Factors explaining the heterogeneity of effects of patient decision aids on knowledge of outcome probabilities: a systematic review sub-analysis
© Gentles et al.; licensee BioMed Central Ltd. 2013
Received: 31 July 2013
Accepted: 2 October 2013
Published: 21 October 2013
There 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.
A 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 Additional unpublished data were requested from relevant study authors to maximize the number of eligible studies.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.
A 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 (R 2 = 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 (R 2 = 0.52); this relationship was slightly strengthened after correcting for the statistical dependence of the effect measure on the control event rate.
Patients’ 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.
KeywordsDecision aid Clinical heterogeneity Meta-analysis Meta-regression Subgroup analysis Effect modification Baseline rate Control event rate
In systematic reviews of binary outcomes, heterogeneity conventionally refers to the variation in relative effects (relative risk, odds ratio) across studies that is greater than one would expect by chance . The causes of such study-level variation can either be artifactual, where methodological differences between studies affect the relative effect measures, or real, where differences may be attributable to variation across studies in factors related to the population included, active interventions used or comparators employed [2, 3]. When present, unexplained heterogeneity complicates the interpretation and usefulness of pooled effect estimates of meta-analyses in decision-making. It is for this reason that the quality of pooled evidence is typically downgraded when assessed using the GRADE framework . Attempts to explain sources of heterogeneity are important for overcoming these limitations and for their potential to contribute knowledge about what types of patients benefit most from a specific intervention [2, 3, 5, 6]. The Cochrane meta-analysis of randomized controlled trials (RCTs) evaluating patient decision aid effects on the accuracy of knowledge of outcome probabilities is an example where interpretation of the pooled effect has been hampered by high heterogeneity.
Patient decision aids are complex interventions used to help patients make specific and deliberative choices among treatment or screening options by providing, at the minimum, information on the options and associated outcomes relevant to the patient’s health status, and implicit methods to clarify their values or preferences . Due to their complex nature – involving multiple interacting components and behaviors – and the diverse clinical settings they are designed for, the exact form of the intervention and populations in which they are evaluated vary considerably. There is thus a corresponding expectation of variation in real decision aid effects across conditions.
The effects of decision aids on numerous decision-related outcomes have been extensively evaluated. Since patients are known to underestimate probabilities of harms or overestimate probabilities of benefits , decision aids are often designed to communicate estimates of probabilities derived from population-based research. Such probabilities apply to possible outcomes of the featured decisions: benefits and harms of an intervention, or true- and false-positive or -negative screening results . Studies that evaluate the effects of decision aids on the accuracy of patient knowledge of these outcome probabilities generally measure the proportion of patients who are able to correctly answer questions about population-derived probability estimations – making this a binary outcome.
The most recent (2011) update to the Cochrane systematic review on patient decision aids includes 86 RCTs where the authors reviewed 23 different outcomes . Accuracy of knowledge of outcome probabilities (labeled ‘accurate risk perception’ in that review) was the second-most frequently measured outcome, and the results of 14 studies were pooled. Meta-analysis revealed a uniform direction of effect favoring decision aids across all studies, and the pooled effect estimate was significant (relative risk = 1.74 [1.46 to 2.08], P < 0.001). The level of heterogeneity, however, was significant (P < 0.001) and considerable (I 2 = 83%). Despite this, the pooled effect is considered informative to a degree since decision aids showed a uniformly positive effect. However, the Cochrane review mentions that ‘the pooled effect size and CI should be interpreted as a range across conditions, which may not be applicable to a specific condition’ , reflecting the limitation to the interpretability and utility of the pooled random effects estimates found in meta-analyses when there exists substantial real variation in intervention effects. In other words, the pooled estimate does not correspond to any individual decision aid, setting or population. Furthermore, it is impossible to predict where any given decision aid would lie within the wide range of possible relative effects .
The 2011 Cochrane update  tentatively explored two sources of heterogeneity affecting this outcome. First, it showed that the effect size of decision aids in which probabilities were represented numerically is larger than for those where probabilities were described with words, suggesting possible effect modification attributable to this specific aspect of the intervention. Secondly, removing three (of 14) studies with the lowest control event rate (selected as outliers by visual inspection) reduced heterogeneity to non-significant levels (P = 0.3), implicating control levels of accurate knowledge as a potential contributor to heterogeneity . While informative, these preliminary analyses were not selected with any overall rationale and did not provide formal tests for effect modification.
The current investigation aims to improve interpretability and usefulness of the available research evidence regarding decisions aid effects on the accuracy of patient knowledge of outcome probabilities by exploring and characterizing potential contributors to the observed heterogeneity [2–4]. Subgroup analysis and meta-regression were employed to investigate the potential effects of three study-level factors (covariates): the type of control intervention, the level of decision aid quality and the control event rate. These covariates were chosen because they represent the best available measures that summarize or combine relevant characteristics of the comparator (control), active intervention or study population, respectively.
