Methods for meta-analysis of pharmacodynamic dose-response data with application to multi-arm studies of alogliptin.

Standard methods for meta-analysis of dose-response data in epidemiology assume a model with a single scalar parameter, such as log-linear relationships between exposure and outcome; such models are implicitly unbounded. In contrast, in pharmacology, multi-parameter models, such as the widely used Emax model, are used to describe relationships that are bounded above and below. We propose methods for estimating the parameters of a dose-response model by meta-analysis of summary data from the results of randomized controlled trials of a drug, in which each trial uses multiple doses of the drug of interest (possibly including dose 0 or placebo). We assume that, for each randomized arm of each trial, the mean and standard error of a continuous response measure and the corresponding allocated dose are available. We consider weighted least squares fitting of the model to the mean and dose pairs from all arms of all studies, and a two-stage procedure in which scalar inverse-variance meta-analysis is performed at each dose, and the dose-response model is fitted to the results by weighted least squares. We then compare these with two further methods inspired by network meta-analysis that fit the model to the contrasts between doses. We illustrate the methods by estimating the parameters of the Emax model to a collection of multi-arm, multiple-dose, randomized controlled trials of alogliptin, a drug for the management of diabetes mellitus, and further examine the properties of the four methods with sensitivity analyses and a simulation study. We find that all four methods produce broadly comparable point estimates for the parameters of most interest, but a single-stage method based on contrasts between doses produces the most appropriate confidence intervals. Although simpler methods may have pragmatic advantages, such as the use of standard software for scalar meta-analysis, more sophisticated methods are nevertheless preferable for their advantages in estimation.

Individual Trade-Offs Between Possible Benefits and Risks of Cancer Treatments: Results from a Stated Preference Study with Patients with Multiple Myeloma.

BACKGROUND: The objectives of this study were to elicit the preferences of patients with multiple myeloma regarding the possible benefits and risks of cancer treatments and to illustrate how such data may be used to estimate patients' acceptance of new treatments. PATIENTS AND METHODS: Patients with multiple myeloma from the cancer charity Myeloma UK were invited to participate in an online survey based on multicriteria decision analysis and swing weighting to elicit individual stated preferences for the following attributes: (a) 1-year progression-free survival (PFS, ranging from 50% to 90%), (b) mild or moderate toxicity for 2 months or longer (ranging from 85% to 45%), and (c) severe or life-threatening toxicity (ranging from 80% to 20%). RESULTS: A total of 560 participants completed the survey. The average weight given to PFS was 0.54, followed by 0.32 for severe or life-threatening toxicity and 0.14 for mild or moderate chronic toxicity. Participants who ranked severe or life-threatening toxicity above mild or moderate chronic toxicity (56%) were more frequently younger, working, and looking after dependent family members and had more frequently experienced severe or life-threatening side effects. The amount of weight given to PFS did not depend on any of the collected covariates. The feasibility of using the collected preference data to estimate the patients' acceptance of specific multiple myeloma treatments was demonstrated in a subsequent decision analysis example. CONCLUSION: Stated preference studies provide a systematic approach to gain knowledge about the distribution of preferences in the population and about what this implies for patients' acceptance of specific treatments. IMPLICATIONS FOR PRACTICE: This study demonstrated how quantitative preference statements from a large group of participants can be collected through an online survey and how such information may be used to explore the acceptability of specific treatments based on the attributes studied. Results from such studies have the potential to become an important new tool for gathering patient views and studying heterogeneity in preferences in a systematic way, along with other methods, such as focus groups and expert opinions.

The Efficacy and Safety of First-line Chemotherapy in Advanced Esophagogastric Cancer: A Network Meta-analysis.

