Derivative survival analyses: Analysis methods to derive survival outcomes for the remainder patient cohort without individual patient data

It is not uncommon for industry-sponsored randomized controlled trials to publish survival curves/data for the overall patient cohort("A+B") and for a favorable subgroup ("A") pre-specified or post hoc, but not the survival curves/data for the remainder cohort("B"). Consequently, following regulatory approval of the intervention treatment for the overall patient population if the primary endpoint is met, it is common for cancer patients representing the remainder cohort (B) to be treated as per the results of the overall cohort (A+B). To overcome this important issue in clinical decision-making, this study aimed to identify methods to accurately derive the survival curves and/or hazard ratio (95% confidence interval) for the remainder cohort (B), utilizing published curves and hazard ratios (95% confidence intervals) of the overall (A+B) and favorable subgroup (A) cohorts. The analysis methods (method I and method II) presented here, termed "derivative survival analyses," enable accurate assessment of survival outcomes in the remainder cohort without individual patient data.

Two-stage targeted maximum likelihood estimation for mixed aggregate and individual participant data analysis with an application to multidrug resistant tuberculosis

In this study, we develop a new method for the meta-analysis of mixed aggregate data (AD) and individual participant data (IPD). The method is an adaptation of inverse probability weighted targeted maximum likelihood estimation (IPW-TMLE), which was initially proposed for two-stage sampled data. Our methods are motivated by a systematic review investigating treatment effectiveness for multidrug resistant tuberculosis (MDR-TB) where the available data include IPD from some studies but only AD from others. One complication in this application is that participants with MDR-TB are typically treated with multiple antimicrobial agents where many such medications were not observed in all studies considered in the meta-analysis. We focus here on the estimation of the expected potential outcome while intervening on a specific medication but not intervening on any others. Our method involves the implementation of a TMLE that transports the estimation from studies where the treatment is observed to the full target population. A second weighting component adjusts for the studies with missing (inaccessible) IPD. We demonstrate the properties of the proposed method and contrast it with alternative approaches in a simulation study. We finally apply this method to estimate treatment effectiveness in the MDR-TB case study.

Heterogeneity in meta-analysis: a comprehensive overview

In recent years, meta-analysis has evolved to a critically important field of Statistics, and has significant applications in Medicine and Health Sciences. In this work we briefly present existing methodologies to conduct meta-analysis along with any discussion and recent developments accompanying them. Undoubtedly, studies brought together in a systematic review will differ in one way or another. This yields a considerable amount of variability, any kind of which may be termed heterogeneity. To this end, reports of meta-analyses commonly present a statistical test of heterogeneity when attempting to establish whether the included studies are indeed similar in terms of the reported output or not. We intend to provide an overview of the topic, discuss the potential sources of heterogeneity commonly met in the literature and provide useful guidelines on how to address this issue and to detect heterogeneity. Moreover, we review the recent developments in the Bayesian approach along with the various graphical tools and statistical software that are currently available to the analyst. In addition, we discuss sensitivity analysis issues and other approaches of understanding the causes of heterogeneity. Finally, we explore heterogeneity in meta-analysis for time to event data in a nutshell, pointing out its unique characteristics.

Investigating the Secondary Use of Clinical Research Data: Protocol for a Mixed Methods Study

BACKGROUND

The increasing emphasis to share patient data from clinical research has resulted in substantial investments in data repositories and infrastructure. However, it is unclear how shared data are used and whether anticipated benefits are being realized.

OBJECTIVE

The purpose of our study is to examine the current utilization of shared clinical research data sets and assess the effects on both scientific research and public health outcomes. Additionally, the study seeks to identify the factors that hinder or facilitate the ethical and efficient use of existing data based on the perspectives of data users.

METHODS

The study will utilize a mixed methods design, incorporating a cross-sectional survey and in-depth interviews. The survey will involve at least 400 clinical researchers, while the in-depth interviews will include 20 to 40 participants who have utilized data from repositories or institutional data access committees. The survey will target a global sample, while the in-depth interviews will focus on individuals who have used data collected from low- and middle-income countries. Quantitative data will be summarized by using descriptive statistics, while multivariable analyses will be used to assess the relationships between variables. Qualitative data will be analyzed through thematic analysis, and the findings will be reported in accordance with the COREQ (Consolidated Criteria for Reporting Qualitative Research) guidelines. The study received ethical approval from the Oxford Tropical Research Ethics Committee in 2020 (reference number: 568-20).

RESULTS

The results of the analysis, including both quantitative data and qualitative data, will be available in 2023.

CONCLUSIONS

The outcomes of our study will offer crucial understanding into the current status of data reuse in clinical research, serving as a basis for guiding future endeavors to enhance the utilization of shared data for the betterment of public health outcomes and for scientific progress.

TRIAL REGISTRATION

Thai Clinical Trials Registry TCTR20210301006; https://tinyurl.com/2p9atzhr.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/44875.

entecavir-based regimens: Individual patient data meta-analysis

BACKGROUND & AIMS

The comparative risk of hepatocellular carcinoma (HCC) in patients with chronic hepatitis B (CHB) receiving tenofovir disoproxil fumarate (TDF) vs. entecavir (ETV) remains controversial. In this individual patient data (IPD) meta-analysis, we aimed to compare HCC risk between the two drugs and identify subgroups who may benefit more from one treatment than the other.

METHODS

Published meta-analyses, electronic databases and congress proceedings were searched to identify eligible studies through January 2021. We compared HCC risk between the two drugs using a multivariable Cox proportional hazards model with anonymised IPD from treatment-naïve patients with CHB receiving TDF or ETV for ≥1 year. Treatment effect consistency was explored in propensity score matching (PSM), weighting (PSW) and subgroup analyses for age, sex, hepatitis B e-antigen (HBeAg) positivity, cirrhosis and diabetes status.

RESULTS

We included 11 studies from Korea, Taiwan and Hong Kong involving 42,939 patients receiving TDF (n = 6,979) or ETV (n = 35,960) monotherapy. Patients receiving TDF had significantly lower HCC risk (adjusted hazard ratio [HR] 0.77; 95% CI 0.61-0.98; p = 0.03). Lower HCC risk with TDF was consistently observed in PSM (HR 0.73; 95% CI 0.59-0.88; p <0.01) and PSW (HR 0.83; 95% CI 0.67-1.03; p = 0.10) analyses and in all subgroups, with statistical significance in the ≥50 years of age (HR 0.76; 95% CI 0.58-1.00; p <0.05), male (HR 0.74; 95% CI 0.58-0.96; p = 0.02), HBeAg-positive (HR 0.69; 95% CI 0.49-0.97; p = 0.03) and non-diabetic (HR 0.79; 95% CI 0.63-1.00; p <0.05) subgroups.

CONCLUSION

TDF was associated with significantly lower HCC risk than ETV in patients with CHB, particularly those with HBeAg positivity. Longer follow-up may be needed to better define incidence differences between the treatments in various subgroups.

IMPACT AND IMPLICATIONS

Previous aggregate data meta-analyses have reported inconsistent conclusions on the relative effectiveness of tenofovir disoproxil fumarate and entecavir in reducing hepatocellular carcinoma risk in patients with chronic hepatitis B (CHB). This individual patient data meta-analysis on 11 studies involving 42,939 patients from Korea, Taiwan and Hong Kong suggested that tenofovir disoproxil fumarate-treated patients have a significantly lower hepatocellular carcinoma risk than entecavir-treated patients, which was observed in all subgroups of clinical interest and by different analytical methodologies. These findings should be taken into account by healthcare providers when determining the optimal course of treatment for patients with CHB and may be considered in ensuring that treatment guidelines for CHB remain pertinent.

An Introduction to Individual Participant Data Meta-analysis

Meta-analysis using individual participant data (IPD-MA) from randomised controlled trials (RCTs) can strengthen evidence used for decision-making, and is considered the 'gold standard' approach. In this paper, we present the importance, properties, and main approaches of conducting an IPD-MA. We exemplify the main approaches of conducting an IPD-MA and how these can be used to obtain subgroup effects through estimation of interaction terms. IPD-MA has several benefits over traditional aggregate data meta-analysis. These include: standardization of definitions of outcomes and/or scales, re-analysis of eligible RCTs using the same analysis model across all studies, accounting for missing outcome data, detecting outliers, using participant-level covariates to explore intervention-by-covariate interactions, and tailoring intervention effects to participant characteristics. IPD-MA can be performed either in a two-stage or a one-stage approach. We exemplify the presented methods using two illustrative examples. The first real-life example includes six studies assessing sonothrombolysis with or without addition of microspheres against intravenous thrombolysis alone (i.e., control) in acute ischemic stroke participants with large vessel occlusions. The second real-life example includes seven studies evaluating the association between blood pressure levels after endovascular thrombectomy and functional improvement of acute ischemic stroke in patients with large vessel occlusion. IPD reviews can be associated with higher-quality statistical analysis and may differ from aggregate data reviews. Unlike individual trials that lack power, and aggregate data meta-analysis results which suffer from confounding and aggregation bias, the use of IPD allows us to explore intervention-by-covariate interactions. However, a key limitation of conducting an IPD-MA is retrieval of IPD from original RCTs. Time and resources should be carefully planned before embarking to retrieving IPD.

