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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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Body:Bioequivalence experimental study designs play a pivotal role in testing the effectiveness of various treatments. Key among these are the repeated measures, cross-over, carry-over, and Latin square designs. In the repeated measures design, each subject receives all treatments, allowing for temporal comparisons. This type of design is useful in reducing variability but requires careful planning to avoid bias.The cross-over design, an economical method, involves sequential administration of...
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Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
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Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs01:20

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Body:Bioequivalence experimental study designs are crucial methodologies used in evaluating and comparing the bioavailability of different drug products. These designs are categorized into various types: completely randomized, randomized block, repeated measures, cross and carry-over, and Latin square designs.Completely randomized designs involve randomly allocating treatments to all subjects participating in the experiment. This allocation is achieved by assigning unique random numbers to...
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Hazard Ratio01:12

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The hazard ratio (HR) is a widely used measure in clinical trials to compare the risk of events, such as death or disease recurrence, between two groups over time. It reflects the ratio of hazard rates—the instantaneous risk of the event occurring—between a treatment group and a control group. This measure provides valuable insights into the relative effectiveness of a treatment by assessing how the risk of an event differs between the two groups.
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Updated: Mar 1, 2026

Observational Study Protocol for Repeated Clinical Examination and Critical Care Ultrasonography Within the Simple Intensive Care Studies
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Effect sizes in ongoing randomized controlled critical care trials.

Elliott E Ridgeon1, Rinaldo Bellomo2,3, Scott K Aberegg4

  • 1Medical Research Institute of New Zealand, Wellington, New Zealand.

Critical Care (London, England)
|June 7, 2017
PubMed
Summary
This summary is machine-generated.

Clinicians believe many critical care trials overestimate mortality reductions. Most experts found plausible absolute mortality reductions to be less than 5%, suggesting a need to reassess trial hypotheses.

Keywords:
Clinical trial designCritical careIntensive careIntensive care unitRandomized clinical trial

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Area of Science:

  • Critical care medicine
  • Clinical trial design
  • Health services research

Background:

  • Critical care trials often hypothesize implausibly large mortality reductions.
  • Systematic assessment of trial hypothesis plausibility is lacking in critical care.
  • This study addresses the need for evaluating clinician perspectives on trial plausibility.

Purpose of the Study:

  • To determine clinicians' views on prior probabilities for ongoing critical care trials.
  • To assess clinicians' opinions on plausible effect sizes for landmark mortality endpoints.

Main Methods:

  • Systematic review of clinical trial registries (September 2015) identified relevant trials.
  • Clinician survey collected opinions on ten large critical care trials with mortality as the primary outcome.
  • Participants estimated the probability of achieving pre-specified mortality effect sizes.

Main Results:

  • Clinician estimates for trial success varied widely, with a median of 15%.
  • The median plausible absolute mortality reduction was 4.5%, lower than the 5% used in sample size calculations.
  • A significant portion of clinicians deemed the hypothesized effects implausible.

Conclusions:

  • Many clinicians perceive low prior probabilities for large critical care trials.
  • Plausible absolute mortality reductions are considered less than 5% by many experts.
  • Further research is needed to validate clinician-pooled estimates for prior probability assessment.