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CDKN2A/B deletions are prognostically relevant in low- and high-grade gliomas. Data on this is derived from heterogeneous series, an accurate estimation of survival risk from homozygous CDKN2A/B deletion is missing. Besides genetic testing, p16-immunohistochemistry (IHC) as a less cost intensive means for indirect detection of CDKN2A/B alterations is possible but not validated in larger datasets. The present meta-analysis aimed to (1) reconstruct individual patient data (IPD) and estimate overall survival (OS) stratified by CDKN2A/B status from all literature and to (2) determine accuracy of p16 testing for CDKNA2/B detection from published studies. For survival analysis according to CDKN2A/B status 460 records were screened, four articles with 714 participants were included. In IDH-wildtype (IDH-wt) gliomas, 57.07% harbored the deletion compared to 9.76% in IDH-mutant (IDH-mut) gliomas. Median OS of patients with IDH-wt gliomas and homozygous CDKN2A/B deletion was 13.0 months compared to 18.0 months with non-deleted CDKN2A/B (p = 0.014, Log-Rank). With homozygous deletion of CDKN2A/B the risk of death was increased by 1.5 (95%-CI 1.1–2.1). Median OS in patients with IDH-mut gliomas without CDKN2A/B deletion was 92.0 months compared to 40.0 months with CDKN2A/B deletion (p < 0.001, Log-Rank). CDKN2A/B deletions were associated with a significantly shorter OS (HR = 3.2; 95%-CI 2.2–5.5). For p16 IHC analysis, 10 eligible studies with 1087 examined samples were included. The cut-off for retention differed between the studies. In 588/662 p16 retained cases CDKN2A/B deletions was not detected, implying a negative predictive value (NPV) of p16 staining of 88.8%. Conversely, 279/425 p16 absent cases showed a CDKN2A/B deletion resulting in a positive predictive value (PPV) of 65.6%. Sensitivity of p16 staining for CDKN2A/B detection was 79.0%, specificity 80.1%. Highest diagnostic accuracy of p16 IHC was reached with a cut-off of > 5% and within IDH-mut glioma. [ABSTRACT FROM AUTHOR]

Abstract CDKN2A/B deletions are prognostically relevant in low- and high-grade gliomas. Data on this is derived from heterogeneous series, an accurate estimation of survival risk from homozygous CDKN2A/B deletion is missing. Besides genetic testing, p16-immunohistochemistry (IHC) as a less cost intensive means for indirect detection of CDKN2A/B alterations is possible but not validated in larger datasets. The present meta-analysis aimed to (1) reconstruct individual patient data (IPD) and estimate overall survival (OS) stratified by CDKN2A/B status from all literature and to (2) determine accuracy of p16 testing for CDKNA2/B detection from published studies. For survival analysis according to CDKN2A/B status 460 records were screened, four articles with 714 participants were included. In IDH-wildtype (IDH-wt) gliomas, 57.07% harbored the deletion compared to 9.76% in IDH-mutant (IDH-mut) gliomas. Median OS of patients with IDH-wt gliomas and homozygous CDKN2A/B deletion was 13.0 months compared to 18.0 months with non-deleted CDKN2A/B (p = 0.014, Log-Rank). With homozygous deletion of CDKN2A/B the risk of death was increased by 1.5 (95%-CI 1.1–2.1). Median OS in patients with IDH-mut gliomas without CDKN2A/B deletion was 92.0 months compared to 40.0 months with CDKN2A/B deletion (p 5% and within IDH-mut glioma.

Abstract Background A deficiency in iron stores is associated with various adverse health complications, which, if left untreated, can progress to states of anaemia, whereby there is significant detriment to an individual’s work capacity and quality of life due to compromised erythropoiesis. The most common methods employed to treat an iron deficiency include oral iron supplementation and, in persistent and/or unresponsive cases, intravenous iron therapy. The efficacy of these treatments, particularly in states of iron deficiency without anaemia, is equivocal. Indeed, both randomised control trials and aggregate data meta-analyses have produced conflicting evidence. Therefore, this study aims to assess the efficacy of both oral and intravenous iron supplementation on physical capacity, quality of life, and fatigue scores in iron-deficient non-anaemic individuals using individual patient data (IPD) meta-analysis techniques. Methods All potential studies, irrespective of design, will be sourced through systematic searches on the following databases: Cochrane Central Register of Controlled Trials, MEDLINE Ovid, Embase Ovid, Web of Science: Science Citation Index Expanded, Web of Science: Conference Proceedings Citation Index-Science, ClinicalTrials.gov, and World Health Organization (WHO) International Clinical Trials Registry Platform. Individual patient data from all available trials will be included and subsequently analysed in a two-stage approach. Predetermined subgroup and sensitivity analyses will be employed to further explain results. Discussion The significance of this IPD meta-analysis is one of consolidating a clear consensus to better inform iron-deficient individuals of the physiological response associated with iron supplementation. The IPD approach, to the best of our knowledge, is novel for this research topic. As such, the findings will significantly contribute to the current body of evidence. Systematic review registration PROSPERO CRD42020191739.

ABSTRACT The innovative use of real‐world data (RWD) can answer questions that cannot be addressed using data from randomized clinical trials (RCTs). While the sponsors of RCTs have a central database containing all individual patient data (IPD) collected from trials, analysts of RWD face a challenge: regulations on patient privacy make access to IPD from all regions logistically prohibitive. In this research, we propose a double inverse probability weighting (DIPW) approach for the analysis sponsor to estimate the population average treatment effect (PATE) for a target population without the need to access IPD. One probability weighting is for achieving comparable distributions in confounders across treatment groups; another probability weighting is for generalizing the result from a subpopulation of patients who have data on the endpoint to the whole target population. The likelihood expressions for propensity scores and the DIPW estimator of the PATE can be written to only rely on regional summary statistics that do not require IPD. Our approach hinges upon the positivity and conditional independency assumptions, prerequisites to most RWD analysis approaches. Simulations are conducted to compare the performances of the proposed method against a modified meta‐analysis and a regular meta‐analysis. [ABSTRACT FROM AUTHOR]

