Primary versus secondary source of data in observational studies and heterogeneity in meta-analyses of drug effects: a survey of major medical journals

Prada-Ramallal, Guillermo; Roque, Fátima; Herdeiro, María Teresa; Takkouche, Bahi; Figueiras, Adolfo

doi:10.1186/s12874-018-0561-3

Cited by 10 publications

(6 citation statements)

References 69 publications

(105 reference statements)

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“…While dispensing records are more detailed in measurement of exposure (though they do not record the over-the-counter or out-of-pocket consumption at an individual level), they nonetheless lack outcome variables [ 1 , 3 , 58 , 59 ]. It is therefore important to link both types of data sources [ 60 , 61 ] and consider, when necessary, the use of additional data collected expressly for research purposes [ 15 , 62 , 63 ], to avoid errors that may generate misleading conclusions [ 64 , 65 ].…”

Section: Discussionmentioning

confidence: 99%

Bias in pharmacoepidemiologic studies using secondary health care databases: a scoping review

Prada-Ramallal

Takkouche

Figueiras

2019

BMC Med Res Methodol

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103

111

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BackgroundThe availability of clinical and therapeutic data drawn from medical records and administrative databases has entailed new opportunities for clinical and epidemiologic research. However, these databases present inherent limitations which may render them prone to new biases. We aimed to conduct a structured review of biases specific to observational clinical studies based on secondary databases, and to propose strategies for the mitigation of those biases.MethodsScoping review of the scientific literature published during the period 2000–2018 through an automated search of MEDLINE, EMBASE and Web of Science, supplemented with manually cross-checking of reference lists. We included opinion essays, methodological reviews, analyses or simulation studies, as well as letters to the editor or retractions, the principal objective of which was to highlight the existence of some type of bias in pharmacoepidemiologic studies using secondary databases.ResultsA total of 117 articles were included. An increasing trend in the number of publications concerning the potential limitations of secondary databases was observed over time and across medical research disciplines. Confounding was the most reported category of bias (63.2% of articles), followed by selection and measurement biases (47.0% and 46.2% respectively). Confounding by indication (32.5%), unmeasured/residual confounding (28.2%), outcome misclassification (28.2%) and “immortal time” bias (25.6%) were the subcategories most frequently mentioned.ConclusionsSuboptimal use of secondary databases in pharmacoepidemiologic studies has introduced biases in the studies, which may have led to erroneous conclusions. Methods to mitigate biases are available and must be considered in the design, analysis and interpretation phases of studies using these data sources.Electronic supplementary materialThe online version of this article (10.1186/s12874-019-0695-y) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Bias in pharmacoepidemiologic studies using secondary health care databases: a scoping review

Prada-Ramallal

Takkouche

Figueiras

2019

BMC Med Res Methodol

Self Cite

103

111

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“…We defined primary data as information collected directly by the researchers using interviews –personal or by telephone– or self-administered questionnaires ( 20 ). Secondary data were defined as data that were previously collected for other purposes than for the study at hand ( 20 ).…”

Section: Methodsmentioning

confidence: 99%

Artificial intelligence-driven prediction of COVID-19-related hospitalization and death: a systematic review

Shakibfar

Nyberg

et al. 2023

Front. Public Health

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AimTo perform a systematic review on the use of Artificial Intelligence (AI) techniques for predicting COVID-19 hospitalization and mortality using primary and secondary data sources.Study eligibility criteriaCohort, clinical trials, meta-analyses, and observational studies investigating COVID-19 hospitalization or mortality using artificial intelligence techniques were eligible. Articles without a full text available in the English language were excluded.Data sourcesArticles recorded in Ovid MEDLINE from 01/01/2019 to 22/08/2022 were screened.Data extractionWe extracted information on data sources, AI models, and epidemiological aspects of retrieved studies.Bias assessmentA bias assessment of AI models was done using PROBAST.ParticipantsPatients tested positive for COVID-19.ResultsWe included 39 studies related to AI-based prediction of hospitalization and death related to COVID-19. The articles were published in the period 2019-2022, and mostly used Random Forest as the model with the best performance. AI models were trained using cohorts of individuals sampled from populations of European and non-European countries, mostly with cohort sample size <5,000. Data collection generally included information on demographics, clinical records, laboratory results, and pharmacological treatments (i.e., high-dimensional datasets). In most studies, the models were internally validated with cross-validation, but the majority of studies lacked external validation and calibration. Covariates were not prioritized using ensemble approaches in most of the studies, however, models still showed moderately good performances with Area under the Receiver operating characteristic Curve (AUC) values >0.7. According to the assessment with PROBAST, all models had a high risk of bias and/or concern regarding applicability.ConclusionsA broad range of AI techniques have been used to predict COVID-19 hospitalization and mortality. The studies reported good prediction performance of AI models, however, high risk of bias and/or concern regarding applicability were detected.

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“…• Data Collection: Primary Data and Secondary Data are two types of data commonly used for research purposes [27]. Data collection is a structured procedure for acquiring observations or measurements.…”

Section: What Are Embeddings and How Are They Generated?mentioning

confidence: 99%

Optimizing Sales Prediction in Online Grocery Shopping using Product Embeddings

Aziegbemhin

2023

Preprint

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The focus of this research is to delve into the world of product embeddings and their potential to improve prediction accuracy. The study included a thorough review of existing literature to highlight the generation of embeddings and their utility in downstream tasks. This review uncovered the significance of embeddings in enhancing prediction accuracy, showing promise for their application in the realm of online grocery shopping. By employing embedding techniques, we aim to examine how they refine sales predictions, ultimately contributing to improved inventory management and optimized supply chain processes in the e-commerce domain. Future work will encompass data acquisition and a comprehensive process involving embedding generation, model training, and hyperparameter tuning to validate these findings.

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Primary versus secondary source of data in observational studies and heterogeneity in meta-analyses of drug effects: a survey of major medical journals

Cited by 10 publications

References 69 publications

Bias in pharmacoepidemiologic studies using secondary health care databases: a scoping review

Bias in pharmacoepidemiologic studies using secondary health care databases: a scoping review

Artificial intelligence-driven prediction of COVID-19-related hospitalization and death: a systematic review

Optimizing Sales Prediction in Online Grocery Shopping using Product Embeddings

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