As a sub-analysis of the previous Cochrane systematic review on decision aids , certain aspects of the original methods were not repeated in detail here – principally the literature search and parts of the literature selection. In addition, the original review can be consulted for further information on individual studies, including setting, patients included, intervention characteristics and risk of bias assessments.
Data sources and study selection
Studies previously identified through electronic database searches (MEDLINE, PsycINFO, CINAHL, EMBASE, Cochrane Central Controlled Trials Register) in the 2011 Cochrane review served as the basis for study selection . Thus, RCTs published up to December 2009 meeting the original selection criteria were considered. As an additional criterion, we included studies where data had been collected on the proportions of participants in both intervention and control groups who had accurate knowledge of outcome probabilities post-intervention. To maximize the number of studies available for analysis, the 86 publications included in the 2011 Cochrane update were rescreened to identify studies where the relevant outcome data might exist but had not been previously published. The corresponding authors were then emailed up to three times requesting unpublished data used to calculate relative risks and copies of the original decision aids.
Data from all studies were extracted in duplicate (SG, MA) using piloted forms. In addition to newly eligible studies, data were re-extracted from the set of 14 studies pooled in the 2011 Cochrane update . In cases of disagreement with the outcome data from the previous Cochrane review, its authors (CB, DS) were consulted and consensus was reached on which data to use for the current review.
Event rates, defined as the proportion of patients in the decision aid group correctly answering questions about probabilities divided by that in the control group, were extracted for calculating relative risk. In eight studies that evaluated knowledge of outcome probabilities with more than one question, the proportion of correct answers was averaged. For purposes of GRADE assessment, the risk of bias items applicable at the outcome level (blinding, incomplete outcome data, specifically for assessments of knowledge of probabilities) were abstracted, as these items were previously reported in the Cochrane update  only at the study level. Information for the three covariates analyzed was abstracted (described below).
Selection of study-level factors (covariates) investigated
Study-level factors with the potential to contribute to heterogeneity (covariates) were considered to represent three principal sources of clinical heterogeneity: characteristics of the comparator (control), the active intervention and the population . To minimize the risk of detecting spurious effect modification due to multiple comparisons, only one covariate was selected to represent each main source, to give a total of three [11, 12]. In each case, the covariates were selected for their availability and biologic plausibility (likelihood based on a mechanistic rationale) as substantial contributors to heterogeneity . For the first category, comparator (or control), only one covariate was available and therefore selected: the type of control intervention. Since multiple covariates were available corresponding to characteristics of active intervention and study population, a top-down approach was used in which the best available measure that combined potentially relevant characteristics was selected in each case. To represent intervention characteristics, a composite measure of relevant decision aid quality characteristics was chosen. For population characteristics, the control event rate was chosen because it provides a convenient summary measure . The rationale, hypothesis and measurement for each covariate are described below.
Type of control intervention
Depending on the context, not all studies evaluating decision aids provide the same degree of standardized information to the control group . Three types of control intervention, from less to more standardized information, are categorized: (1) no standardized information other than usual care; (2) generic standardized information used as a sham, such as basic background on the disease, and containing no outcome information or (3) information on outcomes associated with options, sometimes considered as a less intense form of decision aid. In all cases, control interventions differ from the experimental intervention by providing no information on outcome probabilities. Higher levels of standardized information may have a hidden effect on patients’ ability to answer questions about probabilities. The hypothesis for this covariate was that control interventions that provide more standardized information to the control group, because they may conceivably improve control patients’ ability to answer questions about probabilities, would decrease relative effect size. Possible effect modification by this categorical covariate was investigated with subgroup analysis.
Decision aid quality
The International Patient Decision Aid Standards (IPDAS) collaboration has developed an instrument, the IPDASi, for rating the quality of decision aids . IPDASi includes a probabilities dimension consisting of eight items corresponding to theoretical elements derived from systematic review of the evidence on effective formats for communicating outcome probabilities to patients . The items address factors including the presentation of event rates, specification of a time period, the allowing for comparison of probabilities across options, the reporting of levels of uncertainty around probabilities, the provision of multiple ways of viewing probabilities (for example, words, numbers and diagrams) and providing balanced information to limit framing biases . The probabilities dimension therefore represents a comprehensive composite measure of relevant decision aid characteristics likely to affect knowledge of probabilities. Moreover, its continuous scale probably gives greater statistical power when testing for effect modification than does an equivalent categorical variable. The hypothesis for this covariate was that decision aid scores on the IPDASi probabilities dimension would increase as the effectiveness of decision aids for improving knowledge of outcome probabilities increases – which, if true, would support the predictive validity of the probabilities dimension of IPDASi . Decision aids were scored in duplicate by trained raters on a scale from 1 to 4 points for each of 8 items in this dimension (scores provided by NJW). The possible ratings of 8 to 32 were re-scaled to a range of 0% to 100%. The effects of this continuous covariate were investigated with meta-regression.