Multilevel network meta-regression (ML-NMR) leverages individual patient data (IPD) and aggregate data from a network of randomized controlled trials (RCTs) to assess the comparative efficacy of multiple treatments, while adjusting for between-study differences. We provide an overview of ML-NMR for time-to-event outcomes and apply it to an illustrative case study, including example R code. The case study evaluated the comparative efficacy of idecabtagene vicleucel (ide-cel), selinexor+dexamethasone (Sd), belantamab mafodotin (BM), and conventional care (CC) for patients with triple-class exposed relapsed/refractory multiple myeloma in terms of overall survival. Single-arm clinical trials and real-world data were naively combined to create an aggregate data artificial RCT (aRCT) (MAMMOTH-CC versus DREAMM-2-BM versus STORM-2-Sd) and an IPD aRCT (KarMMa-ide-cel versus KarMMa-RW-CC). With some assumptions, we incorporated continuous covariates with skewed distributions, reported as median and range. The ML-NMR models adjusted for number of prior lines, triple-class refractory status, and age and were compared using the leave-one-out information criterion. We summarized predicted hazard ratios and survival (95% credible intervals) in the IPD aRCT population. The Weibull ML-NMR model had the lowest leave-one-out information criterion. Ide-cel was more efficacious than Sd, BM, and CC in terms of overall survival. Effect modifiers had minimal impact on the model, and only triple-class refractory was a prognostic factor. We demonstrate an application of ML-NMR for time-to-event outcomes and introduce code that can be used to aid implementation. Given its benefits, we encourage practitioners to utilize ML-NMR when population adjustment is necessary for comparisons of multiple treatments. • Multilevel network meta-regression (ML-NMR) can leverage individual patient data and aggregate data when comparing multiple treatments in a network of any size, while adjusting for differences in populations between trials. • We demonstrate an application of ML-NMR for time-to-event outcomes and introduce code for implementation. • ML-NMR reflects an extension of standard network meta-analysis framework to help address challenges of existing alternative indirect treatment methods, which can be applied to time-to-event outcomes to compare multiple direct treatment comparisons simultaneously in health technology assessments. [ABSTRACT FROM AUTHOR]

Background: A deficiency in iron stores is associated with various adverse health complications, which, if left untreated, can progress to states of anaemia, whereby there is significant detriment to an individual's work capacity and quality of life due to compromised erythropoiesis. The most common methods employed to treat an iron deficiency include oral iron supplementation and, in persistent and/or unresponsive cases, intravenous iron therapy. The efficacy of these treatments, particularly in states of iron deficiency without anaemia, is equivocal. Indeed, both randomised control trials and aggregate data meta-analyses have produced conflicting evidence. Therefore, this study aims to assess the efficacy of both oral and intravenous iron supplementation on physical capacity, quality of life, and fatigue scores in iron-deficient non-anaemic individuals using individual patient data (IPD) meta-analysis techniques. Methods: All potential studies, irrespective of design, will be sourced through systematic searches on the following databases: Cochrane Central Register of Controlled Trials, MEDLINE Ovid, Embase Ovid, Web of Science: Science Citation Index Expanded, Web of Science: Conference Proceedings Citation Index-Science, ClinicalTrials.gov, and World Health Organization (WHO) International Clinical Trials Registry Platform. Individual patient data from all available trials will be included and subsequently analysed in a two-stage approach. Predetermined subgroup and sensitivity analyses will be employed to further explain results. Discussion: The significance of this IPD meta-analysis is one of consolidating a clear consensus to better inform iron-deficient individuals of the physiological response associated with iron supplementation. The IPD approach, to the best of our knowledge, is novel for this research topic. As such, the findings will significantly contribute to the current body of evidence. Systematic review registration: PROSPERO CRD42020191739. [ABSTRACT FROM AUTHOR]

Objective: Selecting between programmed cell death ligand 1 (PD-L1) inhibitor or programmed cell death 1 (PD-1) inhibitor plus chemotherapy as first-line treatment for extensive-stage small cell lung cancer (ES-SCLC) patients urgently needs to be answered. Methods: Eligible phase 3 randomized clinical trials evaluating regimens based on PD-1/PD-L1 inhibitors as first-line treatment in ES-SCLC patients were systematically searched on the PubMed and Cochrane Library databases and major international conferences from 01/01/2018 to 18/09/2023. The individual patient data (IPD) were recuperated from the Kaplan–Meier curves of the overall survival (OS) and progression-free survival (PFS) of the included studies using the IPDfromKM method. The reconstructed data were pooled into unified arms, including the PD-L1 inhibitor plus chemotherapy group (PD-L1 group), PD-1 inhibitor plus chemotherapy group (PD-1 group), and PD-1 (L1) inhibitor and chemotherapy plus other (anlotinib group, tiragolumab group, and tremelimumab group). Subsequently, the PD-L1 group was indirectly compared with the other groups. A standard statistical analysis was conducted using the “survival” package for the time-to-event endpoint. The primary outcomes were the OS and PFS of the PD-L1 group and the PD-1 inhibitor group. The secondary outcomes included safety and the 12- and 24-month restricted mean survival time (RMST) of the PD-L1 group and PD-1 group. Results: A total of 9 studies including 11 immunotherapy cohorts were included. No significant difference in PFS (hazard ratio [HR]: 0.96, 95% confidence interval [CI]: 0.86–1.06), OS (HR: 0.94, 95% CI: 0.84–1.05), and 12-month and 24-month RMST for OS (P = 0.198 and P = 0.216, respectively) was observed between the PD-L1 group and the PD-1 group. In contrast, the anlotinib group showed significantly better OS (HR: 0.70, 95% CI: 0.55–0.89), PFS (HR: 0.69, 95% CI: 0.58–0.83), and RMST for OS compared to the PD-L1 group. The tiragolumab group showed similar efficacy to the PD-L1 group. However, the tremelimumab group exhibited inferior efficacy than the PD-L1 group. The incidence of ≥grade 3 treatment-emergent adverse events (TEAEs) was significantly higher in the PD-1 group compared to the PD-L1 group (85.4% vs. 69.6%, P <.001), whereas the incidence of irAEs was similar between the two groups. Conclusion: This reconstructed IPD analysis revealed that PD-1 inhibitors plus chemotherapy achieved similar efficacy to PD-L1 inhibitors plus chemotherapy as first-line treatment in ES-SCLC patients, whereas PD-L1 inhibitors plus chemotherapy had a better safety profile. [ABSTRACT FROM AUTHOR]

