Neoplastic progression risk in females with Barrett's esophagus: a systematic review and meta-analysis of individual patient data.

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) aims to provide more robust evidence on neoplastic progression risk in females. METHODS: Systematic literature search of three electronic databases (Medline, Embase, Google Scholar) from inception until August 2023. Eligible studies (1) reported original data on progression from non-dysplastic BE (NDBE), indefinite for dysplasia (IND) or low-grade dysplasia (LGD) to high-grade dysplasia (HGD) or esophageal adenocarcinoma (EAC), and (2) included female and male patients. IPD were quality controlled by two independent reviewers. 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 11/66 eligible studies, including 2.196 (31%) females. Neoplastic progression risk was lower in females (HR 1.44 for males vs females, 95%CI 1.13-1.82) after adjusting for age, smoking, medication use, hiatal hernia, BE length, and baseline pathology. Annual progression rate was 0.88% in females vs 1.29% in males. Time to progression was similar in both sexes; 3.7 years (IQR 2.1-7.7) in females, and 4.2 years (IQR 2.0-8.1) 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.

Studying the Outcomes in Patients with Tricuspid Regurgitation Treated with Valve Repair or Valve Replacement: Interpreting the Survival Pattern on The Long Term by Application of Artificial Intelligence Methods.

Background: The reconstruction of individual patient data from published Kaplan-Meier survival curves is a new technique (often denoted as the IPDfromKM method) for studying efficacy in cases where multiple trials are available, and the endpoint is long-term mortality. In patients with tricuspid regurgitation, both valve repair and valve replacement have been proposed to improve prognosis; 6 controlled clinical trials (CTs) have been conducted to compare the two therapeutic options mentioned above. The objective of our analysis was to study these six trials through the application of the IPDfromKM method. Methods: In the present report, we applied the IPDfromKM method to carry out a pooled analysis of these 6 CTs to investigate the effectiveness of valve repair vs valve replacement and to assess the between-study heterogeneity from this clinical material. After reconstructing individual patient data from these 6 trials, patients treated with valve repair were pooled together and their Kaplan-Meier curve was generated. Likewise, patients treated with valve replacement were pooled together and their Kaplan-Meier curve was generated. Finally, these two curves were compared by standard survival statistics. The hazard ratio (HR) was determined; death from any cause was the endpoint. Results: These 6 CTs included a total of 552 patients; in each of these CTs, the patient group treated with valve repair was compared with another group treated with valve replacement. Our statistical results showed a significantly better survival for valve repair compared with valve replacement (HR, 0.6098; 95% confidence intervals (CI), 0.445 to 0.835; p = 0.002). Heterogeneity was found to be significant in the 6 patient arms undergoing replacement, but not in those undergoing valve repair. In valve replacement, the classification of patients in class III or IV of New York Heart Association (NYHA) was the main negative prognostic factor. Conclusions: Our analysis confirmed the methodological advantages of the IPDfromKM method in the indirect comparative analysis of multiple trials. These advantages include appropriate analysis of censored patients, original assessment of heterogeneity, and graphical presentation of the results, wherein individual patients retain an important role.

Association of symptom severity and cerebrospinal fluid alterations in recent onset psychosis in schizophrenia-spectrum disorders - An individual patient data meta-analysis.

Neuroinflammation and blood-cerebrospinal fluid barrier (BCB) disruption could be key elements in schizophrenia-spectrum disorders(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 symptoms severity in males, indicating potential difficult-to-treat states. This work highlights the need for considering BCB breakdownand sex-related differences in SSDs clinical trials and treatment strategies.

Influence of donor sex and age on graft outcome in kidney transplantation.

BACKGROUND: There is a known recipient sex-dependent association between donor sex and kidney transplant survival. We hypothesized that donor age also modifies the association between donor sex and graft survival. METHODS: First, deceased donor kidney transplant recipients (1988-2019, n = 461 364) recorded in the Scientific Registry of Transplant Recipients, the Australia and New Zealand Dialysis and Transplant Registry and the Collaborative Transplant Study were analyzed. We used multivariable Cox regression models to estimate the association between donor sex and death censored graft loss, accounting for the modifying effects of recipient sex and donor age; donor age was categorized as 5-19, 20-34, 35-49, 50-59 and ≥60 years. Results from cohort-specific Cox models were combined using individual patient data meta-analysis. RESULTS: Among female recipients of donors aged <60 years, graft loss hazards did not differ by donor sex; recipients of female donors ≥60 years showed significantly lower graft loss hazards than recipients of male donors of the same age [combined adjusted hazard ratio (aHR) 0.90, 95% CI 0.86-0.94]. Among male recipients, female donors aged <50 years were associated with significantly higher graft loss hazards than same-aged male donors (5-19 years: aHR 1.11, 95% CI 1.02-1.21; 20-34 years: aHR 1.08, 95% CI 1.02-1.15; 35-49 years: aHR 1.07, 95% CI 1.04-1.10). There were no significant differences in graft loss by donor sex among male recipients of donors aged ≥50 years. CONCLUSION: Donor age modifies the association between donor sex and graft survival. Older female donors were associated with similar or lower hazards of graft failure than older male donors in both male and female recipients, suggesting a better functional reserve of older female donor kidneys.

