1,721,084 research outputs found
Statistics in pills: meta-analysis of rare events.
Full text linkMeta-analysis of rare events requires special considerations regarding which statistical method to use. This is because standard meta-analytical models are not well suited for the task, especially when some of the identified studies have reported zero events in one or more treatment groups
Practical guide to the meta-analysis of rare events.
Full text linkOBJECTIVE
Meta-analysing studies with low event rates is challenging as some of the standard methods for meta-analysis are not well suited to handle rare outcomes. This is more evident when some studies have zero events in one or both treatment groups. In this article, we discuss why rare events require special attention in meta-analysis, we present an overview of some approaches suitable for meta-analysing rare events and we provide practical recommendations for their use.
METHODS
We go through several models suggested in the literature for performing a rare events meta-analysis, highlighting their respective advantages and limitations. We illustrate these models using a published example from mental health. We provide the software code needed to perform all analyses in the appendix.
RESULTS
Different methods may give different results, and using a suboptimal approach may lead to erroneous conclusions. When data are very sparse, the choice between the available methods may have a large impact on the results. Methods that use the so-called continuity correction (eg, adding 0.5 to the number of events and non-events in studies with zero events in one treatment group) may lead to biased estimates.
CONCLUSIONS
Researchers should define the primary analysis a priori, in order to avoid selective reporting. A sensitivity analysis using a range of methods should be used to assess the robustness of results. Suboptimal methods such as using a continuity correction should be avoided
Meta-analysis of the prevalence of attention-deficit hyperactivity disorder in prison: A comment on Fazel and Favril (2024) and reanalysis of the data.
Full text linkBACKGROUND
Fazel and Favril presented a reanalysis of our previously published systematic review and meta-analysis on the prevalence of attention deficit hyperactivity disorder (ADHD) in prison.
AIMS
The current paper addresses some of the criticisms of Fazel and Favril on our meta-analysis and presents a reanalysis of the data, focusing on adult detained persons.
METHODS
We conducted a meta-regression on 28 studies (n = 7710) to estimae the pooled prevalence of ADHD.
RESULTS
This reanalysis yielded a pooled estimate of 22.2% for the prevalence of ADHD (95% confidence interval [CI]: 15.7; 28.6), which disagrees with the estimate given by Fazel and Favril (8.3%, 95% CI: 3.8; 12.8).
CONCLUSION
We argue that the ADHD prevalence provided by Fazel and Favril was an underestimate due to their use of too restrictive exclusion criteria and suboptimal analysis methods. Our reanalysis on detained adults suggests a higher ADHD prevalence, which highlights the need to diagnose and treat ADHD in prison
Evidence-synthesis methods for personalizing the choice of treatment.
No full textBackground
This thesis comprises work done in several research areas, including meta-analysis, network
meta-analysis and prediction modelling. Below, I briefly provide some background for each
of these areas.
Meta-analysis of individual patient data (IPD) from randomized controlled trials (RCTs) can
potentially be used to identify whether treatment effects substantially differ across clinically
important subgroups and to potentially pinpoint the best treatment for each patient. Statistical
methods for IPD meta-analysis have been established. However, RCTs often collect
information on a large number of patient-level variables (covariates), some of which might be
unrelated to the outcome of interest. Including too many covariates in an IPD meta-analysis
model might lead to worse estimates, and might hinder interpretation of results. Currently
there is a lack of guidance on how to select covariates to include in an IPD meta-analysis
model.
In addition, there has been growing interest in using data from non-randomized studies (NRS)
to complement evidence from RCTs in medical decision-making. This is because, although
RCTs are the best source of evidence regarding relative treatment effects, they often employ
strict experimental settings, which may hamper their ability to predict outcomes in ‘realworld’
clinical settings. Currently, there is a gap in methods for combining IPD from RCTs
and NRS, when aiming to make patient-specific predictions about the real-world effects of
medical interventions.
Moreover, clinical prediction models are widely used in modern clinical practice. Such
models are often developed using 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, i.e., when not all
studies collect all predictors of interest. This situation poses challenges in model
development.
Finally, network meta-analysis (NMA) can be used to compare multiple competing
treatments for the same disease. In practice, usually a range of outcomes are of interest. As
the number of outcomes increases, summarizing results from multiple NMAs becomes a nontrivial
task, especially for larger networks. In addition, NMAs provide results in terms of
relative effect measures that can be difficult to interpret and apply in every-day clinical
practice, such as the odds ratios.
Aims
This thesis has four research aims.
The first aim was to explore whether a systematic approach to the selection of treatmentcovariate
interactions in an IPD meta-analysis can lead to better estimates of patient-specific
treatment effects.
The second aim was to describe a general framework for developing models that combine
individual patient data from RCTs and NRS when aiming to predict outcomes for a set of
competing medical interventions applied in real-world clinical settings.
The third aim was to explore approaches that can be used to develop prediction models for
continuous outcomes, when not all studies collect all predictors of interest, i.e. resulting in
systematically missing predictors.
