Individual response to antidepressants for depression in adults-a meta-analysis and simulation study

Background. The observation that some patients appear to respond better to antidepressants for depression than others encourages the assumption that the effect of antidepressants differs between individuals and that treatment can be personalized. To test this assumption, we compared the outcome variance in the group of patients receiving antidepressants with the outcome variance of the group of patients receiving placebo in randomized controlled trials (RCTs) of adults with major depressive disorder (MDD). An increased variance in the antidepressant group would indicate individual differences in response to antidepressants. In addition, we illustrate in a simulation study why attempts to personalize antidepressant treatment using RCTs might be misguided.Methods. We first illustrated the variance components of trials by simulating RCTs and crossover trials of antidepressants versus placebo. Second, we analyzed data of a large meta-analysis of antidepressants for depression, including a total of 222 placebo-controlled studies from the dataset that reported outcomes on the 17 or 21 item Hamilton Depression Rating Scale or the Montgomery-Åsberg Depression Rating Scale. We performed inverse variance, random-effects meta-analyses of the variability ratio (VR) between the antidepressant and placebo groups. Outcomes. The meta-analyses of the VR comprised 345 comparisons of 19 different antidepressants with placebo in a total of 61144 adults with an MDD diagnosis. Across all comparisons, we found no evidence for a larger variance in the antidepressant group compared with placebo overall (VR = 1.00, 95% CI: 0.98; 1.01, I2 = 0%) or for any individual antidepressant. Interpretation. Our findings did not provide empirical support for individual differences in response to antidepressants.

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Beta-binomial models for meta-analysis with binary outcomes: Variations, extensions, and additional insights from econometrics

Research Methods in Medicine & Health Sciences ◽

10.1177/2632084321996225 ◽

2021 ◽

pp. 263208432199622

Author(s):

Tim Mathes ◽

Oliver Kuss

Keyword(s):

Simulation Study ◽

Count Data ◽

Negative Binomial ◽

Meta Analysis ◽

Negative Binomial Regression ◽

Binary Outcomes ◽

Small Scale ◽

Panel Count Data ◽

Count Data Models ◽

Meta Analyses

Background Meta-analysis of systematically reviewed studies on interventions is the cornerstone of evidence based medicine. In the following, we will introduce the common-beta beta-binomial (BB) model for meta-analysis with binary outcomes and elucidate its equivalence to panel count data models. Methods We present a variation of the standard “common-rho” BB (BBST model) for meta-analysis, namely a “common-beta” BB model. This model has an interesting connection to fixed-effect negative binomial regression models (FE-NegBin) for panel count data. Using this equivalence, it is possible to estimate an extension of the FE-NegBin with an additional multiplicative overdispersion term (RE-NegBin), while preserving a closed form likelihood. An advantage due to the connection to econometric models is, that the models can be easily implemented because “standard” statistical software for panel count data can be used. We illustrate the methods with two real-world example datasets. Furthermore, we show the results of a small-scale simulation study that compares the new models to the BBST. The input parameters of the simulation were informed by actually performed meta-analysis. Results In both example data sets, the NegBin, in particular the RE-NegBin showed a smaller effect and had narrower 95%-confidence intervals. In our simulation study, median bias was negligible for all methods, but the upper quartile for median bias suggested that BBST is most affected by positive bias. Regarding coverage probability, BBST and the RE-NegBin model outperformed the FE-NegBin model. Conclusion For meta-analyses with binary outcomes, the considered common-beta BB models may be valuable extensions to the family of BB models.

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When does the use of individual patient data in network meta-analysis make a difference? A simulation study

BMC Medical Research Methodology ◽

10.1186/s12874-020-01198-2 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Steve Kanters ◽

Mohammad Ehsanul Karim ◽

Kristian Thorlund ◽

Aslam Anis ◽

Nick Bansback

Keyword(s):

Simulation Study ◽

Treatment Effect ◽

Mean Squared Error ◽

Individual Patient Data ◽

Meta Analysis ◽

Model Performance ◽

Patient Data ◽

Meta Regression ◽

Treatment Comparisons ◽

The Impact

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.

