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Type of presentation: poster

Category I: Chance-related Heterogeneity
Type of presentation: Oral

Quantifying the Amount of Heterogeneity in Meta-Analysis: A Comparison of

Knapp G
Department of Statistics, University of Dortmund, Germany

In random effects meta-analysis, several confidence intervals on the between-trial variance have been proposed. These confidence intervals can be broadly categorized in two classes. Hardy and Thompson (1996) as well as Biggerstaff and Tweedie (1997) propose two intervals based on likelihood approaches. Biggerstaff and Tweedie (1997) and Knapp et al. (2006) consider appropriate quadratic forms in the treatment effects estimates and propose intervals based on approximate distributions of their quadratic forms. The performance of the confidence intervals with respect to actual confidence coefficient and average length rather differs for different effect size. Knapp et al. (2006) and Viechtbauer (2006) have conducted simulation studies for the effect sizes mean difference and log odds ratio for the above mentioned confidence interval. In the present paper, further results for normal and binary outcome measures will be provided with focus on the effect size standardized mean difference and risk difference. References Biggerstaff BJ, Tweedie RL (1997). Incorporating variability in estimates of heterogeneity in the random effects model in meta-analysis. Statistics in Medicine 16, 753-768. Hardy RJ, Thompson SG (1996). A likelihood approach to meta-analysis with random effects. Statistics in Medicine 15, 619-629. Knapp G, Biggerstaff BJ, Hartung, J (2006). Assessing the amount of heterogeneity in random effects meta-analysis. Biometrical Journal 48, 271-285. Viechtbauer W (2006). Confidence intervals for the amount of heterogeneity in meta-analysis. Statistics in Medicine (in press). Category I: Chance-related Heterogeneity
Type of presentation: Oral

A Simple Prediction Interval for Random-Effects Meta-Analysis

Higgins JPT, Thompson SG, Spiegelhalter DJ
MRC Biostatistics Unit, Cambridge, UK

For situations in which heterogeneity cannot be suitably explained by study characteristics, a random-effects meta-analysis is a common approach to synthesizing results from a collection of clinical trials. It is usual to present the estimated mean of the random-effects distribution, along with its confidence interval, and often a test for heterogeneity. Indeed, many meta-analysis software packages produce only these statistics. We argue that this standard presentation of a random-effects meta-analysis is insufficient and potentially misleading, since it fails to illustrate the extent of variability in treatment effects across trials. The among-study standard deviation (often called tau) provides a quantification of the amount of heterogeneity, but can be awkward to interpret. We propose a simple prediction interval for the underlying effect in a future trial. This provides a useful description of the variability of effects across studies, and incorporates consideration of uncertainty in both the mean and the standard deviation of the random-effects distribution. Category I: Chance-related Heterogeneity
Type of presentation: Oral

Non-Parametric Modeling of the Relationship between Baseline Risk and
Treatment Benefit

Ghidey W, Stijnen T
Department of Epidemiology and Biostatistics, Erasmus Medical Center, Rotterdam, The Netherlands

In meta-analysis of clinical trials, often meta-regression analyses are performed to explain the heterogeneity in treatment effects that usually exist between trials. A popular explanatory variable is the risk observed in the control group, the baseline risk, which is usually linearly related. To investigate the relationship, however, fitting an ordinary least squares (OLS) regression of the treatment effect on the baseline risk or fitting a standard random effects meta-regression model can be very misleading (Sharp et al., 1996). The main criticism is that both methods do not consider the fact that the baseline risk is estimated from a finite sample and thus subjected to measurement error. In the literature alternative methods are suggested which accommodate this issue by assuming both the true baseline risks and the measurement errors normally distributed. However, in practice, the normality assumption on the baseline risk might be too strong to adequately describe the underlying true distribution. Among others, Ghidey et al. (2006) relax the normality assumption to more flexible distribution function. Here, we propose an alternative non-parametric method, which does not require any distributional assumption about the true baseline risk. We applied the functional measurement error model approach of Carroll et al. (2006). We illustrate our method on a number of simulated data sets and on a published meta-analysis data set. References: Sharp SJ, Thompson SG, and Altman DG. (1996). The relation between treatment benefit and underlying risk in meta-analysis. British Medical Journal; 313 (7059):735-38. Ghidey W, Lesaffre E, and Stijnen T. (2006). Semi-parametric modeling of the distribution of the baseline risk in meta-analysis. (submitted) Carroll RJ, Ruppert D, Stefanski LA, and Crainiceanu CM. (2006). Measurement Error in Nonlinear Models: A Modern Perspective (2nd ed.). Boca Raton: Chapman & Hall/CRC. Category I: Chance-related Heterogeneity
Type of presentation: Poster