BACKGROUND: A globally accepted standard first-line chemotherapy regimen in advanced esophagogastric cancer (AEGC) is not clearly established. We conducted a systematic review to investigate the efficacy and safety of first-line chemotherapy using Network meta-analysis (NMA). METHODS: Medline, EMBASE, CENTRAL, and conferences were searched until June 2015 for randomized controlled trials that compared regimens containing: fluoropyrimidine (F), platinum (cisplatin [C] and oxaliplatin [Ox]), taxane (T), anthracycline (A), irinotecan (I), or methotrexate (M). Direct and indirect evidence for overall survival (OS) and progression-free-survival (PFS) were combined using random-effects NMA on the hazard ratio (HR) scale and calculated as combined hazard ratios and 95% credible intervals (CrIs). RESULTS: The NMA incorporated 17 chemotherapy regimens with 37 direct comparisons between regimens for OS (50 studies, n = 10 249) and 29 direct comparisons for PFS (34 studies, n = 7795). Combining direct and indirect effects showed increased efficacy for fluoropyrimidine noncisplatin doublets (F-doublets) over cisplatin doublets (C-doublets): FI vs CF (combined HR = 0.85, 95% CrI = 0.71 to 0.99), FOx vs CF (combined HR = 0.83, 95% CrI = 0.71 to 0.98) in OS and FOx vs CF (combined HR = 0.82, 95% CrI = 0.66 to 0.99) in PFS. Anthracycline-containing triplets (A-triplets: ACF, AFOx, AFM) and TCF triplet showed no benefit over F-doublets in OS and PFS. The triplet FOxT showed increased PFS vs F-doublets FT (combined HR = 0.61, 95% CrI = 0.38 to 0.99), FI (combined HR = 0.62, 95% CrI = 0.38 to 0.99), and FOx (combined HR = 0.67, 95% CrI = 0.44 to 0.99). Increased grade 3 to 4 toxicity was found for CF vs F-doublets, for ACF vs FI for TCF vs CF, and for FOxT vs FOx. CONCLUSIONS: Based on efficacy and toxicity, F-doublets FOx, FI, and FT are preferred as first-line treatment for AEGC compared with C-doublets, A-triplets, and TCF. FOxT is the most promising triplet.

Previously unidentified duplicate registrations of clinical trials: an exploratory analysis of registry data worldwide.

BACKGROUND: Trial registries were established to combat publication bias by creating a comprehensive and unambiguous record of initiated clinical trials. However, the proliferation of registries and registration policies means that a single trial may be registered multiple times (i.e., "duplicates"). Because unidentified duplicates threaten our ability to identify trials unambiguously, we investigate to what degree duplicates have been identified across registries globally. METHODS: We retrieved all records from the World Health Organization (WHO) International Clinical Trials Registry Platform (ICTRP) search portal and made a list of all records identified as duplicates by the ICTRP. To investigate how to discriminate duplicates from non-duplicates, we applied text-based similarity scoring to various registration fields of both ICTRP-identified duplicates and arbitrary pairs of trials. We then used the best similarity measure to identify the most similar pairs of records and manually assessed a random sample of pairs not identified as duplicates by the ICTRP to estimate the number of previously unidentified (or "hidden") duplicates. RESULTS: Two hundred eighty-five thousand unique records, or 271 thousand unique trials after accounting for known duplicates, were retrieved from the ICTRP portal in April 2015. We found that the title field best discriminated duplicates from non-duplicates. Out of 41 billion total pair-wise comparisons, we identified the 474,000 pairs of titles with the highest similarity scores (>0.5). After manually assessing a random sample of 434 pairs, we estimated that 45 % of all duplicate registrations currently go undetected and remain to be identified and confirmed as duplicates. Thus, the actual number of unique trials represented in this dataset is estimated to be approximately 258,000 (5 % less). CONCLUSIONS: The ICTRP portal does not currently enable the unambiguous identification of trials across registries. Further research is needed to identify and verify the duplicates that currently go undetected. Sponsors, registries, and the ICTRP should consider actions to ensure duplicate registrations are easily identifiable.

Second- and third-line systemic therapy in patients with advanced esophagogastric cancer: a systematic review of the literature.

The optimal second- and third-line chemotherapy and targeted therapy for patients with advanced esophagogastric cancer is still a matter of debate. Therefore, a literature search was carried out in Medline, EMBASE, CENTRAL, and oncology conferences until January 2016 for randomized controlled trials that compared second- or third-line therapy. We included 28 studies with 4810 patients. Second-line, single-agent taxane/irinotecan showed increased survival compared to best supportive care (BSC) (hazard ratio 0.65, 95 % confidence interval 0.53-0.79). Median survival gain ranged from 1.4 to 2.7 months among individual studies. Taxane- and irinotecan-based regimens showed equal survival benefit. Doublet chemotherapy taxane/irinotecan plus platinum and fluoropyrimidine was not different in survival, but showed increased toxicity vs. taxane/irinotecan monotherapy. Compared to BSC, second-line ramucirumab and second- or third-line everolimus and regorafenib showed limited median survival gain ranging from 1.1 to 1.4 months, and progression-free survival gain, ranging from 0.3 to 1.6 months. Third- or later-line apatinib showed increased survival benefit over BSC (HR 0.50, 0.32-0.79). Median survival gain ranged from 1.8 to 2.3 months. Compared to taxane-alone, survival was superior for second-line ramucirumab plus taxane (HR 0.81, 0.68-0.96), and olaparib plus taxane (HR 0.56, 0.35-0.87), with median survival gains of 2.2 and 4.8 months respectively. Targeted agents, either in monotherapy or combined with chemotherapy showed increased toxicity compared to BSC and chemotherapy-alone. This review indicates that, given the survival benefit in a phase III study setting, ramucirumab plus taxane is the preferred second-line treatment. Taxane or irinotecan monotherapy are alternatives, although the absolute survival benefit was limited. In third-line setting, apatinib monotherapy is preferred.