Control event rate
The control event rate (CER) in this context is the proportion of patients in the control group who correctly answer specific questions about probabilities. Note, ‘control event rate’ is used in preference to ‘baseline risk’ to minimize confusion, since ‘risk’ in this case corresponds to a favored outcome (that is, having accurate knowledge of probabilities). Assuming the type of control intervention does not modify its effects (and our investigations found no evidence that it does), the control event rate provides an estimate of the baseline level of accurate knowledge of outcome probabilities in the population studied. Patients’ baseline knowledge of these probabilities may vary widely depending on factors such as whether specific probabilities are likely to be common knowledge, newness of the underlying evidence or patient education levels. The plausibility of effect modification was first suggested in the 2009 Cochrane update where heterogeneity was reduced to non-significant levels after removing three studies with the lowest control event rate . The hypothesis for this covariate was that studies with higher control event rates have lower relative risks. Effects due to this continuous covariate were investigated with meta-regression.
Three types of statistical analysis were performed: meta-analysis of main effects, subgroup analysis to test for effect modification by the one categorical covariate (type of control intervention) and meta-regression to test for and characterize effect modification by the two continuous covariates (decision aid quality and control event rate). Each analysis type is described in further detail. The threshold for statistical significance was P < 0.05.
Meta-analysis of main effects
Consistent with previous meta-analysis of the main effects for this outcome , relative risk was used as the effect measure. The software Review Manager (RevMan, version 5.1, Copenhagen, The Nordic Cochrane Centre, The Cochrane Collaboration, 2011) was used to combine estimates using the DerSimonian and Laird random-effects model. Tau-squared in this model provides an estimate of the between-study variance. A chi-squared test was used to examine the strength of evidence about whether heterogeneity is present, and I 2 provides an estimate of its magnitude.
Subgroup analysis (type of control intervention)
Potential effect modification by the three types of control intervention was tested with a weighted one-way ANOVA. To provide additional support for a lack of effect on the control event rate, a weighted ANOVA between type of control intervention and control event rate was performed. ANOVAs were calculated using the software IBM SPSS Statistics (version 20.0 for Windows, Armonk, NY, IBM Corp.), using the natural logarithm of the odds ratio, ln(OR), as the effect measure for consistency with subsequent covariate analyses.
Meta-regression analysis (decision aid quality and control event rate)
Univariate weighted least squares (WLS) meta-regression analyses were conducted to test for and characterize potential effect modification by IPDASi probabilities dimension score and control event rate, separately.
Study-level covariate values, observed effect size measures and pooled heterogeneity estimates listed in order of increasing relative risk
Type of controla
IPDASi, probability dimension score/32
Rescaled IPDASi, probability dimension score (%)
Lerman et al.
Johnson et al. 
Wolf and Schorling 
Whelan et al. 
McBride et al. 
Schapira and Vanruiswyk 
Dodin et al. 
O’Connor et al. 
Whelan et al. 
Kuppermann et al. 
Vandemheen et al. 
McAlister et al. 
Mathieu et al. 
Man-Son-Hing et al., 
Weymiller et al. 
Laupacis et al. 
Gattellari and Ward 
Since the selected effect measure ln(OR) is not available in RevMan, Excel was used to generate an equivalent meta-analysis for ln(OR) to obtain the tau-based weights for the meta-regressions. Excel formulae were verified by comparing (non-continuity-corrected) back-translated values to the RevMan output for OR. Event frequencies for this meta-analysis were continuity-corrected (adding 0.5). IBM SPSS Statistics was then used to calculate standard WLS regressions using the tau-based weights. Neither regression model (logit control vs ln(OR) or re-scaled IPDAS vs ln(OR)) was found to violate the assumptions of linear regression (linearity, independence, homoscedasticity and normality) upon examination of the residual plots (predicted vs residual, independent vs residual and normal probability (Q-Q) of residual plots).
The meta-regression against control event rate incorporated a bias correction. When baseline response rates (control event rates) are used as the covariate in a meta-regression, the measurement error in control event rate and the functional dependence of the observed treatment effect on the control group response can bias the standard WLS regression and lead to incorrect inference about the degree to which the control event rate modifies effects and underlies heterogeneity [13, 16, 18]. This problem was addressed using a modified WLS approach developed previously , which considers sampling error in the control event rate and generates bias terms that are used to correct the standard regression coefficients. Bias terms and bias-corrected regression coefficients were calculated using Excel, the formulae for which were verified using data from the original article describing this approach .