Meta Analysis of Randomized Controlled Trials Describing the Effectiveness
of Venlafaxine in the Treatment of Major Depressive Disorder in Comparison
with Alternative Antidepressant Therapies – Effect on Response, Remission
and Relative Tolerability

Freemantle N, Tharmanathan P
University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK

Background: A number of different antidepressant types are available, and many randomized trials (most
modest in size and statistical power) have evaluated their relative effectiveness. Venlafaxine is a well
established antidepressant, and previous work has indicated that it may be superior to SSRIs in treating
depression. Various methods for meta analysis of binary data have been developed, including
theoretically exact conditional maximum likelihood methods for fixed effects analysis, and numerical
simulation methods based upon the Gibbs Sampler, both of which are statistically superior to standard
Methods: We conducted a meta analysis of all available trials comparing venlafaxine and SSRIs
examining the outcomes of response, remission and relative tolerability. Trials were identified through
searches of Medline, Embase, Cochrane Library and through accessing unpublished trials held by the
manufacturer. Results based on intention to treat analyses, were pooled using theoretically exact methods
for fixed effects, and numerical simulation using a Gibbs sampler for random effects. In the random
effects analyses the between trial component of variance is parameterized and estimated with uncertainty
from the data, rather than taken from the observed heterogeneity as in standard methods. Where
significant heterogeneity was observed we aimed to explore the causes using meta regression. In the
absence of significant heterogeneity we described the full random effects analyses to incorporate the
observed variability between the results of trials, and the uncertainty of that heterogeneity, in the estimate
of treatment effect.
Results: We identified 34 trials comparing venlafaxine with an SSRI, including 6374 patients.
Venlafaxine was compared with fluoxetine in 18 trials, with paroxetine in 6 trials and with sertraline in 4
trials. Other comparators were citalopram (2 trials), escilatopram (2 trials) and fluvoxamine (2 trials).
Response to venlafaxine was superior to that of alternative SSRIs, odds ratio 1.17 (95% CI 1.05 to 1.30; P
= 0.0052) for the fixed effects analysis, and 1.19 (95% CI 1.05 to 1.34) for the full random effects
analysis. There was no evidence of heterogeneity on this outcome (p 0.29, I² = 12% (95% CI = 0% to
For remission, venlafaxine was superior to SSRIs, odds ratio 1.24 (95% CI 1.10 to 1.40; P = 0.0004). There was some evidence of heterogeneity (P = 0.08, I² (inconsistency) = 31% (95% CI = 0% to 59%), however this had little effect on the full random effects model, odds ratio 1.27 (95% CI 1.11 to 1.49). Overall drop out was similar for SSRIs and venalfaxine. Conclusion: Venlafaxine is more effective then SSRIs in achieving response and remission, and appears similarly tolerated. Theoretically exact meta analysis based upon conditional maximum likelihood, and full random effects meta analysis based upon the empirically Bayesian Gibbs sampler, performed well and provided complementary results. Category I: Chance-related Heterogeneity
Type of presentation: Poster

Illustrating Heterogeneity Graphically - the Inclusion of the Estimated Inter-
Study Variation into Forest Plots for Random Effects Meta-Analyses
Skipka G
Institute for Quality and Efficiency in Health Care (IQWiG), Cologne, Germany

Background: Meta-analyses are widely used to combine the results of clinical studies by calculating
statistics for overall treatment effects. Basically, two different models exist for meta-analyses. The fixed
effects model (FEM) assumes each study is measuring the same treatment effect θ. Different estimations
for θ are expected to arise from sampling error only. By contrast, the random effects model (REM)
incorporates an inter-study variation τ2 taking heterogeneous results into account. Usually, it is assumed
that the true treatment effects are normally distributed with expectation θ and variance τ2. Although these
two approaches estimate different parameters (true effect vs. expectation of the distribution of true effects)
the results are represented in the same way in practice. Commonly, the point and interval estimation of θ
is drawn in a forest plot as a diamond. But the estimation for the inter-study variation τ2 is ignored in the
representation of the results of REMs.
Objectives: To suggest a graphical approach for including the estimated inter-study variation into forest
plots when representing the results of random effects meta-analyses.
Results: We include two rows for the summary statistics in the forest plot in case of REMs: The row
“total expectation (95% CI)” represents the point and interval estimation for θ. The row “total
heterogeneity (95% CI)” represents the interval [a−1.96×b; a+1.96×b], where a and b indicate estimators
for θ and τ, respectively. This “heterogeneity interval” delivers an approximated interval, where 95% of
the true effects are to be expected.
Conclusions: This proposed extension of the forest plot may be helpful to accurately distinguish the
results of meta-analyses from FEMs and REMs and to illustrate the amount of heterogeneity graphically.
Category II: Design- and Analysis-related Heterogeneity
Type of presentation: Oral

