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

Patient-Level Pooled Analysis of Endovascular Ultrasound Renal Denervation or a Sham Procedure 6 Months After Medication Escalation: The RADIANCE Clinical Trial Program

BACKGROUND

The randomized, sham-controlled RADIANCE-HTN (A Study of the Recor Medical Paradise System in Clinical Hypertension) SOLO, RADIANCE-HTN TRIO, and RADIANCE II (A Study of the Recor Medical Paradise System in Stage II Hypertension) trials independently met their primary end point of a greater reduction in daytime ambulatory systolic blood pressure (SBP) 2 months after ultrasound renal denervation (uRDN) in patients with hypertension. To characterize the longer-term effectiveness and safety of uRDN versus sham at 6 months, after the blinded addition of antihypertensive treatments (AHTs), we pooled individual patient data across these 3 similarly designed trials.

METHODS

Patients with mild to moderate hypertension who were not on AHT or with hypertension resistant to a standardized combination triple AHT were randomized to uRDN (n=293) versus sham (n=213); they were to remain off of added AHT throughout 2 months of follow-up unless specified blood pressure (BP) criteria were exceeded. In each trial, if monthly home BP was ≥135/85 mm Hg from 2 to 5 months, standardized AHT was sequentially added to target home BP <135/85 mm Hg under blinding to initial treatment assignment. Six-month outcomes included baseline- and AHT-adjusted change in daytime ambulatory, home, and office SBP; change in AHT; and safety. Linear mixed regression models using all BP measurements and change in AHT from baseline through 6 months were used.

RESULTS

Patients (70% men) were 54.1±9.3 years of age with a baseline daytime ambulatory/home/office SBP of 150.5±9.8/151.0±12.4/155.5±14.4 mm Hg, respectively. From 2 to 6 months, BP decreased in both groups with AHT titration, but fewer uRDN patients were prescribed AHT (P=0.004), and fewer additional AHT were prescribed to uRDN patients versus sham patients (P=0.001). Whereas the unadjusted between-group difference in daytime ambulatory SBP was similar at 6 months, the baseline and medication-adjusted between-group difference at 6 months was -3.0 mm Hg (95% CI, -5.7, -0.2; P=0.033), in favor of uRDN+AHT. For home and office SBP, the adjusted between-group differences in favor of uRDN+AHT over 6 months were -5.4 mm Hg (-6.8, -4.0; P<0.001) and -5.2 mm Hg (-7.1, -3.3; P<0.001), respectively. There was no heterogeneity between trials. Safety outcomes were few and did not differ between groups.

CONCLUSIONS

This individual patient-data analysis of 506 patients included in the RADIANCE trials demonstrates the maintenance of BP-lowering efficacy of uRDN versus sham at 6 months, with fewer added AHTs.

REGISTRATION

URL: https://www.clinicaltrials.gov; Unique identifiers: NCT02649426 and NCT03614260.

Use of multiple covariates in assessing treatment-effect modifiers: A methodological review of individual participant data meta-analyses

Individual participant data (IPD) meta-analyses of randomised trials are considered a reliable way to assess participant-level treatment effect modifiers but may not make the best use of the available data. Traditionally, effect modifiers are explored one covariate at a time, which gives rise to the possibility that evidence of treatment-covariate interaction may be due to confounding from a different, related covariate. We aimed to evaluate current practice when estimating treatment-covariate interactions in IPD meta-analysis, specifically focusing on involvement of additional covariates in the models. We reviewed 100 IPD meta-analyses of randomised trials, published between 2015 and 2020, that assessed at least one treatment-covariate interaction. We identified four approaches to handling additional covariates: (1) Single interaction model (unadjusted): No additional covariates included (57/100 IPD meta-analyses); (2) Single interaction model (adjusted): Adjustment for the main effect of at least one additional covariate (35/100); (3) Multiple interactions model: Adjustment for at least one two-way interaction between treatment and an additional covariate (3/100); and (4) Three-way interaction model: Three-way interaction formed between treatment, the additional covariate and the potential effect modifier (5/100). IPD is not being utilised to its fullest extent. In an exemplar dataset, we demonstrate how these approaches lead to different conclusions. Researchers should adjust for additional covariates when estimating interactions in IPD meta-analysis providing they adjust their main effects, which is already widely recommended. Further, they should consider whether more complex approaches could provide better information on who might benefit most from treatments, improving patient choice and treatment policy and practice.

Mortality in randomised controlled trials using paclitaxel-coated devices for femoropopliteal interventional procedures: an updated patient-level meta-analysis

BACKGROUND

Numerous randomised clinical trials and real-world studies have supported the safety of paclitaxel-coated devices for the treatment of femoropopliteal occlusive disease. However, a 2018 summary-level meta-analysis suggested an increased mortality risk for paclitaxel-coated devices compared with uncoated control devices. This study presents an updated analysis of deaths using the most complete and current data available from pivotal trials of paclitaxel-coated versus control devices.

METHODS

Ten trials comparing paclitaxel-coated versus control devices were included in a patient-level pooled analysis. Cox regression models were used to evaluate the effect of paclitaxel exposure on risk of death in both intention-to-treat (ITT; primary analysis) and three as-treated analysis sets accounting for treatment group crossover at the index procedure and over time. The effect of paclitaxel dose and baseline covariates were also evaluated.

FINDINGS

A total of 2666 participants were included with a median follow-up of 4·9 years. No significant increase in deaths was observed for patients treated with paclitaxel-coated devices. This was true in the ITT analysis (hazard ratio [HR] 1·14, 95% CI 0·93-1·40), the as-treated analysis (HR 1·13, 95% CI 0·92-1·39), and in two crossover analyses: 1·07 (0·87-1·31) when late crossovers were censored and 1·04 (0·84-1·28) when crossovers were analysed from the date of paclitaxel exposure. There was no significant effect of paclitaxel dose on mortality risk.

INTERPRETATION

This meta-analysis found no association between paclitaxel-coated device exposure and risk of death, providing reassurance to patients, physicians, and regulators on the safety of paclitaxel-coated devices.

FUNDING

Becton Dickinson, Boston Scientific, Cook, Medtronic, Philips, Surmodics, and TriReme Medical.

Methods for Integrating Trials and Non-experimental Data to Examine Treatment Effect Heterogeneity

Estimating treatment effects conditional on observed covariates can improve the ability to tailor treatments to particular individuals. Doing so effectively requires dealing with potential confounding, and also enough data to adequately estimate effect moderation. A recent influx of work has looked into estimating treatment effect heterogeneity using data from multiple randomized controlled trials and/or observational datasets. With many new methods available for assessing treatment effect heterogeneity using multiple studies, it is important to understand which methods are best used in which setting, how the methods compare to one another, and what needs to be done to continue progress in this field. This paper reviews these methods broken down by data setting: aggregate-level data, federated learning, and individual participant-level data. We define the conditional average treatment effect and discuss differences between parametric and nonparametric estimators, and we list key assumptions, both those that are required within a single study and those that are necessary for data combination. After describing existing approaches, we compare and contrast them and reveal open areas for future research. This review demonstrates that there are many possible approaches for estimating treatment effect heterogeneity through the combination of datasets, but that there is substantial work to be done to compare these methods through case studies and simulations, extend them to different settings, and refine them to account for various challenges present in real data.

Two-stage or not two-stage? That is the question for IPD meta-analysis projects

Individual participant data meta-analysis (IPDMA) projects obtain, check, harmonise and synthesise raw data from multiple studies. When undertaking the meta-analysis, researchers must decide between a two-stage or a one-stage approach. In a two-stage approach, the IPD are first analysed separately within each study to obtain aggregate data (e.g., treatment effect estimates and standard errors); then, in the second stage, these aggregate data are combined in a standard meta-analysis model (e.g., common-effect or random-effects). In a one-stage approach, the IPD from all studies are analysed in a single step using an appropriate model that accounts for clustering of participants within studies and, potentially, between-study heterogeneity (e.g., a general or generalised linear mixed model). The best approach to take is debated in the literature, and so here we provide clearer guidance for a broad audience. Both approaches are important tools for IPDMA researchers and neither are a panacea. If most studies in the IPDMA are small (few participants or events), a one-stage approach is recommended due to using a more exact likelihood. However, in other situations, researchers can choose either approach, carefully following best practice. Some previous claims recommending to always use a one-stage approach are misleading, and the two-stage approach will often suffice for most researchers. When differences do arise between the two approaches, often it is caused by researchers using different modelling assumptions or estimation methods, rather than using one or two stages per se.

Nonlinear effects and effect modification at the participant-level in IPD meta-analysis part 2: methodological guidance is available

OBJECTIVES

To review methodological guidance for nonlinear covariate-outcome associations (NL), and linear effect modification and nonlinear effect modification (LEM and NLEM) at the participant level in individual participant data meta-analyses (IPDMAs) and their power requirements.

STUDY DESIGN AND SETTING

We searched Medline, Embase, Web of Science, Scopus, PsycINFO and the Cochrane Library to identify methodology publications on IPDMA of LEM, NL or NLEM (PROSPERO CRD42019126768).

RESULTS

Through screening 6,466 records we identified 54 potential articles of which 23 full texts were relevant. Nine further relevant publications were published before or after the literature search and were added. Of these 32 references, 21 articles considered LEM, 6 articles NL or NLEM and 6 articles described sample size calculations. A book described all four. Sample size may be calculated through simulation or closed form. Assessments of LEM or NLEM at the participant level need to be based on within-trial information alone. Nonlinearity (NL or NLEM) can be modeled using polynomials or splines to avoid categorization.

CONCLUSION

Detailed methodological guidance on IPDMA of effect modification at participant-level is available. However, methodology papers for sample size and nonlinearity are rarer and may not cover all scenarios. On these aspects, further guidance is needed.

Nonlinear effects and effect modification at the participant-level in IPD meta-analysis part 1: analysis methods are often substandard

OBJECTIVES

To review analysis methods used for linear effect modification (LEM), nonlinear covariate-outcome associations (NL) and nonlinear effect modification (NLEM) at the participant-level in individual participant data meta-analysis (IPDMA).

STUDY DESIGN AND SETTING

We searched Medline, Embase, Web of Science, Scopus, PsycINFO and the Cochrane Library to identify IPDMA of randomized controlled trials (PROSPERO CRD42019126768). We investigated if and how IPDMA examined LEM, NL and NLEM, including whether aggregation bias was addressed and if power was considered.

RESULTS

We screened 6,466 records, randomly sampled 207 and identified 100 IPDMA of LEM, NL or NLEM. Power for LEM was calculated a priori in 3 IPDMA. Of 100 IPDMA, 94 analyzed LEM, 4 NLEM and 8 NL. One-stage models were favoured for all three (56%, 100%, 50%, respectively). Two-stage models were used in 15%, 0% and 25% of IPDMA with unclear descriptions in 30%, 0% and 25%, respectively. Only 12% of one-stage LEM and NLEM IPDMA provided sufficient detail to confirm they had addressed aggregation bias.

CONCLUSION

Investigation of effect modification at the participant-level is common in IPDMA projects, but methods are often open to bias or lack detailed descriptions. Nonlinearity of continuous covariates and power of IPDMA are rarely assessed.

