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) |