Meta-Analytic Methods for Diagnostic Test
Kaufmann J
Clinical Statistics EU, Schering AG Berlin, Germany

A diagnostic test is a procedure to increase the probability of a correct diagnosis in diseased and non-diseased patients or diseased and non-diseased observational units, e.g. vessel segments or liver segments. In contrast to therapeutic studies, diagnostic studies are applied to a mixed population of patients with and without the interested diseased. The most widely used performance measurement is the accuracy, an index of validity for the total population. In demonstrating the efficacy of a new diagnostic agent the true-positive rate (TPR), or sensitivity and the true- negative rate (TNR), or specificity described the outcome of a study. We consider how to combine independent studies of the same diagnostic test, where each study reports an estimated true-positive rate (TPR) and true-negative rate (TNR). A summary receiver operating characteristic curve SROC has been recommended to present the performance of a diagnostic test, based on data from several independent diagnostic studies (RCT’s). References Les Irwing et al (1994), Guidelines for Meta-Analyses Evaluating Diagnostic Tests Ann.Intern Med., 120; 667-676 Benjamin Littenberg, Lincoln E. Moses (1993), Estimating Diagnostic Accuracy from Multiple Conflicting Reports: A New Meta-analytic Method Med Dec Making, 13 : 313.321 Lincoln E. Moses et al (1993), Combining Independent Studies of a Diagnostic Test into a Summary ROC-Curve: Data Analytic Approaches and some Additional Considerations Statistics in Medicine, Vol. 12; 1293-1316 Category II: Design- and Analysis-related Heterogeneity
Type of presentation: Oral

Random-Effects Meta-Analysis of Studies Reporting Pairs of Sensitivity and
Specificity: A Comparison of Methods

Hamza TH , Reitsma JB2, Stijnen T1
1 Department of Epidemiology and Biostatistics, Erasmus MC - Erasmus University Medical Center, P.O.Box
1738, 3000 DR Rotterdam, The Netherlands
2 Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Academic Medical Center, University of Amsterdam, P.O.Box 22700, 1100 DE Amsterdam, The Netherlands A Random effects meta-analysis of diagnostic accuracy studies take into account both the within study variability and the between studies variability. The standard method to meta-analyze diagnostic tests when studies report a pair of sensitivity and specificity or a two by two table is the method of Littenberg and Moses [1]. This method has several shortcomings, including the following ones. It does not take account the heterogeneity across studies, assumes the proxy for the threshold is measured without error, does not consider the within study correlation between the estimates and 0.5 is added in each of the two by two table whenever a zero count is encountered. In this paper we considered three different random effects approaches: univariate, approximate bivariate and exact bivariate [2-4], which repair some or all of the shortcomings of Littenberg and Moses [1] approach. Besides, the bivariate approach enables one to derive easily different outcome measures, such as diagnostic odds ratio, likelihood ratios and predictive values. The methods are compared through a simulation study, in terms of bias, mean squared error and coverage probabilities of the confidence intervals. We varied the overall sensitivity and specificity, the between studies variances and covariance, the within study sample size and the number of studies included in the meta-analysis. The methods are illustrated using a published meta-analysis data set. The exact bivariate method performs better than the univariate and approximate bivariate methods, and gives unbiased estimates of the accuracy, slope and residual parameters. The coverage probabilities are also reasonably acceptable. In contrast the univariate and approximate bivariate methods can produce large biases with poor coverage probabilities especially if sample sizes of individual studies are small or if sensitivities or specificities are large. Keywords: Meta-analysis, diagnostic test, univariate random effects, bivariate random effects, sensitivity
and specificity.
References: Littenberg B, Moses LE. Estimating diagnostic-accuracy from multiple conflicting reports- a new meta-analytic method. Medical Decision Making. 1993; 13:313-321. Reistma JB, Glas AS, Rutjes AWS, Scholten RJPM, Bossuyt PM, Zwinderman AH. Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews. Journal of Clinical Epidemiology. 2005; 58:982-990. Arends LR, Hamza TH, van Houwelingen JC, Heijenbrok-kal MH, Hunink MGM, Stijnen T. Multivariate random effects meta-analysis of ROC curves. Submitted. Harbord RM, Deeks JJ, Egger M, Whiting P, Sterne JA. A unification of models for meta-analysis of diagnostic accuracy studies. Biostatistics. 2006; 1(1): 1-21. Category II: Design- and Analysis-related Heterogeneity
Type of presentation: Oral