• BCB's role in schizophrenia's pathophysiology might be pivotal, yet poorly understood. • We report IPD of n=531 recent-onset psychosis individuals with CSF-diagnostics. • BCB's disruption appears highly prevalent early in schizophrenia-spectrum disorders. • No symptom severity differences between individuals with and without CSF alterations. • Males with elevated CSF/serum albumin ratios had higher PANSS positive scores. Neuroinflammation and blood-cerebrospinal fluid barrier (BCB) disruption could be key elements in schizophrenia-spectrum disorderś(SSDs) etiology and symptom modulation. We present the largest two-stage individual patient data (IPD) meta -analysis, investigating the association of BCB disruption and cerebrospinal fluid (CSF) alterations with symptom severity in first-episode psychosis (FEP) and recent onset psychotic disorder (ROP) individuals, with a focus on sex-related differences. Data was collected from PubMed and EMBASE databases. FEP, ROP and high-risk syndromes for psychosis IPD were included if routine basic CSF-diagnostics were reported. Risk of bias of the included studies was evaluated. Random-effects meta -analyses and mixed-effects linear regression models were employed to assess the impact of BCB alterations on symptom severity. Published (6 studies) and unpublished IPD from n = 531 individuals was included in the analyses. CSF was altered in 38.8 % of individuals. No significant differences in symptom severity were found between individuals with and without CSF alterations (SMD = -0.17, 95 %CI −0.55–0.22, p = 0.341). However, males with elevated CSF/serum albumin ratios or any CSF alteration had significantly higher positive symptom scores than those without alterations (SMD = 0.34, 95 %CI 0.05–0.64, p = 0.037 and SMD = 0.29, 95 %CI 0.17–0.41p = 0.005, respectively). Mixed-effects and simple regression models showed no association (p > 0.1) between CSF parameters and symptomatic outcomes. No interaction between sex and CSF parameters was found (p > 0.1). BCB disruption appears highly prevalent in early psychosis and could be involved in positive symptomś severity in males, indicating potential difficult-to-treat states. This work highlights the need for considering BCB breakdown and sex-related differences in SSDs clinical trials and treatment strategies. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract Background One key aspect of personalized medicine is to identify individuals who benefit from an intervention. Some approaches have been developed to estimate individualized treatment effects (ITE) with a single randomized control trial (RCT) or observational data, but they are often underpowered for the ITE estimation. Using individual participant data meta-analyses (IPD-MA) might solve this problem. Few studies have investigated how to develop risk prediction models with IPD-MA, and it remains unclear how to combine those methods with approaches used for ITE estimation. In this article, we compared different approaches using both simulated and real data with binary and time-to-event outcomes to estimate the individualized treatment effects from an IPD-MA in a one-stage approach. Methods We compared five one-stage models: naive model (NA), random intercept (RI), stratified intercept (SI), rank-1 (R1), and fully stratified (FS), built with two different strategies, the S-learner and the T-learner constructed with a Monte Carlo simulation study in which we explored different scenarios with a binary or a time-to-event outcome. To evaluate the performance of the models, we used the c-statistic for benefit, the calibration of predictions, and the mean squared error. The different models were also used on the INDANA IPD-MA, comparing an anti-hypertensive treatment to no treatment or placebo ( $$N = 40\,237$$ N = 40 237 , 836 events). Results Simulation results showed that using the S-learner led to better ITE estimation performances for both binary and time-to-event outcomes. None of the risk models stand out and had significantly better results. For the INDANA dataset with a binary outcome, the naive and the random intercept models had the best performances. Conclusions For the choice of the strategy, using interactions with treatment (the S-learner) is preferable. For the choice of the method, no approach is better than the other.

Background: Although the initial functional outcome findings of the International Subarachnoid Aneurysm Trial (ISAT) study favored coiling at one year after aneurysmal subarachnoid hemorrhage (aSAH), concerns arose regarding limited long-term rerupture data. This meta-analysis is the first to analyze longitudinal individual patient data (IPD) of target aneurysm rerupture in terms of treatment modality. Methods: The present meta-analysis included studies that compared clipping with coiling of ruptured aneurysms regarding long-term rerupture. Rerupture rates' individual patient data (IPD) were extracted from published Kaplan–Meier curves utilizing the R package IPDfromKM in R Version 4.3.1. Results: A total of 3153 patients from two studies were included. The clipping arm included 1755 patients, whereas the coiling arm included 1398 patients. Median reconstructed follow-up was 6.1 years (IQR = 0.5–11.7). The rerupture rates in the clipping arm and the coiling arm were 0.5% and 1.5%, respectively (p = 0.002). Kaplan–Meier chart analysis of the 3153 patients revealed a shortened time to rerupture in the coiling arm (log-rank test: p = 0.01). The hazard ratio (HR) for coiling compared with clipping regarding rerupture was 3.62 (95% CI:1.21–10.86, p = 0.02). Conclusion: Target aneurysm rerupture was rare beyond the initial year. Pooled long-term IPD from the 3153 patients revealed that reruptures of target aneurysms are more common after coiling and might be considered in the pretherapeutic decision-making process for aSAH. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract Background This reconstructed individual patient data (IPD)-based meta-analysis is aimed to summarize the current findings and comprehensively investigate the predictive value of circulating tumor DNA (ctDNA) in operable non-small cell lung cancer (NSCLC). Methods PubMed, Cochrane and Embase were searched to include potentially eligible studies. The primary outcomes included progression-free survival (DFS) by ctDNA status at baseline, postoperative, and longitudinal timepoints. The IPD-based survival data was retracted and used in reconstructed IPD-based meta-analysis. Subgroup analysis was implemented based on the baseline characteristics. Results Totally, 28 studies were involved, including 15 full-length articles (1686 patients) for IPD-based synthesis and 20 studies for conventional meta-analysis. The IPD-based meta-analysis discovered that patients with positive ctDNA status at the baseline (hazard ratio, HR = 3.73, 95% confidential interval, CI: 2.95–4.72), postoperative (3.96, 2.19–7.16), or longitudinal timepoints (12.33, 8.72–17.43) showed significantly higher risk of recurrence. Patients with persistent ctDNA-negative status had the lowest recurrence rate, and the negative conversion of ctDNA from baseline to postoperative timepoints was correlated with elevated DFS. Subgroup analyses suggested that stage II–III patients with ctDNA-positive status may achieve preferable therapeutic outcomes. Conclusions Plasm ctDNA monitoring shows excellent clinical significance at the tested timepoints. Perioperative conversion of ctDNA status may indicate the therapeutic effect of radical surgery. Postoperative adjuvant therapy may be determined according to the ctDNA status. Trail registration CRD42022304445.

