Individual Patient Meta-Analysis

An evidence synthesis approach combining original patient-level records from multiple studies into one unified dataset, rather than only summary statistics.

Source: Stewart & Parmar 1993

Individual patient meta-analysis pools the original patient-level records from several studies into one combined dataset, then analyses them together as though they came from a single large study, while still respecting which study each patient belonged to. Combining the raw records this way allows subgroups to be defined uniformly and effects to be explored in ways that published summaries cannot support. The gain in flexibility comes at the cost of the effort needed to collect and harmonise data from many sources. Merging the underlying records is its method. Riley and colleagues (2010) describe this.

Source: Riley et al. 2010

Individual patient meta-analysis works by obtaining the patient-level datasets from the included studies, checking and harmonising them into a consistent format, and analysing them together, either by analysing each study and combining the results or by modelling the combined data with methods that account for the studies. Having the raw data allows uniform outcome definitions, subgroup analyses, and adjustment for patient factors. So individual patient meta-analysis works by collecting and standardising the raw data across studies and analysing them jointly, using the patient-level information to conduct analyses more detailed and consistent than summary data allow.

Source: DerSimonian & Laird 1986

The advantages of individual patient meta-analysis include the ability to standardise outcome definitions and analyses across studies, improving consistency; to examine how treatment effects vary within subgroups and with patient characteristics; to adjust for patient-level factors; to check the data and extend follow-up; and to reduce some reporting biases. These make it more powerful and flexible than synthesis of summary data. So individual patient meta-analysis is advantageous for the depth, consistency, and flexibility it allows, particularly for exploring how effects differ among patients, which is why it is regarded as a gold standard for evidence synthesis where the raw data can be obtained.

Source: Stewart & Parmar 1993

The challenges of individual patient meta-analysis include the difficulty and time of obtaining the raw datasets from investigators, who may be unable or unwilling to share them, so some data may be missing, risking bias if the obtained studies are unrepresentative; the effort of checking and harmonising the data; and the resources and expertise required. Data-sharing and confidentiality issues also arise. These challenges mean individual patient meta-analysis is more demanding and costly than summary-data synthesis, and its feasibility depends on securing the raw data, though its analytic advantages often justify the effort where the data can be obtained.

Source: Stewart & Parmar 1993

Individual patient meta-analysis combines the original patient-level data from each study into one dataset, while summary-data synthesis, or aggregate data meta-analysis, combines the reported summary results. The patient-level approach allows standardised, consistent analysis, examination of subgroups and patient-level effects, and adjustment for patient factors, whereas summary-data synthesis is limited to what is reported and cannot examine patient-level effects directly. However, individual patient meta-analysis is more demanding, requiring the raw data. So the two differ in the level of data used, with patient-level data offering greater depth at the cost of greater effort and feasibility constraints.

Source: DerSimonian & Laird 1986