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Blinding01:11

Blinding

Blinding is a commonly used method of not telling participants which treatment a subject is receiving. Blinding is a critical part of a randomized control trial or RCT. It reduces the bias that affects the results. In an RCT, blinding is used in the form of a placebo. A placebo effect occurs when untreated subjects falsely believe they have received the treatment and report improved symptoms. A placebo or a dummy treatment is administered to subjects to negate the bias caused by such an effect.
Randomized Experiments01:13

Randomized Experiments

The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
Censoring Survival Data01:09

Censoring Survival Data

Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different reasons...
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
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...
Regression Toward the Mean01:52

Regression Toward the Mean

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...

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Related Experiment Video

Updated: Jun 15, 2026

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

Missing inaction: preventing missing outcome data in randomized clinical trials.

Janet Wittes1

  • 1Statistics Collaborative, Washington, DC, USA.

Journal of Biopharmaceutical Statistics
|February 26, 2010
PubMed
Summary
This summary is machine-generated.

Minimizing missing data in clinical trials is crucial for accurate results. Strategies include investigator training and clear communication about trial completion versus stopping medication.

Related Experiment Videos

Last Updated: Jun 15, 2026

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

Area of Science:

  • Clinical Trials Methodology
  • Biostatistics
  • Data Management in Research

Background:

  • Numerous statistical methods exist for handling missing data in randomized clinical trials.
  • High proportions of missing outcome data can lead to inaccurate effect size estimations.
  • Ongoing statistical research addresses missing data challenges.

Purpose of the Study:

  • To advocate for minimizing missing data in randomized clinical trials.
  • To propose practical strategies for reducing missing data proportions.
  • To enhance the integrity of clinical trial results.

Main Methods:

  • Review of existing statistical approaches for missing data.
  • Proposal of investigator and participant training initiatives.
  • Development of specific language for trial documentation (informed consent, protocols, case report forms).

Main Results:

  • Current methods may yield inaccurate estimates when missing data is substantial.
  • Training can improve participant and investigator adherence.
  • Clear documentation distinguishes between medication cessation and trial withdrawal.

Conclusions:

  • Trialists must prioritize minimizing missing data to ensure reliable study outcomes.
  • Proactive strategies, including enhanced communication and documentation, are essential.
  • Reducing missing data improves the validity and accuracy of clinical trial evidence.