Abstract Homozygous CDKN2A/B deletion has been associated with an increased risk of recurrence in meningiomas. However, the evidence is confined to a limited number of studies, and the importance of heterozygous CDKN2A/B deletions remains insufficiently investigated. Hence, the present meta-analysis reconstructs individual patient data (IPD) and reconstructs the probabilities of progression-free survival (PFS) stratified by CDKN2A/B status. IPD of PFS rates were extracted from published Kaplan–Meier plots using the R package IPDfromKM in R studio (RStudio, Boston, MA, USA). Reconstructed Kaplan–Meier Plots of the pooled IPD data were created. One-stage and two-stage meta-analyses were performed. Hazard ratios (HR) were used as effective measures. Of 181 records screened, four articles with 2521 participants were included. The prevalence of homozygous CDKN2A/B deletions in the included studies was 0.049 (95% CI 0.040–0.057), with higher tumor grades associated with a significantly greater proportion of CDKN2A/B deletions. The reconstructed PFS curves for the pooled cohort showed that the median PFS time of patients with a CDKN2A/B wild-type status, heterozygous or homozygous CDKN2A/B deletion was 180.0 (95% CI 145.7–214.3), 26.1 (95% CI 23.3–29.0), and 11.00 (95% CI 8.6–13.3) months, respectively (p

Background: This reconstructed individual patient data (IPD)-based meta-analysis is aimed to summarize the current findings and comprehensively investigate the predictive value of circulating tumor DNA (ctDNA) in operable non-small cell lung cancer (NSCLC). Methods: PubMed, Cochrane and Embase were searched to include potentially eligible studies. The primary outcomes included progression-free survival (DFS) by ctDNA status at baseline, postoperative, and longitudinal timepoints. The IPD-based survival data was retracted and used in reconstructed IPD-based meta-analysis. Subgroup analysis was implemented based on the baseline characteristics. Results: Totally, 28 studies were involved, including 15 full-length articles (1686 patients) for IPD-based synthesis and 20 studies for conventional meta-analysis. The IPD-based meta-analysis discovered that patients with positive ctDNA status at the baseline (hazard ratio, HR = 3.73, 95% confidential interval, CI: 2.95–4.72), postoperative (3.96, 2.19–7.16), or longitudinal timepoints (12.33, 8.72–17.43) showed significantly higher risk of recurrence. Patients with persistent ctDNA-negative status had the lowest recurrence rate, and the negative conversion of ctDNA from baseline to postoperative timepoints was correlated with elevated DFS. Subgroup analyses suggested that stage II–III patients with ctDNA-positive status may achieve preferable therapeutic outcomes. Conclusions: Plasm ctDNA monitoring shows excellent clinical significance at the tested timepoints. Perioperative conversion of ctDNA status may indicate the therapeutic effect of radical surgery. Postoperative adjuvant therapy may be determined according to the ctDNA status. Trail registration: CRD42022304445. [ABSTRACT FROM AUTHOR]

Homozygous CDKN2A/B deletion has been associated with an increased risk of recurrence in meningiomas. However, the evidence is confined to a limited number of studies, and the importance of heterozygous CDKN2A/B deletions remains insufficiently investigated. Hence, the present meta-analysis reconstructs individual patient data (IPD) and reconstructs the probabilities of progression-free survival (PFS) stratified by CDKN2A/B status. IPD of PFS rates were extracted from published Kaplan–Meier plots using the R package IPDfromKM in R studio (RStudio, Boston, MA, USA). Reconstructed Kaplan–Meier Plots of the pooled IPD data were created. One-stage and two-stage meta-analyses were performed. Hazard ratios (HR) were used as effective measures. Of 181 records screened, four articles with 2521 participants were included. The prevalence of homozygous CDKN2A/B deletions in the included studies was 0.049 (95% CI 0.040–0.057), with higher tumor grades associated with a significantly greater proportion of CDKN2A/B deletions. The reconstructed PFS curves for the pooled cohort showed that the median PFS time of patients with a CDKN2A/B wild-type status, heterozygous or homozygous CDKN2A/B deletion was 180.0 (95% CI 145.7–214.3), 26.1 (95% CI 23.3–29.0), and 11.00 (95% CI 8.6–13.3) months, respectively (p < 0.0001). Both hetero- or homozygous CDKN2A/B deletions were significantly associated with shortened time to meningioma progression. One-stage meta-analysis showed that hetero- (HR: 5.5, 95% CI 4.0–7.6, p < 0.00001) and homozygous CDKN2A/B deletions (HR: 8.4, 95% CI 6.4–11.0, p < 0.00001) are significantly associated with shortened time to meningioma progression. Multivariable Cox regression analysis of progression in a subgroup with available covariates (age, sex, WHO grade, and TERT status) and also two-stage meta-analysis confirmed and validated the results of the one-stage analysis that both heterozygous and homozygous CDKN2A/B deletions are of prognostic importance. Further large-scale studies of WHO grade 2 and 3 meningiomas are needed to validate the importance of heterozygous CDKN2A/B deletions with consideration of established factors. [ABSTRACT FROM AUTHOR]

Background and purposeThe Meta-Analysis of Chemotherapy in squamous cell Head and Neck Cancer (MACH-NC) demonstrated that concomitant chemotherapy (CT) improved overall survival (OS) in patients without distant metastasis. We report the updated results.Materials and methodsPublished or unpublished randomized trials including patients with non-metastatic carcinoma randomized between 1965 and 2016 and comparing curative loco-regional treatment (LRT) to LRT + CT or adding another timing of CT to LRT + CT (main question), or comparing induction CT + radiotherapy to radiotherapy + concomitant (or alternating) CT (secondary question) were eligible. Individual patient data were collected and combined using a fixed-effect model. OS was the main endpoint.ResultsFor the main question, 101 trials (18951 patients, median follow-up of 6.5 years) were analyzed. For both questions, there were 16 new (2767 patients) and 11 updated trials. Around 90% of the patients had stage III or IV disease. Interaction between treatment effect on OS and the timing of CT was significant (p

Abstract 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.

