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Pharmacovigilance01:19

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Post-marketing surveillance is a critical component of pharmaceutical regulation, often uncovering unanticipated adverse drug reactions (ADRs) once a drug is widely used over an extended period.
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Analysis of Population Pharmacokinetic Data01:12

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Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

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Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
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Dosage Regimens: Partial Pharmacokinetic Parameters01:01

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It is not uncommon for complete drug pharmacokinetic profiles to remain elusive in pharmacokinetics. This necessitates certain educated assumptions by pharmacokineticists to determine appropriate dosage regimens without comprehensive pharmacokinetic data from animal or human studies. One prevalent assumption is setting the bioavailability factor, denoted as F, to 1 or 100%. This assumption caters to the scenario where a drug doesn't achieve full systemic absorption, resulting in the patient...
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Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

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Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
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Clinical Trials01:16

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Clinical trials are prospective experimental studies conducted on humans to determine the safety and efficacy of treatments, drugs, diet methods, and medical devices. Using statistics in clinical trials enables researchers to derive reasonable and accurate conclusions from the collected data, allowing them to make wise decisions in uncertain situations. In medical research, statistical methods are crucial for preventing errors and bias.
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Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
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Missing data reporting in clinical pharmacy research.

Sujita W Narayan1, Kar Yu Ho1, Jonathan Penm1

  • 1School of Pharmacy, Faculty of Medicine and Health, University of Sydney, Sydney, Australia.

American Journal of Health-System Pharmacy : AJHP : Official Journal of the American Society of Health-System Pharmacists
|December 3, 2019
PubMed
Summary
This summary is machine-generated.

Few clinical pharmacy studies report handling missing data, potentially biasing results. Transparent reporting of methods for addressing missing data is crucial for reliable findings.

Keywords:
biasdataguidelinepharmacyresearch report

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Area of Science:

  • Clinical Pharmacy
  • Research Methodology

Background:

  • Missing data is a common issue in clinical pharmacy research.
  • Inadequate handling of missing data can lead to biased study outcomes.
  • Transparency in reporting data handling is essential for research integrity.

Purpose of the Study:

  • To assess the extent to which missing data handling is reported in clinical pharmacy research.
  • To identify common methods used for managing missing data in this field.

Main Methods:

  • A cross-sectional descriptive study analyzed 178 studies published in 2018 from 10 pharmacy journals.
  • Included study designs were randomized controlled trials, cohort studies, case-control studies, and cross-sectional studies.
  • The primary outcome was the proportion of studies reporting missing data handling in methods or results.

Main Results:

  • Only 19.7% of studies mentioned missing data, and merely 4.5% detailed how it was handled.
  • Multiple imputation was the most frequent method (n=3), followed by complete case analysis (n=2) and indicator methods (n=2).
  • Most studies that mentioned missing data did not specify the imputation or analysis techniques used.

Conclusions:

  • A significant gap exists in reporting missing data handling in clinical pharmacy literature.
  • Lack of transparency in addressing missing data may compromise the validity of research findings.
  • Researchers are urged to clearly document their strategies for handling missing data to enhance transparency and minimize bias.