Introduction: Epidemiological studies often use multiple datasets from different periods (such as longitudinal studies). For pooled meta-analysis of these datasets, it is important to concatenate them. If variables have different specifications over periods, then this concatenation is difficult, therefore a method for automatic re-coding of variable values from different periods or studies to match a common specification is needed.

Method: The re-coding algorithm analyses the specification of each concatenated variable and harmonises them to a common specification. Furthermore, constraints such as missing value codes or values outside specification have to be considered. Afterwards, the harmonised variables will be used for further meta-analyses.

Results: Using this algorithm, we are able to perform a meta-analysis on four studies with 149 variables and over 2500 cases over 12 periods. Furthermore, the error prone process of manually re-coding is now supported by software, and is thus much more reliable.

Conclusions: The shown algorithm allows easier re-coding of variable specifications in complex data structures for pooled meta-analysis. The risk of errors in manually re-coding prior to statistical analysis can be significantly reduced.