In most circumstances, the researcher easily can gather the relevant descriptive statistics (e.g., means, standard errors, sample sizes) from the reports on the eligible studies.
Sometimes, older reports (say, prior to 1980) do not include variability estimates (e.g., standard errors). If possible, the researcher should attempt to contact the authors directly in such situations. This may not be successful, however, because the authors may no longer have the data.
Ideally, the statistical analysis for a systematic review will be based on the raw data from each eligible study. This has rarely occurred. Either the raw data were no longer available or the authors were unwilling to share the raw data. However, the success of shared data in the Human Genome Project has given impetus to increased data sharing to promote rapid scientific progress. Since the US NIH now requires investigators receiving large new NIH grants to have a plan for data-sharing.( NIH Data Sharing Policy Guide ) and has provided more guidance on how federal data are to be shared. we may anticipate more meta-analyses based on raw data.
As we have discussed earlier, problems to be solved before private entities embrace data sharing include proprietary rights, authorship, patient consent and confidentiality, common technology, proper use, enforcement of policy, etc. As these challenges are overcome, the path to a systematic review and meta-analysis based on raw data will be smoother.