Meta-analysis methods involve combining and analysing quantitative evidence from related studies to produce results based on a whole body of research. As such, metaanalyses are an integral part of evidence based medicine. Traditional methods for meta-analysis synthesise aggregate study level data obtained from study publications or study authors, such as a treatment effect estimate (for example, an odds ratio) and its associated uncertainty (for example, a standard error or confidence interval). An alternative but increasingly popular approach is meta-analysis of individual participant data, or individual patient data, in which the raw individual level data for each study are obtained and used for synthesis.1 In this article we describe the rationale for individual participant data meta-analysis and illustrate through applied examples why this strategy offers numerous advantages, both clinically and statistically, over the aggregate data approach.1 2 We outline when and how to initiate an individual participant data meta-analysis, the statistical issues in conducting one, how the findings should be reported, and what challenges this approach may bring. What are individual participant data?The term "individual participant data" relates to the data recorded for each participant in a study. In a hypertension trial, for example, the individual participant data could be the pre-treatment and post-treatment blood pressure, a treatment group indicator, and important baseline clinical characteristics such as age and sex, for each patient in each study (table). A set of individual participant data from multiple studies often comprises thousands of patients; this is the case in the table, so for brevity we do not show all rows of data here. This concept is in contrast to the term "aggregate data," which relates to information averaged or estimated across all individuals in a study, such as the mean treatment effect on blood pressure, the mean age, or the proportion of participants who are male. Such aggregate data are derived from the individual participant data themselves, so individual participant data can be considered the original source material. What is an individual participant data meta-analysis?As with any meta-analysis, an individual participant data meta-analysis aims to summarise the evidence on a particular clinical question from multiple related studies, such as whether a treatment is effective. The statistical implementation of an individual participant data meta-analysis crucially must preserve the clustering of patients within studies; it is inappropriate to simply analyse individual participant data as if they all came from a single study. Clusters can be retained during analysis by using a two step or a one step approach. 3 In the two step approach, the individual participant data are first analysed in each separate study independently by using a statistical method appropriate for the type of data being analysed; for example, a linear regression model might be fitted for continuous responses such as blo...

Many routinely used summary methods provide widely ranging estimates when applied to sparse data with high imbalance between the size of the studies' arms. A sensitivity analysis using several methods and continuity correction factors is advocated for routine practice.

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Background Population-based cancer survival estimates provide valuable insights into the effectiveness of cancer services and can reflect the prospects of cure. As part of the second phase of the International Cancer Benchmarking Partnership (ICBP), the Cancer Survival in High-Income Countries (SURVMARK-2) project aims to provide a comprehensive overview of cancer survival across seven high-income countries and a comparative assessment of corresponding incidence and mortality trends. Methods In this longitudinal, population-based study, we collected patient-level data on 3•9 million patients with cancer from population-based cancer registries in 21 jurisdictions in seven countries (