scholarly journals Comparing the Evidential Strength for Psychotropic Drugs: A Bayesian Meta-Analysis

2021 ◽  
Author(s):  
Merle-Marie Pittelkow ◽  
Ymkje Anna de Vries ◽  
Rei Monden ◽  
Jojanneke A. Bastiaansen ◽  
Don van Ravenzwaaij
Keyword(s):  
Psychotropic Drugs ◽  
Bayes Factor ◽  
Meta Analysis ◽  
Drug Approval ◽  
Clinical Decision ◽  
Marketing Approval ◽  
Strength Of Evidence ◽  
Fda Approval ◽  
Formal Policy

AbstractObjectiveApproval and prescription of drugs should be informed by the strength of evidence for efficacy. While there is no formal policy towards different standards for drug approval, the typical strength of evidence might differ for different psychotropic drug groups. Using a Bayesian framework, we examine (1) whether psychotropic drugs are supported by substantial evidence (at the time of Food and Drug Administration [FDA] approval), and (2) whether there are systematic differences across drug groups.MethodsData from short-term, placebo-controlled phase II/III clinical trials for 15 antipsychotics, 16 antidepressants for depression, nine antidepressants for anxiety, and 20 drugs for ADHD were extracted from FDA reviews evaluating efficacy prior to marketing approval. Bayesian model-averaged meta-analysis was performed and strength of evidence was quantified with the Bayes factor (BFBMA).ResultsWe observed substantial variation in strength of evidence and trialling between approved psychotropic drugs: Median evidential strength was extremely strong for ADHD medication (BFBMA = 1820.4), but considerably lower and more frequently classified as weak or moderate for antidepressants for both depression (BFBMA = 94.2) and anxiety (BFBMA = 49.8). Differences might be accounted for by varying median effect sizes (schizophrenia: ESBMA = 0.45, depression: ESBMA = 0.30, anxiety: ESBMA = 0.37, ADHD: ESBMA = 0.72), sample sizes (schizophrenia: N = 324, depression: N = 218, anxiety: N = 254, ADHD: N = 189.5), and numbers of trials (schizophrenia: Nr = 3, depression: Nr = 5.5, anxiety: Nr = 3, ADHD: Nr = 2).LimitationsThe analysis only included pre-marketing studies.ConclusionEvidential strength varied across drug groups: Although most psychotropic drugs were supported by strong evidence at the time of approval, some drugs only had moderate or even ambiguous evidence. These results show the need for more systematic quantification and classification of statistical evidence for psychotropic drugs, and for transparent and clear communication of evidential strength toward clinical decision makers.Registrationhttps://osf.io/5jn2d

scholarly journals Comparing the evidential strength for psychotropic drugs: a Bayesian meta-analysis

2021 ◽  
pp. 1-10
Author(s):  
Merle-Marie Pittelkow ◽  
Ymkje Anna de Vries ◽  
Rei Monden ◽  
Jojanneke A. Bastiaansen ◽  
Don van Ravenzwaaij
Keyword(s):  
Psychotropic Drugs ◽  
Meta Analysis ◽  
Clinical Decision ◽  
Decision Makers ◽  
Effect Sizes ◽  
Substantial Evidence ◽  
Short Term ◽  
Strength Of Evidence ◽  
Hyperactivity Disorder

Abstract Approval and prescription of psychotropic drugs should be informed by the strength of evidence for efficacy. Using a Bayesian framework, we examined (1) whether psychotropic drugs are supported by substantial evidence (at the time of approval by the Food and Drug Administration), and (2) whether there are systematic differences across drug groups. Data from short-term, placebo-controlled phase II/III clinical trials for 15 antipsychotics, 16 antidepressants for depression, nine antidepressants for anxiety, and 20 drugs for attention deficit hyperactivity disorder (ADHD) were extracted from FDA reviews. Bayesian model-averaged meta-analysis was performed and strength of evidence was quantified (i.e. BFBMA). Strength of evidence and trialling varied between drugs. Median evidential strength was extreme for ADHD medication (BFBMA = 1820.4), moderate for antipsychotics (BFBMA = 365.4), and considerably lower and more frequently classified as weak or moderate for antidepressants for depression (BFBMA = 94.2) and anxiety (BFBMA = 49.8). Varying median effect sizes (ESschizophrenia = 0.45, ESdepression = 0.30, ESanxiety = 0.37, ESADHD = 0.72), sample sizes (Nschizophrenia = 324, Ndepression = 218, Nanxiety = 254, NADHD = 189.5), and numbers of trials (kschizophrenia = 3, kdepression = 5.5, kanxiety = 3, kADHD = 2) might account for differences. Although most drugs were supported by strong evidence at the time of approval, some only had moderate or ambiguous evidence. These results show the need for more systematic quantification and classification of statistical evidence for psychotropic drugs. Evidential strength should be communicated transparently and clearly towards clinical decision makers.

Reassessing the net health benefit (NHB) of FDA approved cancer drugs with evolution of evidence using the American Society of Clinical Oncology Value Framework (ASCO-VF).