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.

Implementing Multilevel Network Meta-Regression for Time-To-Event Outcomes: A Case Study in Relapsed Refractory Multiple Myeloma.

OBJECTIVES: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Analytical Methods for Comparing Uncontrolled Trials With External Controls From Real-World Data: A Systematic Literature Review and Comparison With European Regulatory and Health Technology Assessment Practice.

OBJECTIVES: This study aimed to provide an overview of analytical methods in scientific literature for comparing uncontrolled medicine trials with external controls from individual patient data real-world data (IPD-RWD) and to compare these methods with recommendations made in guidelines from European regulatory and health technology assessment (HTA) organizations and with their evaluations described in assessment reports. METHODS: A systematic literature review (until March 1, 2023) in PubMed and Connected Papers was performed to identify analytical methods for comparing uncontrolled trials with external controls from IPD-RWD. These methods were compared descriptively with methods recommended in method guidelines and encountered in assessment reports of the European Medicines Agency (2015-2020) and 4 European HTA organizations (2015-2023). RESULTS: Thirty-four identified scientific articles described analytical methods for comparing uncontrolled trial data with IPD-RWD-based external controls. The various methods covered controlling for confounding and/or dependent censoring, correction for missing data, and analytical comparative modeling methods. Seven guidelines also focused on research design, RWD quality, and transparency aspects, and 4 of those recommended analytical methods for comparisons with IPD-RWD. The methods discussed in regulatory (n = 15) and HTA (n = 35) assessment reports were often based on aggregate data and lacked transparency owing to the few details provided. CONCLUSIONS: Literature and guidelines suggest a methodological approach to comparing uncontrolled trials with external controls from IPD-RWD similar to target trial emulation, using state-of-the-art methods. External controls supporting regulatory and HTA decision making were rarely in line with this approach. Twelve recommendations are proposed to improve the quality and acceptability of these methods.

Estimating individualized treatment effects using an individual participant data meta-analysis.

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 , 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.

Dropout from exercise trials among cancer survivors-An individual patient data meta-analysis from the POLARIS study.

INTRODUCTION: The number of randomized controlled trials (RCTs) investigating the effects of exercise among cancer survivors has increased in recent years; however, participants dropping out of the trials are rarely described. The objective of the present study was to assess which combinations of participant and exercise program characteristics were associated with dropout from the exercise arms of RCTs among cancer survivors. METHODS: This study used data collected in the Predicting OptimaL cAncer RehabIlitation and Supportive care (POLARIS) study, an international database of RCTs investigating the effects of exercise among cancer survivors. Thirty-four exercise trials, with a total of 2467 patients without metastatic disease randomized to an exercise arm were included. Harmonized studies included a pre and a posttest, and participants were classified as dropouts when missing all assessments at the post-intervention test. Subgroups were identified with a conditional inference tree. RESULTS: Overall, 9.6% of the participants dropped out. Five subgroups were identified in the conditional inference tree based on four significant associations with dropout. Most dropout was observed for participants with BMI >28.4 kg/m2 , performing supervised resistance or unsupervised mixed exercise (19.8% dropout) or had low-medium education and performed aerobic or supervised mixed exercise (13.5%). The lowest dropout was found for participants with BMI >28.4 kg/m2 and high education performing aerobic or supervised mixed exercise (5.1%), and participants with BMI ≤28.4 kg/m2 exercising during (5.2%) or post (9.5%) treatment. CONCLUSIONS: There are several systematic differences between cancer survivors completing and dropping out from exercise trials, possibly affecting the external validity of exercise effects.

Generalizing Treatment Effect to a Target Population Without Individual Patient Data in a Real-World Setting.

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.

Hepatocellular carcinoma risk in patients with chronic hepatitis B receiving tenofovir- vs. 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.

Structure-preserving integrated analysis for risk stratification with application to cancer staging.

