I have two groups of patients, one is a control group and one is an intervention group. All patients have baseline measurements and then measurements at three months. This is in a clinical condition that natural improves over time and i want to see if the intervention confers additional improvement. The challenge is the data is a mix of paired and unpaired data because the primary outcome is cardiac energetics and this doesn't always produce analysable spectra, which means sometimes you have paired visit 1 and 2 data but sometimes you only have useable data from visit 1 OR visit 2. For a simple comparison of visit 1 and 2 within the intervention groups I can use a mixed model to ensure all data is used. My question is can i account for natural improvement in the control group in the mixed model analysis in SPSS? I can't just do an ANCOVA because it will only use paired data and exclude other data so much is lost. I don't particularly want to input missing data at this stage. Any help much appreciated!
To account for natural changes in the control group while analyzing the intervention group, you can use a mixed-effects model in SPSS.
This allows you to include all available data, both paired and unpaired. Follow these steps:
By including time as a fixed factor in the mixed-effects model, you can assess the additional improvement conferred by the intervention group while considering the natural improvement in the control group.
In your case, where you have two groups (control and intervention) and measurements at baseline and three months, with a mix of paired and unpaired data, using a mixed model analysis can be a suitable approach. The mixed model allows you to handle both paired and unpaired data, maximizing the use of available data while accounting for the correlation between measurements within the same individual.
To account for the natural improvement in the control group in the mixed model analysis, you can include time as a variable in the model. By including time as a predictor, the model can capture the natural improvement over time in both the control and intervention groups.
Here's an overview of how you can set up the mixed model analysis in SPSS:
By including time as a predictor in the mixed model analysis, you are implicitly accounting for the natural improvement in the control group. The interaction term between the intervention group and time will help assess whether there is an additional improvement conferred by the intervention, beyond the natural improvement observed in the control group.
It's worth noting that the specific steps and options may vary depending on the version of SPSS you are using. Consult the SPSS documentation or user guide for more detailed instructions on running mixed model analyses in your particular version.
Remember to interpret the results carefully, considering the limitations of your study design and potential confounding factors. Consulting with a statistician or experienced researcher can also help ensure the appropriate analysis and interpretation of your data.
To adjust for natural change in a control group when analyzing an intervention group, you can use a difference-in-differences (DID) approach. This approach involves comparing the change in the outcome variable over time between the intervention group and the control group. The difference between these two changes is the effect of the intervention.
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