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 7011-7011
Author(s):  
Seanthel Delos Santos ◽  
Noah Witzke ◽  
Vanessa Sarah Arciero ◽  
Amanda Putri Rahmadian ◽  
Louis Everest ◽  
...  
Keyword(s):  
Health Benefit ◽  
Progression Free Survival ◽  
Drug Approval ◽  
Secondary Endpoint ◽  
Substantial Benefit ◽  
Primary Endpoints ◽  
Fda Approval ◽  
American Society ◽  
Approved Drugs

7011 Background: Regulatory approval of oncology drugs are often based on data presented in the primary publication of clinical trials (CT). However, clinically relevant data, such as long-term overall survival (OS) and quality of life (QOL), are often reported in subsequent publications. Therefore, this study aimed to evaluate the ASCO-VF NHB at the time of drug approval and over time as further evidence is published. Methods: All FDA approved oncology drug indications from 01/06-12/16 were reviewed to identify CTs that were scorable using the ASCO-VF version 2. Subsequent publications of included CTs relevant for scoring were identified from Web of Science with a follow-up time of 3 years from approval. Using ASCO-defined threshold scores of ≤40 for low benefit and ≥45 for substantial benefit, changes in classification of benefit were assessed at 3-years post-FDA approval. Results: We identified 57 FDA approved indications (40.4% OS, 59.6% progression-free survival (PFS) as primary endpoints) with scorable ASCO-VF CTs. Among those 57 indications, 36.8% at the time of FDA approval demonstrated substantial benefit, 10.5% demonstrated intermediate benefit, and 52.6% demonstrated low benefit. We then identified 96 subsequent publications relevant to scoring within 3-years of FDA approval, consisting of primary endpoint updates (29.2%; 14.6% OS, 12.5% PFS), secondary endpoint updates (44.8%; 16.7% OS, 7.3% PFS), new reporting of secondary endpoint (4.2% OS), safety updates (28.1%), and QOL reporting (43.8%). Upon reassessment of the NHB in subsequent publications, there was an overall change from initial classification of benefit in 36.8% of trials (17.5% became substantial, 8.8% became low, and 10.5% became intermediate). Changes in scores were mainly the result of an updated hazard ratio (35.1%), change in scoring endpoints from PFS to OS as per ASCO-VF endpoint hierarchy (8.8%), toxicity updates (57.9%), new tail of the curve bonus (12.3%), palliation bonus (14.0%), or QOL bonus (22.8%). Overall, at reassessment at 3 years post-FDA approval, 42.1% were substantial, 10.5% were intermediate, and 47.3% were low benefit. Conclusions: Only a modest proportion of FDA approved drugs have demonstrated substantial NHB at time of approval. As further evidence was published, a substantial proportion of indications have a change in classification of NHB, resulting in a small increase in the overall proportion of indications being deemed to have substantial benefit at 3 years post-approval.

scholarly journals The effects of clinical decision support system for prescribing medication on patient outcomes and physician practice performance: a systematic review and meta-analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sharare Taheri Moghadam ◽  
Farahnaz Sadoughi ◽  
Farnia Velayati ◽  
Seyed Jafar Ehsanzadeh ◽  
Shayan Poursharif
Keyword(s):  
Systematic Review ◽  
Decision Support ◽  
Patient Outcomes ◽  
Clinical Decision Support ◽  
Meta Analysis ◽  
Clinical Decision ◽  
Cochrane Library ◽  
Physician Practice ◽  
Positive Effects ◽  
Practice Performance

Abstract Background Clinical Decision Support Systems (CDSSs) for Prescribing are one of the innovations designed to improve physician practice performance and patient outcomes by reducing prescription errors. This study was therefore conducted to examine the effects of various CDSSs on physician practice performance and patient outcomes. Methods This systematic review was carried out by searching PubMed, Embase, Web of Science, Scopus, and Cochrane Library from 2005 to 2019. The studies were independently reviewed by two researchers. Any discrepancies in the eligibility of the studies between the two researchers were then resolved by consulting the third researcher. In the next step, we performed a meta-analysis based on medication subgroups, CDSS-type subgroups, and outcome categories. Also, we provided the narrative style of the findings. In the meantime, we used a random-effects model to estimate the effects of CDSS on patient outcomes and physician practice performance with a 95% confidence interval. Q statistics and I2 were then used to calculate heterogeneity. Results On the basis of the inclusion criteria, 45 studies were qualified for analysis in this study. CDSS for prescription drugs/COPE has been used for various diseases such as cardiovascular diseases, hypertension, diabetes, gastrointestinal and respiratory diseases, AIDS, appendicitis, kidney disease, malaria, high blood potassium, and mental diseases. In the meantime, other cases such as concurrent prescribing of multiple medications for patients and their effects on the above-mentioned results have been analyzed. The study shows that in some cases the use of CDSS has beneficial effects on patient outcomes and physician practice performance (std diff in means = 0.084, 95% CI 0.067 to 0.102). It was also statistically significant for outcome categories such as those demonstrating better results for physician practice performance and patient outcomes or both. However, there was no significant difference between some other cases and traditional approaches. We assume that this may be due to the disease type, the quantity, and the type of CDSS criteria that affected the comparison. Overall, the results of this study show positive effects on performance for all forms of CDSSs. Conclusions Our results indicate that the positive effects of the CDSS can be due to factors such as user-friendliness, compliance with clinical guidelines, patient and physician cooperation, integration of electronic health records, CDSS, and pharmaceutical systems, consideration of the views of physicians in assessing the importance of CDSS alerts, and the real-time alerts in the prescription.

