Multivariate meta-analysis of multiple outcomes: characteristics and predictors of borrowing of strength from Cochrane reviews.

OBJECTIVES: Multivariate meta-analysis allows the joint synthesis of multiple outcomes accounting for their correlation. This enables borrowing of strength (BoS) across outcomes, which may lead to greater efficiency and even different conclusions compared to separate univariate meta-analyses. However, multivariate meta-analysis is complex to apply, so guidance is needed to flag (in advance of analysis) when the approach is most useful. STUDY DESIGN AND SETTING: We use 43 Cochrane intervention reviews to empirically investigate the characteristics of meta-analysis datasets that are associated with a larger BoS statistic (from 0 to 100%) when applying a bivariate meta-analysis of binary outcomes. RESULTS: Four characteristics were identified as strongly associated with BoS: the total number of studies, the number of studies with the outcome of interest, the percentage of studies missing the outcome of interest, and the largest absolute within-study correlation. Using these characteristics, we then develop a model for predicting BoS in a new dataset, which is shown to have good performance (an adjusted R2 of 50%). Applied examples are used to illustrate the use of the BoS prediction model. CONCLUSIONS: Cochrane reviewers mainly use univariate meta-analysis methods, but the identified characteristics associated with BoS and our subsequent prediction model for BoS help to flag when a multivariate meta-analysis may also be beneficial in Cochrane reviews with multiple binary outcomes. Extension to non-Cochrane reviews and other outcome types is still required.

One-stage individual participant data meta-analysis models for continuous and binary outcomes: Comparison of treatment coding options and estimation methods.

A one-stage individual participant data (IPD) meta-analysis synthesizes IPD from multiple studies using a general or generalized linear mixed model. This produces summary results (eg, about treatment effect) in a single step, whilst accounting for clustering of participants within studies (via a stratified study intercept, or random study intercepts) and between-study heterogeneity (via random treatment effects). We use simulation to evaluate the performance of restricted maximum likelihood (REML) and maximum likelihood (ML) estimation of one-stage IPD meta-analysis models for synthesizing randomized trials with continuous or binary outcomes. Three key findings are identified. First, for ML or REML estimation of stratified intercept or random intercepts models, a t-distribution based approach generally improves coverage of confidence intervals for the summary treatment effect, compared with a z-based approach. Second, when using ML estimation of a one-stage model with a stratified intercept, the treatment variable should be coded using "study-specific centering" (ie, 1/0 minus the study-specific proportion of participants in the treatment group), as this reduces the bias in the between-study variance estimate (compared with 1/0 and other coding options). Third, REML estimation reduces downward bias in between-study variance estimates compared with ML estimation, and does not depend on the treatment variable coding; for binary outcomes, this requires REML estimation of the pseudo-likelihood, although this may not be stable in some situations (eg, when data are sparse). Two applied examples are used to illustrate the findings.

Do comorbidities predict pain and function in knee osteoarthritis following an exercise intervention, and do they moderate the effect of exercise? Analyses of data from three randomized controlled trials.

BACKGROUND: Although exercise is a core treatment for people with knee osteoarthritis (OA), it is currently unknown whether those with additional comorbidities respond differently to exercise than those without. We explored whether comorbidities predict pain and function following an exercise intervention in people with knee OA, and whether they moderate response to: exercise versus no exercise; and enhanced exercise versus usual exercise-based care. METHODS: We undertook analyses of existing data from three randomized controlled trials (RCTs): TOPIK (n = 217), APEX (n = 352) and Benefits of Effective Exercise for knee Pain (BEEP) (n = 514). All three RCTs included: adults with knee pain attributable to OA; physiotherapy-led exercise; data on six comorbidities (overweight/obesity, pain elsewhere, anxiety/depression, cardiac problems, diabetes mellitus and respiratory conditions); the outcomes of interest (six-month Western Ontario and McMaster Universities Arthritis Index knee pain and function). Adjusted mixed models were fitted where data was available; otherwise linear regression models were used. RESULTS: Obesity compared with underweight/normal body mass index was significantly associated with knee pain following exercise, as was the presence compared with absence of anxiety/depression. The presence of cardiac problems was significantly associated with the effect of enhanced versus usual exercise-based care for knee function, indicating that enhanced exercise may be less effective for improving knee function in people with cardiac problems. Associations for all other potential prognostic factors and moderators were weak and not statistically significant. CONCLUSIONS: Obesity and anxiety/depression predicted pain and function outcomes in people offered an exercise intervention, but only the presence of cardiac problems might moderate the effect of exercise for knee OA. Further confirmatory investigations are required.