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

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. [ABSTRACT FROM AUTHOR]

Mid- and long-term rhythm outcomes of catheter ablation (CA) for atrial fibrillation (AF) are reported to be suboptimal. Minimally invasive surgical off-pump ablation (MISOA), including both thoracoscopic and trans-diaphragmatic approaches, has been developed to reduce surgical invasiveness and overcome on-pump surgery drawbacks. We sought to compare the efficacy and safety of MISOA and CA for AF treatment. A systematic review and meta-analysis of the literature was performed including studies comparing MISOA and CA. The primary endpoint was survival freedom from AF at follow-up after a 3-month blanking period. Subgroup analysis of the primary endpoint was performed according to the type of surgical incision and hybrid approach. Freedom from AF at 4 years was 52.1% ± 3.2% vs 29.1% ± 3.5%, between MISOA and CA respectively (log-rank p < 0.001; Hazard Ratio: 0.60 [95%Confidence Interval (CI):0.50–0.72], p < 0.001). At landmark analysis, a significant improvement in rhythm outcomes was observed in the MISOA group after the 5th month of follow-up (2 months from the blanking period). The Odds Ratio between MISOA and CA of postoperative cerebrovascular accident incidence and postoperative permanent pacemaker implant (PPM) were 2.00 (95%CI:0.91–4.40, p = 0.084) and 1.55 (95%CI:0.61–3.95, p = 0.358), respectively. The incidence rate ratio of late CVA between MISOA and CA was 0.86 (95%CI:0.28–2.65, p = 0.787), while for late PPM implant was 0.45 (95%CI:0.11–1.78, p = 0.256). The current meta-analysis suggests that MISOA provides superior rhythm outcomes when compared to CA in terms of sinus rhythm restoration. Despite the rhythm outcome superiority of MISOA, it is associated to higher postoperative complications compared to CA. • MISOA showed higher freedom from AF compared to CA at 4 years (log-rank p < 0.001). • The advantage of MISOA over CA was identified after the 5th month of follow-up. • MISOA was found to be a protective factor for AF at univariable Cox regression. • Despite the rhythm advantage, total postoperative adverse events were higher in MISOA. [ABSTRACT FROM AUTHOR]

A significant proportion of patients with chorea-acanthocytosis (ChAc) fail to respond to standard therapies. Recent evidence suggests that globus pallidus internus (GPi) deep brain stimulation (DBS) is a promising treatment option; however, reports are few and limited by sample sizes. We conducted a systematic literature review to evaluate the clinical outcome of GPi-DBS for ChAc. PubMed, Embase, and Cochrane Library databases were searched for relevant articles published before August 2021. The improvement of multiple motor and nonmotor symptoms was qualitatively presented. Improvements in the Unified Huntington’s Disease Rating Scale motor score (UHDRS-MS) were also analyzed during different follow-up periods. A multivariate linear regression analysis was conducted to identify potential predictors of clinical outcomes. Twenty articles, including 27 patients, were eligible. Ninety-six percent of patients with oromandibular dystonia reported significant improvement. GPi-DBS significantly improved the UHDRS-motor score at < 6 months (p < 0.001) and ≥ 6 months (p < 0.001). The UHDRS-motor score improvement rate was over 25% in 75% (15/20 cases) of patients at long-term follow-up (≥ 6 months). The multiple linear regression analysis showed that sex, age at onset, course of disease, and preoperative movement score had no linear relationship with motor improvement at long-term follow-up (p > 0.05). GPi-DBS is an effective and safe treatment in most patients with ChAc, but no reliable predictor of efficacy has been found. Oromandibular dystonia-dominant patients might be the best candidates for GPi-DBS.

Pain assessment in clinical settings can be a challenging task for clinic staff and patients. An automatic and contactless pain estimation system via facial expression is a decisive advantage. In the following article we present a method for pain classification based on mimic descriptors. To automatically process facial expressions from video data we applied the Facial Action Coding System (FACS). First, we employed a logistic regression model to predict pain using a publicly available database. Second, we utilized this procedure to predict pain in individual intraprocedural patient data. We reached a mean accuracy of 80.94% for the publicly available data and 73.14% for our individual patient data by classifying data as pain and non-pain.

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. 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. 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. 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. 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. [Display omitted] • Relative HCC risk for TDF vs. ETV treatment is undetermined in patients with CHB. • Prior meta-analyses are limited by heterogeneity of observational studies. • Using individual patient data enables a consistent analytic approach across studies. • TDF was consistently associated with lower HCC risk than ETV. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this article, we suggest a blueprint for an ideal open-access repository for clinical trial data with a description of a model of such a repository using a business process analysis approach. Firstly, we suggested which features an ideal repository should have. Secondly, we used business process management software to describe the whole process, from the decision to share clinical trial data to either publication of data in a repository or discarding data. The research community, legislators and society at large should be interested in a transparent open-access repository that will host clinical trial data. We hope this work can inspire relevant stakeholders to engage in discussion about the necessity of creating such repository, and that we will witness the creation of such a repository in the near future.

Purpose: Chemotherapy, when added to radiotherapy, improves survival in locally advanced nasopharyngeal carcinoma (NPC). This article presents the second update of the Meta-Analysis of Chemotherapy in NPC. Methods: Published or unpublished randomized trials assessing radiotherapy (±a second chemotherapy timing) with/without chemotherapy in non-metastatic NPC patients were identified. Updated data were sought for studies included in the previous rounds of the meta-analysis. The primary endpoint was overall survival. All trials were analyzed following the intent-to-treat principle using a fixed-effects model. Treatments were classified in five subsets according to chemotherapy timing. The statistical analysis plan was pre-specified. Results: Eighteen new trials were identified. Individual patient data were available for seven. In total, the meta-analysis now included 26 trials and 7,080 patients. The addition of chemotherapy reduced the risk of death, with a hazard ratio (HR) of 0.79 (95% confidence interval (CI) [0.73; 0.85]), and an absolute survival increase at 5 and 10 years of 6.1% [+3.9; +8.3] and + 8.4% [+5.7; +11.1], respectively. The largest effect was observed for concomitant + adjuvant, induction (with concomitant in both arms) and concomitant chemotherapy, with respective HR [95%CI] of 0.68 [0.59; 0.79] (absolute survival increase at 5 years: 12.3% (7.0%;17.6%)), 0.73 [0.63; 0.86] (6.0% (2.5%;9.5%)) and 0.81 [0.70; 0.92] (5.2% (0.8%;9.6%)). The benefit of chemotherapy was also demonstrated by improvement in progression-free survival, cancer mortality, locoregional control and distant control. There was a significant interaction between patient age and chemotherapy effect. Conclusion: This updated meta-analysis confirms the benefit of concomitant chemotherapy and concomitant + adjuvant chemotherapy, and suggests that addition of induction or adjuvant chemotherapy to concomitant chemotherapy improves tumor control and survival. The benefit of chemotherapy decreases with increasing patient age.