The fourth aim was to facilitate the clinical decision-making process by proposing a new
graphical tool, the Kilim plot, for presenting results from NMA on multiple outcomes.
Methods
For the first aim, we compared in simulations the standard approach to IPD meta-analysis (no
variable selection, all treatment-covariate interactions included in the model) with six
alternative methods: stepwise regression, and five regression methods that perform shrinkage
on treatment-covariate interactions. To illustrate our methods, we used dataset from
cardiology comparing new generation drug-eluting and bare-metal stents for percutaneous
coronary intervention and from psychiatry comparing antidepressant treatment of major
depression.
For the second aim, we developed six meta-analytical models and a simpler model for
making predictions about patients in real world settings. We focused on Bayesian approaches
and utilized methods such as shrinkage, calibration of intercept and main effects of
covariates, and weighting approaches to account for different study designs. We used a
dataset of patients with rheumatoid arthritis obtained from three RCTs and two registries to
illustrate our methods.
For the third aim, we compared four approaches: a naïve approach, where the model is
developed using only predictors measured in all studies; a multiple imputation approach that
ignores patient allocation in studies; a multiple imputation approach that accounts for study
allocation; 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.
For the fourth aim, we worked on developing a new plot that compactly summarizes results
on all treatments and all outcomes; it provides information regarding the strength of the
statistical evidence of treatments, while it illustrates absolute, rather than relative, effects of
interventions.
Results
For the first aim, exploring a range of scenarios, we found in simulations that shrinkage
methods performed well for both continuous and dichotomous outcomes, for a variety of
settings. We exemplified all methods in two real examples and saw that using more advanced
methods may lead to different estimates of relative treatment effects.
For the second aim, we developed several evidence-synthesis models. We found that, for our
example, models that pool information from both RCTs and non-randomized studies might
provide the best predictions for patients in a new setting.
For the third aim, we found that in simulations existing multiple imputation methods and our
new method outperform the naïve approach. In several scenarios, our method outperformed
imputation methods, especially for few studies, when predictor effects were small, and in
case of large heterogeneity.
For the fourth aim, we developed the Kilim plot which provide a holistic view of the
available evidence expressed in terms of absolute treatment effects and their corresponding
strength of statistical evidence.
Conclusion
From the first project, we conclude that variable selection is essential in meta-analyzing IPD
from multiple RCTs, especially when there are many reported covariates. Both frequentist
and Bayesian variable selection methods can be used, as long as the information regarding
study allocation of patients in studies is included in the model.
In the second project, we saw that the gain in predictive performance obtained from models
combining RCTs and NRS was modest in our clinical example. Nevertheless, the illustration
of different modelling approaches and the considerations regarding different cross-validation
methods that we provide may be valuable to inform future studies aiming to predict realworld
outcomes of competing interventions.
Based on the results of the third project, we recommend researchers faced with systematically
missing predictors to select among the different methods after using both internal and
internal-external cross-validation approaches. We think that our new ensemble method offers
a potentially powerful alternative to researchers, and that it might be especially useful in the
common case of having IPD from only a handful of studies, reporting different sets of
predictors.
For the fourth aim, we conclude that the Kilim plot can be a valuable aid in summarizing and
communicating results from NMAs on multiple outcomes. It can be especially useful for
larger networks, for the case of many outcomes, and when aiming to communicate NMA
results with patients and/or clinicians, so as to facilitate every-day clinical practice
Predicting treatment effects in unipolar depression: A meta-review.
Full text linkThere is increasing interest in clinical prediction models in psychiatry, which focus on developing multivariate algorithms to guide personalized diagnostic or management decisions. The main target of these models is the prediction of treatment response to different antidepressant therapies. This is because the ability to predict response based on patients' personal data may allow clinicians to make improved treatment decisions, and to provide more efficacious or more tolerable medications to the right patient. We searched the literature for systematic reviews about treatment prediction in the context of existing treatment modalities for adult unipolar depression, until July 2019. Treatment effect is defined broadly to include efficacy, safety, tolerability and acceptability outcomes. We first focused on the identification of individual predictor variables that might predict treatment response, and second, we considered multivariate clinical prediction models. Our meta-review included a total of 10 systematic reviews; seven (from 2014 to 2018) focusing on individual predictor variables and three focusing on clinical prediction models. These identified a number of sociodemographic, phenomenological, clinical, neuroimaging, remote monitoring, genetic and serum marker variables as possible predictor variables for treatment response, alongside statistical and machine-learning approaches to clinical prediction model development. Effect sizes for individual predictor variables were generally small and clinical prediction models had generally not been validated in external populations. There is a need for rigorous model validation in large external data-sets to prove the clinical utility of models. We also discuss potential future avenues in the field of personalized psychiatry, particularly the combination of multiple sources of data and the emerging field of artificial intelligence and digital mental health to identify new individual predictor variables
Second-generation antipsychotics and seizures - a systematic review and meta-analysis of serious adverse events in randomized controlled trials.