Predictors of the effects of treatment for shoulder pain: protocol of an individual participant data meta-analysis.

BACKGROUND: Shoulder pain is one of the most common presentations of musculoskeletal pain with a 1-month population prevalence of between 7 and 26%. The overall prognosis of shoulder pain is highly variable with 40% of patients reporting persistent pain 1 year after consulting their primary care clinician. Despite evidence for prognostic value of a range of patient and disease characteristics, it is not clear whether these factors also predict (moderate) the effect of specific treatments (such as corticosteroid injection, exercise, or surgery). OBJECTIVES: This study aims to identify predictors of treatment effect (i.e. treatment moderators or effect modifiers) by investigating the association between a number of pre-defined individual-level factors and the effects of commonly used treatments on shoulder pain and disability outcomes. METHODS: This will be a meta-analysis using individual participant data (IPD). Eligible trials investigating the effectiveness of advice and analgesics, corticosteroid injection, physiotherapy-led exercise, psychological interventions, and/or surgical treatment in patients with shoulder conditions will be identified from systematic reviews and an updated systematic search for trials, and risk of bias will be assessed. Authors of all eligible trials will be approached for data sharing. Outcomes measured will be shoulder pain and disability, and our previous work has identified candidate predictors. The main analysis will be conducted using hierarchical one-stage IPD meta-analysis models, examining the effect of treatment-predictor interaction on outcome for each of the candidate predictors and describing relevant subgroup effects where significant interaction effects are detected. Random effects will be used to account for clustering and heterogeneity. Sensitivity analyses will be based on (i) exclusion of trials at high risk of bias, (ii) use of restricted cubic splines to model potential non-linear associations for candidate predictors measured on a continuous scale, and (iii) the use of a two-stage IPD meta-analysis framework. DISCUSSION: Our study will collate, appraise, and synthesise IPD from multiple studies to examine potential predictors of treatment effect in order to assess the potential for better and more efficient targeting of specific treatments for individuals with shoulder pain. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42018088298.

Minimum sample size for developing a multivariable prediction model: PART II - binary and time-to-event outcomes.

When designing a study to develop a new prediction model with binary or time-to-event outcomes, researchers should ensure their sample size is adequate in terms of the number of participants (n) and outcome events (E) relative to the number of predictor parameters (p) considered for inclusion. We propose that the minimum values of n and E (and subsequently the minimum number of events per predictor parameter, EPP) should be calculated to meet the following three criteria: (i) small optimism in predictor effect estimates as defined by a global shrinkage factor of ≥0.9, (ii) small absolute difference of ≤ 0.05 in the model's apparent and adjusted Nagelkerke's R2 , and (iii) precise estimation of the overall risk in the population. Criteria (i) and (ii) aim to reduce overfitting conditional on a chosen p, and require prespecification of the model's anticipated Cox-Snell R2 , which we show can be obtained from previous studies. The values of n and E that meet all three criteria provides the minimum sample size required for model development. Upon application of our approach, a new diagnostic model for Chagas disease requires an EPP of at least 4.8 and a new prognostic model for recurrent venous thromboembolism requires an EPP of at least 23. This reinforces why rules of thumb (eg, 10 EPP) should be avoided. Researchers might additionally ensure the sample size gives precise estimates of key predictor effects; this is especially important when key categorical predictors have few events in some categories, as this may substantially increase the numbers required.

Minimum sample size for developing a multivariable prediction model: Part I - Continuous outcomes.

In the medical literature, hundreds of prediction models are being developed to predict health outcomes in individuals. For continuous outcomes, typically a linear regression model is developed to predict an individual's outcome value conditional on values of multiple predictors (covariates). To improve model development and reduce the potential for overfitting, a suitable sample size is required in terms of the number of subjects (n) relative to the number of predictor parameters (p) for potential inclusion. We propose that the minimum value of n should meet the following four key criteria: (i) small optimism in predictor effect estimates as defined by a global shrinkage factor of ≥0.9; (ii) small absolute difference of ≤ 0.05 in the apparent and adjusted R2 ; (iii) precise estimation (a margin of error ≤ 10% of the true value) of the model's residual standard deviation; and similarly, (iv) precise estimation of the mean predicted outcome value (model intercept). The criteria require prespecification of the user's chosen p and the model's anticipated R2 as informed by previous studies. The value of n that meets all four criteria provides the minimum sample size required for model development. In an applied example, a new model to predict lung function in African-American women using 25 predictor parameters requires at least 918 subjects to meet all criteria, corresponding to at least 36.7 subjects per predictor parameter. Even larger sample sizes may be needed to additionally ensure precise estimates of key predictor effects, especially when important categorical predictors have low prevalence in certain categories.