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.

Prognostic value of circulating tumor DNA in operable non-small cell lung cancer: a systematic review and reconstructed individual patient-data based meta-analysis.

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.

Optimising plasma clozapine levels to improve treatment response: an individual patient data meta-analysis and receiver operating characteristic curve analysis.

BACKGROUND: Although clozapine is the most efficacious medication for treatment-refractory schizophrenia, not all patients will have an adequate response. Optimising clozapine dose using therapeutic drug monitoring could therefore maximise response. AIMS: Using individual patient data, we undertook a receiver operating characteristic (ROC) curve analysis to determine an optimal therapeutic range for clozapine levels to guide clinical practice. METHOD: We conducted a systematic review of PubMed, PsycINFO and Embase for studies that provided individual participant level data on clozapine levels and response. These data were analysed using ROC curves to determine the prediction performance of plasma clozapine levels for treatment response. RESULTS: We included data on 294 individual participants from nine studies. ROC analysis yielded an area under the curve of 0.612. The clozapine level at the point of optimal diagnostic benefit was 372 ng/mL; at this level, the response sensitivity was 57.3%, and specificity 65.7%. The interquartile range for treatment response was 223-558 ng/mL. There was no improvement in ROC performance with mixed models including patient gender, age or length of trial. Clozapine dose and clozapine concentration to dose ratio did not provide significantly meaningful prediction of response to clozapine. CONCLUSIONS: Clozapine dose should be optimised based on clozapine therapeutic levels. We found that a range between 250 and 550 ng/mL could be recommended, while noting that a level of >350 ng/mL is the most optimal for response. Although some patients may not respond without clozapine levels >550 ng/mL, the benefits should be weighed against the increased risk of adverse drug reactions.

Direct evidence gap on fixed versus adjusted-dose benznidazole for adults with chronic Chagas disease without cardiomyopathy: Systematic review and individual patient data meta-analysis.

OBJECTIVES: To determine the comparative efficacy and safety of a fixed dose of benznidazole (BZN) with an adjusted-dose for Trypanosoma cruzi-seropositive adults without cardiomyopathy. METHODS: We conducted a systematic review and individual participant data (IPD) meta-analysis following Cochrane methods, and the PRISMA-IPD statement for reporting. Randomised controlled trials (RCTs) allocating participants to fixed or adjusted doses of BZN for T. cruzi-seropositive adults without cardiomyopathy were included. We searched (December 2021) Cochrane, MEDLINE, EMBASE, LILACS and trial registries and contacted Chagas experts. Selection, data extraction, risk of bias assessment using the Cochrane tool, and a GRADE summary of finding tables were performed independently by pairs of reviewers. We conducted a random-effects IPD meta-analysis using the one-stage strategy, or, if that was impossible, the two-stage strategy. RESULTS: Five RCTs (1198 patients) were included, none directly comparing fixed with adjusted doses of BZN. Compared to placebo, BZN therapy was strongly associated with negative qPCR and sustainable parasitological clearance regardless of the type of dose and subgroup analysed. For negative qPCR, the fixed/adjusted rate of odds ratios (RORF/A ) was 8.83 (95% CI 1.02-76.48); for sustained parasitological clearance, it was 4.60 (95% CI 0.40-52.51), probably indicating at least non-inferior effect of fixed doses, with no statistically significant interactions by scheme for global and most subgroup estimations. The RORF/A for treatment interruption due to adverse events was 0.44 (95% CI 0.14-1.38), probably indicating no worse tolerance of fixed doses. CONCLUSIONS: We found no direct comparison between fixed and adjusted doses of BZN. However, fixed doses versus placebo are probably not inferior to weight-adjusted doses of BZN versus placebo in terms of parasitological efficacy and safety. Network IPD meta-analysis, through indirect comparisons, may well provide the best possible answers in the near future. REGISTRATION: The study protocol was registered in PROSPERO (CRD42019120905).

Developing a Bayesian hierarchical model for a prospective individual patient data meta-analysis with continuous monitoring.

