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It is not uncommon for complete drug pharmacokinetic profiles to remain elusive in pharmacokinetics. This necessitates certain educated assumptions by pharmacokineticists to determine appropriate dosage regimens without comprehensive pharmacokinetic data from animal or human studies. One prevalent assumption is setting the bioavailability factor, denoted as F, to 1 or 100%. This assumption caters to the scenario where a drug doesn't achieve full systemic absorption, resulting in the patient...
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Elderly individuals encompass a diverse population with varying degrees of age-related physiological changes. Defining the elderly presents challenges, as the geriatric population is often arbitrarily categorized as individuals older than 65. However, many individuals in this group lead active and healthy lives, with an increasing number surpassing 85 years and falling into the older elderly category. Physiological changes associated with aging impact performance capacity and homeostatic...
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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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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...
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Using Continuous Data Tracking Technology to Study Exercise Adherence in Pulmonary Rehabilitation
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Statistical considerations for medication adherence research.

Josh DeClercq1, Leena Choi1

  • 1Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA.

Current Medical Research and Opinion
|July 8, 2020
PubMed
Summary
This summary is machine-generated.

Medication adherence measurement, like Proportion of Days Covered (PDC), varies significantly with study criteria. Statistical models should account for treatment naivete to avoid errors and improve adherence analysis.

Keywords:
Adherence measurehealth behaviormedication adherenceproportion of days coveredsimulationstatistical methods

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Area of Science:

  • Health Services Research
  • Biostatistics
  • Pharmacoepidemiology

Background:

  • Medication non-adherence is a significant public health issue, leading to adverse health outcomes and increased healthcare expenditures.
  • Existing adherence measures lack consistent application across studies, and their statistical analysis methods require rigorous evaluation.
  • Proportion of Days Covered (PDC) is a widely used adherence metric, but its calculation and interpretation can be complex.

Purpose of the Study:

  • To investigate the variability in adherence measures due to differing study inclusion criteria and PDC calculation methods.
  • To evaluate the statistical properties of common models (logistic, negative binomial, ordinal logistic regression) for analyzing adherence data.
  • To identify best practices for adherence data analysis and PDC calculation to ensure reliable inference.

Main Methods:

  • Sensitivity analyses were conducted using a motivating data example to illustrate the impact of varying PDC calculation rules and cohort definitions.
  • Simulation studies were performed to compare the statistical performance of logistic, negative binomial, and ordinal logistic regression models.
  • The influence of 'naivete to treatment' as a predictor of adherence was examined within the statistical models.

Main Results:

  • Sensitivity analyses revealed substantial variability in parameter estimates based on decisions regarding study end dates and minimum prescription fill requirements for PDC.
  • Logistic regression demonstrated lower statistical power compared to negative binomial and ordinal logistic regression models.
  • Omitting 'naivete to treatment' as a covariate was found to potentially inflate Type I errors in adherence models.

Conclusions:

  • Dichotomizing adherence data is discouraged due to reduced statistical power.
  • The negative binomial model is advantageous for adherence data, avoiding ratio-based regressions. Ordinal logistic regression offers robustness and power but requires adjustment for treatment naivete.
  • Recommendations are provided for defining the observation window in PDC calculations and for appropriate statistical modeling of adherence.