Persistent sex disparities in clinical outcomes with percutaneous coronary intervention: Insights from 6.6 million PCI procedures in the United States.

BACKGROUND: Prior studies have reported inconsistencies in the baseline risk profile, comorbidity burden and their association with clinical outcomes in women compared to men. More importantly, there is limited data around the sex differences and how these have changed over time in contemporary percutaneous coronary intervention (PCI) practice. METHODS AND RESULTS: We used the Nationwide Inpatient Sample to identify all PCI procedures based on ICD-9 procedure codes in the United States between 2004-2014 in adult patients. Descriptive statistics were used to describe sex-based differences in baseline characteristics and comorbidity burden of patients. Multivariable logistic regressions were used to investigate the association between these differences and in-hospital mortality, complications, length of stay and total hospital charges. Among 6,601,526 patients, 66% were men and 33% were women. Women were more likely to be admitted with diagnosis of NSTEMI (non-ST elevation acute myocardial infarction), were on average 5 years older (median age 68 compared to 63) and had higher burden of comorbidity defined by Charlson score ≥3. Women also had higher in-hospital crude mortality (2.0% vs 1.4%) and any complications compared to men (11.1% vs 7.0%). These trends persisted in our adjusted analyses where women had a significant increase in the odds of in-hospital mortality men (OR 1.20 (95% CI 1.16,1.23) and major bleeding (OR 1.81 (95% CI 1.77,1.86). CONCLUSION: In this national unselected contemporary PCI cohort, there are significant sex-based differences in presentation, baseline characteristics and comorbidity burden. These differences do not fully account for the higher in-hospital mortality and procedural complications observed in women.

Individual participant data meta-analysis of continuous outcomes: A comparison of approaches for specifying and estimating one-stage models.

One-stage individual participant data meta-analysis models should account for within-trial clustering, but it is currently debated how to do this. For continuous outcomes modeled using a linear regression framework, two competing approaches are a stratified intercept or a random intercept. The stratified approach involves estimating a separate intercept term for each trial, whereas the random intercept approach assumes that trial intercepts are drawn from a normal distribution. Here, through an extensive simulation study for continuous outcomes, we evaluate the impact of using the stratified and random intercept approaches on statistical properties of the summary treatment effect estimate. Further aims are to compare (i) competing estimation options for the one-stage models, including maximum likelihood and restricted maximum likelihood, and (ii) competing options for deriving confidence intervals (CI) for the summary treatment effect, including the standard normal-based 95% CI, and more conservative approaches of Kenward-Roger and Satterthwaite, which inflate CIs to account for uncertainty in variance estimates. The findings reveal that, for an individual participant data meta-analysis of randomized trials with a 1:1 treatment:control allocation ratio and heterogeneity in the treatment effect, (i) bias and coverage of the summary treatment effect estimate are very similar when using stratified or random intercept models with restricted maximum likelihood, and thus either approach could be taken in practice, (ii) CIs are generally best derived using either a Kenward-Roger or Satterthwaite correction, although occasionally overly conservative, and (iii) if maximum likelihood is required, a random intercept performs better than a stratified intercept model. An illustrative example is provided.

Simulation-based power calculations for planning a two-stage individual participant data meta-analysis.

