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Cross-bridge Cycle01:26

Cross-bridge Cycle

As muscle contracts, the overlap between the thin and thick filaments increases, decreasing the length of the sarcomere—the contractile unit of the muscle—using energy in the form of ATP. At the molecular level, this is a cyclic, multistep process that involves binding and hydrolysis of ATP, and movement of actin by myosin.

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Related Experiment Video

Updated: May 28, 2026

Real-Time Fluorescent Measurement of Synaptic Functions in Models of Amyotrophic Lateral Sclerosis
08:59

Real-Time Fluorescent Measurement of Synaptic Functions in Models of Amyotrophic Lateral Sclerosis

Published on: July 16, 2021

Modeling drop-outs in amyotrophic lateral sclerosis.

Paolo Messina1, Ettore Beghi

  • 1Laboratory of Neurological Disorders, Mario Negri Institute for Pharmacological Research, Via La Masa 19, 20156, Milan, Italy. paolo.messina@marionegri.it

Contemporary Clinical Trials
|October 15, 2011
PubMed
Summary

Pattern mixture models offer a superior solution for handling missing data in amyotrophic lateral sclerosis (ALS) clinical trials, improving accuracy in patient outcome estimations despite high dropout rates.

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Area of Science:

  • Neuroscience
  • Clinical Trials Methodology
  • Biostatistics

Background:

  • Amyotrophic lateral sclerosis (ALS) clinical trials frequently experience high dropout rates, complicating data analysis.
  • Standard methods for handling missing data in ALS trials can introduce bias and reduce statistical power.
  • Existing strategies for managing data missing not at random have significant limitations.

Purpose of the Study:

  • To compare the effectiveness of standard missing data handling procedures against pattern mixture models in ALS clinical trials.
  • To identify the most accurate method for analyzing longitudinal data in the presence of substantial participant dropouts.

Main Methods:

  • Utilized data from a randomized dose-finding trial of lithium for ALS treatment with a 68.4% dropout rate.
  • Applied mixed-effect models to analyze the ALS Functional Rating Scale-Revised (ALSFRS-R) scores under various imputation strategies: Intention-to-Treat, Completers, Last Observation Carried Forward, and 0-imputation.
  • Compared these standard methods with pattern mixture models, which explicitly account for missing data patterns.

Main Results:

  • All standard imputation methods (Intention-to-Treat, Completers, LOCF, 0-imputation) demonstrated limitations, including assumption violations, reduced power, and biased time-effect estimations.
  • Pattern mixture models provided a significantly better fit to the data compared to models ignoring the missing data pattern (p=0.006 and p=0.0002).

Conclusions:

  • Pattern mixture models are superior to conventional methods for analyzing ALS clinical trial data with high dropout rates.
  • Recommends the use of pattern mixture models to achieve more accurate estimations of treatment effects and disease progression when data are missing.