Don't characterize replications as successes or failures

2018 ◽  
Vol 41 ◽  
Author(s):  
Andrew Gelman
Keyword(s):  
Treatment Effects ◽  
Meta Analysis ◽  
Raw Data ◽  
Continuous Measures

AbstractNo replication is truly direct, and I recommend moving away from the classification of replications as “direct” or “conceptual” to a framework in which we accept that treatment effects vary across conditions. Relatedly, we should stop labeling replications as successes or failures and instead use continuous measures to compare different studies, again using meta-analysis of raw data where possible.

scholarly journals AB0116 THE EFFECTS OF DISEASE MODIFYING ANTI-RHEUMATIC DRUGS ON PATIENT REPORTED OUTCOMES; PRELIMINARY RESULTS OF A SYSTEMATIC REVIEW AND NETWORK META-ANALYSIS

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 1087.1-1087
Author(s):  
M. Van den Dikkenberg, Msc ◽  
N. Luurssen-Masurel ◽  
M. Kuijper ◽  
M. R. Kok ◽  
P. De Jong ◽  
...  
Keyword(s):  
Systematic Review ◽  
Patient Reported Outcomes ◽  
Treatment Effects ◽  
Meta Analysis ◽  
Activity Limitations ◽  
Definitive Answer ◽  
Preliminary Results ◽  
Patient Reported ◽  
Standard Set ◽  
Disease Modifying

Background:The need to involve patient reported outcomes (PROs) in the management of rheumatoid arthritis (RA) increases, since PROs quantify patient relevant outcomes. Although PROs have been incorporated in the core-outcome sets in clinical trials, knowledge about the treatment effects on these PROs is scarce. Therefore, we performed a systematic review on the effects of disease modifying anti-rheumatic drugs (DMARDs), of any type, on relevant PRO domains mentioned in the ICHOM standard set. This might support rheumatologists and RA patients during treatment decisions.Objectives:To get insight in the treatment effects of DMARDs of any type on three PRO domains that matter to patients (pain, activity limitations and fatigue).Methods:A systematic review was performed in Embase, Medline, Web of Science, Cochrane and Google Scholar. Included were all studies that were published before August 2019 and showed DMARD treatment effects in RA on PROs that are part of the ICHOM standard set. Three Bayesian network meta-analyses were performed for the PRO domains pain, activity limitations and fatigue. Preliminary results of DMARDs (in)directly compared to placebo were visualized by forest plots using R.Results:The search strategy yielded n=5974 articles. After selection was performed by 2 independent researchers, n=70 individual articles representing n=53 studies were extracted, over the three PRO domains; pain (n=31), activity limitations (n=41) and fatigue (n=21). In all RCTs, PROs were only reported as secondary or tertiary endpoints. In figure 1, we show the effects on PROs for any type of DMARD investigated compared to placebo. Overall, DMARDs show a greater reduction in pain (standardized mean difference (SMD); -0.97 – -0.22) and most of them in activity limitations (SMD; -0.81 – 0.56). In fatigue, this clear direction is lacking (SMD; -0.86 – 3.5). csDMARDs and anti-TNF seem to perform slightly, but nog significantly, worse than other bDMARDs and tsDMARDs in the first two domains.Conclusion:Within in this systematic review we report a reduction for DMARDs of any type on the domains of pain and activity limitations compared to placebo. However, results are still preliminary and should be interpreted with care. A more comprehensive network analysis might give a more definitive answer which DMARD performs best.Figure 1.Disclosure of Interests:None declared

scholarly journals Can Vaccination Trigger Autoimmune Disorders? A Meta-Analysis

Vaccines ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 821
Author(s):  
Marek Petráš ◽  
Ivana Králová Lesná ◽  
Jana Dáňová ◽  
Alexander M. Čelko
Keyword(s):  
Meta Analysis ◽  
Autoimmune Disorders ◽  
Risk Of Bias ◽  
Extensive Literature ◽  
Primary Analysis ◽  
Measures Of Association ◽  
Assessment Development ◽  
Extensive Literature Search ◽  
The Stability