BACKGROUND: Researchers and funders should consider the statistical power of planned Individual Participant Data (IPD) meta-analysis projects, as they are often time-consuming and costly. We propose simulation-based power calculations utilising a two-stage framework, and illustrate the approach for a planned IPD meta-analysis of randomised trials with continuous outcomes where the aim is to identify treatment-covariate interactions. METHODS: The simulation approach has four steps: (i) specify an underlying (data generating) statistical model for trials in the IPD meta-analysis; (ii) use readily available information (e.g. from publications) and prior knowledge (e.g. number of studies promising IPD) to specify model parameter values (e.g. control group mean, intervention effect, treatment-covariate interaction); (iii) simulate an IPD meta-analysis dataset of a particular size from the model, and apply a two-stage IPD meta-analysis to obtain the summary estimate of interest (e.g. interaction effect) and its associated p-value; (iv) repeat the previous step (e.g. thousands of times), then estimate the power to detect a genuine effect by the proportion of summary estimates with a significant p-value. RESULTS: In a planned IPD meta-analysis of lifestyle interventions to reduce weight gain in pregnancy, 14 trials (1183 patients) promised their IPD to examine a treatment-BMI interaction (i.e. whether baseline BMI modifies intervention effect on weight gain). Using our simulation-based approach, a two-stage IPD meta-analysis has < 60% power to detect a reduction of 1 kg weight gain for a 10-unit increase in BMI. Additional IPD from ten other published trials (containing 1761 patients) would improve power to over 80%, but only if a fixed-effect meta-analysis was appropriate. Pre-specified adjustment for prognostic factors would increase power further. Incorrect dichotomisation of BMI would reduce power by over 20%, similar to immediately throwing away IPD from ten trials. CONCLUSIONS: Simulation-based power calculations could inform the planning and funding of IPD projects, and should be used routinely.

Bayesian bivariate meta-analysis of correlated effects: Impact of the prior distributions on the between-study correlation, borrowing of strength, and joint inferences.

Multivariate random-effects meta-analysis allows the joint synthesis of correlated results from multiple studies, for example, for multiple outcomes or multiple treatment groups. In a Bayesian univariate meta-analysis of one endpoint, the importance of specifying a sensible prior distribution for the between-study variance is well understood. However, in multivariate meta-analysis, there is little guidance about the choice of prior distributions for the variances or, crucially, the between-study correlation, ρB; for the latter, researchers often use a Uniform(-1,1) distribution assuming it is vague. In this paper, an extensive simulation study and a real illustrative example is used to examine the impact of various (realistically) vague prior distributions for ρB and the between-study variances within a Bayesian bivariate random-effects meta-analysis of two correlated treatment effects. A range of diverse scenarios are considered, including complete and missing data, to examine the impact of the prior distributions on posterior results (for treatment effect and between-study correlation), amount of borrowing of strength, and joint predictive distributions of treatment effectiveness in new studies. Two key recommendations are identified to improve the robustness of multivariate meta-analysis results. First, the routine use of a Uniform(-1,1) prior distribution for ρB should be avoided, if possible, as it is not necessarily vague. Instead, researchers should identify a sensible prior distribution, for example, by restricting values to be positive or negative as indicated by prior knowledge. Second, it remains critical to use sensible (e.g. empirically based) prior distributions for the between-study variances, as an inappropriate choice can adversely impact the posterior distribution for ρB, which may then adversely affect inferences such as joint predictive probabilities. These recommendations are especially important with a small number of studies and missing data.

Perioperative supplementation with a fruit and vegetable juice powder concentrate and postsurgical morbidity: A double-blind, randomised, placebo-controlled clinical trial.

BACKGROUND & AIMS: Surgical trauma leads to an inflammatory response that causes surgical morbidity. Reduced antioxidant micronutrient (AM)a levels and/or excessive levels of Reactive Oxygen Species (ROS)b have previously been linked to delayed wound healing and presence of chronic wounds. We aimed to evaluate the effect of pre-operative supplementation with encapsulated fruit and vegetable juice powder concentrate (JuicePlus+®) on postoperative morbidity and Quality of Life (QoL)c. METHODS: We conducted a randomised, double-blind, placebo-controlled two-arm parallel clinical trial evaluating postoperative morbidity following lower third molar surgery. Patients aged between 18 and 65 years were randomised to take verum or placebo for 10 weeks prior to surgery and during the first postoperative week. The primary endpoint was the between-group difference in QoL over the first postoperative week, with secondary endpoints being related to other measures of postoperative morbidity (pain and trismus). RESULTS: One-hundred and eighty-three out of 238 randomised patients received surgery (Intention-To-Treat population). Postoperative QoL tended to be higher in the active compared to the placebo group. Furthermore, reduction in mouth opening 2 days after surgery was 3.1 mm smaller (95% CI 0.1, 6.1), the mean pain score over the postoperative week was 8.5 mm lower (95% CI 1.8, 15.2) and patients were less likely to experience moderate to severe pain on postoperative day 2 (RR 0.58, 95% CI 0.35, 0.95), comparing verum to placebo groups. CONCLUSION: Pre-operative supplementation with a fruit and vegetable supplement rich in AM may improve postoperative QoL and reduce surgical morbidity and post-operative complications after surgery. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT01145820; Registered June 16, 2010.