Calcium supplementation to prevent pre-eclampsia: protocol for an individual participant data meta-analysis, network meta-analysis and health economic evaluation

INTRODUCTION

Low dietary calcium intake is a risk factor for pre-eclampsia, a major contributor to maternal and perinatal mortality and morbidity worldwide. Calcium supplementation can prevent pre-eclampsia in women with low dietary calcium. However, the optimal dose and timing of calcium supplementation are not known. We plan to undertake an individual participant data (IPD) meta-analysis of randomised trials to determine the effects of various calcium supplementation regimens in preventing pre-eclampsia and its complications and rank these by effectiveness. We also aim to evaluate the cost-effectiveness of calcium supplementation to prevent pre-eclampsia.

METHODS AND ANALYSIS

We will identify randomised trials on calcium supplementation before and during pregnancy by searching major electronic databases including Embase, CINAHL, MEDLINE, CENTRAL, PubMed, Scopus, AMED, LILACS, POPLINE, AIM, IMSEAR, ClinicalTrials.gov and the WHO International Clinical Trials Registry Platform, without language restrictions, from inception to February 2022. Primary researchers of the identified trials will be invited to join the International Calcium in Pregnancy Collaborative Network and share their IPD. We will check each study's IPD for consistency with the original authors before standardising and harmonising the data. We will perform a series of one-stage and two-stage IPD random-effect meta-analyses to obtain the summary intervention effects on pre-eclampsia with 95% CIs and summary treatment-covariate interactions (maternal risk status, dietary intake, timing of intervention, daily dose of calcium prescribed and total intake of calcium). Heterogeneity will be summarised using tau2, I2 and 95% prediction intervals for effect in a new study. Sensitivity analysis to explore robustness of statistical and clinical assumptions will be carried out. Minor study effects (potential publication bias) will be investigated using funnel plots. A decision analytical model for use in low-income and middle-income countries will assess the cost-effectiveness of calcium supplementation to prevent pre-eclampsia.

ETHICS AND DISSEMINATION

No ethical approvals are required. We will store the data in a secure repository in an anonymised format. The results will be published in peer-reviewed journals.

PROSPERO REGISTRATION NUMBER

CRD42021231276.

Patient-Level Pooled Analysis of Ultrasound Renal Denervation in the Sham-Controlled RADIANCE II, RADIANCE-HTN SOLO, and RADIANCE-HTN TRIO Trials

Importance

Ultrasound renal denervation (uRDN) was shown to lower blood pressure (BP) in patients with uncontrolled hypertension (HTN). Establishing the magnitude and consistency of the uRDN effect across the HTN spectrum is clinically important.

Objective

To characterize the effectiveness and safety of uRDN vs a sham procedure from individual patient-level pooled data across uRDN trials including either patients with mild to moderate HTN on a background of no medications or with HTN resistant to standardized triple-combination therapy.

Data Sources

A Study of the ReCor Medical Paradise System in Clinical Hypertension (RADIANCE-HTN SOLO and TRIO) and A Study of the ReCor Medical Paradise System in Stage II Hypertension (RADIANCE II) trials.

Study Selection

Trials with similar designs, standardized operational implementation (medication standardization and blinding of both patients and physicians to treatment assignment), and follow-up.

Data Extraction and Synthesis

Pooled analysis using individual patient-level data using linear regression models to compare uRDN with sham across the trials.

Main Outcomes and Measures

The primary outcome was baseline-adjusted change in 2-month daytime ambulatory systolic BP (dASBP) between groups.

Results

A total of 506 patients were randomized in the 3 studies (uRDN, 293; sham, 213; mean [SD] age, 54.1 [9.3]; 354 male [70.0%]). After a 1-month medication stabilization period, dASBP was similar between the groups (mean [SD], uRDN, 150.3 [9.2] mm Hg; sham, 150.8 [10.5] mm Hg). At 2 months, dASBP decreased by 8.5 mm Hg to mean (SD) 141.8 (13.8) mm Hg among patients treated with uRDN and by 2.9 mm Hg to 147.9 (14.6) mm Hg among patients treated with a sham procedure (mean difference, -5.9; 95% CI, -8.1 to -3.8 mm Hg; P < .001 in favor of uRDN). BP decreases from baseline with uRDN vs sham were consistent across trials and across BP parameters (office SBP: -10.4 mm Hg vs -3.4 mm Hg; mean difference, -6.4 mm Hg; 95% CI, -9.1 to -3.6 mm Hg; home SBP: -8.4 mm Hg vs -1.4 mm Hg; mean difference, -6.8 mm Hg; 95% CI, -8.7 to -4.9 mm Hg, respectively). The BP reductions with uRDN vs sham were consistent across prespecified subgroups. Independent predictors of a larger BP response to uRDN were higher baseline BP and heart rate and the presence of orthostatic hypertension. No differences in early safety end points were observed between groups.

Conclusions and Relevance

Results of this patient-level pooled analysis suggest that BP reductions with uRDN were consistent across HTN severity in sham-controlled trials designed with a 2-month primary end point to standardize medications across randomized groups.

Trial Registration

ClinicalTrials.gov Identifier: NCT02649426 and NCT03614260.

Linear mixed models for investigating effect modification in subgroup meta-analysis

Subgroup meta-analysis can be used for comparing treatment effects between subgroups using information from multiple trials. If the effect of treatment is differential depending on subgroup, the results could enable personalization of the treatment. We propose using linear mixed models for estimating treatment effect modification in aggregate data meta-analysis. The linear mixed models capture existing subgroup meta-analysis methods while allowing for additional features such as flexibility in modeling heterogeneity, handling studies with missing subgroups and more. Reviews and simulation studies of the best suited models for estimating possible differential effect of treatment depending on subgroups have been studied mostly within individual participant data meta-analysis. While individual participant data meta-analysis in general is recommended over aggregate data meta-analysis, conducting an aggregate data subgroup meta-analysis could be valuable for exploring treatment effect modifiers before committing to an individual participant data subgroup meta-analysis. Additionally, using solely individual participant data for subgroup meta-analysis requires collecting sufficient individual participant data which may not always be possible. In this article, we compared existing methods with linear mixed models for aggregate data subgroup meta-analysis under a broad selection of scenarios using simulation and two case studies. Both the case studies and simulation studies presented here demonstrate the advantages of the linear mixed model approach in aggregate data subgroup meta-analysis.

Order of patient entry and outcomes in phase II clinical trials: A meta-analysis of individual patient data

BACKGROUND

Prior meta-analysis of stem-cell transplantation trials for renal-cell carcinoma observed that clinical outcomes vary by subjects' order of entry, specifically their quartile of accrual. We test this hypothesis using meta-analysis of individual patient data from diverse Phase II trials conducted by an oncology consortium.

METHODS

Eligible were all Phase II trials in hematologic or solid tumors opened and closed by California Cancer Consortium during 2005-2020. Excluded were trials closed in first quartile or currently embargoed pending publication and subjects ineligible per protocol or untreated on study. The primary risk factor was entry by quartile of planned sample size. As a cross-protocol endpoint, primary outcome was time to discontinuation of intervention. One-stage meta-analysis used a shared frailty model with trial as random effect. As covariates, stepwise selection retained tumor type, obesity, their interaction, calendar year, entry at least 3 years post-diagnosis, and performance status but rejected age, sex, randomized design, and class of drug.

RESULTS

Twenty trials (including 8 terminated early, 2 not published) included n = 923 subjects. Most (90.6%) subjects discontinued intervention, usually for disease progression or toxicity. Independently of covariates, risk of discontinuation increased (p < 0.0001) with each quartile of entry (Hazards Ratio 1.13, 95% CI 1.06-1.22), culminating at Quartile 4 (HR 1.46, 1.36-1.57). The 95% prediction interval for the Hazards Ratio in future trials was (1.04-1.24). Progression-free survival similarly worsened by quartile of entry.

CONCLUSION

In Phase II trials, clinical outcome worsens with quartile of entry. This finding merits independent replication, and the cause of this phenomenon merits investigation.

Arthroscopic partial meniscectomy vs non-surgical or sham treatment in patients with MRI-confirmed degenerative meniscus tears: a systematic review and meta-analysis with individual participant data from 605 randomised patients

OBJECTIVE

To identify subgroups of patients with magnetic resonance imaging (MRI)-confirmed degenerative meniscus tears who may benefit from arthroscopic partial meniscectomy (APM) in comparison with non-surgical or sham treatment.

METHODS

Individual participant data (IPD) from four RCTs were pooled (605 patients, mean age: 55 (SD: 7.5), 52.4% female) as to investigate the effectiveness of APM in patients with MRI-confirmed degenerative meniscus tears compared to non-surgical or sham treatment. Primary outcomes were knee pain, overall knee function, and health-related quality of life, at 24 months follow-up (0-100). The IPD were analysed in a one- and two-stage meta-analyses. Identification of potential subgroups was performed by testing interaction effects of predefined patient characteristics (e.g., age, gender, mechanical symptoms) and APM for each outcome. Additionally, generalized linear mixed-model trees were used for subgroup detection.

RESULTS

The APM group showed a small improvement over the non-surgical or sham group on knee pain at 24 months follow-up (2.5 points (95% CI: 0.8-4.2) and 2.2 points (95% CI: 0.9-3.6), one- and two-stage analysis, respectively). Overall knee function and health-related quality of life did not differ between the two groups. Across all outcomes, no relevant subgroup of patients who benefitted from APM was detected. The generalized linear mixed-model trees did also not identify a subgroup.

CONCLUSIONS

No relevant subgroup of patients was identified that benefitted from APM compared to non-surgical or sham treatment. Since we were not able to identify any subgroup that benefitted from APM, we recommend a restrained policy regarding meniscectomy in patients with degenerative meniscus tears.

Estimating interactions and subgroup-specific treatment effects in meta-analysis without aggregation bias: A within-trial framework

Estimation of within-trial interactions in meta-analysis is crucial for reliable assessment of how treatment effects vary across participant subgroups. However, current methods have various limitations. Patients, clinicians and policy-makers need reliable estimates of treatment effects within specific covariate subgroups, on relative and absolute scales, in order to target treatments appropriately-which estimation of an interaction effect does not in itself provide. Also, the focus has been on covariates with only two subgroups, and may exclude relevant data if only a single subgroup is reported. Therefore, in this article we further develop the "within-trial" framework by providing practical methods to (1) estimate within-trial interactions across two or more subgroups; (2) estimate subgroup-specific ("floating") treatment effects that are compatible with the within-trial interactions and make maximum use of available data; and (3) clearly present this data using novel implementation of forest plots. We described the steps involved and apply the methods to two examples taken from previously published meta-analyses, and demonstrate a straightforward implementation in Stata based upon existing code for multivariate meta-analysis. We discuss how the within-trial framework and plots can be utilised with aggregate (or "published") source data, as well as with individual participant data, to effectively demonstrate how treatment effects differ across participant subgroups.

