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Setting up the suggestions for Outcomes (or Diagnoses, if you are doing a Diagnostic Accuracy project) is very similar to setting up Arms, though there are a few other things to think about.

A first principle when setting up the Outcomes or Diagnoses tab is to suggest only the outcomes of interest. Authors generally report many different outcomes, but only some may be of interest for your project. Eager extractors or people new to carrying out systematic reviews may mistakenly think they need to extract every outcome reported in a study. So, if you set the suggestions ahead of time, then you can focus on only those and avoid wasted time.

5.4.1. Setting Up Outcomes

When you open the Outcomes template tab, you’ll notice that SRDR+ has additional fields not found on the Arms suggestion template.

Suggest Domain: This is your outcome measurement of interest. For instance, if my PICO question were “Among children and adolescents, what is the effectiveness of a multi-component weight management program, compared to single-component interventions, in bringing about improvements in weight status?”, I would not want to enter “weight status” as a suggestion because there are a number of different ways of measuring weight status: BMI (body mass index), percent weight change, weight change, percent body fat, etc. So, use specific types of measurements as Domains rather than more general outcomes (like weight status).

Suggest Type of Domain: This is how the outcome measure is, well, measured: as a continuous measure, as a categorical measure. Just by way of reminder:

  • Continuous variables are numeric variables that have an infinite number of values between any two values. For example, BMI can take any numeric value (e.g., 25.2, 25.3, 25.4).
  • Categorical variables contain a finite number of categories or distinct groups. For example, the US Centers for Disease Control BMI Categories (underweight, normal weight, overweight, obese).

There are also discrete (or counting) variables (e.g., numeric variables that have a countable number of values between any two values–like the number of hospital visits in a year), but in SRDR+ you would typically treat discrete variables as if they were continuous.

When you click on the Suggest Type of Domain dropdown, you’ll see both of these options.

Setting the Type of Outcome is important since SRDR+ structures the Results tab based on this setting. For instance,

  • For the BMI (continuous) outcome, SRDR+ will automatically set up the data input structure on the Results tab to “expect” a mean, standard deviation and number analyzed.
  • For the BMI Category (categorical) outcome, SRDR+ will automatically set up the data input structure on the Results tab to “expect” number of “events” and number analyzed.

Below you can see how we’ve set up BMI and BMI Category as different variables (one continuous and one categorical).

What about the Suggest Specific Measurements or Suggest Timepoints fields? These will be useful if your project is focusing very specifically on only certain measurements (e.g., only extracting visual analog scale measures for pain and excluding all other types of pain measurements), or if the project is only interested in very specific timepoints (e.g, Baseline and one-year outcomes and ignoring any other timepoints). We have found it best to let the data extractors capture specific measures and timepoints as reported by the authors of the selected studies and then focus on differentiating between specific measures and timepoints at the analysis phase.

5.4.2. Setting up Diagnoses

In case you missed the section about setting up for a Diagnostic Accuracy template, see section 5.2.2 of this resource. Hint: if you don’t see a Diagnoses tab (but, rather, see an Outcome tab), that means that you have not set up your project right. Pop back to the earlier section to see how to set up your template for a Diagnostic Accuracy project.

Setting up Diagnoses (the outcome of interest in a Diagnostic Accuracy study), is very simple. All you need is to name the diagnosis.