Deriving percentage study weights in multi-parameter meta-analysis models: with application to meta-regression, network meta-analysis and one-stage individual participant data models.

Many meta-analysis models contain multiple parameters, for example due to multiple outcomes, multiple treatments or multiple regression coefficients. In particular, meta-regression models may contain multiple study-level covariates, and one-stage individual participant data meta-analysis models may contain multiple patient-level covariates and interactions. Here, we propose how to derive percentage study weights for such situations, in order to reveal the (otherwise hidden) contribution of each study toward the parameter estimates of interest. We assume that studies are independent, and utilise a decomposition of Fisher's information matrix to decompose the total variance matrix of parameter estimates into study-specific contributions, from which percentage weights are derived. This approach generalises how percentage weights are calculated in a traditional, single parameter meta-analysis model. Application is made to one- and two-stage individual participant data meta-analyses, meta-regression and network (multivariate) meta-analysis of multiple treatments. These reveal percentage study weights toward clinically important estimates, such as summary treatment effects and treatment-covariate interactions, and are especially useful when some studies are potential outliers or at high risk of bias. We also derive percentage study weights toward methodologically interesting measures, such as the magnitude of ecological bias (difference between within-study and across-study associations) and the amount of inconsistency (difference between direct and indirect evidence in a network meta-analysis).

Guidance for deriving and presenting percentage study weights in meta-analysis of test accuracy studies.

Percentage study weights in meta-analysis reveal the contribution of each study toward the overall summary results and are especially important when some studies are considered outliers or at high risk of bias. In meta-analyses of test accuracy reviews, such as a bivariate meta-analysis of sensitivity and specificity, the percentage study weights are not currently derived. Rather, the focus is on representing the precision of study estimates on receiver operating characteristic plots by scaling the points relative to the study sample size or to their standard error. In this article, we recommend that researchers should also provide the percentage study weights directly, and we propose a method to derive them based on a decomposition of Fisher information matrix. This method also generalises to a bivariate meta-regression so that percentage study weights can also be derived for estimates of study-level modifiers of test accuracy. Application is made to two meta-analyses examining test accuracy: one of ear temperature for diagnosis of fever in children and the other of positron emission tomography for diagnosis of Alzheimer's disease. These highlight that the percentage study weights provide important information that is otherwise hidden if the presentation only focuses on precision based on sample size or standard errors. Software code is provided for Stata, and we suggest that our proposed percentage weights should be routinely added on forest and receiver operating characteristic plots for sensitivity and specificity, to provide transparency of the contribution of each study toward the results. This has implications for the PRISMA-diagnostic test accuracy guidelines that are currently being produced.

Subgrouping and TargetEd Exercise pRogrammes for knee and hip OsteoArthritis (STEER OA): a systematic review update and individual participant data meta-analysis protocol.

INTRODUCTION: Knee and hip osteoarthritis (OA) is a leading cause of disability worldwide. Therapeutic exercise is a recommended core treatment for people with knee and hip OA, however, the observed effect sizes for reducing pain and improving physical function are small to moderate. This may be due to insufficient targeting of exercise to subgroups of people who are most likely to respond and/or suboptimal content of exercise programmes. This study aims to identify: (1) subgroups of people with knee and hip OA that do/do not respond to therapeutic exercise and to different types of exercise and (2) mediators of the effect of therapeutic exercise for reducing pain and improving physical function. This will enable optimal targeting and refining the content of future exercise interventions. METHODS AND ANALYSIS: Systematic review and individual participant data meta-analyses. A previous comprehensive systematic review will be updated to identify randomised controlled trials that compare the effects of therapeutic exercise for people with knee and hip OA on pain and physical function to a non-exercise control. Lead authors of eligible trials will be invited to share individual participant data. Trial-level and participant-level characteristics (for baseline variables and outcomes) of included studies will be summarised. Meta-analyses will use a two-stage approach, where effect estimates are obtained for each trial and then synthesised using a random effects model (to account for heterogeneity). All analyses will be on an intention-to-treat principle and all summary meta-analysis estimates will be reported as standardised mean differences with 95% CI. ETHICS AND DISSEMINATION: Research ethical or governance approval is exempt as no new data are being collected and no identifiable participant information will be shared. Findings will be disseminated via national and international conferences, publication in peer-reviewed journals and summaries posted on websites accessed by the public and clinicians. PROSPERO REGISTRATION NUMBER: CRD42017054049.

