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Longitudinal Research02:20

Longitudinal Research

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Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
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Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
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Causality in Epidemiology01:21

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Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
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Observational studies are a type of analytical study where researchers observe events without any interventions. In other words, the researcher does not influence the response variable or the experiment's outcome.
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The Bradford Hill criteria serve as guidelines for establishing causative links in epidemiological research. Beyond Strength, Consistency, Specificity, and Temporality, key criteria also include Biological Gradient, Plausibility, Coherence, Experiment, and Analogy. These principles assist scientists in assessing the likelihood of causation in complex biological contexts. Below is a summary of these concepts:
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The Bradford Hill criteria are a group of principles that provide a framework to determine a causal relationship between a specific factor and a disease. There are nine criteria that are pivotal in assessing causality in epidemiological studies. Here's a closer look at Strength, Consistency, Specificity, and Temporality criteria with definitions and examples:
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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
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Causal inference for longitudinal data based on historical controls.

Jeen Liu1, Jane Zhang1, Alan Mitchell1

  • 1Data and Statistical Sciences, AbbVie Inc, North Chicago, IL, USA.

Journal of Biopharmaceutical Statistics
|December 5, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical method to accurately analyze longitudinal data from clinical trials using historical controls, even with missing patient information. The doubly robust procedure ensures reliable results for drug development and patient treatment comparisons.

Keywords:
Historical controlMMRMdoubly robust estimatorlongitudinal data analysismissing data

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Area of Science:

  • Biostatistics
  • Clinical Trial Methodology
  • Health Economics

Background:

  • Historical data offers potential to reduce patient burden and drug development costs.
  • Comparing new treatments with historical controls requires adjustments for patient characteristic imbalances.
  • Longitudinal data with missing values complicates outcome analysis.

Purpose of the Study:

  • To propose a doubly robust adjustment procedure for longitudinal data analysis with missing data when using historical controls.
  • To provide a statistically valid method for comparing experimental treatments against historical data.

Main Methods:

  • Developed a doubly robust adjustment procedure for longitudinal data.
  • The method requires correct specification of either the propensity score model or the mixed-effects model for repeated measures (MMRM).
  • Validated through extensive numerical simulations and a real clinical trial example.

Main Results:

  • The proposed method provides valid analysis results under specified model assumptions.
  • Demonstrated the procedure's performance in a numerical study.
  • Successfully applied the method to analyze a clinical trial using historical data.

Conclusions:

  • The doubly robust procedure offers a reliable approach for analyzing longitudinal data with missing values in single-arm studies using historical controls.
  • This method can facilitate earlier availability of innovative therapies by improving the use of historical data in clinical research.