To calculate relative risk values predicted by the bias-corrected regression formula for corresponding control event rate values, back-translation was performed using Excel.
The GRADE framework was employed to provide a standardized summary rating of the pooled evidence for the outcome of interest based on key quality dimensions: risk of bias, consistency, directness, precision and publication bias [4, 19, 20]. The software GRADEpro (version 3.2 for Windows, 2008) was used.
Meta-analysis of main effects
Subgroup and meta-regression analysis of covariate effects
Table 1 shows the covariate values and corresponding effect sizes for each study. For the subgroup analysis that tested effect modification due to the type of control intervention used (no standardized information, generic information or simple decision aid without probability information), the weighted ANOVA was not significant (F = 2.33, degrees of freedom, df = 2, P = 0.11). As further support for the lack of effect of the type of control intervention on the control event rate, the second ANOVA between these two covariates also lacked significance (F = 0.49, df = 2, P = 0.62).
Regression coefficients for normalized IPDASi probabilities dimension score vs ln(OR) and logit control vs ln(OR)
Slope (standard errorcpa
Normalized IPDASi probabilities score vs ln(OR)
logit control vs ln(OR), non-bias-corrected
logit control vs ln(OR), bias-corrected
The multiple regression, which combined IPDASi probabilities dimension score and control event rate, was significant (P = 0.007) and accounted for slightly more variability (R 2 = 0.54). While effect modification due to control event rate was still significant in this model (P = 0.018), IPDASi probabilities dimension score lost significance (P = 0.561).
GRADE assessment of evidence quality
GRADE evidence quality assessment for the effect of decision aids on the accuracy of patient knowledge of outcome probabilities
Risk of bias
No serious inconsistency b
No serious indirectness
No serious imprecision c
Our analysis of main effects of decision aids on the accuracy of patient knowledge of outcome probabilities includes unpublished data from three studies in addition to the 14 studies previously included in the 2011 Cochrane analysis for this outcome. Compared to this earlier analysis, the added data slightly increase the pooled relative risk (from 1.74 to 1.80) and maintain the finding that all studies uniformly favor decision aids; additionally, they slightly increase the level of heterogeneity (from I 2 of 83% to 87%) . As recognized in the previous Cochrane review , this substantial level of heterogeneity limits the interpretability of the random effects pooled estimate since it represents an average of possible real effects of decision aids that vary widely from setting to setting. Thus an investigation of the factors that may influence this variation is warranted to better understand the conditions under which decision aids have their greatest effects [2, 3, 5, 6].
Given that factors underlying real variation of intervention effects can include study-level characteristics of the comparator or control intervention, the active intervention or the study population [2, 3], the current investigation therefore analyzed the effects of three covariates chosen to represent each of these sources of variability. There was no evidence that the type of control intervention modifies either the effect size or the control event rate. This negative finding provides incidental support for an assumption integral to the third covariate analysis of effect modification by control event rate (see Methods). That is, any effect modification is unlikely to be confounded by the control intervention manipulating effect size via effects on the control event rate. Thus the control event rate can be more reliably interpreted as representing a study population’s baseline level of knowledge of outcome probabilities.
The second covariate, decision aid quality as represented by the IPDASi probabilities dimension score, was found to modify effect size, the positive relationship observed being consistent with the expectation that higher-quality decision aids produce larger effect sizes. Overall, this result provides tentative support for the predictive validity of the probabilities dimension of the IPDASi, although statistical significance is borderline (P = 0.037). Significance is lost, for example, when IPDASi probabilities dimension score is combined in multiple regression with control event rate – although for any bivariate regression to be sufficiently powered, a larger sample size would generally be advisable. Thus, additional studies are necessary to improve certainty regarding effect modification due to the IPDASi probabilities dimension score.
Nevertheless, there are reasons to expect that decision aid quality defined according to the IPDASi probabilities dimension does in reality modify the effectiveness of decision aids. Firstly, individual components of decision aid design on which the IPDASi probabilities dimension are based  are supported by a review of evidence providing biologic or theoretical plausibility . Secondly, subgroup analysis in the 2011 Cochrane review provides direct evidence for at least one design feature – that using numbers rather than words in decision aids to communicate probabilities improves knowledge of those probabilities to a statistically significantly greater extent . The components of decision aid design that may underlie variation in effect sizes are not restricted to the IPDASi probabilities domain, however, and the updated IPDAS review summarizing recent evidence for presenting probabilities  describes additional promising factors to explore in future analyses of effect modification. The effects of individual design factors were not examined here because of the top-down approach to selecting covariates and the decision to restrict their number to minimize the risk of detecting spurious effect modification due to multiple comparisons. The selection of specific factors for future analyses should consider both the theoretical plausibility of effect modification, and whether the selected design feature is likely to be consistently relevant for all decision aids since some features, such as those relevant only to screening decisions, restrict the sample size of studies available for analysis.