Association of maternal serum PAPP-A levels, nuchal translucency and crown-rump length in first trimester with adverse pregnancy outcomes: retrospective cohort study.

OBJECTIVE: Are first trimester serum pregnancy-associated plasma protein-A (PAPP-A), nuchal translucency (NT) and crown-rump length (CRL) prognostic factors for adverse pregnancy outcomes? METHOD: Retrospective cohort, women, singleton pregnancies (UK 2011-2015). Unadjusted and multivariable logistic regression. OUTCOMES: small for gestational age (SGA), pre-eclampsia (PE), preterm birth (PTB), miscarriage, stillbirth, perinatal mortality and neonatal death (NND). RESULTS: A total of 12 592 pregnancies: 852 (6.8%) PTB, 352 (2.8%) PE, 1824 (14.5%) SGA, 73 (0.6%) miscarriages, 37(0.3%) stillbirths, 73 perinatal deaths (0.6%) and 38 (0.30%) NND. Multivariable analysis: lower odds of SGA [adjusted odds ratio (aOR) 0.88 (95% CI 0.85,0.91)], PTB [0.92 (95%CI 0.88,0.97)], PE [0.91 (95% CI 0.85,0.97)] and stillbirth [0.71 (95% CI 0.52,0.98)] as PAPP-A increases. Lower odds of SGA [aOR 0.79 (95% CI 0.70,0.89)] but higher odds of miscarriage [aOR 1.75 95% CI (1.12,2.72)] as NT increases, and lower odds of stillbirth as CRL increases [aOR 0.94 95% CI (0.89,0.99)]. Multivariable analysis of three factors together demonstrated strong associations: a) PAPP-A, NT, CRL and SGA, b) PAPP-A and PTB, c) PAPP-A, CRL and PE, d) NT and miscarriage. CONCLUSIONS: Pregnancy-associated plasma protein-A, NT and CRL are independent prognostic factors for adverse pregnancy outcomes, particularly PAPP-A and SGA with lower PAPP-A associated with increased risk. © 2017 John Wiley & Sons, Ltd.

One-stage individual participant data meta-analysis models: estimation of treatment-covariate interactions must avoid ecological bias by separating out within-trial and across-trial information.

Stratified medicine utilizes individual-level covariates that are associated with a differential treatment effect, also known as treatment-covariate interactions. When multiple trials are available, meta-analysis is used to help detect true treatment-covariate interactions by combining their data. Meta-regression of trial-level information is prone to low power and ecological bias, and therefore, individual participant data (IPD) meta-analyses are preferable to examine interactions utilizing individual-level information. However, one-stage IPD models are often wrongly specified, such that interactions are based on amalgamating within- and across-trial information. We compare, through simulations and an applied example, fixed-effect and random-effects models for a one-stage IPD meta-analysis of time-to-event data where the goal is to estimate a treatment-covariate interaction. We show that it is crucial to centre patient-level covariates by their mean value in each trial, in order to separate out within-trial and across-trial information. Otherwise, bias and coverage of interaction estimates may be adversely affected, leading to potentially erroneous conclusions driven by ecological bias. We revisit an IPD meta-analysis of five epilepsy trials and examine age as a treatment effect modifier. The interaction is -0.011 (95% CI: -0.019 to -0.003; p = 0.004), and thus highly significant, when amalgamating within-trial and across-trial information. However, when separating within-trial from across-trial information, the interaction is -0.007 (95% CI: -0.019 to 0.005; p = 0.22), and thus its magnitude and statistical significance are greatly reduced. We recommend that meta-analysts should only use within-trial information to examine individual predictors of treatment effect and that one-stage IPD models should separate within-trial from across-trial information to avoid ecological bias. © 2016 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

Meta-analysis using individual participant data: one-stage and two-stage approaches, and why they may differ.