Validating the Assumptions of Population Adjustment: Application of Multilevel Network Meta-regression to a Network of Treatments for Plaque Psoriasis

BACKGROUND

Network meta-analysis (NMA) and indirect comparisons combine aggregate data (AgD) from multiple studies on treatments of interest but may give biased estimates if study populations differ. Population adjustment methods such as multilevel network meta-regression (ML-NMR) aim to reduce bias by adjusting for differences in study populations using individual patient data (IPD) from 1 or more studies under the conditional constancy assumption. A shared effect modifier assumption may also be necessary for identifiability. This article aims to demonstrate how the assumptions made by ML-NMR can be assessed in practice to obtain reliable treatment effect estimates in a target population.

METHODS

We apply ML-NMR to a network of evidence on treatments for plaque psoriasis with a mix of IPD and AgD trials reporting ordered categorical outcomes. Relative treatment effects are estimated for each trial population and for 3 external target populations represented by a registry and 2 cohort studies. We examine residual heterogeneity and inconsistency and relax the shared effect modifier assumption for each covariate in turn.

RESULTS

Estimated population-average treatment effects were similar across study populations, as differences in the distributions of effect modifiers were small. Better fit was achieved with ML-NMR than with NMA, and uncertainty was reduced by explaining within- and between-study variation. We found little evidence that the conditional constancy or shared effect modifier assumptions were invalid.

CONCLUSIONS

ML-NMR extends the NMA framework and addresses issues with previous population adjustment approaches. It coherently synthesizes evidence from IPD and AgD studies in networks of any size while avoiding aggregation bias and noncollapsibility bias, allows for key assumptions to be assessed or relaxed, and can produce estimates relevant to a target population for decision-making.

HIGHLIGHTS

Multilevel network meta-regression (ML-NMR) extends the network meta-analysis framework to synthesize evidence from networks of studies providing individual patient data or aggregate data while adjusting for differences in effect modifiers between studies (population adjustment). We apply ML-NMR to a network of treatments for plaque psoriasis with ordered categorical outcomes.We demonstrate for the first time how ML-NMR allows key assumptions to be assessed. We check for violations of conditional constancy of relative effects (such as unobserved effect modifiers) through residual heterogeneity and inconsistency and the shared effect modifier assumption by relaxing this for each covariate in turn.Crucially for decision making, population-adjusted treatment effects can be produced in any relevant target population. We produce population-average estimates for 3 external target populations, represented by the PsoBest registry and the PROSPECT and Chiricozzi 2019 cohort studies.

Calculating the power to examine treatment-covariate interactions when planning an individual participant data meta-analysis of randomized trials with a binary outcome

Before embarking on an individual participant data meta-analysis (IPDMA) project, researchers and funders need assurance it is worth their time and cost. This should include consideration of how many studies are promising their IPD and, given the characteristics of these studies, the power of an IPDMA including them. Here, we show how to estimate the power of a planned IPDMA of randomized trials to examine treatment-covariate interactions at the participant level (ie, treatment effect modifiers). We focus on a binary outcome with binary or continuous covariates, and propose a three-step approach, which assumes the true interaction size is common to all trials. In step one, the user must specify a minimally important interaction size and, for each trial separately (eg, as obtained from trial publications), the following aggregate data: the number of participants and events in control and treatment groups, the mean and SD for each continuous covariate, and the proportion of participants in each category for each binary covariate. This allows the variance of the interaction estimate to be calculated for each trial, using an analytic solution for Fisher's information matrix from a logistic regression model. Step 2 calculates the variance of the summary interaction estimate from the planned IPDMA (equal to the inverse of the sum of the inverse trial variances from step 1), and step 3 calculates the corresponding power based on a two-sided Wald test. Stata and R code are provided, and two examples given for illustration. Extension to allow for between-study heterogeneity is also considered.

Estimating interactions in individual participant data meta-analysis: a comparison of methods in practice

Medical interventions may be more effective in some types of individuals than others and identifying characteristics that modify the effectiveness of an intervention is a cornerstone of precision or stratified medicine. The opportunity for detailed examination of treatment-covariate interactions can be an important driver for undertaking an individual participant data (IPD) meta-analysis, rather than a meta-analysis using aggregate data. A number of recent modelling approaches are available. We apply these methods to the Perinatal Antiplatelet Review of International Studies (PARIS) Collaboration IPD dataset and compare estimates between them. We discuss the practical implications of applying these methods, which may be of interest to aid meta-analysists in the use of these, often complex models.Models compared included the two-stage meta-analysis of interaction terms and one-stage models which fit multiple random effects and separate within and between trial information. Models were fitted for nine covariates and five binary outcomes and results compared.Interaction terms produced by the methods were generally consistent. We show that where data are sparse and there is low heterogeneity in the covariate distributions across trials, the meta-analysis of interactions may produce unstable estimates and have issues with convergence. In this IPD dataset, varying assumptions by using multiple random effects in one-stage models or using only within trial information made little difference to the estimates of treatment-covariate interaction. Method choice will depend on datasets characteristics and individual preference.

Association Between Patient Factors and the Effectiveness of Wearable Trackers at Increasing the Number of Steps per Day Among Adults With Cardiometabolic Conditions: Meta-analysis of Individual Patient Data From Randomized Controlled Trials

BACKGROUND

Current evidence supports the use of wearable trackers by people with cardiometabolic conditions. However, as the health benefits are small and confounded by heterogeneity, there remains uncertainty as to which patient groups are most helped by wearable trackers.

OBJECTIVE

This study examined the effects of wearable trackers in patients with cardiometabolic conditions to identify subgroups of patients who most benefited and to understand interventional differences.

METHODS

We obtained individual participant data from randomized controlled trials of wearable trackers that were conducted before December 2020 and measured steps per day as the primary outcome in participants with cardiometabolic conditions including diabetes, overweight or obesity, and cardiovascular disease. We used statistical models to account for clustering of participants within trials and heterogeneity across trials to estimate mean differences with the 95% CI.

RESULTS

Individual participant data were obtained from 9 of 25 eligible randomized controlled trials, which included 1481 of 3178 (47%) total participants. The wearable trackers revealed that over the median duration of 12 weeks, steps per day increased by 1656 (95% CI 918-2395), a significant change. Greater increases in steps per day from interventions using wearable trackers were observed in men (interaction coefficient -668, 95% CI -1157 to -180), patients in age categories over 50 years (50-59 years: interaction coefficient 1175, 95% CI 377-1973; 60-69 years: interaction coefficient 981, 95% CI 222-1740; 70-90 years: interaction coefficient 1060, 95% CI 200-1920), White patients (interaction coefficient 995, 95% CI 360-1631), and patients with fewer comorbidities (interaction coefficient -517, 95% CI -1188 to -11) compared to women, those aged below 50, non-White patients, and patients with multimorbidity. In terms of interventional differences, only face-to-face delivery of the tracker impacted the effectiveness of the interventions by increasing steps per day.

CONCLUSIONS

In patients with cardiometabolic conditions, interventions using wearable trackers to improve steps per day mostly benefited older White men without multimorbidity.

TRIAL REGISTRATION

PROSPERO CRD42019143012; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=143012.

MA-cont:pre/post effect size: An interactive tool for the meta-analysis of continuous outcomes using R Shiny

Meta‐analysis is a widely used methodology to combine evidence from different sources examining a common research phenomenon, to obtain a quantitative summary of the studied phenomenon. In the medical field, multiple studies investigate the effectiveness of new treatments and meta‐analysis is largely performed to generate the summary (average) treatment effect. In the meta‐analysis of aggregate continuous outcomes measured in a pretest‐posttest design using differences in means as the effect measure, a plethora of methods exist: analysis of final (follow‐up) scores, analysis of change scores and analysis of covariance. Specialised and general‐purpose statistical software is used to apply the various methods, yet, often the choice among them depends on data availability and statistical affinity. We present a new web‐based tool, MA‐cont:pre/post effect size, to conduct meta‐analysis of continuous data assessed pre‐ and post‐treatment using the aforementioned approaches on aggregate data and a more flexible approach of generating and analysing pseudo individual participant data. The interactive web environment, available by R Shiny, is used to create this free‐to‐use statistical tool, requiring no programming skills by the users. A basic statistical understanding of the methods running in the background is a prerequisite and we encourage the users to seek advice from technical experts when necessary.

Challenges of modelling approaches for network meta-analysis of time-to-event outcomes in the presence of non-proportional hazards to aid decision making: Application to a melanoma network

BACKGROUND

Synthesis of clinical effectiveness from multiple trials is a well-established component of decision-making. Time-to-event outcomes are often synthesised using the Cox proportional hazards model assuming a constant hazard ratio over time. However, with an increasing proportion of trials reporting treatment effects where hazard ratios vary over time and with differing lengths of follow-up across trials, alternative synthesis methods are needed.

OBJECTIVES

To compare and contrast five modelling approaches for synthesis of time-to-event outcomes and provide guidance on key considerations for choosing between the modelling approaches.

METHODS

The Cox proportional hazards model and five other methods of estimating treatment effects from time-to-event outcomes, which relax the proportional hazards assumption, were applied to a network of melanoma trials reporting overall survival: restricted mean survival time, generalised gamma, piecewise exponential, fractional polynomial and Royston-Parmar models.

RESULTS

All models fitted the melanoma network acceptably well. However, there were important differences in extrapolations of the survival curve and interpretability of the modelling constraints demonstrating the potential for different conclusions from different modelling approaches.

CONCLUSION

The restricted mean survival time, generalised gamma, piecewise exponential, fractional polynomial and Royston-Parmar models can accommodate non-proportional hazards and differing lengths of trial follow-up within a network meta-analysis of time-to-event outcomes. We recommend that model choice is informed using available and relevant prior knowledge, model transparency, graphically comparing survival curves alongside observed data to aid consideration of the reliability of the survival estimates, and consideration of how the treatment effect estimates can be incorporated within a decision model.

Pragmatic Precision Psychiatry-A New Direction for Optimizing Treatment Selection

IMPORTANCE

Clinical trials have identified numerous prescriptive predictors of mental disorder treatment response, ie, predictors of which treatments are best for which patients. However, none of these prescriptive predictors is strong enough alone to guide precision treatment planning. This has prompted growing interest in developing precision treatment rules (PTRs) that combine information across multiple prescriptive predictors, but this work has been much less successful in psychiatry than some other areas of medicine. Study designs and analysis schemes used in research on PTR development in other areas of medicine are reviewed, key challenges for implementing similar studies of mental disorders are highlighted, and recent methodological advances to address these challenges are described here.

OBSERVATIONS

Discovering prescriptive predictors requires large samples. Three approaches have been used in other areas of medicine to do this: conduct very large randomized clinical trials, pool individual-level results across multiple smaller randomized clinical trials, and develop preliminary PTRs in large observational treatment samples that are then tested in smaller randomized clinical trials. The third approach is most feasible for research on mental disorders. This approach requires working with large real-world observational electronic health record databases; carefully selecting samples to emulate trials; extracting information about prescriptive predictors from electronic health records along with other inexpensive data augmentation strategies; estimating preliminary PTRs in the observational data using appropriate methods; implementing pragmatic trials to validate the preliminary PTRs; and iterating between subsequent observational studies and quality improvement pragmatic trials to refine and expand the PTRs. New statistical methods exist to address the methodological challenges of implementing this approach.