Impact of Methodological Variations Between Randomized Clinical Trials on
Results of Meta-Analysis: A Systematic Review and Meta-Analyses of
Empirical Evidence
Mukhtar AM
, Timm J
Competence Center for Clinical Trials Bremen, University of Bremen, Germany

Background: Combining randomized clinical trials (RCTs) meta-analytically without considering their
variations in methodological quality ignores the importance of critical appraisal and may lead to biased results. Meta-epidemiological studies yielded inconsistent results in terms of the existence, direction and quantity of design- and analysis-related bias.
Objectives: The purpose of this study is to systematically review the literature comparing effect sizes of
high quality RCTs (HQ-RCTS) and of low quality RCTs (LQ-RCTs). Furthermore, we investigated whether other sources of heterogeneity (chance-, patient- and intervention-related variations) between RCTs were regarded in these comparisons. Methods: Systematic review and meta-analysis of empirical studies relating the quality of RCTs to their effect sizes. We searched the Cochrane Methodology Register, screened “related articles” of six key meta- epidemiological studies in Medline and checked references of included studies. We included studies which (i) compared the effect sizes of LQ-RCTs vs. HQ-RCTs, (ii) included one or more meta-analysis and (iii) assessed quality of RCTs using scores or the following individual components: generation of allocation sequence, concealment of allocation, blinding, handling of withdrawals. We excluded comparisons that used vote counting. We used the ratio of odds ratios (ROR, i.e. summary effect size of LQ-RCTs to that of HQ-RCTs) as an outcome. For quality scores and each quality component, we combined RORs using a random-effects model. We used regression analysis to investigate whether RORs differ as to medical conditions and interventions.
Results: 109 citations were retrieved for detailed evaluation. 23 studies including 103 empirical
comparisons between LQ-RCTs and HQ-RCTs were included in this review. These comparisons were based on 216 meta-analyses and 2273 RCTs. An overestimation of treatment effect by LQ-RCTs in comparison with HQ-RCTs was observed, when Jadad-Score was used for the appraisal of quality [ROR= 0.82 (95% CI: 0.68 - 0.98), 9 comparisons]. RCTs with inadequate allocation concealment and those without blinding exaggerated the effect size by 15% [ROR= 0.85 (0.79 – 0.92), 24 comparisons] and 11% [ROR= 0.89 (0.82 – 0.97), 28 comparisons] respectively, in comparison with HQ-RCTs in these aspects. No relation between the adequacy of allocation sequence and effect estimate was found [ROR= 0.92 (0.82 – 1.02), 12 comparisons]. Similarly, no difference was detected between LQ-RCTs and HQ-RCTs with regard to the appropriateness of reporting or handling of withdrawals (intension-to-treat-analysis) [ROR= 1.01 (0.97 - 1.05), 23 comparisons]. No association was found between RORs and the medical condition in which it was estimated. RORs calculated from one meta-analysis did not differ from those based on more than one meta-analysis. About half of the comparisons investigated other design characteristics and used a random-effects model for synthesis. Patient- and intervention-related variations between RCTs
Conclusions: Randomized trials with low quality score, inadequate concealment of allocation and without
blinding overestimate the efficacy of interventions. However, most of these empirical studies did not consider clinical causes of heterogeneity. This fact may confound the relation between methodological quality and effect size. Simultaneous investigation of diverse sources of heterogeneity is required. Category II: Design- and Analysis-related Heterogeneity
Type of presentation: Oral

Impact of Design Characteristics on Outcome in Studies on Tension-Type

Verhagen AP , Stijnen T2
1 Department of General Practice
2 Department of Epidemiology and Biostatistics
ErasmusMC, PO Box 2040, 3000 CA Rotterdam, The Netherlands

Objective: We studied the influence of the methodological quality of individual trials on the outcome of a
systematic review on conservative treatments in patients with tension-type headache.
Methods: Included were studies included in five systematic reviews on tension-type headache. From each
study we extracted the number of patients in both groups who recovered or improved during follow-up
and we extracted data on headache outcome measures such as severity, intensity and frequency.
Methodological quality was assessed using the Delphi list. Dichotomous as well as continuous outcomes
were used in the analysis. Regression analysis on separate design characteristics and the overall
methodological quality on size of treatment effect is performed.
Results: Out of the original dataset of 134 studies 61 studies fulfilled our selection criteria. The number of
studies presenting dichotomous was slightly larger than studies presenting continuous data. Our study
sample has a higher overall quality compared to the original sample and studies evaluating
pharmacological interventions were over represented.
All criteria show a non-significant relation with the effect estimate. Whether the outcome is measured
dichotomous or continuous appear to have a greater (significant) impact on treatment effect estimates
compared to the design characteristics.
Conclusion: In this study sample design characteristics do not show to have an impact on treatment effect
estimates, but the way the treatment effect is measured has a significant impact.
Category III: Patient- and Intervention-related Heterogeneity
Type of presentation: Oral