For the third covariate, control event rate, the negatively sloped relationship is highly significant (P = 0.001) and is slightly steeper after correcting for dependence of the effect measure on the control event rate, increasing confidence in true effect modification. Furthermore, when combined in multiple regression with IPDASi probabilities dimension scores added to the model, control event rate remained significant despite the low power for this bivariate analysis. In univariate analysis, approximately half the heterogeneity is accounted for by the control event rate. Thus control event rate, reflecting patients’ baseline level of knowledge of outcome probabilities, appears to be an important variable explaining heterogeneity of effects of decision aids on accuracy of knowledge of outcome probabilities, with greater relative effects expected when the baseline proportion of patients with accurate knowledge is lower.
Given the clinical utility of being able to define what types of patients benefit most from an intervention using the relationship between effect size and the control event rate (or level of baseline risk), one may ask how often is such a relationship significant for interventions in other contexts, and why is it not characterized more frequently? An analysis by Schmid and colleagues provides an informative answer . They examined 115 meta-analyses of clinical trials to detect whether there was an effect of control event rate on effect size. After correcting for dependence of the effect measure on control event rate and using a two-standard error rule of significance, they found linear correlations with ln(OR) in only 14% of meta-analyses. They proposed that such effect modification is more likely to be found when the meta-analysis includes a sufficient number of studies (ten or more), and comprises greater variation in control event rates across included studies. The current meta-analysis, which includes 17 studies and has widely ranging control event rates (0.08 to 0.77), is consistent with this observation. This follows from the idea that ‘heterogeneity is your friend’ since more heterogeneity provides a better opportunity to detect a covariate effect [38, 39].
Finally, by providing an explanation for heterogeneity, the quality of the pooled research was assessed with the GRADE framework [19, 20] as ‘moderate’ instead of ‘low’. This reflects that the current investigation of sources of heterogeneity improves the quality of the evidence from a body of 17 pooled studies by improving its interpretability and utility [2–4].
A limitation of investigating study-level sources of heterogeneity is that interpretation may be affected by confounding from other study-level factors, particularly those related to study design . These factors include both methodological aspects known to increase the risk of bias in an RCT (sequence generation, allocation concealment, blinding of patients, blinding of intervention providers, blinding of outcome assessors, and completeness of outcome data) and aspects of outcome measurement . Confounding by aspects of outcome measurement requires considering characteristics of the questions used to measure knowledge of probabilities. Evaluation questions can vary, for example, in the number of categories to select between within a multiple choice question, whether the question forces guessing (by not providing an option for ‘unsure’), whether numbers or words are used to represent probabilities, and in the precision used to define the probability ranges for each category. Some but not all of these characteristics are also design features of decision aids whose influence on improving knowledge of probabilities has been established – for example, that presenting probabilities as numbers is more effective than words [7, 40, 41]. Similarly, specific characteristics would be expected to influence the difficulty of evaluation questions. Although there is extensive research and standards that guide and support the presentation of probabilities in decision aids [14, 15], research into how relevant characteristics affect the difficulty of evaluation questions – and therefore influence the measurement of patient knowledge of probabilities – is lacking. It was not possible to conduct an analysis of these effects in the current study since descriptions of evaluation questions were not available for most studies. Considering how question difficulty has the potential to influence and confound estimates of baseline knowledge (control event rates), future research into this measurement issue is warranted.
The current sub-analysis increases the interpretability and utility of previously pooled evidence on the effects of decision aids for improving accuracy of knowledge of outcome probabilities by adding data for this outcome and characterizing the effects of two potential contributors to heterogeneity of decision aid effects: decision aid quality and the control event rate. While decision aid quality, as measured by the IPDASi probabilities dimension, may increase the effects of decision aids, this finding is of borderline significance and requires further analysis with data from additional studies. The control event rate – representing patients’ baseline level of knowledge of outcome probabilities – is a highly significant and substantial contributor to heterogeneity, with greater relative effects observed when the baseline proportion of patients with accurate knowledge is low. This suggests that decision aids are most effective in populations with low awareness. Further research may be warranted, however, to determine whether aspects of evaluation questions influence the measurement of knowledge of probabilities. Knowledge of how relative risk is expected to vary across a wide range of control event rates may be useful to inform policy judgments about the uptake of decision aids to inform patients of probabilities related to the outcomes of interventions or diagnostic tests in specific settings.
Control event rate
Degrees of freedom
International patient decision aid standards instrument
Randomized controlled trial
Weighted least squares.