CONCLUSIONS AND RELEVANCE

Advances in pragmatic precision psychiatry will require moving beyond the current focus on randomized clinical trials and adopting an iterative discovery-confirmation process that integrates observational and experimental studies in real-world clinical populations.

A frequentist one-step model for a simple network meta-analysis of time-to-event data in presence of an effect modifier

INTRODUCTION

Individual patient data (IPD) present particular advantages in network meta-analysis (NMA) because interactions may lead an aggregated data (AD)-based model to wrong a treatment effect (TE) estimation. However, fewer works have been conducted for IPD with time-to-event contrary to binary outcomes. We aimed to develop a general frequentist one-step model for evaluating TE in the presence of interaction in a three-node NMA for time-to-event data.

METHODS

One-step, frequentist, IPD-based Cox and Poisson generalized linear mixed models were proposed. We simulated a three-node network with or without a closed loop with (1) no interaction, (2) covariate-treatment interaction, and (3) covariate distribution heterogeneity and covariate-treatment interaction. These models were applied to the NMA (Meta-analyses of Chemotherapy in Head and Neck Cancer [MACH-NC] and Radiotherapy in Carcinomas of Head and Neck [MARCH]), which compared the addition of chemotherapy or modified radiotherapy (mRT) to loco-regional treatment with two direct comparisons. AD-based (contrast and meta-regression) models were used as reference.

RESULTS

In the simulated study, no IPD models failed to converge. IPD-based models performed well in all scenarios and configurations with small bias. There were few variations across different scenarios. In contrast, AD-based models performed well when there were no interactions, but demonstrated some bias when interaction existed and a larger one when the modifier was not distributed evenly. While meta-regression performed better than contrast-based only, it demonstrated a large variability in estimated TE. In the real data example, Cox and Poisson IPD-based models gave similar estimations of the model parameters. Interaction decomposition permitted by IPD explained the ecological bias observed in the meta-regression.

CONCLUSION

The proposed general one-step frequentist Cox and Poisson models had small bias in the evaluation of a three-node network with interactions. They performed as well or better than AD-based models and should also be undertaken whenever possible.

Penalized Poisson model for network meta-analysis of individual patient time-to-event data

Network meta-analysis (NMA) allows the combination of direct and indirect evidence from a set of randomized clinical trials. Performing NMA using individual patient data (IPD) is considered as a "gold standard" approach as it provides several advantages over NMA based on aggregate data. For example, it allows to perform advanced modeling of covariates or covariate-treatment interactions. An important issue in IPD NMA is the selection of influential parameters among terms that account for inconsistency, covariates, covariate-by-treatment interactions or nonproportionality of treatments effect for time to event data. This issue has not been deeply studied in the literature yet and in particular not for time-to-event data. A major difficulty is to jointly account for between-trial heterogeneity which could have a major influence on the selection process. The use of penalized generalized mixed effect model is a solution, but existing implementations have several shortcomings and an important computational cost that precludes their use for complex IPD NMA. In this article, we propose a penalized Poisson regression model to perform IPD NMA of time-to-event data. It is based only on fixed effect parameters which improve its computational cost over the use of random effects. It could be easily implemented using existing penalized regression package. Computer code is shared for implementation. The methods were applied on simulated data to illustrate the importance to take into account between trial heterogeneity during the selection procedure. Finally, it was applied to an IPD NMA of overall survival of chemotherapy and radiotherapy in nasopharyngeal carcinoma.

Individual Patient Data Meta-Analysis and Network Meta-Analysis

Meta-analyses are often conducted using trial-level summary data. However, when individual patient data (IPD ) is available, there is greater flexibility in the analysis and a wider range of statistical models that can be fitted. There are two approaches to fitting IPD models. The traditional two-stage approach involves analyzing each trial individually in the first stage and then combining trial estimates of treatment effectiveness in the second stage using methods developed for aggregate data meta-analysis. Growing in popularity is the one-stage approach in which trials are analyzed and synthesized within one statistical model whilst the clustering of patients within trials is accounted for. This chapter outlines both fixed effect and random effects one- and two-stage meta-analysis models for continuous, binary, and time-to-event outcomes. The meta-analysis framework is then extended to the scenario where there are more than two treatments and network meta-analysis models are described.The availability of IPD provides greater statistical power for investigating interactions between treatments and covariates. Treatment-covariate interactions contain both within- and across-trial information where the across-trial information may be subject to ecological bias. This chapter presents network meta-analysis models separating out the within- and across-trial information and finishes by considering practical solutions for dealing with missing covariate data, assessing the consistency assumption, combining IPD and aggregate data and specific considerations for time-to-event outcomes.

Activation of the innate immune response and organ injury after cardiac surgery: a systematic review and meta-analysis of randomised trials and analysis of individual patient data from randomised and non-randomised studies

BACKGROUND

It is unclear whether the innate immune response represents a therapeutic target for organ protection strategies in cardiac surgery.

METHODS

A systematic review of trials of interventions targeting the inflammatory response to cardiac surgery reporting treatment effects on both innate immune system cytokines and organ injury was performed. The protocol was registered at the International Prospective Register of Systematic Reviews: CRD42020187239. Searches of the Cochrane Central Register of Controlled Trials, MEDLINE, and Embase were performed. Random-effects meta-analyses were used for the primary analysis. A separate analysis of individual patient data from six studies (n=785) explored sources of heterogeneity for treatment effects on cytokine levels.

RESULTS

Searches to May 2020 identified 251 trials evaluating 24 interventions with 20 582 participants for inclusion. Most trials had important limitations. Methodological limitations of the included trials and heterogeneity of the treatment effects on cytokine levels between trials limited interpretation. The primary analysis demonstrated inconsistency in the direction of the treatment effects on innate immunity and organ failure or death between interventions. Analyses restricted to important subgroups or trials with fewer limitations showed similar results. Meta-regression, pooling available data from all trials, demonstrated no association between the direction of the treatment effects on inflammatory cytokines and organ injury or death. The analysis of individual patient data demonstrated heterogeneity in the association between the cytokine response and organ injury after cardiac surgery for people >75 yr old and those with some chronic diseases.

CONCLUSIONS

The certainty of the evidence for a causal relationship between innate immune system activation and organ injury after cardiac surgery is low.

Benefits and harms of Risperidone and Paliperidone for treatment of patients with schizophrenia or bipolar disorder: a meta-analysis involving individual participant data and clinical study reports

BACKGROUND

Schizophrenia and bipolar disorder are severe mental illnesses which are highly prevalent worldwide. Risperidone and Paliperidone are treatments for either illnesses, but their efficacy compared to other antipsychotics and growing reports of hormonal imbalances continue to raise concerns. As existing evidence on both antipsychotics are solely based on aggregate data, we aimed to assess the benefits and harms of Risperidone and Paliperidone in the treatment of patients with schizophrenia or bipolar disorder, using individual participant data (IPD), clinical study reports (CSRs) and publicly available sources (journal publications and trial registries).

METHODS

We searched MEDLINE, Central, EMBASE and PsycINFO until December 2020 for randomised placebo-controlled trials of Risperidone, Paliperidone or Paliperidone palmitate in patients with schizophrenia or bipolar disorder. We obtained IPD and CSRs from the Yale University Open Data Access project. The primary outcome Positive and Negative Syndrome Scale (PANSS) score was analysed using one-stage IPD meta-analysis. Random-effect meta-analysis of harm outcomes involved methods for coping with rare events. Effect-sizes were compared across all available data sources using the ratio of means or relative risk. We registered our review on PROSPERO, CRD42019140556.

RESULTS

Of the 35 studies, IPD meta-analysis involving 22 (63%) studies showed a significant clinical reduction in the PANSS in patients receiving Risperidone (mean difference - 5.83, 95% CI - 10.79 to - 0.87, I2 = 8.5%, n = 4 studies, 1131 participants), Paliperidone (- 6.01, 95% CI - 8.7 to - 3.32, I2 = 4.3%, n = 13, 3821) and Paliperidone palmitate (- 7.89, 95% CI - 12.1 to - 3.69, I2 = 2.9%, n = 5, 2209). CSRs reported nearly two times more adverse events (4434 vs. 2296 publication, relative difference (RD) = 1.93, 95% CI 1.86 to 2.00) and almost 8 times more serious adverse events (650 vs. 82; RD = 7.93, 95% CI 6.32 to 9.95) than the journal publications. Meta-analyses of individual harms from CSRs revealed a significant increased risk among several outcomes including extrapyramidal disorder, tardive dyskinesia and increased weight. But the ratio of relative risk between the different data sources was not significant. Three treatment-related gynecomastia events occurred, and these were considered mild to moderate in severity.

CONCLUSION

IPD meta-analysis conclude that Risperidone and Paliperidone antipsychotics had a small beneficial effect on reducing PANSS score over 9 weeks, which is more conservative than estimates from reviews based on journal publications. CSRs also contained significantly more data on harms that were unavailable in journal publications or trial registries. Sharing of IPD and CSRs are necessary when performing meta-analysis on the efficacy and safety of antipsychotics.

Continuation of Antidepressants vs Sequential Psychological Interventions to Prevent Relapse in Depression: An Individual Participant Data Meta-analysis

IMPORTANCE

Depression frequently recurs. To prevent relapse, antidepressant medication is often taken in the long term. Sequentially delivering a psychological intervention while undergoing tapering of antidepressant medication might be an alternative to long-term antidepressant use. However, evidence is lacking on which patients may benefit from tapering antidepressant medication while receiving a psychological intervention and which should continue the antidepressant therapy. A meta-analysis of individual patient data with more power and precision than individual randomized clinical trials or a standard meta-analysis is warranted.

OBJECTIVES

To compare the associations between use of a psychological intervention during and/or after antidepressant tapering vs antidepressant use alone on the risk of relapse of depression and estimate associations of individual clinical factors with relapse.

DATA SOURCES

PubMed, the Cochrane Library, Embase, and PsycInfo were last searched on January 23, 2021. Requests for individual participant data from included randomized clinical trials (RCTs) were sent.

STUDY SELECTION

Randomized clinical trials that compared use of a psychological intervention while tapering antidepressant medication with antidepressant monotherapy were included. Patients had to be in full or partial remission from depression. Two independent assessors conducted screening and study selection.

DATA EXTRACTION AND SYNTHESIS

Of 15 792 screened studies, 236 full-text articles were retrieved, and 4 RCTs that provided individual participant data were included.

MAIN OUTCOMES AND MEASURES

Time to relapse and relapse status over 15 months measured via a blinded assessor using a diagnostic clinical interview.

RESULTS

Individual data from 714 participants (mean [SD] age, 49.2 [11.5] years; 522 [73.1%] female) from 4 RCTs that compared preventive cognitive therapy or mindfulness-based cognitive therapy during and/or after antidepressant tapering vs antidepressant monotherapy were available. Two-stage random-effects meta-analysis found no significant difference in time to depressive relapse between use of a psychological intervention during tapering of antidepressant medication vs antidepressant therapy alone (hazard ratio [HR], 0.86; 95% CI, 0.60-1.23). Younger age at onset (HR, 0.98; 95% CI, 0.97-0.99), shorter duration of remission (HR, 0.99; 95% CI, 0.98-1.00), and higher levels of residual depressive symptoms at baseline (HR, 1.07; 95% CI, 1.04-1.10) were associated with a higher overall risk of relapse. None of the included moderators were associated with risk of relapse.