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Incorporating single-arm studies in meta-analysis of randomised controlled trials: a simulation study

BMC Medical Research Methodology ◽

10.1186/s12874-021-01301-1 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Janharpreet Singh ◽

Keith R. Abrams ◽

Sylwia Bujkiewicz

Keyword(s):

Simulation Study ◽

Randomised Controlled Trials ◽

Meta Analysis ◽

Health Technology ◽

Small Error ◽

Aggregate Data ◽

Controlled Trials ◽

Real World Data ◽

Study Heterogeneity ◽

Randomised Controlled

Abstract Background Use of real world data (RWD) from non-randomised studies (e.g. single-arm studies) is increasingly being explored to overcome issues associated with data from randomised controlled trials (RCTs). We aimed to compare methods for pairwise meta-analysis of RCTs and single-arm studies using aggregate data, via a simulation study and application to an illustrative example. Methods We considered contrast-based methods proposed by Begg & Pilote (1991) and arm-based methods by Zhang et al (2019). We performed a simulation study with scenarios varying (i) the proportion of RCTs and single-arm studies in the synthesis (ii) the magnitude of bias, and (iii) between-study heterogeneity. We also applied methods to data from a published health technology assessment (HTA), including three RCTs and 11 single-arm studies. Results Our simulation study showed that the hierarchical power and commensurate prior methods by Zhang et al provided a consistent reduction in uncertainty, whilst maintaining over-coverage and small error in scenarios where there was limited RCT data, bias and differences in between-study heterogeneity between the two sets of data. The contrast-based methods provided a reduction in uncertainty, but performed worse in terms of coverage and error, unless there was no marked difference in heterogeneity between the two sets of data. Conclusions The hierarchical power and commensurate prior methods provide the most robust approach to synthesising aggregate data from RCTs and single-arm studies, balancing the need to account for bias and differences in between-study heterogeneity, whilst reducing uncertainty in estimates. This work was restricted to considering a pairwise meta-analysis using aggregate data.

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Indicators of response to exercise training: a systematic review and meta-analysis

BMJ Open ◽

10.1136/bmjopen-2020-044676 ◽

2021 ◽

Vol 11 (7) ◽

pp. e044676

Author(s):

Arash Ardavani ◽

Hariz Aziz ◽

Bethan E Phillips ◽

Brett Doleman ◽

Imran Ramzan ◽

...

Keyword(s):

Systematic Review ◽

Exercise Training ◽

Meta Analysis ◽

Statistical Significance ◽

Response Rates ◽

Whole Body ◽

Individual Response ◽

Qualitative Synthesis ◽

Exercise Response ◽

High Intensity Interval

BackgroundMeans-based analysis of maximal rate of oxygen consumption (VO2max) has traditionally been used as the exercise response indicator to assess the efficacy of endurance (END), high intensity interval (HIIT) and resistance exercise training (RET) for improving cardiorespiratory fitness and whole-body health. However, considerable heterogeneity exists in the interindividual variability response to the same or different training modalities.ObjectivesWe performed a systematic review and meta-analysis to investigate exercise response rates in the context of VO2max: (1) in each training modality (END, HIIT and RET) versus controls, (2) in END versus either HIIT or RET and (3) exercise response rates as measured by VO2max versus other indicators of positive exercise response in each exercise modality.MethodsThree databases (EMBASE, MEDLINE, CENTRAL) and additional sources were searched. Both individual response rate and population average data were incorporated through continuous data, respectively. Of 3268 identified manuscripts, a total of 29 studies were suitable for qualitative synthesis and a further 22 for quantitative. Stratification based on intervention duration (less than 12 weeks; more than or equal to 12 weeks) was undertaken.ResultsA total of 62 data points were procured. Both END and HIIT training exhibited differential improvements in VO2max based on intervention duration. VO2max did not adequately differentiate between END and HIIT, irrespective of intervention length. Although none of the other exercise response indicators achieved statistical significance, LT and HRrest demonstrated common trajectories in pooled and separate analyses between modalities. RET data were highly limited. Heterogeneity was ubiquitous across all analyses.ConclusionsThe potential for LT and HRrest as indicators of exercise response requires further elucidation, in addition to the exploration of interventional and intrinsic sources of heterogeneity.