Developing prediction models when there are systematically missing predictors in individual patient data meta-analysis

Clinical prediction models are widely used in modern clinical practice. Such models are often developed using individual patient data (IPD) from a single study, but often there are IPD available from multiple studies. This allows using meta-analytical methods for developing prediction models, increasing power and precision. Different studies, however, often measure different sets of predictors, which may result to systematically missing predictors, that is, when not all studies collect all predictors of interest. This situation poses challenges in model development. We hereby describe various approaches that can be used to develop prediction models for continuous outcomes in such situations. We compare four approaches: a "restrict predictors" approach, where the model is developed using only predictors measured in all studies; a multiple imputation approach that ignores study-level clustering; a multiple imputation approach that accounts for study-level clustering; and a new approach that develops a prediction model in each study separately using all predictors reported, and then synthesizes all predictions in a multi-study ensemble. We explore in simulations the performance of all approaches under various scenarios. We find that imputation methods and our new method outperform the restrict predictors approach. In several scenarios, our method outperformed imputation methods, especially for few studies, when predictor effects were small, and in case of large heterogeneity. We use a real dataset of 12 trials in psychotherapies for depression to illustrate all methods in practice, and we provide code in R.

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.

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.

Immune checkpoint inhibitors as first line in advanced melanoma: Evaluating progression-free survival based on reconstructed individual patient data

BACKGROUND

In patients with advanced melanoma, immune-checkpoint inhibitors (ICIs) represent the mainstay for first line treatment. Recently, relatlimab+nivolumab was proposed as a new combination therapy. This review was aimed at summarizing the current data of effectiveness for ICIs. Progression-free survival (PFS) was the endpoint of our analysis.

METHODS

After a standard literature search, Phase II/III studies comparing different ICI regimens in previously untreated advanced melanoma patients were analyzed. Patient-level data were reconstructed from Kaplan-Meier curves by application of the IPDfromKM method. These reconstructed datasets were used to perform indirect comparisons between treatments. Standard statistical testing was used, including hazard ratio and medians. A secondary analysis employed the restricted mean survival time.

RESULTS

Six trials were included in our analysis. Information on PFS from these trials was pooled according to the following treatments: nivolumab or pembrolizumab as monotherapy, or in combination with ipilimumab, and relatlimab + nivolumab. Pembrolizumab+ipilimumab showed significantly better PFS compared with the other treatments; nivolumab+ipilimumab ranked second; the other treatments showed a similar survival pattern.

CONCLUSIONS

The picture of comparative effectiveness resulting from our analysis is complex. The IPDfromKM method is advantageous because it accounts for the length of follow-up but loses the balance between treatment group and controls determined by randomization. Based on indirect comparisons, the combination of pembrolizumab+ipilimumab showed a particularly high efficacy, and so deserves further investigation. While the effect of between-trial differences in inclusion criteria plays an important role, our results do not support the proposal of relatlimab+nivolumab as a new standard of care.

Bayesian Additive Regression Trees (BART) with covariate adjusted borrowing in subgroup analyses

It is crucial in clinical trials to investigate treatment effect consistency across subgroups defined by patient baseline characteristics. However, there may be treatment effect variability across subgroups due to small subgroup sample size. Various Bayesian models have been proposed to incorporate this variability when borrowing information across subgroups. These models rely on the underlying assumption that patients with similar characteristics will have similar outcomes to the same treatment. Patient populations within each subgroup must subjectively be deemed similar enough Pocock (1976) to borrow response information across subgroups. We propose utilizing the machine learning method of Bayesian Additive Regression Trees (BART) to provide a method for subgroup borrowing that does not rely on an underlying assumption of homogeneity between subgroups. BART is a data-driven approach that utilizes patient-level observations. The amount of borrowing between subgroups automatically adjusts as BART learns the covariate-response relationships. Modeling patient-level data rather than treating the subgroup as a single unit minimizes assumptions regarding homogeneity across subgroups. We illustrate the use of BART in this context by comparing performance from existing subgroup borrowing methods in a simulation study and a case study in non-small cell lung cancer. The application of BART in the context of subgroup analyses alleviates the need to subjectively choose how much information to borrow based on subgroup similarity. Having the amount of borrowing be analytically determined and controlled for based on the similarity of individual patient-level characteristics allows for more objective decision making in the drug development process with many other applications including basket trials.

Combining individual patient data from randomized and non-randomized studies to predict real-world effectiveness of interventions

Meta-analysis of randomized controlled trials is generally considered the most reliable source of estimates of relative treatment effects. However, in the last few years, there has been interest in using non-randomized studies to complement evidence from randomized controlled trials. Several meta-analytical models have been proposed to this end. Such models mainly focussed on estimating the average relative effects of interventions. In real-life clinical practice, when deciding on how to treat a patient, it might be of great interest to have personalized predictions of absolute outcomes under several available treatment options. This paper describes a general framework for developing models that combine individual patient data from randomized controlled trials and non-randomized study when aiming to predict outcomes for a set of competing medical interventions applied in real-world clinical settings. We also discuss methods for measuring the models' performance to identify the optimal model to use in each setting. We focus on the case of continuous outcomes and illustrate our methods using a data set from rheumatoid arthritis, comprising patient-level data from three randomized controlled trials and two registries from Switzerland and Britain.

Evaluating the stability of opioid efficacy over 12 months in patients with chronic noncancer pain who initially demonstrate benefit from extended release oxycodone or hydrocodone: harmonization of Food and Drug Administration patient-level drug safety study data

ABSTRACT

Opioids relieve acute pain, but there is little evidence to support the stability of the benefit over long-term treatment of chronic noncancer pain. Previous systematic reviews consider only group level published data which did not provide adequate detail. Our goal was to use patient-level data to explore the stability of pain, opioid dose, and either physical function or pain interference in patients treated for 12 months with abuse deterrent formulations of oxycodone and hydrocodone. All available studies in the Food and Drug Administration Document Archiving, Reporting, and Regulatory Tracking System were included. Patient-level demographics, baseline data, exposure, and outcomes were harmonized. Individual patient slopes were calculated from a linear model of pain, physical function, and pain interference to determine response over time. Opioid dose was summarized by change between baseline and the final month of observation. Patients with stable or less pain, stable or lower opioid dose, and stable or better physical function (where available) met our prespecified criteria for maintaining long-term benefit from chronic opioids. Of the complete data set of 3192 patients, 1422 (44.5%) maintained their pain level and opioid dose. In a secondary analysis of 985 patients with a measured physical function, 338 (34.3%) maintained their physical function in addition to pain and opioid dose. Of 2040 patients with pain interference measured, 788 (38.6%) met criteria in addition. In a carefully controlled environment, about one-third of patients successfully titrated on opioids to treat chronic noncancer pain demonstrated continued benefit for up to 12 months.

Modeling treatment effect modification in multidrug-resistant tuberculosis in an individual patientdata meta-analysis

Effect modification occurs while the effect of the treatment is not homogeneous across the different strata of patient characteristics. When the effect of treatment may vary from individual to individual, precision medicine can be improved by identifying patient covariates to estimate the size and direction of the effect at the individual level. However, this task is statistically challenging and typically requires large amounts of data. Investigators may be interested in using the individual patient data from multiple studies to estimate these treatment effect models. Our data arise from a systematic review of observational studies contrasting different treatments for multidrug-resistant tuberculosis, where multiple antimicrobial agents are taken concurrently to cure the infection. We propose a marginal structural model for effect modification by different patient characteristics and co-medications in a meta-analysis of observational individual patient data. We develop, evaluate, and apply a targeted maximum likelihood estimator for the doubly robust estimation of the parameters of the proposed marginal structural model in this context. In particular, we allow for differential availability of treatments across studies, measured confounding within and across studies, and random effects by study.