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

Transcranial magnetic stimulation (TMS) is a non-invasive treatment for adolescent major depressive disorder (MDD). Existing evidence on the efficacy of TMS in adolescent MDD awaits quantitative synthesis. A systematic literature search was conducted, and data from eligible studies were synthesized using random-effects models. Treatment-covariate interactions were examined in exploratory analyses of individual-patient data (IPD). Systematic search of the literature yielded 1264 hits, of which 10 individual studies (2 randomized trials) were included for quantitative synthesis of mainly uncontrolled studies. Individual patient data (IPD) were available from five trials (all uncontrolled studies). Quantitative synthesis of aggregated data revealed a statistically significant negative overall standardized mean change (pooled SMCC = 2.04, 95% CI [1.46; 2.61], SE = 0.29, p <.001), as well as a significant overall treatment response rate (Transformed Proportion = 41.30%, 95% CI [31.03; 51.57], SE = 0.05; p < 0.001), considering data from baseline to post-treatment. Exploratory IPD analyses suggests TMS might be more effective in younger individuals and individuals with more severe depression, and efficacy might be enhanced with certain treatment modality settings, including higher number of TMS sessions, longer treatment durations, and unilateral and not bilateral stimulation. Existing studies exhibit methodological shortcomings, including small-study effects and lack of control group, blinding, and randomization—compromising the credibility of the present results. To date, two randomized controlled trials on TMS in adolescent depression have been published, and the only large-scale randomized trial suggests TMS is not more effective than sham stimulation. Future large-scale, randomized, and sham-controlled trials are warranted. Future trials should ensure appropriate selection of patients for TMS treatment and guide precision medicine approaches for stimulation protocols. [ABSTRACT FROM AUTHOR]

Purpose: The efficacy of several novel combinations of anti-programmed cell death protein 1 or its ligand antibodies with chemotherapy, which have become the new standard first-line combination therapy with favorable outcomes, was still not certain in patients with different combined positive score (CPS) grades. This research aimed to evaluate the efficacy of immune checkpoint inhibitor immunotherapy or immunochemotherapy at different CPS grades, compared with chemotherapy., Methods: Kaplan-Meier (KM) curve reconstruction was employed to assess the overall survival (OS) and progression-free survival (PFS) of patients with gastric cancer. The graphical reconstruction algorithm was used to estimate the time-to-event outcomes from Kaplan-Meier curves of the overall cohort or reported subgroups (depending on CPS). KMSubtraction was used to derive the unreported survival data by matching participants in the overall cohort and known subgroups., Findings: This analysis included 5072 patients in 5 trials (CheckMate 649, KEYNOTE-859, ORIENT-16, KEYNOTE-062, and JAVELIN Gastric 100). Immunochemotherapy exhibited more effectiveness than chemotherapy in most cases. For the overall cohort, sintilimab + chemotherapy exhibited the best effect in OS (hazard ratio [HR], 0.65; 95% CI, 0.55-0.76). Nivolumab + chemotherapy (HR, 0.75; 95% CI, 0.67-0.84), sintilimab + chemotherapy (HR, 0.52; 95% CI, 0.41-0.65), and pembrolizumab + chemotherapy (HR, 0.68; 95% CI, 0.58-0.81) exhibited favorable outcomes in OS in patients with a CPS ≥1, 5, and 10, respectively, and similarly in PFS. Avelumab + chemotherapy performed similarly to chemotherapy in OS but had poor PFS in the reported subgroup., Implications: Finding suggests that immune checkpoint inhibitors combined with chemotherapy could enrich patients with benefits regardless of CPS grades, though subtle efficacy in low CPS subgroups. This study compared the efficacy of different immunotherapies combined with chemotherapy in patients with gastric cancer, but we acknowledge some differences between reconstructed and original data. Hopefully there will be more research investigating comparisons between current therapies rather than with chemotherapy only in the future., Competing Interests: Declaration of competing interest None., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Background and Aims: Females with Barrett's esophagus (BE) have a lower risk of neoplastic progression than males, but sufficiently powered risk analyses are lacking. This systematic review and meta-analysis of individual patient data (IPD) aimed to provide more robust evidence on neoplastic progression risk in females., Methods: We conducted a systematic literature search of 3 electronic databases (Medline, Embase, Google Scholar) from inception until August 2023. Eligible studies (1) reported original data on progression from nondysplastic BE, indefinite for dysplasia, or low-grade dysplasia to high-grade dysplasia or esophageal adenocarcinoma; and (2) included female and male patients. IPD were quality controlled by 2 independent reviewers. The primary outcome was the association between sex and neoplastic progression risk, adjusted for risk factors using multivariable Cox regression analysis. Secondary outcomes were sex differences in time to progression and annual progression rate., Results: IPD were obtained from 11 of 66 eligible studies, including 2196 (31%) females. Neoplastic progression risk was lower in females (hazard ratio for males vs females, 1.44; 95% confidence interval, 1.13-1.82) after adjusting for age, smoking, medication use, hiatal hernia, BE length, and baseline pathology. The annual progression rate was 0.88% in females vs 1.29% in males. Time to progression was similar in both sexes: 3.7 years (interquartile range, 2.1-7.7 years) in females and 4.2 years (interquartile range, 2.0-8.1 years) in males., Conclusion: Although females had a lower neoplastic progression risk, sex differences were smaller than previously reported, and time to progression was similar for both sexes. Future research should focus on other factors than sex to identify low- and high-risk BE patients., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

