This is the seventh paper in the evidence-based medicine glossary series. In this paper, We mainlyintroduced five terms related to meta-analysis——prospective meta-analysis, individual patient data meta-analysis,cumulative meta-analysis, multiple-treatments meta-analysis and meta regression.We also gave some examples to helpreaders better understand and use them.
Individual patient data meta-analyses are conducted through development of collaboration with trial investigators, central collection and checking of individual patient data of all eligible trials, and pooling of patient data to produce the best estimate of effects of health care interventions. They ensure study data to be update, accessible, reliable and complete so as to minimize the risk of bias, and are the gold standard of systematic reviews addressing effects of health care interventions. Meta-analyses using individual patient data enable higher flexibility of data analyses and more completeness and balance of results interpretation. The study conduct differs between individual patient data versus conventional meta-analyses. This article discussed the steps of conducting individual patient data meta-analyses.
Survival data include the occurrence and duration of an event. As most survival data are distributed irregularly, the Kaplan-Meier method is often used in survival analysis; however, studies usually only report the Kaplan-Meier curve and median survival time and do not provide the original survival data, which creates issues for subsequent secondary research. This study introduced a systematic method whereby image processing software and R software were used to process and extract survival data from published Kaplan-Meier curves. It also introduced the specific steps required to obtain survival data using an example to show the accuracy and feasibility of the extraction method and provided references for the effective secondary use of survival data.
When there is a lack of head-to-head randomized controlled trials between two interventions of interest, indirect comparison methods can be employed to estimate their relative treatment effects. Matching-adjusted indirect comparison (MAIC) is a population-adjusted indirect comparison method that utilizes a weighting approach. Unanchored MAIC is particularly applicable in scenarios where a common control group between the two interventions is not available. This article introduces the background and mathematical theory of unanchored MAIC, along with a demonstration of the operational steps and interpretation of results through an application example.
Time-to-event outcomes are a key component in survival analyses. Effect modification by time, also known as interaction between effect and time, can exist in time-to-event data and influence the analysis process. Our objective is to discuss the proper methods to conduct evidence synthesis of time-to-event data when effect modification by time exists.
With the continuous progress of national medical insurance strategic purchasing and value-based healthcare, pharmacoeconomic evaluation, serving as a technical tool for assessing the cost-effectiveness of healthcare interventions, has played an important role in policy decision support. Comparative efficacy evidence is the core data source for pharmacoeconomic evaluation, and also the foundation for conducting pharmacoeconomic research. In recent years, the number of innovative drugs approved based on single-arm trial has been increasing. Most existing randomized controlled clinical trials are also placebo-controlled or compared with traditional treatments, unable to directly meet the need for efficacy evidence of comparisons with conventional or standard treatments in pharmacoeconomic evaluations. In the absence of direct comparative efficacy evidence, exploring indirect comparison methods for efficacy has become a cutting-edge direction in pharmacoeconomic evaluation. Through a comprehensive literature review and systematic analysis, this study focuses on five indirect comparison methods based on individual patient data for population adjustment, including match adjusted indirect comparison (MAIC), simulated treatment comparison (STC), propensity score matching (PSM), inverse probability of treatment weighting (IPTW) and network meta regression (NMR), and discussing their basic concepts, advantages and disadvantages and application comparisons. Finally, it provides methodological suggestions on how to choose an indirect comparison method for efficacy, with the aim of promoting the generation of higher-quality indirect comparison evidence for efficacy and advancing pharmacoeconomic evaluation to provide high-quality evidence references for healthcare policy decision-making.