Meta-analyses of Adverse Effects Data Derived from Randomised Controlled Trials as Compared to Observational Studies: Methodological Overview

Golder, Su; Loke, Yoon K.; Bland, Martin

doi:10.1371/journal.pmed.1001026

Cited by 240 publications

(178 citation statements)

References 118 publications

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“…These study designs, in real-world clinics and with larger samples, allow for collecting data on adverse events not found in RCTs such as rare events, longterm effects, events that occur after treatment discontinuation, as well as events incurred by malpractice [59]. In addition, safety monitoring could be prioritized in pilot and case studies of new interventions.…”

Section: Accepted M Manuscriptmentioning

confidence: 99%

Reporting of harms in randomized controlled trials of psychological interventions for mental and behavioral disorders: A review of current practice

Jönsson

Alaie

Parling

et al. 2014

Contemporary Clinical Trials

141

123

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Section: Accepted M Manuscriptmentioning

confidence: 99%

Reporting of harms in randomized controlled trials of psychological interventions for mental and behavioral disorders: A review of current practice

Jönsson

Alaie

Parling

et al. 2014

Contemporary Clinical Trials

141

123

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“…Moreover, confounding is a matter of degree. For the association between a vegetarian diet and thrombosis, the confounding will be almost intractable, whereas it has been shown that side effects can reliably be assessed in observational studies, as confounding is only marginally an issue 11, 12. Importantly, standard statistical techniques (including propensity scores) cannot fully adjust for unmeasured confounding.…”

Section: Risk Of Bias Analysismentioning

confidence: 99%

Meta‐analysis: Key features, potentials and misunderstandings

Dekkers

2018

Research and Practice in Thrombosis and Haemostasis

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A meta‐analysis consists of a systematic approach to combine different studies in one design. Preferably, a protocol is written and published spelling out the research question, eligibility criteria, risk of bias assessment, and statistical approach. Included studies are likely to display some diversity regarding populations, calendar period, or treatment settings. Such diversity should be considered when deciding whether to combine (some) studies in a formal meta‐analysis. Statistically, the fixed effect model assumes that all studies estimate the same underlying true effect. This assumption is relaxed in a random effects model and given the expected study diversity a random effects approach will often be more realistic. In the absence of statistical heterogeneity, fixed and random effects models give identical estimates. Meta‐analyses are especially useful to provide a broader scope of the literature; they should carefully explore sources of between study heterogeneity and may show a treatment effect or an exposure–outcome association where individual studies are not powered. However, its validity largely depends on the validity of included studies.

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“…Recently, Golder studied how individual mood varies from hour-to-hour, day-to-day, and across seasons and cultures by measuring positive and negative effect in Twitter posts, using the lexicon LIWC [10]. Studies were included where a pooled relative measure of an adverse effect (odds ratio or risk ratio) from RCTs could be directly compared, using the ratio of odds ratios, with the pooled estimate for the same adverse effect arising from observational studies.…”

Section: Related Workmentioning

confidence: 99%

Two Step Approach for Emotion Detection on Twitter Data

Suhasini¹,

Badugu²

2018

IJCA

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ABSTRACT"Emotional states of individuals, also known as moods, are central to the expression of thoughts, ideas and opinions, and in turn impact attitudes and behavior". In this paper we have proposed a method which detects the emotion or mood of the tweet and classify the twitter message under appropriate emotional category. Our approach is a two-step approach, it is so called as it uses two approaches for the classification process, one is Rule Based approach and the other is Machine Learning approach. The first approach is the Rule Based Approach (RBA), our minor contributions in this approach are pre-processing, tagging, feature selection and Knowledge base creation. Feature selection is based on tags. Our second approach is Machine Learning Approach (MLA), in this the classifier is based on supervised machine learning algorithm called Naïve Bayes which requires labeled data. Naïve Bayes is used to detect and classify the emotion of a tweet. The output of RBA is given to MLA as input because MLA requires labeled data which we have already created through RBA. We have compared the accuracies of both the approaches, observed that, with the rule based approach we are able to classify the tweets with accuracy around 85% and with the machine learning approach the accuracy is around 88%. Machine learning approach performance is better than rule based approach, the performance has been improved as we have removed the error data while training the model. The approaches are involved with the concepts of Natural Language Processing, Artificial Intelligence, and Machine Learning for the development of the system. Our major contributions in this paper are detection of emotion for non hashtagged data and the labeled data creation for machine learning approach without manual creation.

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Meta-analyses of Adverse Effects Data Derived from Randomised Controlled Trials as Compared to Observational Studies: Methodological Overview

Cited by 240 publications

References 118 publications

Reporting of harms in randomized controlled trials of psychological interventions for mental and behavioral disorders: A review of current practice

Reporting of harms in randomized controlled trials of psychological interventions for mental and behavioral disorders: A review of current practice

Meta‐analysis: Key features, potentials and misunderstandings

Two Step Approach for Emotion Detection on Twitter Data

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