conscious level, stroke syndrome) and outcome at 14 days and 6 months (death, complications, dependency, recovery, place of residence). We developed a specific logistic regression model to adjust for case-mix in order to evaluate the separate influence of gender on outcome. RESULTS: Female patients were older, suffered more frequently from atrial fibrillation, had higher systolic blood pressure at randomization and generally had more severe strokes (a higher proportion were unconscious or drowsy or had a total anterior circulation syndrome). Females had higher 14-day and 6-month case fatality and were more likely to be dead or dependent at six months (and consequently more likely to require institutional or residential care). Gender was an independent predictor of death or dependency at 6 months. CONCLUSIONS: The adverse effect of female gender on outcome indicates that further research to explore the underlying biological mechanism is justified, and that more intensive acute and long-term treatment may be needed to improve outcome among female patients with stroke.

Acute Disease↗

PubMed2003

Evaluating non-randomised intervention studies.

OBJECTIVES: To consider methods and related evidence for evaluating bias in non-randomised intervention studies. DATA SOURCES: Systematic reviews and methodological papers were identified from a search of electronic databases; handsearches of key medical journals and contact with experts working in the field. New empirical studies were conducted using data from two large randomised clinical trials. METHODS: Three systematic reviews and new empirical investigations were conducted. The reviews considered, in regard to non-randomised studies, (1) the existing evidence of bias, (2) the content of quality assessment tools, (3) the ways that study quality has been assessed and addressed. (4) The empirical investigations were conducted generating non-randomised studies from two large, multicentre randomised controlled trials (RCTs) and selectively resampling trial participants according to allocated treatment, centre and period. RESULTS: In the systematic reviews, eight studies compared results of randomised and non-randomised studies across multiple interventions using meta-epidemiological techniques. A total of 194 tools were identified that could be or had been used to assess non-randomised studies. Sixty tools covered at least five of six pre-specified internal validity domains. Fourteen tools covered three of four core items of particular importance for non-randomised studies. Six tools were thought suitable for use in systematic reviews. Of 511 systematic reviews that included non-randomised studies, only 169 (33%) assessed study quality. Sixty-nine reviews investigated the impact of quality on study results in a quantitative manner. The new empirical studies estimated the bias associated with non-random allocation and found that the bias could lead to consistent over- or underestimations of treatment effects, also 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. 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. 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. 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 that are free of selection bias indicate that non-randomised studies should only be undertaken when RCTs are infeasible or unethical. Recommendations for further research include: applying the resampling methodology in other clinical areas to ascertain whether the biases described are typical; developing or refining existing quality assessment tools for non-randomised studies; investigating how quality assessments of non-randomised studies can be incorporated into reviews and the implications of individual quality features for interpretation of a review's results; examination of the reasons for the apparent failure of case-mix adjustment methods; and further evaluation of the role of the propensity score.

Bias↗