GetReal in network meta-analysis: a review of the methodology.

Pairwise meta-analysis is an established statistical tool for synthesizing evidence from multiple trials, but it is informative only about the relative efficacy of two specific interventions. The usefulness of pairwise meta-analysis is thus limited in real-life medical practice, where many competing interventions may be available for a certain condition and studies informing some of the pairwise comparisons may be lacking. This commonly encountered scenario has led to the development of network meta-analysis (NMA). In the last decade, several applications, methodological developments, and empirical studies in NMA have been published, and the area is thriving as its relevance to public health is increasingly recognized. This article presents a review of the relevant literature on NMA methodology aiming to pinpoint the developments that have appeared in the field. Copyright © 2016 John Wiley & Sons, Ltd.

Automated generation of node-splitting models for assessment of inconsistency in network meta-analysis.

Network meta-analysis enables the simultaneous synthesis of a network of clinical trials comparing any number of treatments. Potential inconsistencies between estimates of relative treatment effects are an important concern, and several methods to detect inconsistency have been proposed. This paper is concerned with the node-splitting approach, which is particularly attractive because of its straightforward interpretation, contrasting estimates from both direct and indirect evidence. However, node-splitting analyses are labour-intensive because each comparison of interest requires a separate model. It would be advantageous if node-splitting models could be estimated automatically for all comparisons of interest. We present an unambiguous decision rule to choose which comparisons to split, and prove that it selects only comparisons in potentially inconsistent loops in the network, and that all potentially inconsistent loops in the network are investigated. Moreover, the decision rule circumvents problems with the parameterisation of multi-arm trials, ensuring that model generation is trivial in all cases. Thus, our methods eliminate most of the manual work involved in using the node-splitting approach, enabling the analyst to focus on interpreting the results.

Get real in individual participant data (IPD) meta-analysis: a review of the methodology.

Individual participant data (IPD) meta-analysis is an increasingly used approach for synthesizing and investigating treatment effect estimates. Over the past few years, numerous methods for conducting an IPD meta-analysis (IPD-MA) have been proposed, often making different assumptions and modeling choices while addressing a similar research question. We conducted a literature review to provide an overview of methods for performing an IPD-MA using evidence from clinical trials or non-randomized studies when investigating treatment efficacy. With this review, we aim to assist researchers in choosing the appropriate methods and provide recommendations on their implementation when planning and conducting an IPD-MA.

Applying Multiple Criteria Decision Analysis to Comparative Benefit-Risk Assessment: Choosing among Statins in Primary Prevention.

Decision makers in different health care settings need to weigh the benefits and harms of alternative treatment strategies. Such health care decisions include marketing authorization by regulatory agencies, practice guideline formulation by clinical groups, and treatment selection by prescribers and patients in clinical practice. Multiple criteria decision analysis (MCDA) is a family of formal methods that help make explicit the tradeoffs that decision makers accept between the benefit and risk outcomes of different treatment options. Despite the recent interest in MCDA, certain methodological aspects are poorly understood. This paper presents 7 guidelines for applying MCDA in benefit-risk assessment and illustrates their use in the selection of a statin drug for the primary prevention of cardiovascular disease. We provide guidance on the key methodological issues of how to define the decision problem, how to select a set of nonoverlapping evaluation criteria, how to synthesize and summarize the evidence, how to translate relative measures to absolute ones that permit comparisons between the criteria, how to define suitable scale ranges, how to elicit partial preference information from the decision makers, and how to incorporate uncertainty in the analysis. Our example on statins indicates that fluvastatin is likely to be the most preferred drug by our decision maker and that this result is insensitive to the amount of preference information incorporated in the analysis.