To provide appropriate and practical level of health care, it is critical to group patients into relatively few strata that have distinct prognosis. Such grouping or stratification is typically based on well-established risk factors and clinical outcomes. A well-known example is the American Joint Committee on Cancer staging for cancer that uses tumor size, node involvement, and metastasis status. We consider a statistical method for such grouping based on individual patient data from multiple studies. The method encourages a common grouping structure as a basis for borrowing information, but acknowledges data heterogeneity including unbalanced data structures across multiple studies. We build on the "lasso-tree" method that is more versatile than the well-known classification and regression tree method in generating possible grouping patterns. In addition, the parametrization of the lasso-tree method makes it very natural to incorporate the underlying order information in the risk factors. In this article, we also strengthen the lasso-tree method by establishing its theoretical properties for which Lin and others (2013. Lasso tree for cancer staging with survival data. Biostatistics 14, 327-339) did not pursue. We evaluate our method in extensive simulation studies and an analysis of multiple breast cancer data sets. [ABSTRACT FROM AUTHOR]

Abstract Background Building a dataset of individual participant data (IPD) for meta-analysis represents considerable research investment as well as collaboration across multiple institutions and researchers. Making arrangements to curate and share the dataset beyond the IPD meta-analysis project for which it was established, for reuse in future research projects, would maximise the value of this investment. Methods Our aim was to establish the Cochrane repository for individual patient data from clinical trials in pregnancy and childbirth (CRIB) as an example of how an IPD repository could become part of Cochrane infrastructure. We believed that establishing CRIB under Cochrane auspices would engender trust and encourage trial investigators to share data, and at the same time position Cochrane to take steps towards expanding the number of reviews with IPD synthesis. Results CRIB was designed as a web-based platform to receive, host and facilitate onward sharing of de-identified data. Development was not straightforward and we did not fully achieve our aim as intended. We describe the challenges encountered and suggest ways that future repositories might overcome these. In particular, securing the legal agreements required to facilitate data sharing proved to be the main barrier, being time-consuming and more complex than anticipated. Conclusions We would recommend that researchers conducting IPD meta-analysis should consider discussing the option to transfer the curated IPD datasets to a repository at the end of the initial meta-analysis and this should be recognised within the data sharing agreements made with the original data contributors.

Abstract Background A subcutaneous (SC) formulation of infliximab biosimilar CT-P13 is approved in Europe for the treatment of adult patients with rheumatoid arthritis (RA). It may offer improved efficacy versus intravenous (IV) infliximab formulations. Methods A network meta-regression was conducted using individual patient data from two randomised trials in patients with RA, which compared CT-P13 SC with CT-P13 IV, and CT-P13 IV with reference infliximab IV. In this analysis, CT-P13 SC was compared with CT-P13 IV, reference infliximab IV and pooled data for both reference infliximab IV and CT-P13 IV. Outcomes included changes from baseline in 28-joint Disease Activity Score based on C-reactive protein (DAS28-CRP), Simplified Disease Activity Index (SDAI) and Clinical Disease Activity Index (CDAI), and rates of remission, low disease activity or clinically meaningful improvement in functional disability per Health Assessment Questionnaire–Disability Index (HAQ-DI). Results The two studies enrolled 949 patients with RA; pooled data for 840 and 751 patients were evaluable at weeks 30 and 54, respectively. For the CT-P13 SC versus pooled IV treatment arm comparison, differences in changes from baseline in DAS28-CRP (− 0.578; 95% confidence interval [CI] − 0.831, − 0.325; p

Now that immunotherapy plus chemotherapy (CT) is one standard option in first-line treatment of advanced non-small cell lung cancer (NSCLC), there exists a medical need to assess the efficacy of second-line treatments (2LT) with antiangiogenics (AA). We performed an individual patient data meta-analysis to validate the efficacy of these combinations as 2LT. Randomised trials of AA plus standard 2LT compared to 2LT alone that ended accrual before 2015 were eligible. Fixed-effect models were used to compute pooled hazard ratios (HRs) for overall survival (OS, main end-point), progression-free survival (PFS) and subgroup analyses. Sixteen trials were available (8,629 patients, 64% adenocarcinoma). AA significantly prolonged OS (HR = 0.93 [95% confidence interval {CI}: 0.89; 0.98], p = 0.005) and PFS (0.80 [0.77; 0.84], p < 0.0001) compared with 2LT alone. Absolute 1-year OS and PFS benefit for AA were +1.8% [−0.4; +4.0] and +3.5% [+1.9; +5.1], respectively. The OS benefit of AA was higher in younger patients (HR = 0.87 [95% CI: 0.76; 1.00], 0.89 [0.81; 0.97], 0.94 [0.87; 1.02] and 1,04 [0.93; 1.17] for patients <50, 50–59, 60–69 and ≥ 70 years old, respectively; trend test: p = 0.02) and in patients who started AA within 9 months after starting the first-line therapy (0.88 [0.82; 0.99]) than in patients who started AA later (0.99 [0.91; 1.08]) (interaction: p = 0.03). Results were similar for PFS. AA increased the risk of hypertension (p < 0.0001), but not the risk of pulmonary thromboembolic events (p = 0.21). In the 2LT of advanced NSCLC, adding AA significantly prolongs OS and PFS, but the benefit is clinically limited, mainly observed in younger patients and after shorter time since the start of first-line therapy. • The addition of antiangiogenics (AA) in second line reduces 20% the risk of progression. • The addition of AA in second line reduces 7% the risk of death. • The benefit is higher in younger patients and in those with platinum-refractory tumours. • However, addition of AA significantly increased the risk of grade ≥3 toxicities. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract Background The 2018 World Health Organization HIV guidelines were based on the results of a network meta-analysis (NMA) of published trials. This study employed individual patient-level data (IPD) and aggregate data (AgD) and meta-regression methods to assess the evidence supporting the WHO recommendations and whether they needed any refinements. Methods Access to IPD from three trials was granted through ClinicalStudyDataRequest.com (CSDR). Seven modelling approaches were applied and compared: 1) Unadjusted AgD network meta-analysis (NMA) – the original analysis; 2) AgD-NMA with meta-regression; 3) Two-stage IPD-AgD NMA; 4) Unadjusted one-stage IPD-AgD NMA; 5) One-stage IPD-AgD NMA with meta-regression (one-stage approach); 6) Two-stage IPD-AgD NMA with empirical-priors (empirical-priors approach); 7) Hierarchical meta-regression IPD-AgD NMA (HMR approach). The first two were the models used previously. Models were compared with respect to effect estimates, changes in the effect estimates, coefficient estimates, DIC and model fit, rankings and between-study heterogeneity. Results IPD were available for 2160 patients, representing 6.5% of the evidence base and 3 of 24 edges. The aspect of the model affected by the choice of modeling appeared to differ across outcomes. HMR consistently generated larger intervals, often with credible intervals (CrI) containing the null value. Discontinuations due to adverse events and viral suppression at 96 weeks were the only two outcomes for which the unadjusted AgD NMA would not be selected. For the first, the selected model shifted the principal comparison of interest from an odds ratio of 0.28 (95% CrI: 10.17, 0.44) to 0.37 (95% CrI: 0.23, 0.58). Throughout all outcomes, the regression estimates differed substantially between AgD and IPD methods, with the latter being more often larger in magnitude and statistically significant. Conclusions Overall, the use of IPD often impacted the coefficient estimates, but not sufficiently as to necessitate altering the final recommendations of the 2018 WHO Guidelines. Future work should examine the features of a network where adjustments will have an impact, such as how much IPD is required in a given size of network.