Syncope Time Frames for Adverse Events after Emergency Department Presentation: An Individual Patient Data Meta-Analysis

Background and Objectives: Knowledge of the incidence and time frames of the adverse events of patients presenting syncope at the ED is essential for developing effective management strategies. The aim of the present study was to perform a meta-analysis of the incidence and time frames of adverse events of syncope patients. Materials and Methods: We combined individual patients’ data from prospective observational studies including adult patients who presented syncope at the ED. We assessed the pooled rate of adverse events at 24 h, 72 h, 7–10 days, 1 month and 1 year after ED evaluation. Results: We included nine studies that enrolled 12,269 patients. The mean age varied between 53 and 73 years, with 42% to 57% females. The pooled rate of adverse events was 5.1% (95% CI 3.4% to 7.7%) at 24 h, 7.0% (95% CI 4.9% to 9.9%) at 72 h, 8.4% (95% CI 6.2% to 11.3%) at 7–10 days, 10.3% (95% CI 7.8% to 13.3%) at 1 month and 21.3% (95% CI 15.8% to 28.0%) at 1 year. The pooled death rate was 0.2% (95% CI 0.1% to 0.5%) at 24 h, 0.3% (95% CI 0.1% to 0.7%) at 72 h, 0.5% (95% CI 0.3% to 0.9%) at 7–10 days, 1% (95% CI 0.6% to 1.7%) at 1 month and 5.9% (95% CI 4.5% to 7.7%) at 1 year. The most common adverse event was arrhythmia, for which its rate was 3.1% (95% CI 2.0% to 4.9%) at 24 h, 4.8% (95% CI 3.5% to 6.7%) at 72 h, 5.8% (95% CI 4.2% to 7.9%) at 7–10 days, 6.9% (95% CI 5.3% to 9.1%) at 1 month and 9.9% (95% CI 5.5% to 17) at 1 year. Ventricular arrhythmia was rare. Conclusions: The risk of death or life-threatening adverse event is rare in patients presenting syncope at the ED. The most common adverse events are brady and supraventricular arrhythmias, which occur during the first 3 days. Prolonged ECG monitoring in the ED in a short stay unit with ECG monitoring facilities may, therefore, be beneficial.

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.

Patient Outcomes in Disorders of Consciousness Following Transcranial Magnetic Stimulation: A Systematic Review and Meta-Analysis of Individual Patient Data

Background: There are few treatments with limited efficacy for patients with disorders of consciousness (DoC), such as minimally conscious and persistent vegetative state (MCS and PVS).

Objective: In this meta-analysis of individual patient data (IPD), we examine studies utilizing transcranial magnetic stimulation (TMS) as a treatment in DoC to determine patient and protocol-specific factors associated with improved outcomes.

Methods: We conducted a systematic review of PubMed, Ovid Medline, and Clinicaltrials.gov through April 2020 using the following terms: “minimally conscious state,” or “persistent vegetative state,” or “unresponsive wakefulness syndrome,” or “disorders of consciousness” and “transcranial magnetic stimulation.” Studies utilizing TMS as an intervention and reporting individual pre- and post-TMS Coma Recovery Scale-Revised (CRS-R) scores and subscores were included. Studies utilizing diagnostic TMS were excluded. We performed a meta-analysis at two time points to generate a pooled estimate for absolute change in CRS-R Index, and performed a second meta-analysis to determine the treatment effect of TMS using data from sham-controlled crossover studies. A linear regression model was also created using significant predictors of absolute CRS-R index change.

Results: The search yielded 118 papers, of which 10 papers with 90 patients were included. Patients demonstrated a mean pooled absolute change in CRS-R Index of 2.74 (95% CI, 0.62–4.85) after one session of TMS and 5.88 (95% CI, 3.68–8.07) at last post-TMS CRS-R assessment. The standardized mean difference between real rTMS and sham was 2.82 (95% CI, −1.50 to 7.14), favoring rTMS. The linear regression model showed that patients had significantly greater CRS-R index changes if they were in MCS, had an etiology of stroke or intracranial hemorrhage, received 10 or more sessions of TMS, or if TMS was initiated within 3 months from injury.

Conclusions: TMS may improve outcomes in MCS and PVS. Further evaluation with randomized, clinical trials is necessary to determine its efficacy in this patient population.

Ganglioneuromas across age groups: Systematic review of individual patient data

BACKGROUND

Ganglioneuromas are very rare tumours of the sympathetic nervous system. Clinical and pathological knowledge is currently based on largely incomparable registries and case series that focus on paediatric or adrenal cases. To comprehensively characterize the full clinical spectrum across ages and locations, a meta-analysis was performed where amenable and complemented by systematic literature review of individual patient data (IPD).

DESIGN

Articles containing "ganglioneuroma" in English on humans, published from 1/1/1995-6/27/2018, were identified from PubMed. Aggregate data from 10 eligible patient series on 19 variables were considerably inhomogeneous, restricting meta-analysis to age and gender distribution. To determine basic disease characteristics across ages and locations, IPD were retrieved from case reports and small case series (PROSPERO CRD42018010247).

RESULTS

Individual patient data representing 364 cases revealed that 65.7% (60.6%-70.4%) were diagnosed in adults, more frequently in females (62%, 56.9%-66.9%). 24.5% (20.3%-39.1%) were discovered incidentally. Most often, ganglioneuromas developed in abdomen/pelvis (66.2, 32.1% adrenal). With age, the proportion of ganglioneuroma localizations with high post-surgical complication rate (35.6% head/neck and 16.3% thorax) decreased. Contrarily, the diagnosis of adrenal ganglioneuromas (<1% post-surgical complications) increased with age. Hormone production, hypertension or coincidence with another non-neuroblastic neural-crest-derived tumour component was more common for adrenal location. Recurrence and metastatic spread have not been reported for ganglioneuromas without secondary tumour component.

CONCLUSIONS

This work summarizes characteristics of the currently largest number of international GN patients across all ages. The data confirm a benign nature of GN, independent of age. Age-related differences in predominant tumour location, associated post-surgical complications and hormone production suggest case-centred management strategies.

Assessing the performance of population adjustment methods for anchored indirect comparisons: A simulation study

Standard network meta-analysis and indirect comparisons combine aggregate data from multiple studies on treatments of interest, assuming that any factors that interact with treatment effects (effect modifiers) are balanced across populations. Population adjustment methods such as multilevel network meta-regression (ML-NMR), matching-adjusted indirect comparison (MAIC), and simulated treatment comparison (STC) relax this assumption using individual patient data from one or more studies, and are becoming increasingly prevalent in health technology appraisals and the applied literature. Motivated by an applied example and two recent reviews of applications, we undertook an extensive simulation study to assess the performance of these methods in a range of scenarios under various failures of assumptions. We investigated the impact of varying sample size, missing effect modifiers, strength of effect modification and validity of the shared effect modifier assumption, validity of extrapolation and varying between-study overlap, and different covariate distributions and correlations. ML-NMR and STC performed similarly, eliminating bias when the requisite assumptions were met. Serious concerns are raised for MAIC, which performed poorly in nearly all simulation scenarios and may even increase bias compared with standard indirect comparisons. All methods incur bias when an effect modifier is missing, highlighting the necessity of careful selection of potential effect modifiers prior to analysis. When all effect modifiers are included, ML-NMR and STC are robust techniques for population adjustment. ML-NMR offers additional advantages over MAIC and STC, including extending to larger treatment networks and producing estimates in any target population, making this an attractive choice in a variety of scenarios.

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.

Comprehensive database and individual patient data meta-analysis of randomised controlled trials on psychotherapies reducing suicidal thoughts and behaviour: study protocol

INTRODUCTION

Psychotherapy may reduce suicidal thoughts and behaviour, but its effectiveness is not well examined. Furthermore, conventional meta-analyses are unable to test possible effects of moderators affecting this relationship. This protocol outlines the building of a comprehensive database of the literature in this research field. In addition, we will conduct an individual patient data meta-analysis (IPD-MA) to establish the effectiveness of psychotherapy in reducing suicidality, and to examine which factors moderate the efficacy of these interventions.