Evaluation of CADD Scores in Curated Mismatch Repair Gene Variants Yields a Model for Clinical Validation and Prioritization.

Next-generation sequencing in clinical diagnostics is providing valuable genomic variant data, which can be used to support healthcare decisions. In silico tools to predict pathogenicity are crucial to assess such variants and we have evaluated a new tool, Combined Annotation Dependent Depletion (CADD), and its classification of gene variants in Lynch syndrome by using a set of 2,210 DNA mismatch repair gene variants. These had already been classified by experts from InSiGHT's Variant Interpretation Committee. Overall, we found CADD scores do predict pathogenicity (Spearman's ρ = 0.595, P < 0.001). However, we discovered 31 major discrepancies between the InSiGHT classification and the CADD scores; these were explained in favor of the expert classification using population allele frequencies, cosegregation analyses, disease association studies, or a second-tier test. Of 751 variants that could not be clinically classified by InSiGHT, CADD indicated that 47 variants were worth further study to confirm their putative pathogenicity. We demonstrate CADD is valuable in prioritizing variants in clinically relevant genes for further assessment by expert classification teams.

A mixed treatment comparison of gabapentin enacarbil, pramipexole, ropinirole and rotigotine in moderate-to-severe restless legs syndrome.

OBJECTIVE: A mixed treatment comparison (MTC) was performed to investigate the relative efficacy and safety of licensed pharmaceuticals for moderate-to-severe restless legs syndrome (RLS). METHODS: RLS trials published over the past 10 years were identified via systematic literature searches of MEDLINE, Embase, Cochrane CENTRAL, and manufacturers' websites. MTC was performed with WinBUGS software using a Bayesian approach. Identified primary outcomes: change in International RLS Study Group Rating Scale (IRLS) at week 12 and end of maintenance (EoM). SECONDARY OUTCOMES: IRLS and Clinical Global Impression - Improvement Scale (CGI-I) responders, RLS-6 items and adverse events (AEs). RESULTS: Twenty-eight clinical trials were identified. Fifteen were included in the primary analysis. Indirect comparisons were established among gabapentin enacarbil, pramipexole, ropinirole, rotigotine and placebo. Overall, the four active treatments showed similar efficacies as assessed by changes in IRLS scores, IRLS responders, CGI-I responders, and RLS-6 scores. The sole exception was change in IRLS at week 12, for which rotigotine was likely more efficacious than ropinirole (mean difference: -2.52 [95% CrI: -4.74, -0.40]). Indirect comparisons on safety endpoints indicated ropinirole was associated with a higher risk of nausea than the other agents, and was more likely to result in discontinuations due to lack of efficacy than pramipexole. Nausea was likely more frequent with pramipexole than gabapentin enacarbil, and rotigotine was more likely to result in discontinuation due to AEs than ropinirole and pramipexole. CONCLUSIONS: This MTC confirmed the superiority of gabapentin enacarbil, pramipexole, ropinirole, and rotigotine above placebo in alleviating RLS symptoms. Compared to ropinirole, rotigotine showed some additional benefit in terms of change in IRLS at Week 12. Choice of RLS drugs requires careful evaluation of effectiveness and safety profiles in clinical practice. Due to lack of head-to-head trials, inconsistency could not be assessed in our analysis. Head-to-head trials on a more homogeneous population are needed to validate the MTC results.

A multi-criteria decision analysis perspective on the health economic evaluation of medical interventions.

A standard practice in health economic evaluation is to monetize health effects by assuming a certain societal willingness-to-pay per unit of health gain. Although the resulting net monetary benefit (NMB) is easy to compute, the use of a single willingness-to-pay threshold assumes expressibility of the health effects on a single non-monetary scale. To relax this assumption, this article proves that the NMB framework is a special case of the more general stochastic multi-criteria acceptability analysis (SMAA) method. Specifically, as SMAA does not restrict the number of criteria to two and also does not require the marginal rates of substitution to be constant, there are problem instances for which the use of this more general method may result in a better understanding of the trade-offs underlying the reimbursement decision-making problem. This is illustrated by applying both methods in a case study related to infertility treatment.

Multicriteria benefit-risk assessment using network meta-analysis.

