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Table 2 Methodological challenges when developing and validating a risk prediction model using IPD from multiple studies as identified from those 15 articles in our review (written below in a framework similar to recommendations by Abo-Zaid et al.[37] for prognostic factors)

From: Developing and validating risk prediction models in an individual participant data meta-analysis

Methodological issue

Challenge

Identifying relevant studies

• Unavailability of IPD in some studies

Issues within studies

• How to assess quality of studies available

• Inability of IPD to overcome deficiencies of original studies, such as missing participant data or of being low methodological quality.

Heterogeneity across studies

• Dealing with different definitions of disease or outcome

• Dealing with different (or out-dated) treatment strategies, especially when a mixture of older and newer studies are combined

Statistical issues for meta-analysis

• Dealing with a mixture of IPD from retrospective and prospective studies

• Missing data, including: missing predictor values and missing outcome data for some participants within a study, and completely unavailable predictors in some studies

• Difficulty in using a continuous scale for continuous factors in meta-analysis when some IPD studies give values on a continuous scale and others do not

• Dealing with IPD from trials where both control and treatment groups are available

Assessment of potential biases

• How to assess the impact of excluded studies who did not provide IPD

Model development

• Accounting for clustering of patients within different IPD studies

• Allowing for heterogeneity in baseline risk (intercept term) across studies

• Allowing for heterogeneity in predictor effects across studies

Model validation

• Lack of external validation if all studies used for model development

• Sample size required to implement the internal-external approach (i.e. sample size of studies to be excluded, and also the total number of IPD studies needed)