Vaccination as an important tool in the fight against infections has been suggested as a possible trigger of autoimmunity over the last decades. To confirm or refute this assumption, a Meta-analysis of Autoimmune Disorders Association With Immunization (MADAWI) was conducted. Included in the meta-analysis were a total of 144 studies published in 1968–2019 that were available in six databases and identified by an extensive literature search conducted on 30 November 2019. The risk of bias classification of the studies was performed using the Newcastle–Ottawa Quality Assessment Scale. The strength of evidence was assessed using the Grading of Recommendations Assessment, Development, and Evaluation. While our primary analysis was conducted in terms of measures of association employed in studies with a low risk of bias, the robustness of the MADAWI outcome was tested using measures independent of each study risk of bias. Additionally, subgroup analyses were performed to determine the stability of the outcome. The pooled association of 0.99 (95% confidence interval, 0.97–1.02), based on a total of 364 published estimates, confirmed an equivalent occurrence of autoimmune disorders in vaccinated and unvaccinated persons. The same level of association reported by studies independently of the risk of bias was supported by a sufficient number of studies, and no serious limitation, inconsistency, indirectness, imprecision, and publication bias. A sensitivity analysis did not reveal any discrepancy in the primary result. Current common vaccination is not the cause of any of the examined autoimmune disorders in the medium and long terms.

Evaluation of treatment effects in patients with endometrial cancer and POLE mutations: An individual patient data meta‐analysis

Cancer ◽  
2021 ◽  
Author(s):  
Jessica N. McAlpine ◽  
Derek S. Chiu ◽  
Remi A. Nout ◽  
David N. Church ◽  
Pascal Schmidt ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
Treatment Effects ◽  
Individual Patient Data ◽  
Meta Analysis ◽  
Patient Data

Kraepelin revisited: a reassessment and statistical analysis of dementia praecox and manic-depressive insanity in 1908

1993 ◽  
Vol 23 (4) ◽  
pp. 843-858 ◽  
Author(s):  
A. Jablensky ◽  
H. Hugler ◽  
M. Von Cranach ◽  
K. Kalinov
Keyword(s):  
Meta Analysis ◽  
Original Data ◽  
Dementia Praecox ◽  
Schizoaffective Disorders ◽  
Homogeneous Groups ◽  
Manic Depressive ◽  
Original Case ◽  
State Examination ◽  
Dichotomous Classification

SynopsisA meta-analysis was carried out on 53 cases of dementia praecox (DP) and 134 cases of manic-depressive insanity (MDI) originally diagnosed by Kraepelin or his collaborators in Munich in 1908. The original case material was coded in terms of Present State Examination syndromes and analysed statistically for internal consistency and discrimination between the two diagnostic entities. Kraepelin's DP and MDI were found to define homogeneous groups of disorders which could be clearly distinguished from one another. A CATEGO re-classification of the cases revealed an 80·2% concordance rate between Kraepelin's diagnoses and ICD-9. Cluster analysis of the original data reproduced closely Kraepelin's dichotomous classification of the psychoses but suggested that DP was a narrower concept than schizophrenia today, while MDI was a composite group including both ‘typical’ manic-depressive illnesses and schizoaffective disorders.

Meta-analysis of effect sizes reported at multiple time points: A multivariate approach

2012 ◽  
Vol 9 (5) ◽  
pp. 610-620 ◽  
Author(s):  
Thomas A Trikalinos ◽  
Ingram Olkin
Keyword(s):  
Random Effects ◽  
Treatment Effects ◽  
Multivariate Analyses ◽  
Meta Analysis ◽  
Effect Sizes ◽  
Multiple Time ◽  
Time Points ◽  
Fixed And Random Effects ◽  
Multiple Time Points ◽  
Meta Analyses