OBJECTIVE: To enable multicriteria benefit-risk (BR) assessment of any number of alternative treatments using all available evidence from a network of clinical trials. STUDY DESIGN AND SETTING: We design a general method for multicriteria decision aiding with criteria measurements from Mixed Treatment Comparison (MTC) analyses. To evaluate the method, we apply it to BR assessment of four second-generation antidepressants and placebo in the setting of a published peer-reviewed systematic review. RESULTS: The analysis without preference information shows that placebo is supported by a wide range of possible preferences. Preference information provided by a clinical expert showed that although treatment with antidepressants is warranted for severely depressed patients, for mildly depressed patients placebo is likely to be the best option. It is difficult to choose between the four antidepressants, and the results of the model indicate a high degree of uncertainty. CONCLUSIONS: The designed method enables quantitative BR analysis of alternative treatments using all available evidence from a network of clinical trials. The preference-free analysis can be useful in presenting the results of an MTC considering multiple outcomes.

Automating network meta-analysis.

Mixed treatment comparison (MTC) (also called network meta-analysis) is an extension of traditional meta-analysis to allow the simultaneous pooling of data from clinical trials comparing more than two treatment options. Typically, MTCs are performed using general-purpose Markov chain Monte Carlo software such as WinBUGS, requiring a model and data to be specified using a specific syntax. It would be preferable if, for the most common cases, both could be derived from a well-structured data file that can be easily checked for errors. Automation is particularly valuable for simulation studies in which the large number of MTCs that have to be estimated may preclude manual model specification and analysis. Moreover, automated model generation raises issues that provide additional insight into the nature of MTC. We present a method for the automated generation of Bayesian homogeneous variance random effects consistency models, including the choice of basic parameters and trial baselines, priors, and starting values for the Markov chain(s). We validate our method against the results of five published MTCs. The method is implemented in freely available open source software. This means that performing an MTC no longer requires manually writing a statistical model. This reduces time and effort, and facilitates error checking of the dataset. Copyright © 2012 John Wiley & Sons, Ltd.

Culprit vessel only versus multivessel and staged percutaneous coronary intervention for multivessel disease in patients presenting with ST-segment elevation myocardial infarction: a pairwise and network meta-analysis.

OBJECTIVES: The purposes of this study were to investigate whether, in patients with ST-segment elevation myocardial infarction (STEMI) and multivessel disease (MVD), percutaneous coronary intervention (PCI) should be confined to the culprit or also nonculprit vessels and, when performing PCI for nonculprit vessels, whether it should take place during primary PCI or staged procedures. BACKGROUND: A significant percentage of STEMI patients have MVD. However, the best PCI strategy for nonculprit vessel lesions is unknown. METHODS: Pairwise and network meta-analyses were performed on 3 PCI strategies for MVD in STEMI patients: 1) culprit vessel only PCI strategy (culprit PCI), defined as PCI confined to culprit vessel lesions only; 2) multivessel PCI strategy (MV-PCI), defined as PCI of culprit vessel as well as ≥1 nonculprit vessel lesions; and 3) staged PCI strategy (staged PCI), defined as PCI confined to culprit vessel, after which ≥1 nonculprit vessel lesions are treated during staged procedures. Prospective and retrospective studies were included when research subjects were patients with STEMI and MVD undergoing PCI. The primary endpoint was short-term mortality. RESULTS: Four prospective and 14 retrospective studies involving 40,280 patients were included. Pairwise meta-analyses demonstrated that staged PCI was associated with lower short- and long-term mortality as compared with culprit PCI and MV-PCI and that MV-PCI was associated with highest mortality rates at both short- and long-term follow-up. In network analyses, staged PCI was also consistently associated with lower mortality. CONCLUSIONS: This meta-analysis supports current guidelines discouraging performance of multivessel primary PCI for STEMI. When significant nonculprit vessel lesions are suitable for PCI, they should only be treated during staged procedures.

A stochastic multicriteria model for evidence-based decision making in drug benefit-risk analysis.

Drug benefit-risk (BR) analysis is based on firm clinical evidence regarding various safety and efficacy outcomes. In this paper, we propose a new and more formal approach for constructing a supporting multi-criteria model that fully takes into account the evidence on efficacy and adverse drug reactions. Our approach is based on the stochastic multi-criteria acceptability analysis methodology, which allows us to compute the typical value judgments that support a decision, to quantify decision uncertainty, and to compute a comprehensive BR profile. We construct a multi-criteria model for the therapeutic group of second-generation antidepressants. We assess fluoxetine and venlafaxine together with placebo according to incidence of treatment response and three common adverse drug reactions by using data from a published study. Our model shows that there are clear trade-offs among the treatment alternatives.