CONCLUSIONS AND RELEVANCE

The findings of this individual participant data meta-analysis suggest that regardless of the clinical factors included in these studies, the sequential delivery of a psychological intervention during and/or after tapering may be an effective relapse prevention strategy instead of long-term use of antidepressants. These results could be used to inform shared decision-making in clinical practice.

Comparison of Multimodal Therapies and Outcomes Among Patients With High-Risk Prostate Cancer With Adverse Clinicopathologic Features

IMPORTANCE

The optimal management strategy for high-risk prostate cancer and additional adverse clinicopathologic features remains unknown.

OBJECTIVE

To compare clinical outcomes among patients with high-risk prostate cancer after definitive treatment.

DESIGN, SETTING, AND PARTICIPANTS

This retrospective cohort study included patients with high-risk prostate cancer (as defined by the National Comprehensive Cancer Network [NCCN]) and at least 1 adverse clinicopathologic feature (defined as any primary Gleason pattern 5 on biopsy, clinical T3b-4 disease, ≥50% cores with biopsy results positive for prostate cancer, or NCCN ≥2 high-risk features) treated between 2000 and 2014 at 16 tertiary centers. Data were analyzed in November 2020.

EXPOSURES

Radical prostatectomy (RP), external beam radiotherapy (EBRT) with androgen deprivation therapy (ADT), or EBRT plus brachytherapy boost (BT) with ADT. Guideline-concordant multimodal treatment was defined as RP with appropriate use of multimodal therapy (optimal RP), EBRT with at least 2 years of ADT (optimal EBRT), or EBRT with BT with at least 1 year ADT (optimal EBRT with BT).

MAIN OUTCOMES AND MEASURES

The primary outcome was prostate cancer-specific mortality; distant metastasis was a secondary outcome. Differences were evaluated using inverse probability of treatment weight-adjusted Fine-Gray competing risk regression models.

RESULTS

A total of 6004 men (median [interquartile range] age, 66.4 [60.9-71.8] years) with high-risk prostate cancer were analyzed, including 3175 patients (52.9%) who underwent RP, 1830 patients (30.5%) who underwent EBRT alone, and 999 patients (16.6%) who underwent EBRT with BT. Compared with RP, treatment with EBRT with BT (subdistribution hazard ratio [sHR] 0.78, [95% CI, 0.63-0.97]; P = .03) or with EBRT alone (sHR, 0.70 [95% CI, 0.53-0.92]; P = .01) was associated with significantly improved prostate cancer-specific mortality; there was no difference in prostate cancer-specific mortality between EBRT with BT and EBRT alone (sHR, 0.89 [95% CI, 0.67-1.18]; P = .43). No significant differences in prostate cancer-specific mortality were found across treatment cohorts among 2940 patients who received guideline-concordant multimodality treatment (eg, optimal EBRT alone vs optimal RP: sHR, 0.76 [95% CI, 0.52-1.09]; P = .14). However, treatment with EBRT alone or EBRT with BT was consistently associated with lower rates of distant metastasis compared with treatment with RP (eg, EBRT vs RP: sHR, 0.50 [95% CI, 0.44-0.58]; P < .001).

CONCLUSIONS AND RELEVANCE

These findings suggest that among patients with high-risk prostate cancer and additional unfavorable clinicopathologic features receiving guideline-concordant multimodal therapy, prostate cancer-specific mortality outcomes were equivalent among those treated with RP, EBRT, and EBRT with BT, although distant metastasis outcomes were more favorable among patients treated with EBRT and EBRT with BT. Optimal multimodality treatment is critical for improving outcomes in patients with high-risk prostate cancer.

The effects of dietary and lifestyle interventions among pregnant women with overweight or obesity on early childhood outcomes: an individual participant data meta-analysis from randomised trials

BACKGROUND

The impact of maternal obesity extends beyond birth, being independently associated with an increased risk of child obesity. Current evidence demonstrates that women provided with a dietary intervention during pregnancy improve their dietary quality and have a modest reduction in gestational weight gain. However, the effect of this on longer-term childhood obesity-related outcomes is unknown.

METHODS

We conducted an individual participant data meta-analysis from RCTs in which women with a singleton, live gestation between 10⁺⁰ and 20⁺⁰ weeks and body mass index (BMI) ≥ 25 kg/m² in early pregnancy were randomised to a diet and/or lifestyle intervention or continued standard antenatal care and in which longer-term maternal and child follow-up at 3-5 years of age had been undertaken. The primary childhood outcome was BMI z-score above the 90th percentile. Secondary childhood outcomes included skinfold thickness measurements and body circumferences, fat-free mass, dietary and physical activity patterns, blood pressure, and neurodevelopment.

RESULTS

Seven primary trials where follow-up of participants occurred were identified by a systematic literature search within the International Weight Management in Pregnancy (i-WIP) Collaborative Group collaboration, with six providing individual participant data. No additional studies were identified after a systematic literature search. A total of 2529 children and 2383 women contributed data. Approximately 30% of all child participants had a BMI z-score above the 90th percentile, with no significant difference between the intervention and control groups (aRR 0.97; 95% CI 0.87, 1.08; p=0.610). There were no statistically significant differences identified for any of the secondary outcome measures.

CONCLUSIONS

In overweight and obese pregnant women, we found no evidence that maternal dietary and/or lifestyle intervention during pregnancy modifies the risk of early childhood obesity. Future research may need to target the pre-conception period in women and early childhood interventions.

TRIAL REGISTRATION

PROSPERO, CRD42016047165.

Protocol for individual participant data meta-analysis of randomised controlled trials of patients with psychosis to investigate treatment effect modifiers for CBT versus treatment as usual or other psychosocial interventions

INTRODUCTION

Aggregate data meta-analyses have shown heterogeneous treatment effects for cognitive-behavioural therapy (CBT) for patients with schizophrenia spectrum diagnoses. This heterogeneity could stem from specific intervention or patient characteristics that could influence the clinical effectiveness of CBT, termed treatment effect modifiers. This individual participant data meta-analysis will investigate a range of potential treatment effect modifiers of the efficacy of CBT.

METHODS AND ANALYSIS

We will perform a systematic review and meta-analysis of studies investigating CBT versus treatment as usual, or CBT versus other psychosocial interventions, for patients with schizophrenia spectrum diagnoses. The Cochrane Central Register of Controlled Trials (CENTRAL), PubMed, EMBASE and the online clinical trials registers of the US government, European Union, WHO and Current Controlled Trials will be searched. Two researchers will screen titles and abstracts identified by the search. Individual participant data will be requested for any eligible study, for the primary outcome (overall psychotic symptoms), secondary outcomes and treatment effect modifiers. Data will be checked and recoded according to an established statistical analysis plan. One-stage and two-stage random effects meta-analyses investigating potential treatment effect modifiers will be conducted. A list of potential treatment effect modifiers for CBT will be produced, motivating future research into particular modifiers.

ETHICS AND DISSEMINATION

This study does not require ethical approval as it is based on data from existing studies, although best ethical practice for secondary analysis of clinical data will be followed. The findings will be submitted for publication in peer-reviewed journals, and promoted to relevant stakeholders.

PROSPERO REGISTRATION NUMBER

CRD42017060068.

Biodegradable polymer sirolimus-eluting stents vs durable polymer everolimus-eluting stents in patients undergoing percutaneous coronary intervention: A meta-analysis of individual patient data from 5 randomized trials

BACKGROUND

Newest generation drug-eluting stents combine biodegradable polymers with ultrathin stent platforms in order to minimize vessel injury and inflammatory response. Evidence from randomized controlled trials suggested that differences in stent design translate into differences in clinical outcome. The aim of the present study was to evaluate the safety and efficacy of ultrathin strut, biodegradable polymer sirolimus eluting stents (BP SES) compared with thin strut, durable polymer everolimus-eluting stents (DP EES) among patients undergoing percutaneous coronary intervention (PCI).

METHODS

We pooled individual participant data from 5 randomized trials (NCT01356888, NCT01939249, NCT02389946, NCT01443104, NCT02579031) including a total of 5,780 patients, and performed a one-stage meta-analysis using a mixed effects Cox regression model.

RESULTS

At a median duration of follow-up of 739 days (interquartile range 365-1,806 days), target-lesion failure occurred in 337 (10.3%) and 304 (12.2%) patients treated with BP SES and DP EES (HR 0.86, 95%CI 0.71-1.06, P = .16). There were no significant differences between BP SES and DP EES with regards to cardiac death (111 (3.4%) vs 102 (4.1%); HR 1.05, 95%CI 0.80-1.37, P = .73), target-vessel myocardial infarction (136 (4.1%) vs 126 (5.0%), HR 0.79, 95%CI 0.62-1.01, P = .061), and clinically-driven target-lesion revascularization (163 (5.0%) vs 147 (5.9%); HR 0.94, 95%CI 0.75-1.18, P = .61). The effect was consistent across major subgroups. In a landmark analysis, there was no significant interaction between treatment effect and timing of events.

CONCLUSIONS

In this patient-level meta-analysis of 5 randomized controlled trials, BP SES were associated with a similar risk of target-lesion failure compared with DP EES among patients undergoing PCI.

STUDY REGISTRATION

PROSPERO registry (CRD42018109098).

The methodological quality of individual participant data meta-analysis on intervention effects: systematic review

OBJECTIVE

To assess the methodological quality of individual participant data (IPD) meta-analysis and to identify areas for improvement.

DESIGN

Systematic review.

DATA SOURCES

Medline, Embase, and Cochrane Database of Systematic Reviews.

ELIGIBILITY CRITERIA FOR SELECTING STUDIES

Systematic reviews with IPD meta-analyses of randomised controlled trials on intervention effects published in English.

RESULTS

323 IPD meta-analyses covering 21 clinical areas and published between 1991 and 2019 were included: 270 (84%) were non-Cochrane reviews and 269 (84%) were published in journals with a high impact factor (top quarter). The IPD meta-analyses showed low compliance in using a satisfactory technique to assess the risk of bias of the included randomised controlled trials (43%, 95% confidence interval 38% to 48%), accounting for risk of bias when interpreting results (40%, 34% to 45%), providing a list of excluded studies with justifications (32%, 27% to 37%), establishing an a priori protocol (31%, 26% to 36%), prespecifying methods for assessing both the overall effects (44%, 39% to 50%) and the participant-intervention interactions (31%, 26% to 36%), assessing and considering the potential of publication bias (31%, 26% to 36%), and conducting a comprehensive literature search (19%, 15% to 23%). Up to 126 (39%) IPD meta-analyses failed to obtain IPD from 90% or more of eligible participants or trials, among which only 60 (48%) provided reasons and 21 (17%) undertook certain strategies to account for the unavailable IPD.