Natalie Joseph-Williams (Institute of Primary Care and Public Health, Cardiff University) provided duplicate ratings for decision aids using the probabilities dimension of the IPDASi.
- Cochrane Handbook for Systematic Reviews of Interventions 4.2.6. The Cochrane Library, Issue 4. Edited by: Higgins JPT, Green S. 2006, Chichester, UK: John Wiley & Sons, Ltd
- Glasziou PP, Sanders SL: Investigating causes of heterogeneity in systematic reviews. Stat Med. 2002, 21: 1503-1511. 10.1002/sim.1183.View ArticlePubMedGoogle Scholar
- Thompson SG: Why sources of heterogeneity in meta-analysis should be investigated. BMJ. 1994, 309: 1351-1355. 10.1136/bmj.309.6965.1351.View ArticlePubMedPubMed CentralGoogle Scholar
- Guyatt GH, Oxman AD, Kunz R, Vist GE, Falck-Ytter Y, Schunemann HJ: What is ‘quality of evidence’ and why is it important to clinicians?. BMJ. 2008, 336: 995-998. 10.1136/bmj.39490.551019.BE.View ArticlePubMedPubMed CentralGoogle Scholar
- Schmid CH, Lau J, McIntosh MW, Cappelleri JC: An empirical study of the effect of the control rate as a predictor of treatment efficacy in meta-analysis of clinical trials. Stat Med. 1998, 17: 1923-1942. 10.1002/(SICI)1097-0258(19980915)17:17<1923::AID-SIM874>3.0.CO;2-6.View ArticlePubMedGoogle Scholar
- Sutton AJ, Abrams KR, Jones DR, Sheldon TA, Song F: Methods for Meta-Analysis in Medical Research. 2000, Chichester, England: WileyGoogle Scholar
- Stacey D, Bennett CL, Barry MJ, Col NF, Eden KB, Holmes-Rovner M, Llewellyn-Thomas H, Lyddiatt A, Legare F, Thomson R: Decision aids for people facing health treatment or screening decisions. Cochrane Database Syst Rev. 2011, CD001431Google Scholar
- O’Connor AM, Tugwell P, Wells GA, Elmslie T, Jolly E, Hollingworth G, McPherson R, Drake E, Hopman W, Mackenzie T: Randomized trial of a portable, self-administered decision aid for postmenopausal women considering long-term preventive hormone therapy. Med Decis Making. 1998, 18: 295-303. 10.1177/0272989X9801800307.View ArticlePubMedGoogle Scholar
- Trevena LJ, Zikmund-Fisher B, Edwards A, Gaissmaier W, Galesic M, Han P, King J, Lawson M, Linder S, Lipkus I, Ozanne E, Peters E, Timmermans D, Woloshin S: Presenting probabilities. Update of the International Patient Decision Aids Standards (IPDAS) Collaboration’s Background Document. Edited by: Volk R, Llewellyn-Thomas H. 2012, Ottawa, Canada: IPDAS CollaborationGoogle Scholar
- O’Connor AM, Bennett CL, Stacey D, Barry M, Col NF, Eden KB, Entwistle VA, Fiset V, Holmes-Rovner M, Khangura S, Llewellyn-Thomas H, Rovner D: Decision aids for people facing health treatment or screening decisions. Cochrane Database Syst Rev. 2009, CD001431Google Scholar
- Sun X, Briel M, Walter SD, Guyatt GH: Is a subgroup effect believable? Updating criteria to evaluate the credibility of subgroup analyses. BMJ. 2010, 340: c117-10.1136/bmj.c117.View ArticlePubMedGoogle Scholar
- Guyatt GH, Oxman AD, Kunz R, Woodcock J, Brozek J, Helfand M, Alonso-Coello P, Glasziou P, Jaeschke R, Akl EA, Norris S, Vist G, Dahm P, Shukla VK, Higgins J, Falck-Ytter Y, Schünemann HJ, GRADE Working Group: GRADE guidelines: 7. Rating the quality of evidence – inconsistency. J Clin Epidemiol. 2011, 64: 1294-1302. 10.1016/j.jclinepi.2011.03.017.View ArticlePubMedGoogle Scholar
- Sharp SJ, Thompson SG, Altman DG: The relation between treatment benefit and underlying risk in meta-analysis. BMJ. 1996, 313: 735-738. 10.1136/bmj.313.7059.735.View ArticlePubMedPubMed CentralGoogle Scholar
- Elwyn G, O’Connor AM, Bennett C, Newcombe RG, Politi M, Durand MA, Drake E, Joseph-Williams N, Khangura S, Saarimaki A, Sivell S, Stiel M, Bernstein SJ, Col N, Coulter A, Eden K, Härter M, Rovner MH, Moumjid N, Stacey D, Thomson R, Whelan T, van der Weijden T, Edwards A: Assessing the quality of decision support technologies using the International Patient Decision Aid Standards instrument (IPDASi). PLoS ONE. 2009, 4: e4705-10.1371/journal.pone.0004705.View ArticlePubMedPubMed CentralGoogle Scholar