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.

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.

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.

Do sexist jokes increase rape proclivity among males high in hostile sexism? Evidence from two pre-registered direct replications of Thomae & Viki (2013)

2021 ◽  
Author(s):  
Neil McLatchie ◽  
Manuela Thomae
Keyword(s):  
Bayes Factor ◽  
Alternative Hypothesis ◽  
Meta Analysis ◽  
Statistical Significance ◽  
Original Study ◽  
Bayes Factors ◽  
Hostile Sexism ◽  
Replication Studies ◽  
Open Research ◽  
Rape Proclivity

Thomae and Viki (2013) reported that increased exposure to sexist humour can increase rape proclivity among males, specifically those who score high on measures of Hostile Sexism. Here we report two pre-registered direct replications (N = 530) of Study 2 from Thomae and Viki (2013) and assess replicability via (i) statistical significance, (ii) Bayes factors, (iii) the small-telescope approach, and (iv) an internal meta-analysis across the original and replication studies. The original results were not supported by any of the approaches. Combining the original study and the replications yielded moderate evidence in support of the null over the alternative hypothesis with a Bayes factor of B = 0.13. In light of the combined evidence, we encourage researchers to exercise caution before claiming that brief exposure to sexist humour increases male’s proclivity towards rape, until further pre-registered and open research demonstrates the effect is reliably reproducible.

Enrollment disparities in cancer clinical trials leading to FDA approvals between 2008 and 2020.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e18501-e18501
Author(s):  
Ryan Huu-Tuan Nguyen ◽  
Yomaira Silva ◽  
Vijayakrishna K. Gadi
Keyword(s):  
Prostate Cancer ◽  
United States ◽  
Clinical Trials ◽  
Drug Approval ◽  
The United States ◽  
Cancer Clinical Trials ◽  
Cancer Prevalence ◽  
Fda Approval ◽  
The Us ◽  
Enrollment Data

e18501 Background: Cancer clinical trials based in the United States (US) have lacked adequate representation of racial and ethnic minorities, the elderly, and women. Pivotal clinical trials leading to United States Food and Drug Administration (FDA) approval are often multi-national trials and may also lack generalizability to underrepresented populations in the United States. We determined the racial, ethnic, age, and sex enrollment in pivotal trials relative to the US cancer population. Methods: We reviewed the FDA’s Drug Approvals and Databases for novel and new use drug approvals for breast, colorectal, lung, and prostate cancer indications from 2008 through 2020. Drugs@FDA was searched for drug approval summaries and FDA labels to identify clinical trials used to justify clinical efficacy that led to FDA approval. For eligible trials, enrollment data were obtained from FDA approval summaries, FDA labels, ClinicalTrials.gov, and corresponding journal manuscripts. Enrollment Fraction (EF) was calculated as enrollment in identified clinical trials divided by 2017 SEER cancer prevalence. All data sources were publicly available. Results: From 2008 through 2020, 60 drugs received novel or new use drug approval for breast, colorectal, lung, or prostate cancer indications based on 66 clinical trials with a total enrollment of 36,830. North America accounted for 9,259 (31%) enrollees of the 73% of trials reporting location of enrollment. Racial demographics were reported in 78% of manuscripts, 66% of ClinicalTrials.gov pages, and 98% of FDA labels or approval summaries. Compared with a 0.4% enrollment fraction among White patients, lower enrollment fractions were noted in Hispanic (0.2%, odds ratio [OR] vs White, 0.46; 95% confidence interval [CI], 0.43 to 0.49, P< 0.001) and Black (0.1%, OR 0.29; 95% CI 0.28 to 0.31, P< 0.001) patients. Elderly patients (age ≥ 65 years) were less likely than younger patients to be enrollees (EF 0.3% vs 0.9%, OR 0.27; 95% CI 0.26 to 0.27, P< 0.001) despite accounting for 61.3% of cancer prevalence. For colorectal and lung cancer trials, females were less likely than males (EF 0.7% vs 1.1%, OR 0.66; 95% CI 0.63 to 0.68, P< 0.001) to be enrolled. Conclusions: Black, Hispanic, elderly, and female patients were less likely to enroll in cancer clinical trials leading to FDA approvals from 2008 to 2020. Race and geographic enrollment data were inconsistently reported in journal manuscripts and ClinicalTrials.gov. The lack of appropriate representation of specific patient populations in these key clinical trials limits their generalizability. Future efforts must be made to ensure equitable access, representation, and reporting of enrollees that adequately represent the US population of patients with cancer.

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