Importance of trends in the interpretation of an overall odds ratio in the meta‐analysis of clinical trials

Brand, Roland; Kragt, H.

doi:10.1002/sim.4780130310

Cited by 55 publications

(29 citation statements)

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“…For instance, consider the debate regarding how to investigate the underlying risk as a source of heterogeneity in meta-analyses [48][49][50][51][52]. The approach of Oldham's method has been criticized for yielding misleading results when there is variation in the treatment e ects across di erent clinical trials [50][51][52].…”

Section: Discussionmentioning

confidence: 99%

Revisiting the relation between change and initial value: a review and evaluation

Gilthorpe

2006

Statistics in Medicine

180

204

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SUMMARYThe relation between initial disease status and subsequent change following treatment has attracted great interest in clinical research. However, statisticians have repeatedly warned against correlating=regressing change with baseline due to two methodological concerns known as mathematical coupling and regression to the mean. Oldham's method and Blomqvist's formula are the two most often adopted methods to rectify these problems. The aims of this article are to review brie y the proposed solutions in the statistical and psychological literature, and to clarify the popular misconception that Blomqvist's formula is superior to Oldham's method. We argue that this misconception is due to a failure to recognize that the heterogeneity of individual responses to treatment is a source of regression to the mean in the analysis of the relation between change and initial value. Furthermore, we demonstrate how each method actually answers di erent research questions, and how confusion arises when this is not always understood.

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Section: Discussionmentioning

confidence: 99%

Revisiting the relation between change and initial value: a review and evaluation

Gilthorpe

2006

Statistics in Medicine

180

204

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“…The problem of using the observed proportion of events in the control group was first discussed11 in response to a meta-analysis of 14 placebo controlled clinical trials to evaluate the effect of tocolysis with β mimetics on the risk of preterm birth 18. An example where the outcome was a continuous measure appeared in a review of 18 randomised controlled trials of prophylactic desmopressin to reduce perioperative blood loss during cardiac surgery,22 where one conclusion was that the efficacy of desmopressin was a function of blood loss in the control group.…”

Section: Discussionmentioning

confidence: 99%

“…The size of the bias can be surprisingly large. In the extreme case when the treated and control group outcomes are unrelated the expected correlation can be -0.71 11. Underlying risk may indeed be a source of heterogeneity, but such a graph and regression will misrepresent any true effect.…”

Section: The Datamentioning

confidence: 99%

The relation between treatment benefit and underlying risk in meta-analysis

Sharp

Thompson²,

Altman³

1996

BMJ

157

114

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In meta-analyses of clinical trials comparing a treated group with a control group it has been common to ask whether the treatment benefit varies according to the underlying risk of the patients in the different trials, with the hope of defining which patients would benefit most and which least from medical interventions. The usual analysis used to investigate this issue, however, which uses the observed proportions of events in the control groups of the trials as a measure of the underlying risk, is flawed and produces seriously misleading results. This arises through a bias due to regression to the mean and will be particularly acute in meta-analyses which include some small trials or in which the variability in the true underlying risks across trials is small. Approaches which previously have been thought to be more appropriate are to substitute the average proportion of events in the control and treated groups as the measure of underlying risk or to plot the proportion of events in the treated group against that in the control group (L'Abbé plot). However, these are still subject to bias in most circumstances. Because of the potentially seriously flawed conclusions that can result from such analyses, they should be replaced either by statistically appropriate (but more complex) approaches or, preferably, by analyses which investigate the dependence of the treatment effect on measured baseline characteristics of the patients in each trial.

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“…Changing the effect measure (between relative risk (RR), OR, or ln(OR)) and scale for representing the relationship has been recommended as a strategy to minimize apparent heterogeneity and effect modification as a first step in reducing the chance of detecting a spurious interaction in meta-regression where control event rate is a covariate [6,16,17]. Of the three effect measures, ln(OR) had the lowest heterogeneity ( I 2 , Table 1) and was found in exploratory analyses to have the least significant slope vs control event rate, providing justification for using this effect measure in the meta-regression.…”

Section: Methodsmentioning

confidence: 99%

Factors explaining the heterogeneity of effects of patient decision aids on knowledge of outcome probabilities: a systematic review sub-analysis

et al. 2013

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BackgroundThere is considerable unexplained heterogeneity in previous meta-analyses of randomized controlled trials (RCTs) evaluating the effects of patient decision aids on the accuracy of knowledge of outcome probabilities. The purpose of this review was to explore possible effect modification by three covariates: the type of control intervention, decision aid quality and patients' baseline knowledge of probabilities.MethodsA sub-analysis of studies previously identified in the 2011 Cochrane review on decision aids for people facing treatment and screening decisions was conducted. Additional unpublished data were requested from relevant study authors to maximize the number of eligible studies. RCTs (to 2009) comparing decision aids with standardized probability information to control interventions (lacking such information) and assessing the accuracy of patient knowledge of outcome probabilities were included. The proportions of patients with accurate knowledge of outcome probabilities in each group were converted into relative effect measures. Intervention quality was assessed using the International Patient Decision Aid Standards instrument (IPDASi) probabilities domain.ResultsA main effects analysis of 17 eligible studies confirmed that decision aids significantly improve the accuracy of patient knowledge of outcome probabilities (relative risk = 1.80 [1.51, 2.16]), with considerable heterogeneity (87%). The type of control did not modify effects. Meta-regression suggested that the IPDASi probabilities domain score (reflecting decision aid quality) is a potential effect modifier (P = 0.037), accounting for a quarter of the variability (R2 = 0.28). Meta-regression indicated the control event rate (reflecting baseline knowledge) is a significant effect modifier (P = 0.001), with over half the variability in ln(OR) explained by the linear relationship with log-odds for the control group (R2 = 0.52); this relationship was slightly strengthened after correcting for the statistical dependence of the effect measure on the control event rate.ConclusionsPatients’ baseline level of knowledge of outcome probabilities is an important variable that explains the heterogeneity of effects of decision aids on improving accuracy of this knowledge. Greater relative effects are observed when the baseline proportion of patients with accurate knowledge is lower. This may indicate that decision aids are more effective in populations with lower knowledge.

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Importance of trends in the interpretation of an overall odds ratio in the meta‐analysis of clinical trials

Cited by 55 publications

References 1 publication

Revisiting the relation between change and initial value: a review and evaluation

Revisiting the relation between change and initial value: a review and evaluation

The relation between treatment benefit and underlying risk in meta-analysis

Factors explaining the heterogeneity of effects of patient decision aids on knowledge of outcome probabilities: a systematic review sub-analysis

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