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Related Experiment Videos

The hidden effect of time.

D G Altman1, J P Royston

  • 1Section of Medical Statistics, MRC Clinical Research Centre, Harrow, U.K.

Statistics in Medicine
|June 1, 1988
PubMed
Summary
This summary is machine-generated.

Datasets are often assumed to be homogeneous, but hidden time trends can impact study design and interpretation. Visualizing data by collection time, potentially using cumulative sum (cusum) plots, is recommended when observation order is known.

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Area of Science:

  • Statistics
  • Data Analysis
  • Research Methodology

Background:

  • Standard practice assumes dataset homogeneity regarding measurement collection order.
  • This assumption can be violated, leading to potential biases.
  • Understanding temporal dependencies is crucial for valid scientific conclusions.

Purpose of the Study:

  • To highlight the implications of violating the homogeneity assumption in datasets.
  • To introduce the concept of hidden time trends in data.
  • To propose methods for detecting and addressing temporal effects in observational data.

Main Methods:

  • Illustrating scenarios where the order of data collection is not random.
  • Discussing the impact of time trends on study design and analysis.

Related Experiment Videos

  • Suggesting graphical methods for temporal trend detection, such as plotting data against time or using cumulative sum (cusum) plots.

    • Main Results:

    • Demonstrated that datasets may not be homogeneous over time.
    • Identified hidden time trends as a significant factor affecting data interpretation.
    • Showcased the utility of time-based plots for revealing underlying trends.

    Conclusions:

    • The assumption of dataset homogeneity with respect to collection order should be critically evaluated.
    • Incorporating temporal analysis, like time-series plotting or cusum analysis, is essential when observation order is known.
    • Addressing hidden time trends improves the rigor of study design, analysis, and interpretation.