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Related Concept Videos

Clinical Trials01:16

Clinical Trials

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.
There are four phases in a clinical trial. A phase one...
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

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.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...
Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5% chance...
Dosage Regimens: Partial Pharmacokinetic Parameters01:01

Dosage Regimens: Partial Pharmacokinetic Parameters

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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Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Using audit information to adjust parameter estimates for data errors in clinical trials.

Bryan E Shepherd1, Pamela A Shaw, Lori E Dodd

  • 1Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, TN 37232-2158, USA. bryan.shepherd@vanderbilt.edu

Clinical Trials (London, England)
|August 1, 2012
PubMed
Summary
This summary is machine-generated.

Clinical trial audits can correct biased estimates when treatment assignment correlates with data errors. Incorporating audit data improves accuracy, especially with larger audit sizes, for continuous outcomes.

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Published on: January 8, 2020

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials
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Area of Science:

  • Biostatistics
  • Clinical Trials
  • Data Quality Assurance

Background:

  • Clinical trial audits are crucial for data quality assessment.
  • Audit data are often underutilized beyond fraud detection.
  • Prior research demonstrated audit data's potential to eliminate bias in nonrandomized studies.

Purpose of the Study:

  • To evaluate the utility of audit-based error-correction methods.
  • Focus on clinical trials with continuous outcomes.
  • Assess methods for bias reduction in trial data.

Main Methods:

  • Demonstrated bias in multiple linear regression with error-prone outcomes/covariates.
  • Assessed bias under different assumptions of data error independence.
  • Reviewed moment-based estimators and proposed multiple imputation estimators.
  • Evaluated estimator performance via simulations.

Main Results:

  • Unbiased treatment effect estimates when randomization is independent of data errors.
  • Increased variability of audit-incorporating methods in error-independent settings.
  • Bias in standard estimates when treatment assignment correlates with data errors or is non-randomized.
  • Correction of treatment and covariate effects using audit data (moment or multiple imputation).
  • Improved bias, precision, and confidence interval coverage with larger audit sizes.

Conclusions:

  • Standard analyses are recommended when treatment assignment is independent of data errors.
  • Audit incorporation methods are valuable when treatment assignment correlates with data errors or covariates.
  • Method performance depends on error extent, nature, and audit size.
  • Further research needed for settings beyond linear models.