METHODS AND ANALYSIS

To build a comprehensive database, randomised controlled trials examining the effect of any psychotherapy targeting any psychiatric disorder on suicidal thoughts or behaviour will be identified by running a systematic search in PubMed, Embase, PsycINFO, Web of Science, Scopus and The Cochrane Central Register of Controlled Trials from data inception to 12 August 2019. For the IPD-MA, we will focus on adult outpatients with suicidal ideation or behaviour. In addition, as a comparison group we will focus on a control group (waiting-list, care as usual or placebo). A 1-stage IPD-MA will be used to determine the effectiveness of psychotherapy on suicidal ideation, suicide attempts and/or suicide deaths, and to investigate potential patient-related and intervention-related moderators. Subgroup and sensitivity analyses will be conducted to test the robustness of the findings. Additionally, a conventional MA will be conducted to determine the differences between studies that provided IPD and those that did not. IPD-MA may determine the effectiveness of psychotherapy in reducing suicidality and provide insights into the moderating factors influencing the efficacy of psychotherapy. Answering these questions will inform mental healthcare practitioners about optimal treatments for different groups of individuals with suicidal ideation and/or behaviour and consequently help to reduce suicide risk.

ETHICS AND DISSEMINATION

An ethical approval is not required for this study. The results will be published in a peer-review journal.

PROSPERO REGISTRATION NUMBER

CRD42020140573.

Efficacy of Once-Weekly Semaglutide vs Empagliflozin Added to Metformin in Type 2 Diabetes: Patient-Level Meta-analysis

CONTEXT

No head-to-head trials have directly compared once-weekly (OW) semaglutide, a human glucagon-like peptide-1 analog, with empagliflozin, a sodium-glucose co-transporter-2 inhibitor, in type 2 diabetes (T2D).

OBJECTIVE

We indirectly compared the efficacy of OW semaglutide 1 mg vs once-daily (OD) empagliflozin 25 mg in patients with T2D inadequately controlled on metformin monotherapy, using individual patient data (IPD) and meta-regression methodology.

DESIGN, SETTING, PARTICIPANTS, AND INTERVENTIONS

IPD for patients with T2D receiving metformin monotherapy and randomized to OW semaglutide 1 mg (SUSTAIN 2, 3, 8 trials), or to OD empagliflozin 25 mg (PIONEER 2 trial) were included. Meta-regression analyses were adjusted for potential prognostic factors and effect modifiers.

MAIN OUTCOME MEASURES

The primary efficacy outcomes were change from baseline to end-of-treatment (~1 year) in HbA1c (%-point) and body weight (kg). Responder outcomes and other clinically relevant efficacy measures were analyzed.

RESULTS

Baseline characteristics were similar between OW semaglutide (n = 995) and empagliflozin (n = 410). Our analyses showed that OW semaglutide significantly reduced mean HbA1c and body weight vs empagliflozin (estimated treatment difference: -0.61%-point [95% confidence interval (CI): -0.72; -0.49] and -1.65 kg [95% CI: -2.22; -1.08], respectively; both P < 0.0001). Complementary analyses supported the robustness of these results. A significantly greater proportion of patients on OW semaglutide vs empagliflozin also achieved HbA1c targets and weight-loss responses.

CONCLUSIONS

This indirect comparison suggests that OW semaglutide 1 mg provides superior reductions in HbA1c and body weight vs OD empagliflozin 25 mg in patients with T2D when added to metformin monotherapy.

Comparative effectiveness of nivolumab versus standard of care for third-line patients with small-cell lung cancer

Aim: To estimate the comparative effectiveness of nivolumab versus standard of care (SOC) in terms of overall survival (OS) for small-cell lung cancer patients treated with two prior lines of chemotherapy, in other words, third line in the USA. Materials & methods: Data were from CheckMate 032, a single-arm trial of nivolumab, and real-world electronic patient records. Comparisons of OS were conducted using three different methods to adjust for differences (regression, weighting and doubly robust) between the populations. Results: Nivolumab was associated with longer survival compared with SOC (hazard ratio for OS: 0.58-0.70) across all methods for adjustment. Conclusion: Nivolumab was more efficacious in terms of OS as third-line treatment for small-cell lung cancer compared with current SOC in the USA.

False individual patient data and zombie randomised controlled trials submitted to Anaesthesia

Concerned that studies contain false data, I analysed the baseline summary data of randomised controlled trials when they were submitted to Anaesthesia from February 2017 to March 2020. I categorised trials with false data as 'zombie' if I thought that the trial was fatally flawed. I analysed 526 submitted trials: 73 (14%) had false data and 43 (8%) I categorised zombie. Individual patient data increased detection of false data and categorisation of trials as zombie compared with trials without individual patient data: 67/153 (44%) false vs. 6/373 (2%) false; and 40/153 (26%) zombie vs. 3/373 (1%) zombie, respectively. The analysis of individual patient data was independently associated with false data (odds ratio (95% credible interval) 47 (17-144); p = 1.3 × 10^-12 ) and zombie trials (odds ratio (95% credible interval) 79 (19-384); p = 5.6 × 10^-9 ). Authors from five countries submitted the majority of trials: China 96 (18%); South Korea 87 (17%); India 44 (8%); Japan 35 (7%); and Egypt 32 (6%). I identified trials with false data and in turn categorised trials zombie for: 27/56 (48%) and 20/56 (36%) Chinese trials; 7/22 (32%) and 1/22 (5%) South Korean trials; 8/13 (62%) and 6/13 (46%) Indian trials; 2/11 (18%) and 2/11 (18%) Japanese trials; and 9/10 (90%) and 7/10 (70%) Egyptian trials, respectively. The review of individual patient data of submitted randomised controlled trials revealed false data in 44%. I think journals should assume that all submitted papers are potentially flawed and editors should review individual patient data before publishing randomised controlled trials.

Clinical outcomes in COVID-19 patients treated with tocilizumab: An individual patient data systematic review

Background

Current evidence suggests an important role of the interleukin‐6 (IL‐6) pathway in severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2)‐related cytokine release storm in severely ill coronavirus disease 2019 (COVID‐19) patients. Inhibition of the IL‐6 pathway with tocilizumab has been employed successfully in some of these patients but the data is mostly consistent of case reports and series.

Methods

We performed a systematic search of PubMed, Embase, and Medline from 22nd April 2020 and again on 27th April 2020 using the following search terms alone or in combination: “COVID‐19,” “coronavirus,” “SARS‐CoV‐2,” “COVID,” “anti‐interleukin‐6 receptor antibodies,” “anti‐IL‐6,” “tocilizumab,” “sarilumab,” “siltuximab.” We included studies that reported individual patient data. We extracted and analyzed individual level data on baseline characteristics, laboratory findings, and clinical outcomes. The primary endpoint was in‐hospital mortality. Secondary endpoints included in‐hospital complications, recovery rates, effect of patient characteristics on the primary outcome and changes in levels of inflammatory markers.

Results

Three hundred fifty‐two records were identified through a systematic search, of which 10 studies met the inclusion criteria. A single study currently under review was also added. Eleven observational studies encompassing 29 patients were included in the present review. There were more males (24 [82.8%]), and hypertension was the most common comorbidity (16 [48.3%]). Over an average of 5.4 hospital days, the primary endpoint occurred in 6 (20.7%) patients. Among surviving patients, about 10% had worsened disease and 17% recovered. The most common complication was acute respiratory distress syndrome (8 [27.6%]). The IL‐6 level was significantly higher after the initiation of tocilizumab with median (interquartile range) of 376.6 (148‐900.6) pg/mL compared to the baseline of 71.1 (31.9‐122.8) pg/mL (P = .002). Mean (standard deviation) levels of C‐reactive protein (CRP) were significantly decreased following treatment 24.6 (26.9) mg/L compared to baseline 140.4 (77) mg/L (P < .0001). Baseline demographics were not significantly different among survivors and nonsurvivors by Fisher's exact test.

Conclusion

In COVID‐19 patients treated with tocilizumab, IL‐6 levels are significantly elevated, which are supportive of cytokine storm. Following initiation of tocilizumab, there is elevation in the IL‐6 levels and CRP levels dramatically decrease, suggesting an improvement in this hyperinflammatory state. Ongoing randomized control trials will allow for further evaluation of this promising therapy.

Importance

Recent data indicate that severe COVID‐19 causes a cytokine release storm and is associated with worse clinical outcomes and IL‐6 plays an important role. It is suggestive that anti‐IL‐6 results in the improvement of this hyperinflammatory state. However, to our knowledge, there is no individual patient data systematic review performed to summarize baseline characteristics and clinical outcomes of COVID‐19 patients who received tocilizumab.

Interleukin‐6 (IL‐6) may play an important role in the pathogenesis of COVID‐19.

Data show that tocilizumab, an IL‐6 receptor antagonist, reduces COVID‐19 complications.

Our systematic review suggests that tocilizumab may improve survival in COVID‐19.