BACKGROUND: Numerous clinical trials have been initiated to find effective treatments for COVID-19. These trials have often been initiated in regions where the pandemic has already peaked. Consequently, achieving full enrollment in a single trial might require additional COVID-19 surges in the same location over several years. This has inspired us to pool individual patient data (IPD) from ongoing, paused, prematurely-terminated, or completed randomized controlled trials (RCTs) in real-time, to find an effective treatment as quickly as possible in light of the pandemic crisis. However, pooling across trials introduces enormous uncertainties in study design (e.g., the number of RCTs and sample sizes might be unknown in advance). We sought to develop a versatile treatment efficacy assessment model that accounts for these uncertainties while allowing for continuous monitoring throughout the study using Bayesian monitoring techniques. METHODS: We provide a detailed look at the challenges and solutions for model development, describing the process that used extensive simulations to enable us to finalize the analysis plan. This includes establishing prior distribution assumptions, assessing and improving model convergence under different study composition scenarios, and assessing whether we can extend the model to accommodate multi-site RCTs and evaluate heterogeneous treatment effects. In addition, we recognized that we would need to assess our model for goodness-of-fit, so we explored an approach that used posterior predictive checking. Lastly, given the urgency of the research in the context of evolving pandemic, we were committed to frequent monitoring of the data to assess efficacy, and we set Bayesian monitoring rules calibrated for type 1 error rate and power. RESULTS: The primary outcome is an 11-point ordinal scale. We present the operating characteristics of the proposed cumulative proportional odds model for estimating treatment effectiveness. The model can estimate the treatment's effect under enormous uncertainties in study design. We investigate to what degree the proportional odds assumption has to be violated to render the model inaccurate. We demonstrate the flexibility of a Bayesian monitoring approach by performing frequent interim analyses without increasing the probability of erroneous conclusions. CONCLUSION: This paper describes a translatable framework using simulation to support the design of prospective IPD meta-analyses.

Exploration of an alternative reconstructed individual patient data-based approach for budget impact analysis of anticancer drugs.

BACKGROUND: The duration of treatment (DOT) of the initial intervention and subsequent treatment is the key to determining the accuracy of anticancer-drug budget impact analysis (BIA) calculations. However, existing studies only use simple assumptions as a proxy for DOT, resulting in a high degree of bias. OBJECTIVES: To enhance the accuracy and reliability of anticancer-drug BIA and solve the problem regarding DOT, we propose an alternative individual patient data (IPD)-based approach that reconstructs IPD from the published Kaplan Meier survival curves to estimate DOT. METHODS: We developed a four-step methodological framework for this new approach, taking the use of pembrolizumab in treating microsatellite-instability-high (MSI-H) advanced colorectal cancer as an example: (1) reconstructing the IPD; (2) calculating the total DOT of the initial intervention and subsequent treatment for each patient; (3) assigning a randomized time and DOT; and (4) multiple replacement sampling and calculation of the mean value. RESULTS: Using this approach, the average DOT for the initial intervention and subsequent treatment in each year of the BIA time horizon can be calculated and used to calculate the resources consumed and costs in each year. In our example, the average DOT for the initial intervention with pembrolizumab from the first to the fourth year was 4.90, 6.60, 5.24, and 5.06 months, respectively, while the average DOT for subsequent treatment was 0.75, 2.84, 2.99, and 2.50 months, respectively. CONCLUSIONS: The reconstructed IPD-based approach can improve the accuracy and reliability of anticancer-drug BIA compared with conventional methods, and can be widely used, especially for anticancer drugs with excellent efficacy.

Lower or higher oxygenation targets for acute Hypoxaemic respiratory failure: Protocol for an individual patient data meta-analysis.

BACKGROUND: Supplemental oxygen therapy is central to the treatment of acute hypoxaemic respiratory failure, a condition which remains a major driver for morbidity and mortality in intensive care. Despite several large randomised clinical trials comparing a higher versus a lower oxygenation target for these patients, significant differences in study design impede analysis of aggregate data and final clinical recommendations. METHODS: This paper presents the protocol for conducting an individual patient data meta-analysis where full individual patient data according to the intention-to-treat principle will be pooled from the HOT-ICU and HOT-COVID trials in a one-step procedure. The two trials are near-identical in design. We plan to use a hierarchical general linear mixed model that accounts for data clustering at a trial and site level. The primary outcome will be 90-day all-cause mortality while the secondary outcome will be days alive without life-support at 90 days. Further, we outline 14 clinically relevant predefined subgroups which we will analyse for heterogeneity in the intervention effects and interactions, and we present a plan for assessing the credibility of the subgroup analyses. CONCLUSION: The presented individual patient data meta-analysis will synthesise individual level patient data from two of the largest randomised clinical trials on targeted oxygen therapy in intensive care. The results will provide a re-analysis of the intervention effects on the pooled intention-to-treat populations and facilitate subgroup analyses with an increased power to detect clinically important effect modifications.

The clinical implication of minimally invasive versus open pancreatoduodenectomy for non-pancreatic periampullary cancer: a systematic review and individual patient data meta-analysis.