Abstract Background The use of individual patient data (IPD) in network meta-analyses (NMA) is rapidly growing. This study aimed to determine, through simulations, the impact of select factors on the validity and precision of NMA estimates when combining IPD and aggregate data (AgD) relative to using AgD only. Methods Three analysis strategies were compared via simulations: 1) AgD NMA without adjustments (AgD-NMA); 2) AgD NMA with meta-regression (AgD-NMA-MR); and 3) IPD-AgD NMA with meta-regression (IPD-NMA). We compared 108 parameter permutations: number of network nodes (3, 5 or 10); proportion of treatment comparisons informed by IPD (low, medium or high); equal size trials (2-armed with 200 patients per arm) or larger IPD trials (500 patients per arm); sparse or well-populated networks; and type of effect-modification (none, constant across treatment comparisons, or exchangeable). Data were generated over 200 simulations for each combination of parameters, each using linear regression with Normal distributions. To assess model performance and estimate validity, the mean squared error (MSE) and bias of treatment-effect and covariate estimates were collected. Standard errors (SE) and percentiles were used to compare estimate precision. Results Overall, IPD-NMA performed best in terms of validity and precision. The median MSE was lower in the IPD-NMA in 88 of 108 scenarios (similar results otherwise). On average, the IPD-NMA median MSE was 0.54 times the median using AgD-NMA-MR. Similarly, the SEs of the IPD-NMA treatment-effect estimates were 1/5 the size of AgD-NMA-MR SEs. The magnitude of superior validity and precision of using IPD-NMA varied across scenarios and was associated with the amount of IPD. Using IPD in small or sparse networks consistently led to improved validity and precision; however, in large/dense networks IPD tended to have negligible impact if too few IPD were included. Similar results also apply to the meta-regression coefficient estimates. Conclusions Our simulation study suggests that the use of IPD in NMA will considerably improve the validity and precision of estimates of treatment effect and regression coefficients in the most NMA IPD data-scenarios. However, IPD may not add meaningful validity and precision to NMAs of large and dense treatment networks when negligible IPD are used.

Literature-based meta-analysis is a standard technique applied to pool results of individual studies used in medicine and social sciences. It has been criticized for being too malleable to constrain results, averaging incomparable values, lacking a measure of evidence's strength, and problems with a systematic bias of individual studies. We argue against using literature-based meta-analysis of RCTs to assess treatment efficacy and show that therapeutic decisions based on meta-analytic average are not optimal given the full scope of existing evidence. The argument proceeds with discussing examples and analyzing the properties of some standard meta-analytic techniques. First, we demonstrate that meta-analysis can lead to reporting statistically significant results despite the treatment's limited efficacy. Second, we show that meta-analytic confidence intervals are too narrow compared to the variability of treatment outcomes reported by individual studies. Third, we argue that literature-based meta-analysis is not a reliable measurement instrument. Finally, we show that meta-analysis averages out the differences among studies and leads to a loss of information. Despite these problems, literature-based meta-analysis is useful for the assessment of harms. We support two alternative approaches to evidence amalgamation: meta-analysis of individual patient data (IPD) and qualitative review employing mechanistic evidence. • Literature-based meta-analysis overemphasizes the certainty of treatment effect estimates. • Meta-analysis can show statistical significance despite the limited clinical efficacy of a treatment. • Using meta-analytic results in the clinic leads to inefficient or harmful treatment decisions. • Meta-analysis is useful for the assessment of harms. • Meta-analysis of individual patient data is better suited for pooling individual RCTs. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Objective: The efficacy of globus pallidus internus-deep brain stimulation (GPi-DBS) for the treatment of Huntington’s disease (HD) has not been validated in large-scale studies. We conducted an individual patient analysis to pool outcomes of all of the published HD-GPi-DBS studies. Methods: PubMed, Embase and the Cochrane Library were searched for relevant articles. The Unified Huntington’s Disease Rating Scale (UHDRS)-motor and UHDRS-chorea improvements were analyzed during different follow-up periods. Secondary outcomes, including UHDRS-motor subitem scores and functional assessment results, were also analyzed. Correlation and regression analyses were conducted to find improvement predictors. This study was registered in PROSPERO (CRD42018105995). Results: Eighteen studies including 39 patients with 124 visits were analyzed. GPi-DBS significantly improved the UHDRS-motor score in 30-month follow-up (p = 0.003). Functional assessment was not improved 12 months postoperatively (p = 0.196). The Westphal variant of HD (W-HD) gained no motor benefits 6 months postoperatively (p = 0.178). The Westphal variant was the only risk factor for DBS efficacy (p = 0.044). The rate of stimulation-related adverse events was 87.2%. Conclusions: GPi-DBS has a stable effect on chorea symptoms in HD patients. Chorea-dominant patients may be the best candidates for surgery, while attention should be paid to postoperative stimulation-related complications. Given that GPi-DBS has limited effects on other motor symptoms, W-HD patients are not surgical candidates.