Effect of heat-clearing and detoxifying Chinese medicines combined with conventional therapy on mild hand, foot, and mouth disease with fever: An individual patient data meta-analysis

BACKGROUND

In China, heat-clearing and detoxifying Chinese medicines combined with conventional therapy are commonly applied to treat the mild hand, foot, and mouth disease (HFMD). However, there is lack of solid evidence on the efficacy and safety of such therapies.

METHODS

We conducted a pooled analysis with individual patient data from 5 strictly randomized controlled clinical trials to assess the efficacy and safety of this combination therapy for mild HFMD. An intention-to-treat analysis was performed. A 2-stage meta-analysis method was adopted to analyze the pooled effect size.

RESULTS

In total, 947 patients were included. Compared with conventional therapy, the combination therapy significantly reduced the progression rate of HFMD from mild to severe (odds ratio [OR] 0.43, 95% confidence interval [CI]: 0.22 to 0.83, P = .01). Meanwhile, the healing time of skin rash and oral ulcer in the combination therapy group was significantly shorter than that of conventional therapy. The overall hazard ratio (HR) of healing time of the skin rash or oral ulcer was 1.22 (95%CI: 1.04 to 1.43; P = .02). However, except Jinlianqingre effervescent tablets, the combination therapy cannot shorten the time to fever resolution (HR 1.12, 95%CI: 0.97 to 1.29, P = .14). Because of the heterogeneity, Jinlianqingre effervescent tablets were analyzed separately and the HRs of the time to fever resolution and the healing time of skin rash or oral ulcer were 3.88 (95%CI: 3.19 to 4.72; P < .0001) and 3.79 (95%CI: 2.81 to 5.11; P < .0001), respectively. There were 30 adverse events reported in total; 2 cases were related to Chinese medicines.

CONCLUSION

In conclusion, the heat-clearing and detoxifying Chinese medicines on top of conventional therapy can effectively reduce the progressive rate of mild HFMD and improve healing of skin and oral mucosal lesions. More studies are needed for the time to fever resolution.

Equivalence of entropy balancing and the method of moments for matching-adjusted indirect comparison

Indirect comparisons are used to obtain estimates of relative effectiveness between two treatments that have not been compared in the same randomized controlled trial, but have instead been compared against a common comparator in separate trials. Standard indirect comparisons use only aggregate data, under the assumption that there are no differences in effect‐modifying variables between the trial populations. Population‐adjusted indirect comparisons aim to relax this assumption by using individual patient data (IPD) from one trial to adjust for differences in effect modifiers between populations. At present, the most commonly used approach is matching‐adjusted indirect comparison (MAIC), where weights are estimated that match the covariate distributions of the reweighted IPD to the aggregate trial. MAIC was originally proposed using the method of moments to estimate the weights, but more recently entropy balancing has been proposed as an alternative. Entropy balancing has an additional “optimality” property ensuring that the weights are as uniform as possible, reducing the standard error of the estimates. In this brief method note, we show that MAIC weights are mathematically identical whether estimated using entropy balancing or the method of moments. Importantly, this means that the standard MAIC (based on the method of moments) also enjoys the “optimality” property. Moreover, the additional flexibility of entropy balancing suggests several interesting avenues for further research, such as combining population adjustment via MAIC with adjustments for treatment switching or nonparametric covariate adjustment.

Estimating treatment importance in multidrug-resistant tuberculosis using Targeted Learning: An observational individual patient data network meta-analysis

Persons with multidrug-resistant tuberculosis (MDR-TB) have a disease resulting from a strain of tuberculosis (TB) that does not respond to at least isoniazid and rifampicin, the two most effective anti-TB drugs. MDR-TB is always treated with multiple antimicrobial agents. Our data consist of individual patient data from 31 international observational studies with varying prescription practices, access to medications, and distributions of antibiotic resistance. In this study, we develop identifiability criteria for the estimation of a global treatment importance metric in the context where not all medications are observed in all studies. With stronger causal assumptions, this treatment importance metric can be interpreted as the effect of adding a medication to the existing treatments. We then use this metric to rank 15 observed antimicrobial agents in terms of their estimated add-on value. Using the concept of transportability, we propose an implementation of targeted maximum likelihood estimation, a doubly robust and locally efficient plug-in estimator, to estimate the treatment importance metric. A clustered sandwich estimator is adopted to compute variance estimates and produce confidence intervals. Simulation studies are conducted to assess the performance of our estimator, verify the double robustness property, and assess the appropriateness of the variance estimation approach.

Outcomes of Bedaquiline Treatment in Patients with Multidrug-Resistant Tuberculosis

Bedaquiline is recommended by the World Health Organization for the treatment of multidrug-resistant (MDR) and extensively drug-resistant (XDR) tuberculosis (TB). We pooled data from 5 cohorts of patients treated with bedaquiline in France, Georgia, Armenia, and South Africa and in a multicountry study. The rate of culture conversion to negative at 6 months (by the end of 6 months of treatment) was 78% (95% CI 73.5%-81.9%), and the treatment success rate was 65.8% (95% CI 59.9%-71.3%). Death rate was 11.7% (95% CI 7.0%-19.1%). Up to 91.1% (95% CI 82.2%-95.8%) of the patients experienced >1 adverse event, and 11.2% (95% CI 5.0%-23.2%) experienced a serious adverse event. Lung cavitations were consistently associated with unfavorable outcomes. The use of bedaquiline in MDR and XDR TB treatment regimens appears to be effective and safe across different settings, although the certainty of evidence was assessed as very low.

Integrated evaluation of targeted and non-targeted therapies in a network meta-analysis

Individualized therapies for patients with biomarkers are moving more and more into the focus of research interest when developing new treatments. Hereby, the term individualized (or targeted) therapy denotes a treatment specifically developed for biomarker-positive patients. A network meta-analysis model for a binary endpoint combining the evidence for a targeted therapy from individual patient data with the evidence for a non-targeted therapy from aggregate data is presented and investigated. The biomarker status of the patients is either available at patient-level in individual patient data or at study-level in aggregate data. Both types of biomarker information have to be included. The evidence synthesis model follows a Bayesian approach and applies a meta-regression to the studies with aggregate data. In a simulation study, we address three treatment arms, one of them investigating a targeted therapy. The bias and the root-mean-square error of the treatment effect estimate for the subgroup of biomarker-positive patients based on studies with aggregate data are investigated. Thereby, the meta-regression approach is compared to approaches applying alternative solutions. The regression approach has a surprisingly small bias even in the presence of few studies. By contrast, the root-mean-square error is relatively greater. An illustrative example is provided demonstrating implementation of the presented network meta-analysis model in a clinical setting.

Comparing Strategies for Modeling Competing Risks in Discrete-Event Simulations: A Simulation Study and Illustration in Colorectal Cancer

BACKGROUND

Different strategies toward implementing competing risks in discrete-event simulation (DES) models are available. This study aims to provide recommendations regarding modeling approaches that can be defined based on these strategies by performing a quantitative comparison of alternative modeling approaches.

METHODS

Four modeling approaches were defined: 1) event-specific distribution (ESD), 2) event-specific probability and distribution (ESPD), 3) unimodal joint distribution and regression model (UDR), and 4) multimodal joint distribution and regression model (MDR). Each modeling approach was applied to uncensored individual patient data in a simulation study and a case study in colorectal cancer. Their performance was assessed in terms of relative event incidence difference, relative absolute event incidence difference, and relative entropy of time-to-event distributions. Differences in health economic outcomes were also illustrated for the case study.

RESULTS

In the simulation study, the ESPD and MDR approaches outperformed the ESD and UDR approaches, in terms of both event incidence differences and relative entropy. Disease pathway and data characteristics, such as the number of competing risks and overlap between competing time-to-event distributions, substantially affected the approaches' performance. Although no considerable differences in health economic outcomes were observed, the case study showed that the ESPD approach was most sensitive to low event rates, which negatively affected performance.

CONCLUSIONS

Based on overall performance, the recommended modeling approach for implementing competing risks in DES models is the MDR approach, which is defined according to the general strategy of selecting the time-to-event first and the corresponding event second. The ESPD approach is a less complex and equally performing alternative if sufficient observations are available for each competing event (i.e., the internal validity shows appropriate data representation).