Meta-analysis using individual participant data (IPD) obtains and synthesises the raw, participant-level data from a set of relevant studies. The IPD approach is becoming an increasingly popular tool as an alternative to traditional aggregate data meta-analysis, especially as it avoids reliance on published results and provides an opportunity to investigate individual-level interactions, such as treatment-effect modifiers. There are two statistical approaches for conducting an IPD meta-analysis: one-stage and two-stage. The one-stage approach analyses the IPD from all studies simultaneously, for example, in a hierarchical regression model with random effects. The two-stage approach derives aggregate data (such as effect estimates) in each study separately and then combines these in a traditional meta-analysis model. There have been numerous comparisons of the one-stage and two-stage approaches via theoretical consideration, simulation and empirical examples, yet there remains confusion regarding when each approach should be adopted, and indeed why they may differ. In this tutorial paper, we outline the key statistical methods for one-stage and two-stage IPD meta-analyses, and provide 10 key reasons why they may produce different summary results. We explain that most differences arise because of different modelling assumptions, rather than the choice of one-stage or two-stage itself. We illustrate the concepts with recently published IPD meta-analyses, summarise key statistical software and provide recommendations for future IPD meta-analyses. © 2016 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

Meta-analysis of randomized phase II trials to inform subsequent phase III decisions.

BACKGROUND: If multiple Phase II randomized trials exist then meta-analysis is favorable to increase statistical power and summarize the existing evidence about an intervention's effect in order to help inform Phase III decisions. We consider some statistical issues for meta-analysis of Phase II trials for this purpose, as motivated by a real example involving nine Phase II trials of bolus thrombolytic therapy in acute myocardial infarction with binary outcomes. METHODS: We propose that a Bayesian random effects logistic regression model is most suitable as it models the binomial distribution of the data, helps avoid continuity corrections, accounts for between-trial heterogeneity, and incorporates parameter uncertainty when making inferences. The model also allows predictions that inform Phase III decisions, and we show how to derive: (i) the probability that the intervention will be truly beneficial in a new trial, and (ii) the probability that, in a new trial with a given sample size, the 95% credible interval for the odds ratio will be entirely in favor of the intervention. As Phase II trials are potentially optimistic due to bias in design and reporting, we also discuss how skeptical prior distributions can reduce this optimism to make more realistic predictions. RESULTS: In the example, the model identifies heterogeneity in intervention effect missed by an I-squared of 0%. Prediction intervals accounting for this heterogeneity are shown to support subsequent Phase III trials. The probability of success in Phase III trials increases as the sample size increases, up to 0.82 for intracranial hemorrhage and 0.79 for reinfarction outcomes. CONCLUSIONS: The choice of meta-analysis methods can influence the decision about whether a trial should proceed to Phase III and thus need to be clearly documented and investigated whenever a Phase II meta-analysis is performed.

Prostacyclin inhibits IFN-gamma-stimulated cytokine expression by reduced recruitment of CBP/p300 to STAT1 in a SOCS-1-independent manner.

Increasing evidence indicates that pulmonary arterial hypertension is a vascular inflammatory disease. Prostacyclin (PGI(2)) is widely used to treat pulmonary arterial hypertension and is believed to benefit patients largely through vasodilatory effects. PGI(2) is also increasingly believed to have anti-inflammatory effects, including decreasing leukocyte cytokine production, yet few mechanistic details exist to explain how these effects are mediated at the transcriptional level. Because activated monocytes are critical sources of MCP-1 and other cytokines in cardiovascular inflammation, we examined the effects of iloprost on IFN-gamma- and IL-6-stimulated cytokine production in human monocytes. We found that iloprost inhibited IFN-gamma- and IL-6-induced MCP-1, IL-8, RANTES, and TNF-alpha production in monocytes, indicating wide-ranging anti-inflammatory action. We found that activation of STAT1 was critical for IFN-gamma-induced MCP-1 production and demonstrated that iloprost inhibited STAT1 activation by several actions as follows: 1) iloprost inhibited the phosphorylation of STAT1-S727 in the transactivation domain, thereby reducing recruitment of the histone acetylase and coactivator CBP/p300 to STAT1; 2) iloprost selectively inhibited activation of JAK2 but not JAK1, both responsible for activation of STAT1 via phosphorylation of STAT1-Y701, resulting in reduced nuclear recruitment and activation of STAT1; and 3) SOCS-1, which normally terminates IFN-gamma-signaling, was not involved in iloprost-mediated inhibition of STAT1, indicating divergence from the classical pathway for terminating IFN-gamma-signaling. We conclude that PGI(2) exerts anti-inflammatory action by inhibiting STAT1-induced cytokine production, in part by targeting the transactivation domain-induced recruitment of the histone acetylase CBP/p300.