CONCLUSIONS

The methodological quality of IPD meta-analyses is unsatisfactory. Future IPD meta-analyses need to establish an a priori protocol with prespecified data syntheses plan, comprehensively search the literature, critically appraise included randomised controlled trials with appropriate technique, account for risk of bias during data analyses and interpretation, and account for unavailable IPD.

Moderators of the Effect of Spinal Manipulative Therapy on Pain Relief and Function in Patients with Chronic Low Back Pain: An Individual Participant Data Meta-analysis

STUDY DESIGN

Individual participant data (IPD) meta-analysis.

OBJECTIVE

The aim of this study was to identify which participant characteristics moderate the effect of spinal manipulative therapy (SMT) on pain and functioning in chronic LBP.

SUMMARY OF BACKGROUND

The effects of SMT are comparable to other interventions recommended in guidelines for chronic low back pain (LBP); however, it is unclear which patients are more likely to benefit from SMT compared to other therapies.

METHODS

IPD were requested from randomized controlled trials (RCTs) examining the effect of SMT in adults with chronic LBP for pain and function compared to various other therapies (stratified by comparison). Potential patient moderators (n = 23) were a priori based on their clinical relevance. We investigated each moderator using a one-stage approach with IPD and investigated this interaction with the intervention for each time point (1, 3, 6, and 12 months).

RESULTS

We received IPD from 21 of 46 RCTs (n = 4223). The majority (12 RCTs, n = 2249) compared SMT to recommended interventions. The duration of LBP, baseline pain (confirmatory), smoking, and previous exposure to SMT (exploratory) had a small moderating effect across outcomes and follow-up points; these estimates did not represent minimally relevant differences in effects; for example, patients with <1 year of LBP demonstrated more positive point estimates for SMT versus recommended therapy for the outcome pain (mean differences ranged from 4.97 (95% confidence interval, CI: -3.20 to 13.13) at 3 months, 10.76 (95% CI: 1.06 to 20.47) at 6 months to 5.26 (95% CI: -2.92 to 13.44) at 12 months in patients with over a year LBP. No other moderators demonstrated a consistent pattern across time and outcomes. Few moderator analyses were conducted for the other comparisons because of too few data.

CONCLUSION

We did not identify any moderators that enable clinicians to identify which patients are likely to benefit more from SMT compared to other treatments.Level of Evidence: 2.

Optimizing Dose and Timing in Magnetic Tracer Techniques for Sentinel Lymph Node Detection in Early Breast Cancers: The Prospective Multicenter SentiDose Trial

Simple Summary

Superparamagnetic iron oxide (SPIO) nanoparticles have comparable performance to the combination of radioisotope and blue dye (RI + BD) for sentinel lymph node (SLN) biopsy in breast cancer. In this multicenter prospective study, lower SPIO doses (undiluted 1.5 vs. 1.0 mL) in different timeframes (perioperative vs. 1–7 days preoperative) and injection sites (subareolar vs. peritumoral) were compared to the previous standard (diluted 2.0 mL perioperatively) from the earlier Nordic trial. RI + BD were co-administered as background. In total, 534 patients were analyzed. SPIO SLN detection rates were similar (97.5% vs. 100% vs. 97.6%, p = 0.11) and respectively non-inferior to the dual technique. Significantly more SLNs were retrieved in the preoperative 1.0 mL cohort compared with 1.5 mL and the Nordic cohorts (2.18 vs. 1.85 vs. 1.83, p = 0.003). Thus, SPIO at 1.5 and 1.0 mL was non-inferior to both Sienna+^® and the dual technique for SLN detection.

Abstract

Superparamagnetic iron oxide nanoparticles (SPIO) are non-inferior to radioisotope and blue dye (RI + BD) for sentinel lymph node (SLN) detection. Previously, 2 mL SPIO (Sienna+^®) in 3 mL NaCl was used. In this dose-optimizing study, lower doses of a new refined SPIO solution (Magtrace^®) (1.5 vs. 1.0 mL) were tested in different timeframes (0–24 h perioperative vs. 1–7 days preoperative) and injections sites (subareolar vs. peritumoral). Two consecutive breast cancer cohorts (n = 328) scheduled for SLN-biopsy were included from 2017 to 2019. All patients received isotope ± blue dye as back-up. SLNs were identified primarily with the SentiMag^® probe and thereafter a gamma-probe. The primary endpoint was SLN detection rate with SPIO. Analyses were performed as a one-step individual patient-level meta-analysis using patient-level data from the previously published Nordic Trial (n = 206) as a third, reference cohort. In 534 patients, the SPIO SLN detection rates were similar (97.5% vs. 100% vs. 97.6%, p = 0.11) and non-inferior to the dual technique. Significantly more SLNs were retrieved in the preoperative 1.0 mL cohort compared with 1.5 and the 2.0 mL cohorts (2.18 vs. 1.85 vs. 1.83, p = 0.003). Lower SPIO volumes injected up to 7 days before the operation have comparable efficacy to standard SPIO dose and RI + BD for SLN detection.

Optimizing Dose and Timing in Magnetic Tracer Techniques for Sentinel Lymph Node Detection in Early Breast Cancers: The Prospective Multicenter SentiDose Trial

Superparamagnetic iron oxide nanoparticles (SPIO) are non-inferior to radioisotope and blue dye (RI + BD) for sentinel lymph node (SLN) detection. Previously, 2 mL SPIO (Sienna+^®) in 3 mL NaCl was used. In this dose-optimizing study, lower doses of a new refined SPIO solution (Magtrace^®) (1.5 vs. 1.0 mL) were tested in different timeframes (0-24 h perioperative vs. 1-7 days preoperative) and injections sites (subareolar vs. peritumoral). Two consecutive breast cancer cohorts (n = 328) scheduled for SLN-biopsy were included from 2017 to 2019. All patients received isotope ± blue dye as back-up. SLNs were identified primarily with the SentiMag^® probe and thereafter a gamma-probe. The primary endpoint was SLN detection rate with SPIO. Analyses were performed as a one-step individual patient-level meta-analysis using patient-level data from the previously published Nordic Trial (n = 206) as a third, reference cohort. In 534 patients, the SPIO SLN detection rates were similar (97.5% vs. 100% vs. 97.6%, p = 0.11) and non-inferior to the dual technique. Significantly more SLNs were retrieved in the preoperative 1.0 mL cohort compared with 1.5 and the 2.0 mL cohorts (2.18 vs. 1.85 vs. 1.83, p = 0.003). Lower SPIO volumes injected up to 7 days before the operation have comparable efficacy to standard SPIO dose and RI + BD for SLN detection.

Predicting response to topical non-steroidal anti-inflammatory drugs in osteoarthritis: an individual patient data meta-analysis of randomized controlled trials

Objectives

To identify predictors of the specific (difference between treatment and placebo) and overall (change from baseline in treatment arm) treatment effects of topical NSAIDs in OA.

Methods

Randomized controlled trials (RCTs) of topical NSAIDs in OA were identified through systematic literature searching and inquiry to pharmaceutical companies. The raw, de-identified data were analysed in one-stage individual patient data meta-analysis (IPD-MA). Negative values for treatment effects (0–100 scale) indicate pain reduction.

Results

Of 63 eligible RCTs, 15 provided IPD (n = 1951 on topical NSAID), including 11 placebo-controlled RCTs (n = 1587 on topical NSAIDs, 1553 on placebo). Seven potential predictors of response were examined. Topical NSAIDs were superior to placebo [−6 (95% CI −9, −4)], with a small, but statistically significant greater effect in women than men [difference −4 (95% CI −8, −1)]. The overall treatment effect was 4-fold larger than the specific effect [−25 (95% CI −31, −19)] and increased with greater baseline pain severity (P < 0.001). No differences in efficacy were observed for age, BMI, features of inflammation, duration of complaints or radiographic OA severity.

Conclusion

Topical NSAIDs are effective for OA pain relief. Greater overall pain relief in individuals with more baseline pain might be due to contextual and non-specific effects, including regression to the mean. Additional factors that have been linked either mechanistically or through empirical evidence to outcomes should be selected for inclusion across future RCTs in order to facilitate the identification of response predictors through IPD-MA.

Comparing methods for estimating patient-specific treatment effects in individual patient data meta-analysis

Meta‐analysis of individual patient data (IPD) is increasingly used to synthesize data from multiple trials. IPD meta‐analysis offers several advantages over meta‐analyzing aggregate data, including the capacity to individualize treatment recommendations. Trials usually collect information on many patient characteristics. Some of these covariates may strongly interact with treatment (and thus be associated with treatment effect modification) while others may have little effect. It is currently unclear whether a systematic approach to the selection of treatment‐covariate interactions in an IPD meta‐analysis can lead to better estimates of patient‐specific treatment effects. We aimed to answer this question by comparing in simulations the standard approach to IPD meta‐analysis (no variable selection, all treatment‐covariate interactions included in the model) with six alternative methods: stepwise regression, and five regression methods that perform shrinkage on treatment‐covariate interactions, that is, least absolute shrinkage and selection operator (LASSO), ridge, adaptive LASSO, Bayesian LASSO, and stochastic search variable selection. Exploring a range of scenarios, we found that shrinkage methods performed well for both continuous and dichotomous outcomes, for a variety of settings. In most scenarios, these methods gave lower mean squared error of the patient‐specific treatment effect as compared with the standard approach and stepwise regression. We illustrate the application of these methods in two datasets from cardiology and psychiatry. We recommend that future IPD meta‐analysis that aim to estimate patient‐specific treatment effects using multiple effect modifiers should use shrinkage methods, whereas stepwise regression should be avoided.

Meta-analysis of continuous outcomes: Using pseudo IPD created from aggregate data to adjust for baseline imbalance and assess treatment-by-baseline modification

Meta‐analysis of individual participant data (IPD) is considered the “gold‐standard” for synthesizing clinical study evidence. However, gaining access to IPD can be a laborious task (if possible at all) and in practice only summary (aggregate) data are commonly available. In this work we focus on meta‐analytic approaches of comparative studies where aggregate data are available for continuous outcomes measured at baseline (pre‐treatment) and follow‐up (post‐treatment). We propose a method for constructing pseudo individual baselines and outcomes based on the aggregate data. These pseudo IPD can be subsequently analysed using standard analysis of covariance (ANCOVA) methods. Pseudo IPD for continuous outcomes reported at two timepoints can be generated using the sufficient statistics of an ANCOVA model, i.e., the mean and standard deviation at baseline and follow‐up per group, together with the correlation of the baseline and follow‐up measurements. Applying the ANCOVA approach, which crucially adjusts for baseline imbalances and accounts for the correlation between baseline and change scores, to the pseudo IPD, results in identical estimates to the ones obtained by an ANCOVA on the true IPD. In addition, an interaction term between baseline and treatment effect can be added. There are several modeling options available under this approach, which makes it very flexible. Methods are exemplified using reported data of a previously published IPD meta‐analysis of 10 trials investigating the effect of antihypertensive treatments on systolic blood pressure, leading to identical results compared with the true IPD analysis and of a meta‐analysis of fewer trials, where baseline imbalance occurred.