Patient-Related Heterogeneity may Cause Problems in the Interpretation of
Meta-Analysis Data from Cancer Studies

Hinke A , Louvet C2, Heinemann V3
1 WiSP Research Institute, Langenfeld, Germany
2 Service d'Oncologie, Médecine Interne, Hôpital St. Antoine, Paris, France 3 Med. Klinik III, Klinikum Großhadern, Munich, Germany During the past twenty years meta-analyses proved to be a valid tool to detect minor to moderate, yet clinically worthwhile improvements in the treatment of malignant diseases1. Often (especially when the analysis is based on published data or even conference presentations only) no or limited data on important covariates are available. On the other hand, cancer patients typically exhibit a large amount of disease heterogeneity even within a single trial. Advanced, inoperable pancreatic cancer is a malignancy with an extremely unfavourable course. After standard chemotherapy, consisting of single drug gemcitabine (GEM) since its introduction in the mid-90s, the median survival time is around six months. Since 2000, a considerable number of randomised trials examined the question, whether the addition of a second cytostatic drug might improve the prognosis. However, while progression-free survival was significantly prolonged in some of the individual studies, only one trial was able to show this for overall survival. A meta-analysis including 14 trials (reported between 2002 and 2006) comparing GEM vs. GEM+X, comprising a total of 4335 patients and predominantly based on published data (partly in form of conference presentations only), revealed a risk reduction in survival by combination chemotherapy with a pooled hazard ratio of 0.91 (95% conf. interval: 0.85 – 0.97, p=0.004). The overall test for heterogeneity resulted in p=0.78 (I²=0%) and consequently, the application of a random effects model gave virtually the same results as the fixed effects one, although there were some hints that the effect size depended on the class of drug X. Nevertheless, these presumably clear results proved to be only one side of the coin and probably clinically misleading. The publication on evidence of a strong (possibly qualitative) treatment by performance status interaction from one trial office2 led to similar analyses in several of the other trials (with 1682 patients in total). A meta-analysis of the trials with information on this patient-level covariate resulted in a much more relevant effect on survival in the good performance patients (hazard ratio: 0.76, 0.67 – 0.87, p<0.0001), while in those with an initial poor performance status combination therapy seems to be inefficient or even harmful to the patient (hazard ratio: 1.08, 0.90 – 1.29). In conclusion, a minimum of information on the treatment effects in selected subgroups based on established prognostic factors should be recommended in publications of large randomised cancer studies. References 1. Girlin DJ, Parmar MKB, Stenning S, Stephens RJ, Stewart LA. Clinical trials in cancer: principles and practice. Oxford Univ. Press, Oxford, 2003, pp 294ff 2. Boeck S, Hinke A, Wilkowski R, Heinemann V. Analysis of prognostic factors in patients with advanced pancreatic cancer: subgroup analysis of a randomized phase III trial comparing single-agent gemcitabine to the gemcitabine plus cisplatin combination. J Clin Oncol 2005; 23: 334s Category III: Patient- and Intervention-related Heterogeneity
Type of presentation: Poster

A Systematic Overview of Economic Analyses of Routine Influenza-Virus
Vaccination of Children During Inter-Pandemic Periods
Mau J
Heinrich Heine University Hospital, Institute of Statistics in Medicine, Düsseldorf, Germany