Full text linkSeizures are suspected to be side effects of antipsychotics. To examine a possible causal relationship, we compared the risk of seizures on second-generation antipsychotics to the risk on placebo in randomized controlled clinical trials (RCTs) across diagnostic groups. The primary outcome was any seizure reported as International Conference on Harmonisation-Good Clinical Practice (ICH-GCP)-defined serious adverse event (SAEs). The risk ratio (RR) with antipsychotics versus placebo was synthesized in a pairwise common effects Mantel-Haenszel meta-analysis. For 314 of 597 idenitified placebo-controlled RCTs information about all SAEs could be retrieved from publications, original investigators, pharmaceutical companies and the European Medical Agency. In those, 37 seizures occurred in 42,600 participants on antipsychotics (0.09%) and 28 in 25,042 participants on placebo (0.11%). The meta-analytic results (RR 0,68; 95% Confidence Interval 0.41-1.12) indicated a reduced risk on antipsychotics with a confidence interval including no difference (i.e. RR=1). Neither in sensitivity analyses (excluding events in the safety-follow-up of trials or first-generation antipsychotics; using odds ratios) nor in subgroup analyses (on specific antipsychotics, drug combinations, diagnostic categories, age groups, and study duration) there was evidence for an increased risk on antipsychotics, except for some weak indications of an increased risk on antipsychotics in older and/or demented participants (RRs 1.11 and 1.48, respectively, but with 95% CIs of 0.35-3.49 and 0.41-5.26 including no difference and subgroup tests with p=0.54 and p=0.66 not indicating differences between age groups or diagnostic categories). Consequently, there are no indications that second-generation antipsychotics cause seizures in middle-aged adults and children in most diagnostic groups; rather our results provide some weak evidence for a protective effect. However, there was no data on SAEs available for clozapine, for which observational studies provide the strongest associations with increased seizure rates, and for older and/or demented patients a small additional risk on antipsychotics cannot be excluded
The dark side of the force: multiplicity issues in network meta-analysis and how to address them.
Full text linkStandard models for network meta-analysis simultaneously estimate multiple relative treatment effects. In practice, after estimation, these multiple estimates usually pass through a formal or informal selection procedure, e.g. when researchers draw conclusions about the effects of the best performing treatment in the network. In this paper, we present theoretical arguments as well as results from simulations to illustrate how such practices might lead to exaggerated and overconfident statements regarding relative treatment effects. We discuss how the issue can be addressed via multi-level Bayesian modeling, where treatment effects are modeled exchangeably, and hence estimates are shrunk away from large values. We present a set of alternative models for network meta-analysis, and we show in simulations that in several scenarios, such models perform better than the usual network meta-analysis model
Comparative Efficacy and Acceptability of Treatment Strategies for Antipsychotic-Induced Akathisia: A Systematic Review and Network Meta-analysis.
No full textBACKGROUND
Antipsychotics are the treatment of choice for schizophrenia, but they often induce akathisia. However, comparative efficacy of treatment strategies for akathisia remains unclear.
DESIGN
We performed a systematic review and network meta-analyses (PROSPERO CRD42023450720). We searched multiple databases on July 24, 2023. We included randomized clinical trials comparing 1 or more treatment strategies for antipsychotic-induced akathisia against each other or control conditions. We included adults with schizophrenia or other psychiatric disorders treated with antipsychotics. The primary outcome was akathisia severity at posttreatment. Secondary outcomes included akathisia response, all-cause dropout, psychotic symptoms, and long-term akathisia severity. We synthesized data in random effects frequentist network meta-analyses and assessed confidence in the evidence using CINeMA.
RESULTS
We identified 19 trials with 661 randomized participants (mean age 35.9 [standard deviation 12.0]; 36.7% [195 of 532] women). No trials examined dose reduction or switching of antipsychotics. Findings suggested 5-HT2A antagonists (k = 6, n = 108; standardized mean difference [SMD] -1.07 [95% confidence interval, -1.42; -0.71]) and beta-blockers (k = 8, n = 105; SMD -0.46 [-0.85; -0.07]) may improve akathisia severity, but confidence in the evidence was deemed low. We also found that benzodiazepines (k = 2, n = 13; SMD -1.62 [-2.64; -0.59]) and vitamin B6 (k = 3, n = 67; SMD -0.99 [-1.49; -0.50]) might also be beneficial, but confidence in the evidence was very low. Analyses of secondary outcomes did not provide additional insights.
CONCLUSIONS
Our findings suggest that 5-HT2A antagonists, beta-blockers, and with a lesser certainty, benzodiazepines, and vitamin B6 might improve akathisia. Given the low to very low confidence in the evidence of add-on agents and the absence of evidence of their long-term efficacy, careful consideration of side effects is warranted. These recommendations are extremely preliminary and further trials are needed
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