Guided or self-guided internet-based cognitive-behavioural therapy (iCBT) for depression? Study protocol of an individual participant data network meta-analysis

INTRODUCTION

Although guided forms of internet-based cognitive-behavioural therapy (iCBT) result in a substantial reduction in depression, it seems that the most scalable way to deliver iCBT is without guidance. However, direct evidence on the comparison between guided and self-guided iCBT is scarce. Moreover, it is unclear which types of patients may benefit more from each of these two forms of iCBT. Network meta-analysis (NMA) using individual participant data (IPD) offers a way to assess the relative efficacy of multiple (>2) interventions. Moreover, it maximises our power to detect patient-level characteristics (covariates) that have an important effect on the efficacy of interventions. This protocol describes the procedures of an IPD-NMA, which aims at examining the relative efficacy of guided compared with self-guided iCBT and at identifying predictors and moderators of treatment outcome.

METHODS AND ANALYSIS

We will use an existing database on psychotherapies for adult depression to identify eligible studies. This database has been updated up to 1 January 2018, through literature searches in PubMed, Embase, PsycINFO and Cochrane Library. The outcome of this IPD-NMA is reduction in depressive symptoms severity. We will fit the model in a Bayesian setting. After fitting the model, we will report the relative treatment effects for different types of patients, and we will discuss the clinical implications of our findings. Based on the results from the IPD-NMA model, we will develop and validate a personalised prediction model, aiming to provide patient-level predictions about the effects of the interventions.

ETHICS AND DISSEMINATION

An ethical approval is not required for this study. The results will be published in a peer-review journal. These results will guide clinical decisions about the most efficient way to allocate iCBT resources, thereby increasing the scalability of this innovative therapeutic approach.

Pre-post changes in main outcomes of medical rehabilitation in Germany: protocol of a systematic review and meta-analysis of individual participant and aggregated data

INTRODUCTION

Multidisciplinary, complex rehabilitation interventions are an important part of the treatment of chronic diseases. However, little is known about the effectiveness of routine rehabilitation interventions within the German healthcare system. Due to the nature of the social insurance system in Germany, randomised controlled trials examining the effects of rehabilitation interventions are challenging to implement and scarcely accessible. Consequently, alternative pre-post designs can be employed to assess pre-post effects of medical rehabilitation programmes. We present a protocol of systematic review and meta-analysis methods to assess the pre-post effects of rehabilitation interventions in Germany.

METHODS AND ANALYSIS

The respective study will be conducted within the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. A systematic literature review will be conducted to identify studies reporting the pre-post effects (start of intervention vs end of intervention or later) in German healthcare. Studies investigating the following disease groups will be included: orthopaedics, rheumatology, oncology, pulmonology, cardiology, endocrinology, gastroenterology and psychosomatics. The primary outcomes of interest are physical/mental quality of life, physical functioning and social participation for all disease groups as well as pain (orthopaedic and rheumatologic patients only), blood pressure (cardiac patients only), asthma control (patients with asthma only), dyspnoea (patients with chronic obstructive pulmonary disease only) and depression/anxiety (psychosomatic patients only). We will invite the principal investigators of the identified studies to provide additional individual patient data. We aim to perform the meta-analyses using individual patient data as well as aggregate data. We will examine the effects of both study-level and patient-level moderators by using a meta-regression method.

ETHICS AND DISSEMINATION

Only studies that have received institutional approval from an ethics committee and present anonymised individual patient data will be included in the meta-analysis. The results will be presented in a peer-reviewed publication and at research conferences. A declaration of no objection by the ethics committee of the University of Würzburg is available (number 20180411 01).

TRIAL REGISTRATION NUMBER

CRD42018080316.

Meta-analysis of Individual Patient Data After Kissing Stent Treatment for Aortoiliac Occlusive Disease

PURPOSE

To evaluate short- and long-term technical and clinical outcomes after kissing stent treatment of aortoiliac occlusive disease (AIOD) based on an individual participant data (IPD) meta-analysis.

MATERIALS AND METHODS

A search of the Scopus database identified 156 articles on KS treatment of AIOD; of these 22 met the inclusion criteria. Authors of 19 articles with contact information were approached to join an IPD consortium. Eight author groups responded and 5 provided anonymized data for merging into an IPD database. The number of included procedures was equal before and after 2005. The primary study outcome was the cumulative patency at 24 months. Secondary outcomes were patency at up to 60 months, complications, and changes in Rutherford category and ankle-brachial index. The predictive value of stent protrusion length, pre-/postdilation, stent type, and patient demographics on primary patency were examined with Cox proportional hazard modeling; outcomes are reported as the hazard ratio (HR). The Kaplan-Meier method was employed to estimate patency rates.

RESULTS

In total, 605 (40.9%) of 1480 patients presented in the literature were included in the IPD analysis. The indication for intervention was intermittent claudication in 84.2% and critical limb ischemia in 15.8%. Lesions were classified as TransAtlantic Inter-Society Consensus (TASC) A or B in 52.8% and TASC C and D in 47.2%. The overall primary patency estimate was 81% at 24 months. Primary patency significantly increased after 2005 (p=0.005). Cox regression analysis revealed only age as a significant predictor of sustained primary patency (HR 0.60, p<0.005). Any previous endovascular intervention (HR 2.52, p=0.02) was the main predictor for loss of secondary patency; history of cardiovascular disease (HR 0.27, p=0.04) was the main predictor of sustained secondary patency.

CONCLUSION

The kissing stent technique has a good safety profile and acceptable patency rates up to 2 years, even in TASC C and D lesions, supporting an endovascular-first approach for AIOD.

Impact of missing individual patient data on 18 meta-analyses of randomised trials in oncology: Gustave Roussy experience

OBJECTIVE

To compare the characteristics, quality and treatment effects of randomised clinical trials (RCTs) by individual patient data (IPD) availability, in trials eligible for 18 IPD meta-analyses (MA).

DESIGN

Trial characteristics, risk of bias (RoB) and hazard ratio (HR) for overall survival were extracted from IPD-MA publications and/or RCTs publications. Data for the RoB assessment were extracted for a subset of 73 RCTs. Two investigators blinded to whether IPD was available or not evaluated the RoB for these trials. Treatment effects were compared using ratios of global HRs (RHRs) of IPD-unavailable trials and IPD-available trials. RHR were pooled using a fixed-effect model.

DATA SOURCES

We examined the IPD availability for each trial eligible for each IPD-MA; when the IPD was not available for a trial, we used information from published sources.

ELIGIBILITY CRITERIA FOR SELECTING STUDIES

We selected all published IPD-MAs conducted at Gustave Roussy and the RCTs eligible for each.

RESULTS

349 RCTs (73 018 patients) from 18 MAs were eligible: 60 RCTs (5890 patients) had unavailable IPD and 289 RCTs (67 128 patients) had available IPD. The main reason for IPD unavailability was data loss by investigators. IPD-unavailable trials were smaller (p<0.001), more often monocentric (p<0.001) and non-international (p=0.0004) than IPD-available trials. Geographical areas differed (p=0.054) between IPD-unavailable IPD-available trials. RoB was higher in IPD-unavailable RCTs for random sequence generation (p=0.007) and allocation concealment (p=0.006). The HR and 95% confidence interval (CI) for overall survival were extractable from publications in 23/60 IPD-unavailable trials included in 10 different MAs. Treatment effects were significantly greater for IPD-unavailable trials compared with IPD-available trials (RHR=0.86 (95% CI 0.75 to 0.98)).

CONCLUSIONS

IPD-unavailable RCTs were significantly different from IPD-available RCTs in terms of trial characteristics and were at greater RoB. IPD-unavailable RCTs had a significantly greater treatment effect.

A comparison of one-stage vs two-stage individual patient data meta-analysis methods: A simulation study

Background

Individual patient data (IPD) meta‐analysis allows for the exploration of heterogeneity and can identify subgroups that most benefit from an intervention (or exposure), much more successfully than meta‐analysis of aggregate data. One‐stage or two‐stage IPD meta‐analysis is possible, with the former using mixed‐effects regression models and the latter obtaining study estimates through simpler regression models before aggregating using standard meta‐analysis methodology. However, a comprehensive comparison of the two methods, in practice, is lacking.

Methods

We generated 1000 datasets for each of many simulation scenarios covering different IPD sizes and different between‐study variance (heterogeneity) assumptions at various levels (intercept and exposure). Numerous simulation settings of different assumptions were also used, while we evaluated performance both on main effects and interaction effects. Performance was assessed on mean bias, mean error, coverage, and power.

Results

Fully specified one‐stage models (random study intercept or fixed study‐specific intercept; random exposure effect; and fixed study‐specific effects for covariate) were the best performers overall, especially when investigating interactions. For main effects, performance was almost identical across models unless intercept heterogeneity was present, in which case the fully specified one‐stage and the two‐stage models performed better. For interaction effects, differences across models were greater with the two‐stage model consistently outperformed by the two fully specified one‐stage models.