Background Many comparative studies report results at multiple time points. Such data are correlated because they pertain to the same patients, but are typically meta-analyzed as separate quantitative syntheses at each time point, ignoring the correlations between time points. Purpose To develop a meta-analytic approach that estimates treatment effects at successive time points and takes account of the stochastic dependencies of those effects. Methods We present both fixed and random effects methods for multivariate meta-analysis of effect sizes reported at multiple time points. We provide formulas for calculating the covariance (and correlations) of the effect sizes at successive time points for four common metrics (log odds ratio, log risk ratio, risk difference, and arcsine difference) based on data reported in the primary studies. We work through an example of a meta-analysis of 17 randomized trials of radiotherapy and chemotherapy versus radiotherapy alone for the postoperative treatment of patients with malignant gliomas, where in each trial survival is assessed at 6, 12, 18, and 24 months post randomization. We also provide software code for the main analyses described in the article. Results We discuss the estimation of fixed and random effects models and explore five options for the structure of the covariance matrix of the random effects. In the example, we compare separate (univariate) meta-analyses at each of the four time points with joint analyses across all four time points using the proposed methods. Although results of univariate and multivariate analyses are generally similar in the example, there are small differences in the magnitude of the effect sizes and the corresponding standard errors. We also discuss conditional multivariate analyses where one compares treatment effects at later time points given observed data at earlier time points. Limitations Simulation and empirical studies are needed to clarify the gains of multivariate analyses compared with separate meta-analyses under a variety of conditions. Conclusions Data reported at multiple time points are multivariate in nature and are efficiently analyzed using multivariate methods. The latter are an attractive alternative or complement to performing separate meta-analyses.

Phenotypic and Genotypic Associations Between Migraine and Lipoprotein Subtractions

Neurology ◽  
2021 ◽  
pp. 10.1212/WNL.0000000000012919
Author(s):  
Yanjun Guo ◽  
Iyas Daghlas ◽  
Padhraig Gormley ◽  
Franco Giulianini ◽  
Paul M Ridker ◽  
...  
Keyword(s):  
Genetic Correlation ◽  
Genetic Relationships ◽  
Meta Analysis ◽  
European Ancestry ◽  
Health Study ◽  
Lipoprotein Subfractions ◽  
Genetic Components ◽  
The Uk ◽  
Summary Data

Background and Objective:To evaluate phenotypic and genetic relationships between migraine and lipoprotein subfractions.Methods:We evaluated phenotypic associations between migraine and 19 lipoprotein subfractions measures in the Women’s Genome Health Study (WGHS, N=22,788). We then investigated genetic relationships between these traits using summary statistics from the International Headache Genetics Consortium (IHGC) for migraine (Ncase=54,552, Ncontrol=297,970) and combined summary data for lipoprotein subfractions (N up to 47,713).Results:There was a significant phenotypic association (odds ratio=1.27 [95% confidence interval:1.12-1.44]) and a significant genetic correlation at 0.18 (P=0.001) between migraine and triglyceride-rich lipoproteins (TRLP) concentration but not for LDL or HDL subfractions. Mendelian randomization (MR) estimates were largely null implying that pleiotropy rather than causality underlies the genetic correlation between migraine and lipoprotein subfractions. Pleiotropy was further supported in cross-trait meta-analysis revealing significant shared signals at four loci (chr2p21 harboring THADA, chr5q13.3 harboring HMGCR, chr6q22.31 harboring HEY2, and chr7q11.23 harboring MLXIPL) between migraine and lipoprotein subfractions. Three of these loci were replicated for migraine (P<0.05) in a smaller sample from the UK Biobank. The shared signal at chr5q13.3 colocalized with expression of HMGCR, ANKDD1B, and COL4A3BP in multiple tissues.Conclusions:The current study supports the association between certain lipoprotein subfractions, especially for TRLP, and migraine in populations of European ancestry. The corresponding shared genetic components may be help identify potential targets for future migraine therapeutics.Classification of Evidence:This study provides Class I evidence that migraine is significantly associated with some lipoprotein subfractions.

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