Thread by @Lucyy31

NEW(-ish) PAPER

Ever wanted to harness repeatedly-measured predictors within a clinical prediction model but found the existing literature overwhelming?

Look no further, our recent methodological review can help! 👇👇👇

#epitwitter #statstwitter #phdchat #epidemiology

💡 To make this field more accessible for applied researchers, the extracted modelling techniques were grouped based on similarity, and how they used repeated observations to enhance prediction. 💡

The three main motivations to incorporate repeatedly-measured predictors were:

1⃣ To improve model specification and applicability over time
2⃣ To infer an error-free predictor value at a pre-specified time
3⃣ To account for the effects of predictor change over time

...and the following approaches have been discussed:

✅ Time-dependent covariate modelling
✅ Generalised estimating equations
✅ Landmark analysis
✅ Two-stage models
✅ Trajectory classification
✅ Joint models
✅ Machine learning algorithms

📚📉📈📚

Unfortunately, there is no straight-forward method here, but the choice of methods can be reduced by considering:

1⃣ the type and amount of data available at prediction time
2⃣ how the model can benefit from longitudinal information
3⃣ what is known before model development

‼️ Note: some of these approaches can harness repeated observations on a population for model development, and only SINGLE predictor measurements from a patient at the time of prediction. Others can also harness a patient's repeated observations at the time of risk prediction..💡

Also, key stages of prediction model development were often overlooked in this context & provide motivation for further methods research:

1⃣ sample size
2⃣ handling of missing data
3⃣ variable selection
4⃣ model validation
5⃣ quantification of change in predictive performance

This methodological review was completed with @jamiecsergeant, Mark Lunt, @glen_martin1, and @khyrich, funded by @NIHRresearch, and supported by @CfE_UoM. ✨

Thank you for taking the time to read all or even part of this thread, and I hope you enjoy reading the paper😊🥳

Apologies, I thought I had posted the link with image.

Please find the link to the paper here: diagnprognres.biomedcentral.com/track/pdf/10.1…

📚📚