BACKGROUND: Most studies on minimally invasive pancreatoduodenectomy (MIPD) combine patients with pancreatic and periampullary cancers even though there is substantial heterogeneity between these tumors. Therefore, this study aimed to evaluate the role of MIPD compared to open pancreatoduodenectomy (OPD) in patients with non-pancreatic periampullary cancer (NPPC). METHODS: A systematic review of Pubmed, Embase, and Cochrane databases was performed by two independent reviewers to identify studies comparing MIPD and OPD for NPPC (ampullary, distal cholangio, and duodenal adenocarcinoma) (01/2015-12/2021). Individual patient data were required from all identified studies. Primary outcomes were (90-day) mortality, and major morbidity (Clavien-Dindo 3a-5). Secondary outcomes were postoperative pancreatic fistula (POPF), delayed gastric emptying (DGE), postpancreatectomy hemorrhage (PPH), blood-loss, length of hospital stay (LOS), and overall survival (OS). RESULTS: Overall, 16 studies with 1949 patients were included, combining 928 patients with ampullary, 526 with distal cholangio, and 461 with duodenal cancer. In total, 902 (46.3%) patients underwent MIPD, and 1047 (53.7%) patients underwent OPD. The rates of 90-day mortality, major morbidity, POPF, DGE, PPH, blood-loss, and length of hospital stay did not differ between MIPD and OPD. Operation time was 67 min longer in the MIPD group (P = 0.009). A decrease in DFS for ampullary (HR 2.27, P = 0.019) and distal cholangio (HR 1.84, P = 0.025) cancer, as well as a decrease in OS for distal cholangio (HR 1.71, P = 0.045) and duodenal cancer (HR 4.59, P < 0.001) was found in the MIPD group. CONCLUSIONS: This individual patient data meta-analysis of MIPD versus OPD in patients with NPPC suggests that MIPD is not inferior in terms of short-term morbidity and mortality. Several major limitations in long-term data highlight a research gap that should be studied in prospective maintained international registries or randomized studies for ampullary, distal cholangio, and duodenum cancer separately. PROTOCOL REGISTRATION: PROSPERO (CRD42021277495) on the 25th of October 2021.

Chest X-ray Analysis With Deep Learning-Based Software as a Triage Test for Pulmonary Tuberculosis: An Individual Patient Data Meta-Analysis of Diagnostic Accuracy.

BACKGROUND: Automated radiologic analysis using computer-aided detection software (CAD) could facilitate chest X-ray (CXR) use in tuberculosis diagnosis. There is little to no evidence on the accuracy of commercially available deep learning-based CAD in different populations, including patients with smear-negative tuberculosis and people living with human immunodeficiency virus (HIV, PLWH). METHODS: We collected CXRs and individual patient data (IPD) from studies evaluating CAD in patients self-referring for tuberculosis symptoms with culture or nucleic acid amplification testing as the reference. We reanalyzed CXRs with three CAD programs (CAD4TB version (v) 6, Lunit v3.1.0.0, and qXR v2). We estimated sensitivity and specificity within each study and pooled using IPD meta-analysis. We used multivariable meta-regression to identify characteristics modifying accuracy. RESULTS: We included CXRs and IPD of 3727/3967 participants from 4/7 eligible studies. 17% (621/3727) were PLWH. 17% (645/3727) had microbiologically confirmed tuberculosis. Despite using the same threshold score for classifying CXR in every study, sensitivity and specificity varied from study to study. The software had similar unadjusted accuracy (at 90% pooled sensitivity, pooled specificities were: CAD4TBv6, 56.9% [95% confidence interval {CI}: 51.7-61.9]; Lunit, 54.1% [95% CI: 44.6-63.3]; qXRv2, 60.5% [95% CI: 51.7-68.6]). Adjusted absolute differences in pooled sensitivity between PLWH and HIV-uninfected participants were: CAD4TBv6, -13.4% [-21.1, -6.9]; Lunit, +2.2% [-3.6, +6.3]; qXRv2: -13.4% [-21.5, -6.6]; between smear-negative and smear-positive tuberculosis was: were CAD4TBv6, -12.3% [-19.5, -6.1]; Lunit, -17.2% [-24.6, -10.5]; qXRv2, -16.6% [-24.4, -9.9]. Accuracy was similar to human readers. CONCLUSIONS: For CAD CXR analysis to be implemented as a high-sensitivity tuberculosis rule-out test, users will need threshold scores identified from their own patient populations and stratified by HIV and smear status.