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Intervention Effects allows you to answer these questions while reading clinical evidence:

Intervention Effects

by James Tomlinson

What is it about?

Intervention Effects allows you to answer these questions while reading clinical evidence:

App Details

Version
1.31.1
Rating
NA
Size
14Mb
Genre
Medical Education
Last updated
September 22, 2019
Release date
September 7, 2016
More info

App Screenshots

App Store Description

Intervention Effects allows you to answer these questions while reading clinical evidence:
How much change should my patient expect if they get the reported intervention or the comparison intervention (often control or usual care)?
Should my patient expect important change if they get the reported intervention or the comparison?
By entering results from an intervention trial you will have a deeper and patient relevant understanding of the research outcomes.
Generally, clinical trial statistical analysis is aimed at determining if there is a statistical difference between groups in order to test the research hypothesis. The result is often reported with a less-than-helpful p-value.
The effect a patient may get from an intervention is best predicted from the change seen in the group receiving the same intervention as the patient would get. This within group effect is frequently not easily found in articles.
You will need to enter means, variability and sample sizes from the research results. Once these data are entered, the within group effects, the between groups effect and the standardized mean difference are presented graphically as forest plots and summarized in text.
Useful to clinicians, residents, interns and students, Intervention Effects allows you to enter results whether reported as baseline and post-intervention means or change values. Variability usually is reported as standard deviation but you can also enter standard error or confidence intervals.
You can choose to have results reported in 90% or 95% confidence intervals.
If you enter a value for clinically important change cited in the article, the literature or a change you or your patient would consider important the forest plot will include a MCID marker to aid interpretation of the within group effects.
If you enter values for standard error of measurement (the expected amount of variability in measurements when there is no actual change) cited in the article or literature, a rectangle will be shown on the forest plot allowing you to see the how much of the within group effects represent actual change.
The article citation can be entered so that the forest plot will include the information source if it is entered. A screenshot of the app will include all of the entered information for ease of sharing with colleagues or patients and for your own use at another time.
Data and forest plots can be saved and reordered to meet your needs.

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