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Interval estimation of the overall treatment effect in random-effects meta-analyses: Recommendations from a simulation study comparing frequentist, Bayesian, and bootstrap methods

10.31219/osf.io/5zbh6 ◽

2020 ◽

Author(s):

Frank Weber ◽

Guido Knapp ◽

Anne Glass ◽

Günther Kundt ◽

Katja Ickstadt

Keyword(s):

Literature Review ◽

Random Effects ◽

Simulation Study ◽

Treatment Effect ◽

Profile Likelihood ◽

Meta Analysis ◽

Artery Bypass ◽

Interval Length ◽

Interval Estimators ◽

Meta Analyses

There exists a variety of interval estimators for the overall treatment effect in a random-effects meta-analysis. A recent literature review summarizing existing methods suggested that in most situations, the Hartung-Knapp/Sidik-Jonkman (HKSJ) method was preferable. However, a quantitative comparison of those methods in a common simulation study is still lacking. Thus, we conduct such a simulation study for continuous and binary outcomes, focusing on the medical field for application.Based on the literature review and some new theoretical considerations, a practicable number of interval estimators is selected for this comparison: the classical normal-approximation interval using the DerSimonian-Laird heterogeneity estimator, the HKSJ interval using either the Paule-Mandel or the Sidik-Jonkman heterogeneity estimator, the Skovgaard higher-order profile likelihood interval, a parametric bootstrap interval, and a Bayesian interval using different priors. We evaluate the performance measures (coverage and interval length) at specific points in the parameter space, i.e. not averaging over a prior distribution. In this sense, our study is conducted from a frequentist point of view.We confirm the main finding of the literature review, the general recommendation of the HKSJ method (here with the Sidik-Jonkman heterogeneity estimator). For meta-analyses including only 2 studies, the high length of the HKSJ interval limits its practical usage. In this case, the Bayesian interval using a weakly informative prior for the heterogeneity may help. Our recommendations are illustrated using a real-world meta-analysis dealing with the efficacy of an intramyocardial bone marrow stem cell transplantation during coronary artery bypass grafting.

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Individual Data Meta-analysis Provides No Evidence Of Individual Response Variation For Individuals Supplementing With Beta-alanine

Medicine & Science in Sports & Exercise ◽

10.1249/01.mss.0000762380.76625.5b ◽

2021 ◽

Vol 53 (8S) ◽

pp. 282-282

Author(s):

Gabriel Perri Esteves ◽

Paul Swinton ◽

Craig Sale ◽

Ruth James ◽

Guilherme Giannini Artioli ◽

...

Keyword(s):

Meta Analysis ◽

Individual Response ◽

Individual Data ◽

Beta Alanine ◽

Response Variation

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The Number of Patients and Events Required to Limit the Risk of Overestimation of Intervention Effects in Meta-Analysis—A Simulation Study

PLoS ONE ◽

10.1371/journal.pone.0025491 ◽

2011 ◽

Vol 6 (10) ◽

pp. e25491 ◽

Cited By ~ 219

Author(s):

Kristian Thorlund ◽

Georgina Imberger ◽

Michael Walsh ◽

Rong Chu ◽

Christian Gluud ◽

...