Individual participant data meta-analysis to examine interactions between treatment effect and participant-level covariates: Statistical recommendations for conduct and planning

Precision medicine research often searches for treatment‐covariate interactions, which refers to when a treatment effect (eg, measured as a mean difference, odds ratio, hazard ratio) changes across values of a participant‐level covariate (eg, age, gender, biomarker). Single trials do not usually have sufficient power to detect genuine treatment‐covariate interactions, which motivate the sharing of individual participant data (IPD) from multiple trials for meta‐analysis. Here, we provide statistical recommendations for conducting and planning an IPD meta‐analysis of randomized trials to examine treatment‐covariate interactions. For conduct, two‐stage and one‐stage statistical models are described, and we recommend: (i) interactions should be estimated directly, and not by calculating differences in meta‐analysis results for subgroups; (ii) interaction estimates should be based solely on within‐study information; (iii) continuous covariates and outcomes should be analyzed on their continuous scale; (iv) nonlinear relationships should be examined for continuous covariates, using a multivariate meta‐analysis of the trend (eg, using restricted cubic spline functions); and (v) translation of interactions into clinical practice is nontrivial, requiring individualized treatment effect prediction. For planning, we describe first why the decision to initiate an IPD meta‐analysis project should not be based on between‐study heterogeneity in the overall treatment effect; and second, how to calculate the power of a potential IPD meta‐analysis project in advance of IPD collection, conditional on characteristics (eg, number of participants, standard deviation of covariates) of the trials (potentially) promising their IPD. Real IPD meta‐analysis projects are used for illustration throughout.

Arthroscopic meniscectomy versus non-surgical or sham treatment in patients with MRI confirmed degenerative meniscus lesions: a protocol for an individual participant data meta-analysis

INTRODUCTION

Arthroscopic partial meniscectomy (APM) after degenerative meniscus tears is one of the most frequently performed surgeries in orthopaedics. Although several randomised controlled trials (RCTs) have been published that showed no clear benefit compared with sham treatment or non-surgical treatment, the incidence of APM remains high. The common perception by most orthopaedic surgeons is that there are subgroups of patients that do need APM to improve, and they argue that each study sample of the existing trials is not representative for the day-to-day patients in the clinic. Therefore, the objective of this individual participant data meta-analysis (IPDMA) is to assess whether there are subgroups of patients with degenerative meniscus lesions who benefit from APM in comparison with non-surgical or sham treatment.

METHODS AND ANALYSIS

An existing systematic review will be updated to identify all RCTs worldwide that evaluated APM compared with sham treatment or non-surgical treatment in patients with knee symptoms and degenerative meniscus tears. Time and effort will be spent in contacting principal investigators of the original trials and encourage them to collaborate in this project by sharing their trial data. All individual participant data will be validated for missing data, internal data consistency, randomisation integrity and censoring patterns. After validation, all datasets will be combined and analysed using a one-staged and two-staged approach. The RCTs' characteristics will be used for the assessment of clinical homogeneity and generalisability of the findings. The most important outcome will be the difference between APM and control groups in knee pain, function and quality of life 2 years after the intervention. Other outcomes of interest will include the difference in adverse events and mental health.

ETHICS AND DISSEMINATION

All trial data will be anonymised before it is shared with the authors. The data will be encrypted and stored on a secure server located in the Netherlands. No major ethical concerns remain. This IPDMA will provide the evidence base to update and tailor diagnostic and treatment protocols as well as (international) guidelines for patients for whom orthopaedic surgeons consider APM. The results will be submitted for publication in a peer-reviewed journal.

PROSPERO REGISTRATION NUMBER

CRD42017067240.

Individual participant data meta-analysis of intervention studies with time-to-event outcomes: A review of the methodology and an applied example

Many randomized trials evaluate an intervention effect on time-to-event outcomes. Individual participant data (IPD) from such trials can be obtained and combined in a so-called IPD meta-analysis (IPD-MA), to summarize the overall intervention effect. We performed a narrative literature review to provide an overview of methods for conducting an IPD-MA of randomized intervention studies with a time-to-event outcome. We focused on identifying good methodological practice for modeling frailty of trial participants across trials, modeling heterogeneity of intervention effects, choosing appropriate association measures, dealing with (trial differences in) censoring and follow-up times, and addressing time-varying intervention effects and effect modification (interactions).We discuss how to achieve this using parametric and semi-parametric methods, and describe how to implement these in a one-stage or two-stage IPD-MA framework. We recommend exploring heterogeneity of the effect(s) through interaction and non-linear effects. Random effects should be applied to account for residual heterogeneity of the intervention effect. We provide further recommendations, many of which specific to IPD-MA of time-to-event data from randomized trials examining an intervention effect.We illustrate several key methods in a real IPD-MA, where IPD of 1225 participants from 5 randomized clinical trials were combined to compare the effects of Carbamazepine and Valproate on the incidence of epileptic seizures.

Individual participant data (IPD) meta-analysis of psychological relapse prevention interventions versus control for patients in remission from depression: a protocol

INTRODUCTION

Psychological interventions and antidepressant medication can be effective interventions to prevent depressive relapse for patients currently in remission of depression. Less is known about overall factors that predict or moderate treatment response for patients receiving a psychological intervention for recurrent depression. This is a protocol for an individual participant data (IPD) meta-analysis which aims to assess predictors and moderators of relapse or recurrence for patients currently in remission from depression.

METHODS AND ANALYSIS

Searches of PubMed, PsycINFO, Embase and Cochrane Central Register of Controlled Trials were completed on 13 October 2019. Study extractions and risk of bias assessments have been completed. Study authors will be asked to contribute IPD. Standard aggregate meta-analysis and IPD analysis will be conducted, and the outcomes will be compared with assess whether results differ between studies supplying data and those that did not. IPD files of individual data will be merged and variables homogenised where possible for consistency. IPD will be analysed via Cox regression and one and two-stage analyses will be conducted.

ETHICS AND DISSEMINATION

The results will be published in peer review journals and shared in a policy briefing as well as accessible formats and shared with a range of stakeholders. The results will inform patients and clinicians and researchers about our current understanding of more personalised ways to prevent a depressive relapse. No local ethics approval was necessary following consultation with the legal department. Guidance on patient data storage and management will be adhered to.

PROSPERO REGISTRATION NUMBER

CRD42019127844.

Association of Dietary Fiber and Yogurt Consumption With Lung Cancer Risk: A Pooled Analysis

Importance

Dietary fiber (the main source of prebiotics) and yogurt (a probiotic food) confer various health benefits via modulating the gut microbiota and metabolic pathways. However, their associations with lung cancer risk have not been well investigated.

Objective

To evaluate the individual and joint associations of dietary fiber and yogurt consumption with lung cancer risk and to assess the potential effect modification of the associations by lifestyle and other dietary factors.

Design, Setting, and Participants

This pooled analysis included 10 prospective cohorts involving 1 445 850 adults from studies that were conducted in the United States, Europe, and Asia. Data analyses were performed between November 2017 and February 2019. Using harmonized individual participant data, hazard ratios and 95% confidence intervals for lung cancer risk associated with dietary fiber and yogurt intakes were estimated for each cohort by Cox regression and pooled using random-effects meta-analysis. Participants who had a history of cancer at enrollment or developed any cancer, died, or were lost to follow-up within 2 years after enrollment were excluded.

Exposures

Dietary fiber intake and yogurt consumption measured by validated instruments.

Main Outcomes and Measures

Incident lung cancer, subclassified by histologic type (eg, adenocarcinoma, squamous cell carcinoma, and small cell carcinoma).

Results

The analytic sample included 627 988 men, with a mean (SD) age of 57.9 (9.0) years, and 817 862 women, with a mean (SD) age of 54.8 (9.7) years. During a median follow-up of 8.6 years, 18 822 incident lung cancer cases were documented. Both fiber and yogurt intakes were inversely associated with lung cancer risk after adjustment for status and pack-years of smoking and other lung cancer risk factors: hazard ratio, 0.83 (95% CI, 0.76-0.91) for the highest vs lowest quintile of fiber intake; and hazard ratio, 0.81 (95% CI, 0.76-0.87) for high vs no yogurt consumption. The fiber or yogurt associations with lung cancer were significant in never smokers and were consistently observed across sex, race/ethnicity, and tumor histologic type. When considered jointly, high yogurt consumption with the highest quintile of fiber intake showed more than 30% reduced risk of lung cancer than nonyogurt consumption with the lowest quintile of fiber intake (hazard ratio, 0.67 [95% CI, 0.61-0.73] in total study populations; hazard ratio, 0.69 [95% CI, 0.54-0.89] in never smokers), suggesting potential synergism.

Conclusions and Relevance

Dietary fiber and yogurt consumption was associated with reduced risk of lung cancer after adjusting for known risk factors and among never smokers. Our findings suggest a potential protective role of prebiotics and probiotics against lung carcinogenesis.

Exercise treatment effect modifiers in persistent low back pain: an individual participant data meta-analysis of 3514 participants from 27 randomised controlled trials

Background

Low back pain is one of the leading causes of disability worldwide. Exercise therapy is widely recommended to treat persistent non-specific low back pain. While evidence suggests exercise is, on average, moderately effective, there remains uncertainty about which individuals might benefit the most from exercise.

Methods

In parallel with a Cochrane review update, we requested individual participant data (IPD) from high-quality randomised clinical trials of adults with our two primary outcomes of interest, pain and functional limitations, and calculated global recovery. We compiled a master data set including baseline participant characteristics, exercise and comparison characteristics, and outcomes at short-term, moderate-term and long-term follow-up. We conducted descriptive analyses and one-stage IPD meta-analysis using multilevel mixed-effects regression of the overall treatment effect and prespecified potential treatment effect modifiers.

Results

We received IPD for 27 trials (3514 participants). For studies included in this analysis, compared with no treatment/usual care, exercise therapy on average reduced pain (mean effect/100 (95% CI) −10.7 (−14.1 to –7.4)), a result compatible with a clinically important 20% smallest worthwhile effect. Exercise therapy reduced functional limitations with a clinically important 23% improvement (mean effect/100 (95% CI) −10.2 (−13.2 to –7.3)) at short-term follow-up. Not having heavy physical demands at work and medication use for low back pain were potential treatment effect modifiers—these were associated with superior exercise outcomes relative to non-exercise comparisons. Lower body mass index was also associated with better outcomes in exercise compared with no treatment/usual care. This study was limited by inconsistent availability and measurement of participant characteristics.

Conclusions

This study provides potentially useful information to help treat patients and design future studies of exercise interventions that are better matched to specific subgroups.

Protocol publication

https://doi.org/10.1186/2046-4053-1-64

Statistical approaches to identify subgroups in meta-analysis of individual participant data: a simulation study

Background

Individual participant data meta-analysis (IPD-MA) is considered the gold standard for investigating subgroup effects. Frequently used regression-based approaches to detect subgroups in IPD-MA are: meta-regression, per-subgroup meta-analysis (PS-MA), meta-analysis of interaction terms (MA-IT), naive one-stage IPD-MA (ignoring potential study-level confounding), and centred one-stage IPD-MA (accounting for potential study-level confounding). Clear guidance on the analyses is lacking and clinical researchers may use approaches with suboptimal efficiency to investigate subgroup effects in an IPD setting. Therefore, our aim is to overview and compare the aforementioned methods, and provide recommendations over which should be preferred.

