Individual Participant Data Meta-Analysis. Группа авторов

Скачать книгу

Data completeness and uniformity Include data from trials that are unpublished or not reported in full^*Include unreported data (e.g. unpublished subgroups, outcomes and time‐points), more complete information on outcomes, and data on participants excluded from original trial analyses^*Check each trial’s IPD for completeness, validity and consistency, and resolve any queries with trial investigatorsDerive new or standardised outcome definitions across trials or translate different definitions to a common scaleDerive new or standardised classifications of participant‐level characteristics, or translate different definitions to a common scaleUpdate follow‐up of time‐to‐event or other time‐related outcomes beyond those reported^* Risk of bias assessment Clarify trial design, conduct and analysis methods with trial investigators^*Resolve unclear risk of bias assessments (i.e. based on trial reports) through direct contact with investigators^*Examine trial IPD directly for evidence of potential bias in trial design and conduct, and resolve any queries with trial investigatorsObtain extra data where necessary to alleviate or mitigate against potential biases^* Analyses Apply a consistent method of analysis for each trial (independent of original trial analyses)Analyse all important outcomes irrespective of whether published^*Explore validity of analytical assumptions e.g. normality of residuals in a linear regression analysisDerive outcomes and measures of effect directly from IPD (independent of trial reporting), potentially at multiple time‐points of interestUse a consistent unit of analysis for each trial (e.g. consistently analyse preterm birth events per mother rather a mix of per mother and per baby in trials that include twin pregnancies)Account for complexities in each trial in the analysis, such as cluster randomised trials or multi‐centre trialsAnalyse continuous outcomes on their continuous scale and adjust for baseline valueAdjust for a pre‐defined set of prognostic factorsApply consistent definitions for categorised data (e.g. stage of cancer)Conduct more detailed and appropriate analysis of time‐to‐event outcomes (e.g. handling of censored observations, generating Kaplan Meier curves, examination of non‐proportional hazards)Achieve greater power for assessing interactions between effects of interventions and participant‐level characteristicsModel associations at the participant level, including potential non‐linear relationshipsUse appropriate but non‐standard models (e.g. that account for repeated measurements or correlation between multiple outcomes) or measures of effectExplain potential heterogeneity and inconsistency in network meta‐analysisAddress additional important questions over and above efficacy, or not considered by original trials e.g. to explore the natural history of disease, prognostic factors or surrogate outcomes Interpretation Discuss implications for clinical practice and research with a multi‐disciplinary group of collaborators including trial investigators who supplied data, and patient research partners^* Dissemination Achieve more widespread dissemination though collaborative group networks and patient groups

^*These advantages accrue from direct contact with trial investigators (rather than the IPD per se), so potentially could be achieved for conventional systematic reviews if more active communication with trial investigators were adopted. This is seldom done in practice.

      In general, having access to IPD also supports more flexible and sophisticated analyses than are possible with only existing aggregate data. IPD are vital for a thorough investigation of participant‐level associations, for example to identify treatment effect modifiers (Chapter 7).^7,9,43 For instance, an IPD meta‐analysis project by the Early Breast Cancer Trialists Group, which combined IPD from 37,000 women in 55 randomised trials, established that the drug tamoxifen works better in the subgroup of breast cancer patients who are classed as oestrogen receptor positive.¹⁵

      With IPD, there is no need to rely on, or be restricted by, the original trial methods of analysis. For example, the IPD meta‐analysis research team could opt for alternative effect measures (e.g. hazard ratios rather than odds ratios) or assumptions (e.g. non‐proportional rather than proportional hazards), as appropriate, and consider a broader set of outcomes than originally reported. Collecting IPD also allows continuous variables to be analysed on their continuous scale; potential non‐linear relationships to be examined; and the analysis of outcomes, covariates (e.g. prognostic factors) and time‐points that were recorded, but not originally analysed by trial investigators.

      As most IPD meta‐analysis projects are collaborative endeavours, direct contact with trial investigators can help to identify trials that may not be easily identifiable via other forms of searching,^7,9,43 and to clarify the eligibility of potentially relevant trials. Trial investigators can also provide extra information leading to more reliable risk of bias assessments than are achievable from trial reports (Section 4.6),⁴⁸ and if potential biases or errors are identified, they may be able to supply additional data to resolve or minimise these (Section 4.5.4).^7,9,43 Bringing together a group of international and multi‐disciplinary collaborators can also facilitate wider discussion and interpretation of results, and aid dissemination of key findings (Chapter 10).

The advantages shown in Table 2.1 focus on the synthesis of randomised trials to evaluate treatment effects, but IPD meta‐analyses have further advantages for non‐efficacy questions. In particular, they are pivotal in guiding and tailoring diagnostic strategies (Chapter 15), identifying risk and prognostic factors (Chapter 16), and individualising risk prediction (Chapter 17) to guide healthcare policy and practice.³²

2.4 What Are the Potential Challenges of an IPD Meta‐Analysis Project?

      The processes for collecting, checking and analysing IPD are more involved and complex than for conventional aggregate data reviews of the same topic, and therefore, more time and resources are required (Chapter 3).^7,9,43 Thus, before embarking on an IPD project, careful consideration about whether it is an appropriate course of action is needed (Section 2.6).^47,49

      As most IPD projects rely on collaboration with the teams responsible for the included trials, negotiating and maintaining relationships with investigators from different countries, settings and disciplines can take considerable time, effort, diplomacy and careful management (Section

Скачать книгу