Keyword(s):

Simulation Study ◽

Meta Analysis ◽

Intervention Effects ◽

Number Of Patients

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Individual response to antidepressants for depression in adults – a meta-analysis and simulation study

10.31219/osf.io/srzx5 ◽

2019 ◽

Author(s):

Klaus Munkholm ◽

Stephanie Winkelbeiner ◽

Philipp Homan

Keyword(s):

Rating Scales ◽

Rating Scale ◽

Meta Analysis ◽

Simulated Data ◽

Individual Treatment ◽

Depression Symptom ◽

Individual Response ◽

Inverse Variance ◽

The Individual ◽

Symptom Rating

Background The observation that some patients appear to respond better to antidepressants for depression than others encourages the assumption that the effect of antidepressants differs between individuals and that treatment can be personalized. Objective To compare the outcome variance in patients receiving antidepressants with the outcome variance in patients receiving placebo in randomized controlled trials (RCTs) of adults with major depressive disorder (MDD) and to illustrate, using simulated data, components of variation of RCTs. Methods From a dataset comprising 522 RCTs of antidepressants for adult MDD, we selected the placebo-controlled RCTs reporting outcomes on the 17 or 21 item Hamilton Depression Rating Scale or the Montgomery-Asberg Depression Rating Scale and extracted the means and SDs of raw endpoint scores or baseline to endpoint changes scores on eligible depression symptom rating scales. We conducted inverse variance random-effects meta-analysis with the variability ratio (VR), the ratio between the outcome variance in the group of patients receiving antidepressants and the outcome variance in the group receiving placebo, as the primary outcome. An increased variance in the antidepressant group would indicate individual differences in response to antidepressants. Results We analysed 222 RCTs that investigated 19 different antidepressants compared with placebo in 345 comparisons, comprising a total of 61144 adults with an MDD diagnosis. Across all comparisons, the VR for raw endpoint scores was 0.98 (95% CI 0.96 to 1.00, I^2^ = 0%) and 1.00 (95% CI 0.99 to 1.02, I^2^ = 0%) for baseline-to-endpoint change scores. Conclusion Based on these data, we cannot reject the null hypothesis of equal variances in the antidepressant group and the placebo group. Given that RCTs cannot provide direct evidence for individual treatment effects, it may be most reasonable to assume that the average effect of antidepressants applies also to the individual patient.

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Histamine Antagonists to Temper the Cytokine Overproduction in Gastrointestinal Cells Infected by SARS-CoV-2

10.20944/preprints202004.0542.v1 ◽

2020 ◽

Author(s):

Han Geurdes ◽

Ivo Koutsaroff

Keyword(s):

Meta Analysis ◽

Individual Response ◽

Natural Immunity ◽

Cytokine Storm ◽

Histamine Antagonists ◽

Virus Particles ◽

Gastrointestinal Problems ◽

On Line ◽

H4 Receptor ◽

The Individual

The premise regarding COVID-19 disease is that it is a spectrum which begins with infection with viral SARS-CoV-2 exposure via airborne or oral virus particles. The individual response to it depends on many factors including co-morbid conditions. An important aspect of SARS-CoV-2 virus infection is the cytokine storm that develops after the infection. The immuno-chemical chaos created in this cytokine storm is to the benefit of the virus. In this meta analysis the authors explore ways to let the cytokine storm die down by looking into the role of histamine. Histamine is a metabolic product of the essential aminoacid histidine. Histamine has 4 known receptors: H1, H2, H3 and H4. The immunoglobulines IgE and IgM are indicative for a COVID-19 infection. This immune response is related to inflammation. Inflammation, in turn, runs mainly via histamine after e.g. virus inoculation. The goal of the meta-study is to gather evidence to primarily block the H4 receptor (H4R) in gastrointestinal cells to diminish the cytokine overproduction in the $\approx$ 30\% of the patients suffering from gastrointestinal problems caused by SARS-CoV-2. Our concept is as follows. If we can strike a careful balance between hampering the gastrointestinal spreading of the virus and histamine antagonists to tackle the cytokine storm, then the natural immunity can later on come on line again and attack the virus without being led astray by cytokine chaos. We will concentrate on H4R but also look at H1R and H2R related effects. The proposed substances in our systemic approach can be balanced for an effective early treatment. The nature of our work is by its method and results theoretical. In that respect we also may note the structural chemistry indol skeleton resemblance among a number of different drugs.

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