- Trevena LJ, Davey HM, Barratt A, Butow P, Caldwell P: A systematic review on communicating with patients about evidence. J Eval Clin Pract. 2006, 12: 13-23. 10.1111/j.1365-2753.2005.00596.x.View ArticlePubMedGoogle Scholar
- Senn S: Importance of trends in the interpretation of an overall odds ratio in the meta-analysis of clinical trials. Stat Med. 1994, 13: 293-296. 10.1002/sim.4780130310.View ArticlePubMedGoogle Scholar
- van Houwelingen H, Senn S: Investigating underlying risk as a source of heterogeneity in meta-analysis. Stat Med. 1999, 18: 110-115. 10.1002/(SICI)1097-0258(19990115)18:1<110::AID-SIM14>3.0.CO;2-C.View ArticlePubMedGoogle Scholar
- Walter SD: Variation in baseline risk as an explanation of heterogeneity in meta-analysis. Stat Med. 1997, 16: 2883-2900. 10.1002/(SICI)1097-0258(19971230)16:24<2883::AID-SIM825>3.0.CO;2-B.View ArticlePubMedGoogle Scholar
- Guyatt GH, Oxman AD, Vist GE, Kunz R, Falck-Ytter Y, Alonso-Coello P, Schunemann HJ: GRADE: an emerging consensus on rating quality of evidence and strength of recommendations. BMJ. 2008, 336: 924-926. 10.1136/bmj.39489.470347.AD.View ArticlePubMedPubMed CentralGoogle Scholar
- Balshem H, Helfand M, Schunemann HJ, Oxman AD, Kunz R, Brozek J, Vist GE, Falck-Ytter Y, Meerpohl J, Norris S, Guyatt GH: GRADE guidelines: 3. Rating the quality of evidence. J Clin Epidemiol. 2011, 64: 401-406. 10.1016/j.jclinepi.2010.07.015.View ArticlePubMedGoogle Scholar
- Whelan T, Sawka C, Levine M, Gafni A, Reyno L, Willan A, Julian J, Dent S, Abu-Zahra H, Chouinard E, Tozer R, Pritchard K, Bodendorfer I: Helping patients make informed choices: a randomized trial of a decision aid for adjuvant chemotherapy in lymph node-negative breast cancer. J Natl Cancer Inst. 2003, 95: 581-587. 10.1093/jnci/95.8.581.View ArticlePubMedGoogle Scholar
- Gattellari M, Ward JE: Does evidence-based information about screening for prostate cancer enhance consumer decision-making? A randomised controlled trial. J Med Screen. 2003, 10: 27-39. 10.1258/096914103321610789.View ArticlePubMedGoogle Scholar
- Schapira MM, VanRuiswyk J: The effect of an illustrated pamphlet decision-aid on the use of prostate cancer screening tests. J Fam Pract. 2000, 49: 418-424.PubMedGoogle Scholar
- Wolf AM, Schorling JB: Does informed consent alter elderly patients’ preferences for colorectal cancer screening? Results of a randomized trial. J Gen Intern Med. 2000, 15: 24-30. 10.1046/j.1525-1497.2000.01079.x.View ArticlePubMedPubMed CentralGoogle Scholar
- Whelan T, Levine M, Willan A, Gafni A, Sanders K, Mirsky D, Chambers S, O’Brien MA, Reid S, Dubois S: Effect of a decision aid on knowledge and treatment decision making for breast cancer surgery: a randomized trial. JAMA. 2004, 292: 435-441. 10.1001/jama.292.4.435.View ArticlePubMedGoogle Scholar
- Bastian LA, McBride CM, Fish L, Lyna P, Farrell D, Lipkus IM, Rimer BK, Siegler IC: Evaluating participants’ use of a hormone replacement therapy decision-making intervention. Patient Educ Couns. 2002, 48: 283-291. 10.1016/S0738-3991(02)00048-4.View ArticlePubMedGoogle Scholar
- McBride CM, Bastian LA, Halabi S, Fish L, Lipkus IM, Bosworth HB, Rimer BK, Siegler IC: A tailored intervention to aid decision-making about hormone replacement therapy. Am J Public Health. 2002, 92: 1112-1114. 10.2105/AJPH.92.7.1112.View ArticlePubMedPubMed CentralGoogle Scholar
- Lerman C, Biesecker B, Benkendorf JL, Kerner J, Gomez-Caminero A, Hughes C, Reed MM: Controlled trial of pretest education approaches to enhance informed decision-making for BRCA1 gene testing. J Natl Cancer Inst. 1997, 89: 148-157. 10.1093/jnci/89.2.148.View ArticlePubMedGoogle Scholar