Methods

We conducted a simulation study where we generated IPD of randomised trials and varied the magnitude of subgroup effect (0, 25, 50% relative reduction), between-study treatment effect heterogeneity (none, medium, large), ecological bias (none, quantitative, qualitative), sample size (50,100,200), and number of trials (5,10) for binary, continuous and time-to-event outcomes. For each scenario, we assessed the power, false positive rate (FPR) and bias of aforementioned five approaches.

Results

Naive and centred IPD-MA yielded the highest power, whilst preserving acceptable FPR around the nominal 5% in all scenarios. Centred IPD-MA showed slightly less biased estimates than naïve IPD-MA. Similar results were obtained for MA-IT, except when analysing binary outcomes (where it yielded less power and FPR < 5%). PS-MA showed similar power as MA-IT in non-heterogeneous scenarios, but power collapsed as heterogeneity increased, and decreased even more in the presence of ecological bias. PS-MA suffered from too high FPRs in non-heterogeneous settings and showed biased estimates in all scenarios. Meta-regression showed poor power (< 20%) in all scenarios and completely biased results in settings with qualitative ecological bias.

Conclusions

Our results indicate that subgroup detection in IPD-MA requires careful modelling. Naive and centred IPD-MA performed equally well, but due to less bias of the estimates in the presence of ecological bias, we recommend the latter.

Electronic supplementary material

The online version of this article (10.1186/s12874-019-0817-6) contains supplementary material, which is available to authorized users.

Effects and moderators of exercise on sleep in adults with cancer: Individual patient data and aggregated meta-analyses

OBJECTIVES

To evaluate the effects of exercise interventions on sleep disturbances and sleep quality in patients with mixed cancer diagnoses, and identify demographic, clinical, and intervention-related moderators of these effects.

METHODS

Individual patient data (IPD) and aggregated meta-analyses of randomized controlled trials (RCTs). Using data from the Predicting OptimaL cAncer RehabIlitation and Supportive care project, IPD of 2173 adults (mean age = 54.8) with cancer from 17 RCTs were analyzed. A complementary systematic search was conducted (until November 2018) to study the overall effects and test the representativeness of analyzed IPD. Effect sizes of exercise effects on self-reported sleep outcomes were calculated for all included RCTs. Linear mixed-effect models were used to evaluate the effects of exercise on post-intervention outcome values, adjusting for baseline values. Moderator effects were studied by testing interactions for demographic, clinical and intervention-related characteristics.

RESULTS

For all 27 eligible RCTs from the updated search, exercise interventions significantly decreased sleep disturbances in adults with cancer (g = -0.09, 95% CI [-0.16; -0.02]). No significant effect was obtained for sleep quality. RCTs included in IPD analyses constituted a representative sample of the published literature. The intervention effects on sleep disturbances were not significantly moderated by any demographic, clinical, or intervention-related factor, nor by sleep disturbances.

CONCLUSIONS

This meta-analysis provides some evidence that, compared to control conditions, exercise interventions may improve sleep disturbances, but not sleep quality, in cancer patients, although this effect is of a small magnitude. Among the investigated variables, none was found to significantly moderate the effect of exercise interventions on sleep disturbances.

Scoping Review of Pokémon Go: Comprehensive Assessment of Augmented Reality for Physical Activity Change

Pokémon Go™ (PG) is a mobile videogame that requires real-world walking to "catch" augmented reality (AR) virtual creatures. Media attention speculated that extensive physical activity (PA) could result from PG play, which could have public health benefit. Little is known about contextual factors related to PG play and how they may impact play initiation or duration. A systematic search of articles reporting the words PG was conducted with PubMed and Google Scholar. To understand the many possible influences on and outcomes of PG play, a scoping review was conducted by employing a conceptual model to organize the literature. Although large numbers of people started playing PG, these were a relatively small proportion of the relevant populations, but PG may have activated some of those most in need of PA. Diverse factors predicted who initiated PG play, but they tended to emphasize anticipated fun, escapism, nostalgia, social ties, and desire for PA. Environmental factors (e.g., unavailable GPS signals, trespassing laws) limited PG play for some. Diverse factors predicted duration of gameplay, but fun appeared to be prominent. The level of increases in PA from PG among youth and young adults appeared to be small or undetected, and of a relatively short duration (<2 months). Among older adults, however, there were modest increases in PA for up to 7 months post-release. This intensity and duration of increased PA appears to be inadequate to stem the epidemic of obesity but may have mental and social health benefits. Although many adverse outcomes from playing PG were reported, these appear to be low incidence, which should primarily influence PG players to knowingly exercise caution. Many research issues were identified to specify who might play AR games and effective strategies to enhance game design to increase PA.

Exercise Interventions in Huntington's Disease: An Individual Patient Data Meta-Analysis

Background

Physical activity may be beneficial in Huntington's disease (HD); however, studies to date have been underpowered to detect change. We combined data from five randomized controlled feasibility trials using individual patient data meta-analyses.

Methods/Design

All trial interventions comprised a combination of supervised and self-directed physical activity, with varied emphasis on aerobic, strength, endurance, flexibility, and task training. Duration ranged from 8 to 16 weeks. The primary outcome was the modified Unified Huntington's Disease Rating Motor Score. Secondary outcomes included the Symbol Digit Modality Test, Berg Balance Scale, 30-second Chair stand, Timed Up and Go, Gait Speed, Physical Performance Test, Six-Minute Walk, International Physical Activity Questionnaire, Hospital Anxiety and Depression Scale, EuroQol Health Utility Index, and Short-Form 36 Health Related Quality of Life Scale. The primary analysis utilized a two-stage approach. A one-stage approach was explored as a sensitivity analysis using a cross-classified (by study site) linear mixed-effects model.

Results

One hundred twenty-one participants provided complete data. Risk of bias was moderate; however, primary outcomes were blind assessed. Primary pooled effect estimates adjusted for baseline modified motor score (95% confidence interval) were 0.2 (-2.1 to 2.6) favoring control. There was considerable heterogeneity between the studies.

Conclusions

There was no evidence of an exercise effect on the modified motor score in these relatively short-duration interventions. Longer-duration trials incorporating supervised components meeting frequency, intensity, time, and type principles are required. Lack of common outcomes limited the analysis and highlight the importance of a core outcome set for evaluating exercise in HD.

Drug-eluting or bare-metal stents for percutaneous coronary intervention: a systematic review and individual patient data meta-analysis of randomised clinical trials

BACKGROUND

New-generation drug-eluting stents (DES) have mostly been investigated in head-to-head non-inferiority trials against early-generation DES and have typically shown similar efficacy and superior safety. How the safety profile of new-generation DES compares with that of bare-metal stents (BMS) is less clear.

METHODS

We did an individual patient data meta-analysis of randomised clinical trials to compare outcomes after implantation of new-generation DES or BMS among patients undergoing percutaneous coronary intervention. The primary outcome was the composite of cardiac death or myocardial infarction. Data were pooled in a one-stage random-effects meta-analysis and examined at maximum follow-up and a 1-year landmark. Risk estimates are reported as hazard ratios (HRs) with 95% CIs. This study is registered in PROSPERO, number CRD42017060520.

FINDINGS

We obtained individual data for 26 616 patients in 20 randomised trials. Mean follow-up was 3·2 (SD 1·8) years. The risk of the primary outcome was reduced in DES recipients compared with BMS recipients (HR 0·84, 95% CI 0·78-0·90, p<0·001) owing to a reduced risk of myocardial infarction (0·79, 0·71-0·88, p<0·001) and a possible slight but non-significant cardiac mortality benefit (0·89, 0·78-1·01, p=0·075). All-cause death was unaffected (HR with DES 0·96, 95% CI 0·88-1·05, p=0·358), but risk was lowered for definite stent thrombosis (0·63, 0·50-0·80, p<0·001) and target-vessel revascularisation (0·55, 0·50-0·60, p<0·001). We saw a time-dependent treatment effect, with DES being associated with lower risk of the primary outcome than BMS up to 1 year after placement. While the effect was maintained in the longer term, there was no further divergence from BMS after 1 year.

INTERPRETATION

The performance of new-generation DES in the first year after implantation means that BMS should no longer be considered the gold standard for safety. Further development of DES technology should target improvements in clinical outcomes beyond 1 year.

FUNDING

Bern University Hospital.

Association of Diabetes With All-Cause and Cause-Specific Mortality in Asia: A Pooled Analysis of More Than 1 Million Participants

IMPORTANCE

Asia is home to the largest diabetic populations in the world. However, limited studies have quantified the association of diabetes with all-cause and cause-specific mortality in Asian populations.

OBJECTIVES

To evaluate the association of diabetes with all-cause and cause-specific mortality in Asia and to investigate potential effect modifications of the diabetes-mortality associations by participants' age, sex, education level, body mass index, and smoking status.

DESIGN, SETTING, AND PARTICIPANTS

This pooled analysis incorporated individual participant data from 22 prospective cohort studies of the Asia Cohort Consortium conducted between 1963 and 2006. A total of 1 002 551 Asian individuals (from mainland China, Japan, South Korea, Singapore, Taiwan, India, and Bangladesh) were followed up for more than 3 years. Cohort-specific hazard ratios and 95% confidence intervals for all-cause and cause-specific mortality were estimated using Cox regression models and then pooled using random-effects meta-analysis. Analysis was conducted between January 10, 2018, and August 31, 2018.

EXPOSURES

Doctor-diagnosed diabetes, age, sex, education level, body mass index, and smoking status.

MAIN OUTCOMES AND MEASURES

All-cause and cause-specific mortality.

RESULTS

Of 1 002 551 participants (518 537 [51.7%] female; median [range] age, 54.0 [30.0-98.0] years), 148 868 deaths were ascertained during a median (range) follow-up of 12.6 (3.0-38.9) years. The overall prevalence of diabetes reported at baseline was 4.8% for men and 3.6% for women. Patients with diabetes had a 1.89-fold risk of all-cause death compared with patients without diabetes (hazard ratio [HR], 1.89; 95% CI, 1.74-2.04), with the highest relative risk of death due to diabetes itself (HR, 22.8; 95% CI, 18.5-28.1), followed by renal disease (HR, 3.08; 95% CI, 2.50-3.78), coronary heart disease (HR, 2.57; 95% CI, 2.19-3.02), and ischemic stroke (HR, 2.15; 95% CI, 1.85-2.51). The adverse diabetes-mortality associations were more evident among women (HR, 2.09; 95% CI, 1.89-2.32) than among men (HR, 1.74; 95% CI, 1.62-1.88) (P for interaction < .001) and more evident among adults aged 30 to 49 years (HR, 2.43; 95% CI, 2.08-2.84) than among adults aged 70 years and older (HR, 1.51; 95% CI, 1.40-1.62) (P for interaction < .001). A similar pattern of association was found between diabetes and cause-specific mortality, with significant variations noted by sex and age.

CONCLUSIONS AND RELEVANCE

This study found that diabetes was associated with increased risk of death from several diseases among Asian populations. Development and implementation of diabetes management programs are urgently needed to reduce the burden of diabetes in Asia.