Previous Versions

10th of April, 2008


Primary Outcomes

Mixing actual and potential ADEs is not a good idea for two main reasons. First, these variable are defined in a different way. Second, the way of reporting actual ADEs is problematic for two reasons: 1) the patient is not blinded to the treatment, 2) the side effects and actual adverse drug events are mixed together, which will probably lead to the absence of intervention effect for actual ADEs outcome. Because of these issues, we should consider potential serious ADEs as a main primary outcome, and preventable/ameliorable serious adverse drug events (ADEs) as an additional primary outcome.
  1. Number of potential serious ADEs (see PILL-CVD definitions) per patient during the first 30 days after hospital discharge.
  2. Number of preventable/ameliorable serious adverse drug events (ADEs) (see PILL-CVD definitions) per patient during the first 30 days after hospital discharge.

Effect Modification (Interaction) Variables
  • Level of health literacy (according to the protocol)
  • Investigation site (It is possible that the intervention effect will be different in two sites. If it turns out that it is not different we can omit it when interpreting the results.)

Possible Confounders
It is true that randomization makes sure that we have two very similar samples. Nevertheless the model would benefit from having all possible variables that the outcome might depend on. I would definitely put the following (the investigator should decide on that):
  • Number of Prescribed Medications
  • Age
  • Social support
  • Educational attainment
  • insurance
  • income
  • Cognitive function
I would not put the following variables in the model (the investigator should decide on that):
  • hospital service
  • Primary language (QUESTION? don't you think that cognitive function would account for primary language differences)
  • Gender (QUESTION?)
  • Race (QUESTION?)
Not Confounders
  • Number of Medication Changes During Hospitalization (Changes in prescription might be caused by intervention, therefore, this variable should not be included as a confounder)

Secondary Outcomes

  1. Number of actual severe ADEs
  2. Disease-Specific Quality of Life / Disease Control
  3. Health Care Utilization (QUESTION? How is it measured ?)
Confounders
Confounders listed under Primary outcomes.


ANALYSIS

Baseline Analysis

The following baseline variables are summarized for the control and intervention groups and compared using Wilcoxon's rank-sum (for continuous variables) and Chi-Square test (for categorical variables):
  • Level of health literacy
  • Cognitive function
  • Educational attainment
  • Primary language
  • Gender
  • Race
  • Age
  • Number of prescribed medications

Preliminary Statistical Summary

The following variables are summarized for the control and intervention groups and compared using Wilcoxon's rank-sum test:
  • Number of preventable/ameliorable ADEs per subject
  • Number of potential ADEs per subject The following variables are summarized for the control and intervention groups and compared using Chi-Square test:
  • Number of preventable/ameliorable ADEs by severity

Primary Analyses

  1. Poisson regression (or proportional odds ordinal logistic regression) is used to assess the association between the number of potential ADEs and intervention. The model includes health literacy and site as effect modification variables and controls for variables listed under "Possible confounders".
  2. Poisson regression (or proportional odds ordinal logistic regression) is used to assess the association between the number of preventable/ameliorable ADEs and intervention. The model includes health literacy and site as effect modification variables and controls for variables listed under "Possible confounders".

Secondary Analysis

The effects of the intervention on quality of life(/disease control) is assessed using proportional odds ordinal logistic regression, controlling for variables listed under "Possible Confounders". The effect of the intervention on health care utilization is assessed using Cox proportional hazard model, controlling for the variables listed under "Possible Confounders".

Studio

  1. Notes for the Studio



Topic revision: r10 - 02 May 2008, SvetlanaEden
 

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