Influenza is a recurrent epidemic with a potential of world-wide "mega-kill".
An adequate level of immunization in populations is believed to provide a time window for production
and delivery of antiviral drug treatment. Though the issue seems compelling, decision-makers will not
engage into a costly program of immunization of a significant part of the population, easily.
Previous research found a reduction to one-third of influenza-like illnesses when more than 85% of
children were vaccinated, while the Japanese experience amounts to having prevented about 11,000 deaths
from pneumonia and influ-enza per year by rou-tinely vaccinating school-age children [2], and modelling
studies would imply containment of annual influenza epidemics by vacci-nation of about 60% of children
in the USA [1].
Therefore, targeted immunization of major spreaders and in pools of high contact rates seems a reasonable strategy under several aspects: (i) reduced incidence in targeted subpopulation as well as at community level ('herd immunity'), (ii) reduced influenza-related morbidity and mortality in vulnerable subpopulations, (iii) reduced health costs, (iv) increased productivity, and also (v) increased readiness for pandemic situations through expanded production capacity and logistics. With regard to these considerations, immunization of children, of day care, preschool and school ages, has come into focus. The discussion has mainly been led in the USA, and was taken up in Spring 2006 in the EU. The monetary commitment that routine immunization of children during inter-pandemic periods would entail has motivated economic studies of cost-benefit, too. Economic analyses differ in methodologies, age groups of chil-d-ren, health systems, assumptions about cost factors, and in including influ-enza-related mort-a-lity and vaccination-related adverse events or not, and results are clearly not uniform. Studies published in 2005 and 2006 are less optimistic, due to more conservative assumptions and a wider set of cost factors. Consequently, routine vaccination of all children is not seen as cost saving in general anymore, and routine vaccination of high-risk children of all ages plus routine vaccination of all children aged 6 to 23 months seems to emerge as currently best founded from a theoretical and empirical viewpoint. The systematic overview is based on a common decision-tree model in order to identify differences in modelling assumptions and lacking data. The dependence of most influential cost factors on differences in socio-cultural setting between the US, Asia, and Europe is the most consequential, though not unexpected finding. References [1] Halloran, M. E., et al.: Community interventions and the epidemic prevention potential. Vaccine 20 (2002) 3254 [2] Reichert, T. A., et al.: The Japanese experience with vaccinating schoolchildren against influenza. N Engl J Med 344 (2001) 889 Category III: Patient- and Intervention-related Heterogeneity
Type of presentation: Oral

Meta-Analysis Based on Individual Versus Aggregate Patient Data: A
Systematic Review of Empirical Comparisons
Mukhtar AM
, Timm J
Competence Center for Clinical Trials Bremen, University of Bremen, Germany

Background: Meta-analyses based on individual patient data (MA-IPD) from randomized clinical trials
(RCTs) are considered as the “gold standard” for performing systematic reviews by many authors 1, 2.
However, published mathematical derivations emphasize the equivalence of summary effect sizes
estimated by MA-IPD and by meta-analysis using aggregate patient data (MA-APD) 3, 4.The main
advantage of MA-IPD over MA-APD is suggested to be related to the investigation of differential
treatment effects in subsets of patients and interventions.
Objectives: The purpose of this study is to systematically review the literature dealing with empirical
comparisons between MA-IPD and MA-APD with regard to: (1) the incorporation of chance- related
heterogeneity when combining RCTs (synthetic function of MA) and (2) the investigation of
methodological and clinical causes of variations between RCTs (analytic function of MA).
Methods: We searched the Cochrane Methodology Register and Medline for citations related to MA-IPD.
Moreover, we screened fifty-one trialists' collaborations, the Cochrane Database of IPD Reviews and
references of included studies for relevant comparisons. We included comparisons which (i) were based
on RCTs and (ii) presented quantitative results for MA-IPD and MA-APD or from which these can be
calculated. We excluded citations comparing only the structures, costs or organization of both types. We
calculated the ratio of odds ratios (ROR, i.e. summary effect size of MA-APD to that of MA-IPD) for each
comparison and combined them using a random-effects model. Furthermore, we studied the relation
between summary effect sizes of MA-IPD and MA-APD using a weighted least squares regression.
Results: 53 citations were retrieved for detailed evaluation. 18 studies were included in this review.
Almost one third of them was in oncology. 23 empirical comparisons were extracted. Half of them used
mortality or survival as outcome measure. Only one comparison used a positive outcome. All MA-IPD
considered stratification by RCT and all but two of them combined the data after the intention-to-treat
principle. Most of the comparisons do not report on test of heterogeneity and combine the results in fixed
effects model. Weighted regression showed no difference between summary effect sizes of the studied
MA-IPD and MA-APD [OR= 0.99 (95%-CI 0.93 – 1.15). Meta-analysis of the RORs yielded similar
results [summary ROR= 0.99 (95% CI 0.55-1.78)]. Methodological variations of RCTs were considered in
13 comparisons, two of them found a relation, however inconsistent, between the quality of RCTs and the
effect size. Clinical heterogeneity was investigated by both types of meta-analysis in only 4 comparisons,
with no consistent differences in this regard.
Conclusions: (1) In accordance with the mathematical derivations 3, 4, no empirical evidence of
difference in the synthetic function of MA-IPD and MA-APD was found. (2) Most of the empirical studies
did not compare MA-IPD and MA-APD with regard to their analytic function. Therefore, this function
requires more investigation.
1. Clarke M, Stewart L. Individual patient data or published meta-analysis: a systematic review. Second International Conference Scientific Basis of Health Services & Fifth Annual Cochrane Colloquium; 1997, Oct 8-12; Amsterdam 2. Pignon JP, Courtial F, et al. Difficulties in searching meta-analyses based on individual patient data on potentially curative treatment in oncology. 9th Annual Cochrane Colloquium Abstracts; 2001, Oct; Lyon 3. Olkin I, Sampson A. Comparison of meta-analysis versus analysis of variance of individual patient data. Biometrics, 1998, 54 (1): 317-322 4. Mathew T, Nordstrom K. On the equivalence of meta-analysis using literature and using individual patient data. Biometrics, 1999, 55 (4): 1221-1223 Category III: Patient- and Intervention-related Heterogeneity
Type of presentation: Poster