- McAlister FA, Man-Son-Hing M, Straus SE, Ghali WA, Anderson D, Majumdar SR, Gibson P, Cox JL, Fradette M: Impact of a patient decision aid on care among patients with nonvalvular atrial fibrillation: a cluster randomized trial. CMAJ. 2005, 173: 496-501. 10.1503/cmaj.050091.View ArticlePubMedPubMed CentralGoogle Scholar
- Man-Son-Hing M, Laupacis A, O’Connor AM, Biggs J, Drake E, Yetisir E, Hart RG: A patient decision aid regarding antithrombotic therapy for stroke prevention in atrial fibrillation: a randomized controlled trial. JAMA. 1999, 282: 737-743. 10.1001/jama.282.8.737.View ArticlePubMedGoogle Scholar
- Dodin S, Legare F, Daudelin G, Tetroe J, O’Connor A: Making a decision about hormone replacement therapy. A randomized controlled trial. Can Fam Physician. 2001, 47: 1586-1593.PubMedPubMed CentralGoogle Scholar
- Johnson BR, Schwartz A, Goldberg J, Koerber A: A chairside aid for shared decision making in dentistry: a randomized controlled trial. J Dent Educ. 2006, 70: 133-141.PubMedGoogle Scholar
- Laupacis A, O’Connor AM, Drake ER, Rubens FD, Robblee JA, Grant FC, Wells PS: A decision aid for autologous pre-donation in cardiac surgery – a randomized trial. Patient Educ Couns. 2006, 61: 458-466. 10.1016/j.pec.2005.05.014.View ArticlePubMedGoogle Scholar
- Mathieu E, Barratt A, Davey HM, McGeechan K, Howard K, Houssami N: Informed choice in mammography screening: a randomized trial of a decision aid for 70-year-old women. Arch Intern Med. 2007, 167: 2039-2046. 10.1001/archinte.167.19.2039.View ArticlePubMedGoogle Scholar
- Weymiller AJ, Montori VM, Jones LA, Gafni A, Guyatt GH, Bryant SC, Christianson TJ, Mullan RJ, Smith SA: Helping patients with type 2 diabetes mellitus make treatment decisions: statin choice randomized trial. Arch Intern Med. 2007, 167: 1076-1082. 10.1001/archinte.167.10.1076.View ArticlePubMedGoogle Scholar
- Kuppermann M, Norton ME, Gates E, Gregorich SE, Learman LA, Nakagawa S, Feldstein VA, Lewis J, Washington AE, Nease RF: Computerized prenatal genetic testing decision-assisting tool: a randomized controlled trial. Obstet Gynecol. 2009, 113: 53-63.View ArticlePubMedGoogle Scholar
- Vandemheen KL, O’Connor A, Bell SC, Freitag A, Bye P, Jeanneret A, Berthiaume Y, Brown N, Wilcox P, Ryan G, Brager N, Rabin H, Morrison N, Gibson P, Jackson M, Paterson N, Middleton P, Aaron SD: Randomized trial of a decision aid for patients with cystic fibrosis considering lung transplantation. Am J Respir Crit Care Med. 2009, 180: 761-768. 10.1164/rccm.200903-0421OC.View ArticlePubMedGoogle Scholar
- Kim CK, Berlin JA: The use of meta-analysis in pharmacoepidemiology. Textbook of Pharmacoepidemiology. Edited by: Strom BL, Kimmel SE. 2006, Chichester, UK: John Wiley & Sons, Inc, 356-357.Google Scholar
- Berlin JA: Invited commentary: benefits of heterogeneity in meta-analysis of data from epidemiologic studies. Am J Epidemiol. 1995, 142: 383-387.PubMedGoogle Scholar
- Marteau TM, Saidi G, Goodburn S, Lawton J, Michie S, Bobrow M: Numbers or words? A randomized controlled trial of presenting screen negative results to pregnant women. Prenat Diagn. 2000, 20: 714-718. 10.1002/1097-0223(200009)20:9<714::AID-PD906>3.0.CO;2-4.View ArticlePubMedGoogle Scholar
- Man-Son-Hing M, O’Connor AM, Drake E, Biggs J, Hum V, Laupacis A: The effect of qualitative vs. quantitative presentation of probability estimates on patient decision-making: a randomized trial. Health Expect. 2002, 5: 246-255. 10.1046/j.1369-6513.2002.00188.x.View ArticlePubMedGoogle Scholar
This article is published under license to 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 cited.