Conclusions

A fully specified one‐stage model should be preferred (accounting for potential exposure, intercept, and, possibly, interaction heterogeneity), especially when investigating interactions. If non‐convergence is encountered with a random study intercept, the fixed study‐specific intercept one‐stage model should be used instead.

A recursive partitioning approach for subgroup identification in individual patient data meta-analysis

Background

Motivated by the setting of clinical trials in low back pain, this work investigated statistical methods to identify patient subgroups for which there is a large treatment effect (treatment by subgroup interaction). Statistical tests for interaction are often underpowered. Individual patient data (IPD) meta‐analyses provide a framework with improved statistical power to investigate subgroups. However, conventional approaches to subgroup analyses applied in both a single trial setting and an IPD setting have a number of issues, one of them being that factors used to define subgroups are investigated one at a time. As individuals have multiple characteristics that may be related to response to treatment, alternative exploratory statistical methods are required.

Methods

Tree‐based methods are a promising alternative that systematically searches the covariate space to identify subgroups defined by multiple characteristics. A tree method in particular, SIDES, is described and extended for application in an IPD meta‐analyses setting by incorporating fixed‐effects and random‐effects models to account for between‐trial variation. The performance of the proposed extension was assessed using simulation studies. The proposed method was then applied to an IPD low back pain dataset.

Results

The simulation studies found that the extended IPD‐SIDES method performed well in detecting subgroups especially in the presence of large between‐trial variation. The IPD‐SIDES method identified subgroups with enhanced treatment effect when applied to the low back pain data.

Conclusions

This work proposes an exploratory statistical approach for subgroup analyses applicable in any research discipline where subgroup analyses in an IPD meta‐analysis setting are of interest.

Network meta-analysis including treatment by covariate interactions: Consistency can vary across covariate values

BACKGROUND

Many reviews aim to compare numerous treatments and report results stratified by subgroups (eg, by disease severity). In such cases, a network meta-analysis model including treatment by covariate interactions can estimate the relative effects of all treatment pairings for each subgroup of patients. Two key assumptions underlie such models: consistency of treatment effects and consistency of the regression coefficients for the interactions. Consistency may differ depending on the covariate value at which consistency is assessed. For valid inference, we need to be confident of consistency for the relevant range of covariate values. In this paper, we demonstrate how to assess consistency of treatment effects from direct and indirect evidence at various covariate values.

METHODS

Consistency is assessed using visual inspection, inconsistency estimates, and probabilities. The method is applied to an individual patient dataset comparing artemisinin combination therapies for treating uncomplicated malaria in children using the covariate age.

RESULTS

The magnitude of the inconsistency appears to be decreasing with increasing age for each comparison. For one comparison, direct and indirect evidence differ for age 1 (P = .05), and this brings results for age 1 for all comparisons into question.

CONCLUSION

When fitting models including interactions, the consistency of direct and indirect evidence must be assessed across the range of covariates included in the trials. Clinical inferences are only valid for covariate values for which results are consistent.

Meta-analysis of gene-environment interaction exploiting gene-environment independence across multiple case-control studies

Multiple papers have studied the use of gene-environment (G-E) independence to enhance power for testing gene-environment interaction in case-control studies. However, studies that evaluate the role of G-E independence in a meta-analysis framework are limited. In this paper, we extend the single-study empirical Bayes type shrinkage estimators proposed by Mukherjee and Chatterjee (2008) to a meta-analysis setting that adjusts for uncertainty regarding the assumption of G-E independence across studies. We use the retrospective likelihood framework to derive an adaptive combination of estimators obtained under the constrained model (assuming G-E independence) and unconstrained model (without assumptions of G-E independence) with weights determined by measures of G-E association derived from multiple studies. Our simulation studies indicate that this newly proposed estimator has improved average performance across different simulation scenarios than the standard alternative of using inverse variance (covariance) weighted estimators that combines study-specific constrained, unconstrained, or empirical Bayes estimators. The results are illustrated by meta-analyzing 6 different studies of type 2 diabetes investigating interactions between genetic markers on the obesity related FTO gene and environmental factors body mass index and age.

Methods for Population-Adjusted Indirect Comparisons in Health Technology Appraisal

Standard methods for indirect comparisons and network meta-analysis are based on aggregate data, with the key assumption that there is no difference between the trials in the distribution of effect-modifying variables. Methods which relax this assumption are becoming increasingly common for submissions to reimbursement agencies, such as the National Institute for Health and Care Excellence (NICE). These methods use individual patient data from a subset of trials to form population-adjusted indirect comparisons between treatments, in a specific target population. Recently proposed population adjustment methods include the Matching-Adjusted Indirect Comparison (MAIC) and the Simulated Treatment Comparison (STC). Despite increasing popularity, MAIC and STC remain largely untested. Furthermore, there is a lack of clarity about exactly how and when they should be applied in practice, and even whether the results are relevant to the decision problem. There is therefore a real and present risk that the assumptions being made in one submission to a reimbursement agency are fundamentally different to—or even incompatible with—the assumptions being made in another for the same indication. We describe the assumptions required for population-adjusted indirect comparisons, and demonstrate how these may be used to generate comparisons in any given target population. We distinguish between anchored and unanchored comparisons according to whether a common comparator arm is used or not. Unanchored comparisons make much stronger assumptions, which are widely regarded as infeasible. We provide recommendations on how and when population adjustment methods should be used, and the supporting analyses that are required to provide statistically valid, clinically meaningful, transparent and consistent results for the purposes of health technology appraisal. Simulation studies are needed to examine the properties of population adjustment methods and their robustness to breakdown of assumptions.

Investigating the heterogeneity of alkylating agents' efficacy and toxicity between sexes: A systematic review and meta-analysis of randomized trials comparing cyclophosphamide and ifosfamide (MAIAGE study)

BACKGROUND

A marginal interaction between sex and the type of alkylating agent was observed for event-free survival in the Euro-EWING99-R1 randomized controlled trial (RCT) comparing cyclophosphamide and ifosfamide in Ewing sarcoma. To further evaluate this interaction, we performed an individual patient data meta-analysis of RCTs assessing cyclophosphamide versus ifosfamide in any type of cancer.

METHODS

A literature search produced two more eligible RCTs (EICESS92 and IRS-IV). The endpoints were progression-free survival (PFS, main endpoint) and overall survival (OS). The hazard ratios (HRs) of the treatment-by-sex interaction and their 95% confidence interval (95% CI) were assessed using stratified multivariable Cox models. Heterogeneity of the interaction across age categories and trials was explored. We also assessed this interaction for severe acute toxicity using logistic models.

RESULTS

The meta-analysis comprised 1,528 pediatric and young adult sarcoma patients from three RCTs: Euro-EWING99-R1 (n = 856), EICESS92 (n = 155), and IRS-IV (n = 517). There were 224 PFS events in Euro-EWING99-R1 and 200 in the validation set (EICESS92 + IRS-IV), and 171 and 154 deaths in each dataset, respectively. The estimated treatment-by-sex interaction for PFS in Euro-EWING99-R1 (HR = 1.73, 95% CI = 1.00-3.00) was not replicated in the validation set (HR = 0.97, 95% CI = 0.55-1.72), without heterogeneity across trials (P = 0.62). In the pooled analysis, the treatment-by-sex interaction was not significant (HR = 1.31, 95% CI = 0.89-1.95, P = 0.17), without heterogeneity across age categories (P = 0.88) and trials (P = 0.36). Similar results were observed for OS. No significant treatment-by-sex interaction was observed for leucopenia/neutropenia (P = 0.45), infection (P = 0.64), or renal toxicity (P = 0.20).

CONCLUSION

Our meta-analysis did not confirm the hypothesis of a treatment-by-sex interaction on efficacy or toxicity outcomes.

Is orthostatic hypotension related to falling? A meta-analysis of individual patient data of prospective observational studies

Background

orthostatic hypotension (OH) is one out of many risk factors believed to contribute to an increased fall risk in elderly subjects but it is unclear whether an independent association between OH and falling exists.

Objectives

to perform an individual patient data (IPD) meta-analysis of prospective observational studies investigating the relationship between OH and falling.

Design

MEDLINE, EMBASE, the Cochrane Library and the abstracts of annual meetings of selected hypertension societies were searched. Both one-stage (analysing all IPD from all studies simultaneously) and two-stage (analysing IPD per study, and then pooling the results) methods were used, and both logistic and cox regression analyses were performed. The study protocol was published on PROSPERO (2015:CRD42015019178).