Individual Patient Data for Meta-Analysis: Policy and Legal Frameworks of
Collaborative Research and Data Sharing in an Information Era

Mukhtar AM , König S2, Wolters R3
1 Competence Center for Clinical Trials Bremen, University of Bremen, Germany
2 Institute for Health Law and Medical Law, University of Bremen, Germany 3 Center for Applied Information Technologies, University of Bremen, Germany A growing interest in sharing research data can be observed in many disciplines, especially in genetics and molecular biology [Noor et al., 2006]. Thereby we recognize a shift towards decentralization of data, integration of various databases and sophistication as well as simplification of data linkage, manipulation and analysis. However this interest is confined mainly to data from observational studies and routinely collected data. The superiority of meta-analysis based on individual patient data (MA-IPD) in contrast to meta-analysis using aggregated patient data, is suggested to be mainly related to enabling: (1) the investigation of differential treatment effects in subgroups of patients and interventions - enabling more reliable investigation of clinical heterogeneity and tailoring of health care (2) the checking of data within trials for completeness – combating inadequate reporting and reducing reporting bias - and for plausibility - reducing errors and detecting fraud - (3) the standardization of variables between trials - enabling more synthesis – (4) the execution of time to event analysis according to the intention-to-treat principle as well as (5) the collection of more data by including unpublished randomized clinical trials – reducing publication bias -, and more patients and follow up data - reducing patient exclusion bias -. However, collaborative meta-analyses of randomized clinical trials based on individual patient data are still uncommon [Koopman et al., 2005; Mukhtar, 2006]. It is well recognized, that MA-IPD is a more time and resource consuming endeavour. Moreover, sharing individual patient data is facing limitations related to research cultures/ policies, privacy/ confidentiality, intellectual proprietary/ copy rights, patents and data infrastructure /deposition /management. The main focus of our study is confidentiality and privacy issues related to sharing individual patient data for research purposes. We reviewed the legal situation in Germany with regard to anonymisation, pseudonymisation and data security. Accordingly, we developed a model structure for data sharing. We addressed controversial issues such as balancing omission of identifiers and extending data analysis as well as the question whether patient consent for data sharing is required. Furthermore, we appraised relevant policy and legal frameworks from UK and USA. Literature Koopman L, van der Heijden G, et al. (2005) Methodology of subgroup analyses used in individual patient data meta-analyses versus meta-analyses on published data. XIII Cochrane Colloquium; Oct 22-26; Melbourne, Australia Mukhtar AM, Timm J (2006) Meta-analysis based on individual vs. aggregate patient data. A systematic review of empirical comparisons (unpublished) 06) Data sharing: how much doesn't get submitted to GenBank?(7): e228 Related Topics
Type of presentation: Oral

Sample Size Distribution - An Overlooked Source of Spurious Bias in Funnel
Rücker G
, Schwarzer G, Carpenter JR
Institute of Medical Biometry and Medical Informatics, University Medical Centre Freiburg, Germany

Background: Comparing the recently proposed arcsine test with established tests for publication bias in
meta-analyses with binary outcomes (1), we found that power and size depended on the variability of the
sample sizes of the included trials. With little variation in sample size, and no small (n < 20) trials,
established tests (2; 3) showed greatly inflated type 1 error, which was corrected by recent proposals (4;
5). By contrast, when the included trials had a range of sample sizes from 6 to 1000 or more, established
tests performed not worse. This talk explains these observations, and outlines the advantages of our newly
proposed arcsine test.
Methods: Tests on publication bias are based on a funnel plot, which plots standard error versus treatment
effect. If the outcome is binary, treatment is often measured by the log odds ratio (logOR). For this
measure, the standard error depends on the estimated treatment effect (4). This induces a relationship
between the performance of tests for publication bias and the sizes of the trials in the meta-analysis. We
discuss the effects and consequences of this.
We further propose using the variance stabilizing property of the arcsine transform, thus replacing logOR
by the arcsine risk difference in tests for publication bias. We show how this simple proposal avoids many
problems with existing tests.
Results: For fixed sample size and population treatment effects, the log odds ratio is approximately
related to its standard error through the hyperbolic cosine. Thus, if a meta-analysis contains a range of
study sizes, the final look of the funnel plot arises from a mix of these curves.
We found that excluding small sample sizes, regardless of the treatment effect, systematically removes
some of these curves from the mixture and creates an artificial appearance of publication bias. Further, if
trial sizes are small, we may get trials with no events in one or possibly both arms. Excluding these also
artificially creates the appearance of publication bias and additionally leads to an overestimate of the
treatment effect.
Conclusion: Using an arcsine risk difference alleviates the key problems with the log odds ratio
and log risk ratio. Specifically, it
• reduces error inflation in tests for publication bias, • reduces dependence of tests on sample size distribution, • does not require to exclude trials with no events in one or both groups, • is not affected by reversion of the outcome direction (in contrast to the risk ratio) , • has a geometric interpretation as an arc length corresponding to the risk difference, References [1] Rücker G, Schwarzer G, Carpenter JR. Arcsine test for publication bias in meta-analyses with binary outcomes. Statistics in Medicine 2006; Submitted. [2] Begg CB, Mazumdar M. Operating characteristics of a rank correlation test for publication bias. Biometrics 1994; 50:1088–1101. [3] Egger M, Smith GD, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. British Medical Journal 1997; 315:629–634. [4] Schwarzer G, Antes G, Schumacher M. A test for publication bias in meta-analysis with sparse binary data. Statistics in Medicine 2006; E-publ: 5 June 2006. [5] Harbord RM, Egger M, Sterne JA. A modified test for small-study effects in meta-analyses of controlled trials with binary endpoints. Statistics in Medicine 2005; E-publ: 12 Dec 2005. Related Topics
Type of presentation: Poster

Complexity and Reproducibility
Glattre E
Centre for Epidemiology and Biostatistics, Norwegian School of Veterinary Science, Norway

In fractal epidemiology causal analysis is carried out by the use of conventional statistical tools and fractal methods. The latter are necessary for investigating the causal complexity/heterogeneity of studies and their setting. Fractals, ubiquitous in the living world, are statistically self-similar quantities whose dimension is non-Euclidean. Fractal epidemiology contains an uncertainty-relation in which uncertainty related to the reproducibility of a study (uR) is inversely associated to the simultaneous uncertainty related to the accuracy of the study outcome (uO), that is, the more one reduces uO the more uR will increase and vice versa: uR ⋅ uO ≥ k > 0. Both uR and uO, defined in (0,1], are functions of the frequency of fractal sequences in the sequence set that describes the study. Linear and logistic (non-fractal) functions that mimic uR and uO have been developed. The surrogate functions make it easy to compute uR and uO. The uncertainty-relation has consequences of some interest: The minimum of uO is easy to determine for every study and the corresponding, simultaneous, maximal uRM follows directly from the uncertainty-relation. ℜ = 1 − uRM becomes a stochastic measure of the reproducibility of the study. For ℜ-values close to one, the study and its outcome may be regarded solid; for small ℜ-values, on the other hand, the study may be considered futile, a solitary phenomenon on the basis of which no firm conclusions can be drawn as to the tested hypothesis. ℜ is a quantity of great value in fractal meta-analysis and prediction. The uncertainty-relation may even be applied to traditional studies. Thus, the outcome of a prospective study presented as easy to reproduce, may well be considered worthless if there is good reason to believe that the study setting is ‘highly contaminated’ with fractals. Related Topics
Type of presentation: Oral

Calibrating, Interpreting and Combining the Evidence in Experiments
Kulinskaya E
, Morgenthaler S, Staudte RG
Imperial College London, UK

We propose a new approach to defining evidence in experiments. If adopted, this approach makes meta-analysis a much easier task. Many simple applications of statistics can be transformed onto the probit scale by means of variance stabilizing transformations (vst). Denote a statistic of an appropriate test by T and its vst by h(T). Then under both the null hypothesis and the alternatives, the evidence measure h(T) has a normal distribution with standard deviation one. Thus evidence as defined here is a random quantity with a well known distribution, and it has a standard error of one unit when estimating its expected value. The above statements are only approximately true, but true enough where it matters, for effect sizes and small to moderate sample sizes arising in applications. Each application requires its own special transformation, and the mathematical level required for applying them is minimal. Examples in the talk include standard 1-sample t-test, Welch t-test for two samples with unequal variances ( Kulinskaya and Staudte, 2006, Statistics in Medicine (in press)), and 1- and 2-sample binomial applications. The standard tasks such as sample size calculation, or finding confidence intervals for a parameter of interest become very easy to perform. The quality of achieved variance stabilisation and coverage probabilities of resulting confidence intervals are demonstrated by simulation. It is also very easy to combine the evidence from several studies. Transform observed values of k statistics T1,T2, …,Tk and calculate the combined evidence The book is to be published by Wiley in 2007.



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