Results

from 34 selected abstracts, 6 studies were included. IPD were provided in 1,022 patients from 3 cohorts and were included in the IPD meta-analysis. The one-stage meta-analysis showed a significant relationship between OH and time to first fall incident (hazard ratio (HR) 1.52 (95% Confidence Interval (CI) 1.23-1.88)). No significant relationship between OH and falling was found in the one-stage logistic regression analysis and the two-stage logistic and cox regression analyses.

Conclusions

this IPD meta-analysis of prospective observational studies showed a clear and significant relationship between OH and time to first fall incident. Although the ORs of falling was not significantly different for patients with and without OH, a the cox regression analyses reporting HRs and including time to incident provided more clinically relevant information in present meta-analysis.

Findings from the Adjuvant Colon Cancer End Points (ACCENT) Collaborative Group: the power of pooled individual patient data from multiple clinical trials

The Adjuvant Colon Cancer End Points (ACCENT) Collaborative Group was formed 15 years ago to address scientific questions in early stage colon cancer that could best be answered by pooling individual patient data across many randomized clinical trials. Today, the ACCENT database contains detailed information collected from over 40,000 patients enrolled onto 27 major adjuvant colon cancer trials conducted between 1977 and 2009. Since its inception, the ACCENT group has led many sophisticated analyses addressing a variety of clinical questions, such as the long-term survivorship of colon cancer patients by treatment, the time course of oxaliplatin benefit, and support for the use of disease-free survival (DFS) as a surrogate endpoint for overall survival (OS), among many others. Here, we provide an updated overview of recent important results and future directions of the ACCENT collaboration.

The impact of second generation antipsychotics on insight in schizophrenia: Results from 14 randomized, placebo controlled trials

Despite the negative impact of lack of insight on the prognosis, general functioning and treatment adherence, the effect of antipsychotic medication on insight has been investigated only in small samples and uncontrolled studies. In this study we examine whether previously reported effects of antipsychotics on insight from uncontrolled studies can be confirmed in a database including 14 randomized, double-blind, placebo-controlled trials. The database contained placebo-controlled RCTs of five second-generation antipsychotics (SGAs: olanzapine, paliperidone, quetiapine, risperidone and sertindole) and included a total of 4243 patients with schizophrenia. Insight was assessed with item G12 of the Positive and Negative Syndrome Scale (PANSS) at baseline and at six weeks. Overall, SGA treatment resulted in a significantly larger improvement in insight than placebo (0.43 points versus 0.15 points; Hedge׳s g 0.23; p<0.001). However this difference in improvement in insight was largely explained by improvement in other symptoms. In the initial analysis, one of the compounds was significantly less effective in improving insight than the other SGAs, but this difference no longer persisted when improvement in other symptoms was taken into account. The effect of SGAs on improvement in insight was not moderated by geographic region, illness duration or drop-out. The present study showed that SGA treatment of patients with schizophrenia is associated with improved insight, but that this improvement is associated with SGA induced improvements in other symptoms, though the causal relationship may not be established.

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

Meta‐analysis using individual participant data (IPD) obtains and synthesises the raw, participant‐level data from a set of relevant studies. The IPD approach is becoming an increasingly popular tool as an alternative to traditional aggregate data meta‐analysis, especially as it avoids reliance on published results and provides an opportunity to investigate individual‐level interactions, such as treatment‐effect modifiers. There are two statistical approaches for conducting an IPD meta‐analysis: one‐stage and two‐stage. The one‐stage approach analyses the IPD from all studies simultaneously, for example, in a hierarchical regression model with random effects. The two‐stage approach derives aggregate data (such as effect estimates) in each study separately and then combines these in a traditional meta‐analysis model. There have been numerous comparisons of the one‐stage and two‐stage approaches via theoretical consideration, simulation and empirical examples, yet there remains confusion regarding when each approach should be adopted, and indeed why they may differ.

In this tutorial paper, we outline the key statistical methods for one‐stage and two‐stage IPD meta‐analyses, and provide 10 key reasons why they may produce different summary results. We explain that most differences arise because of different modelling assumptions, rather than the choice of one‐stage or two‐stage itself. We illustrate the concepts with recently published IPD meta‐analyses, summarise key statistical software and provide recommendations for future IPD meta‐analyses. © 2016 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

Meta-analysis of a continuous outcome combining individual patient data and aggregate data: a method based on simulated individual patient data

When some trials provide individual patient data (IPD) and the others provide only aggregate data (AD), meta-analysis methods for combining IPD and AD are required. We propose a method that reconstructs the missing IPD for AD trials by a Bayesian sampling procedure and then applies an IPD meta-analysis model to the mixture of simulated IPD and collected IPD. The method is applicable when a treatment effect can be assumed fixed across trials. We focus on situations of a single continuous outcome and covariate and aim to estimate treatment-covariate interactions separated into within-trial and across-trial effect. An illustration with hypertension data which has similar mean covariates across trials indicates that the method substantially reduces mean square error of the pooled within-trial interaction estimate in comparison with existing approaches. A simulation study supposing there exists one IPD trial and nine AD trials suggests that the method has suitable type I error rate and approximately zero bias as long as the available IPD contains at least 10% of total patients, where the average gain in mean square error is up to about 40%. However, the method is currently restricted by the fixed effect assumption, and extension to random effects to allow heterogeneity is required.

Feasibility of individual patient data meta-analyses in orthopaedic surgery

BACKGROUND

The number of individual patient data meta-analyses published is very low especially in surgical domains. Our aim was to assess the feasibility of individual patient data (IPD) meta-analyses in orthopaedic surgery by determining whether trialists agree to send IPD for eligible trials.

METHODS

We performed a literature search to identify relevant research questions in orthopaedic surgery. For each question, we developed a protocol synopsis for an IPD meta-analysis and identified all related randomized controlled trials (RCTs) with results published since 2000. Corresponding authors of these RCTs were sent personalized emails that presented a project for an IPD meta-analysis corresponding to one of the research questions, with a link to the protocol synopsis, and asking for IPD from their RCT. We guaranteed patient confidentiality and secure data storage, and offered co-authorship and coverage of costs related to extraction.

RESULTS

We identified 38 research questions and 273 RCTs related to these questions. We could contact 217 of the 273 corresponding authors (79 %; 56 had unavailable or non-functional email addresses) and received 68/273 responses (25 %): 21 authors refused to share IPD, 10 stated that our request was under consideration and 37 agreed to send IPD. Four corresponding authors required authorship and three others asked for financial support to send the IPD. Overall, we could obtain IPD for 5,110 of 33,602 eligible patients (15 %). Among the 38 research questions, only one IPD meta-analysis could be potentially initiated because we could receive IPD for more than 50 % of participants.

CONCLUSION

The present study illustrates the difficulties in initiating IPD meta-analyses in orthopaedic surgery. Significant efforts must be made to improve data sharing.

Methods and characteristics of published network meta-analyses using individual patient data: protocol for a scoping review

INTRODUCTION

Individual patient data (IPD) meta-analysis (MA) offers advantages over aggregate MA of using standardised criteria for patient characteristics across trials, and allowing reliable investigation of subgroup effects of interventions. Network meta-analysis (NMA) allows for the comparison of multiple treatments in a comprehensive analysis and the determination of the best treatment among several competing treatments, including those that have never been compared in a head-to-head study. Including IPD in NMA may enable the prevention of misleading inferences due to several biases, such as aggregation bias. Application of IPD-NMA methods in healthcare have begun to appear in medical journals. Our objective is to conduct a scoping review of existing IPD-NMA methods, and summarise their properties. We also aim to describe the characteristics of empirical IPD-NMAs, and examine how their results are reported.

METHODS AND ANALYSIS

We will search relevant electronic databases from inception until October 2014 (eg, MEDLINE), grey literature, and Google. The scoping review will consider published and unpublished papers that report completion of an IPD-NMA, describe a method, or report the methodological quality of IPD-NMA. We will include IPD-NMA of any quantitative study (eg, experimental, quasiexperimental, observational studies). Two reviewers will independently screen titles, abstracts and full-text articles, and will complete data abstraction. The anticipated outcome will be a collection of all the IPD-NMAs completed to date, and a description of their methods and reporting of results. We will create summary tables providing the characteristics of the included studies, and the various methods. Quantitative data (eg, number of patients) will be summarised by medians and IQRs, and categorical data (eg, type of effect size) by frequencies and percentages.

ETHICS AND DISSEMINATION

Ethical approval is not required as our study will not include confidential participant data and interventions. We will disseminate our results through an open access, peer-reviewed publication.