Individual Participant Data Meta-Analysis: A Handbook for Healthcare Research provides a comprehensive introduction to the fundamental principles and methods that healthcare researchers need when ...considering, conducting or using individual participant data (IPD) meta-analysis projects. Written and edited by researchers with substantial experience in the field, the book details key concepts and practical guidance for each stage of an IPD meta-analysis project, alongside illustrated examples and summary learning points. Split into five parts, the book chapters take the reader through the journey from initiating and planning IPD projects to obtaining, checking, and meta-analysing IPD, and appraising and reporting findings. The book initially focuses on the synthesis of IPD from randomised trials to evaluate treatment effects, including the evaluation of participant-level effect modifiers (treatment-covariate interactions). Detailed extension is then made to specialist topics such as diagnostic test accuracy, prognostic factors, risk prediction models, and advanced statistical topics such as multivariate and network meta-analysis, power calculations, and missing data. Intended for a broad audience, the book will enable the reader to: Understand the advantages of the IPD approach and decide when it is needed over a conventional systematic review Recognise the scope, resources and challenges of IPD meta-analysis projects Appreciate the importance of a multi-disciplinary project team and close collaboration with the original study investigators Understand how to obtain, check, manage and harmonise IPD from multiple studies Examine risk of bias (quality) of IPD and minimise potential biases throughout the project Understand fundamental statistical methods for IPD meta-analysis, including two-stage and one-stage approaches (and their differences), and statistical software to implement them Clearly report and disseminate IPD meta-analyses to inform policy, practice and future research Critically appraise existing IPD meta-analysis projects Address specialist topics such as effect modification, multiple correlated outcomes, multiple treatment comparisons, non-linear relationships, test accuracy at multiple thresholds, multiple imputation, and developing and validating clinical prediction models Detailed examples and case studies are provided throughout.
To examine the effect of a (fictional) doctor working during the festive period on population health.
Natural experiment.
England, Wales, and the UK.
Age standardised annual mortality rates in ...England, Wales, and the UK from 1963, when the BBC first broadcast
, a fictional programme with a character called the Doctor who fights villains and intervenes to save others while travelling through space and time. Mortality rates were modelled in a time series analysis accounting for non-linear trends over time, and associations were estimated in relation to a new
episode broadcast during the previous festive period, 24 December to 1 January. An interrupted time series analysis modelled the shift in mortality rates from 2005, when festive episodes of
could be classed as a yearly Christmas intervention.
31 festive periods from 1963 have featured a new
episode, including 14 broadcast on Christmas Day. In time series analyses, an association was found between broadcasts during the festive period and subsequent lower annual mortality rates. In particular, episodes shown on Christmas Day were associated with 0.60 fewer deaths per 1000 person years (95% confidence interval 0.21 to 0.99; P=0.003) in England and Wales and 0.40 fewer deaths per 1000 person years (0.08 to 0.73; P=0.02) in the UK. The interrupted time series analysis showed a strong shift (reduction) in mortality rates from 2005 onwards in association with the
Christmas intervention, with a mean 0.73 fewer deaths per 1000 person years (0.21 to 1.26; P=0.01) in England and Wales and a mean 0.62 fewer deaths per 1000 person years (0.16 to 1.09; P=0.01) in the UK.
A new
episode shown every festive period, especially on Christmas Day, was associated with reduced mortality rates in England, Wales, and the UK, suggesting that a doctor working over the festive period could lower mortality rates. This finding reinforces why healthcare provision should not be taken for granted and may prompt the BBC and Disney+ to televise new episodes of
every festive period, ideally on Christmas Day.
Prognostic factor research aims to identify factors associated with subsequent clinical outcome in people with a particular disease or health condition. In this article, the second in the PROGRESS ...series, the authors discuss the role of prognostic factors in current clinical practice, randomised trials, and developing new interventions, and explain why and how prognostic factor research should be improved.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
When designing a study to develop a new prediction model with binary or time‐to‐event outcomes, researchers should ensure their sample size is adequate in terms of the number of participants (n) and ...outcome events (E) relative to the number of predictor parameters (p) considered for inclusion. We propose that the minimum values of n and E (and subsequently the minimum number of events per predictor parameter, EPP) should be calculated to meet the following three criteria: (i) small optimism in predictor effect estimates as defined by a global shrinkage factor of ≥0.9, (ii) small absolute difference of ≤ 0.05 in the model's apparent and adjusted Nagelkerke's R2, and (iii) precise estimation of the overall risk in the population. Criteria (i) and (ii) aim to reduce overfitting conditional on a chosen p, and require prespecification of the model's anticipated Cox‐Snell R2, which we show can be obtained from previous studies. The values of n and E that meet all three criteria provides the minimum sample size required for model development. Upon application of our approach, a new diagnostic model for Chagas disease requires an EPP of at least 4.8 and a new prognostic model for recurrent venous thromboembolism requires an EPP of at least 23. This reinforces why rules of thumb (eg, 10 EPP) should be avoided. Researchers might additionally ensure the sample size gives precise estimates of key predictor effects; this is especially important when key categorical predictors have few events in some categories, as this may substantially increase the numbers required.
IMPORTANCE: Systematic reviews and meta-analyses of individual participant data (IPD) aim to collect, check, and reanalyze individual-level data from all studies addressing a particular research ...question and are therefore considered a gold standard approach to evidence synthesis. They are likely to be used with increasing frequency as current initiatives to share clinical trial data gain momentum and may be particularly important in reviewing controversial therapeutic areas. OBJECTIVE: To develop PRISMA-IPD as a stand-alone extension to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) Statement, tailored to the specific requirements of reporting systematic reviews and meta-analyses of IPD. Although developed primarily for reviews of randomized trials, many items will apply in other contexts, including reviews of diagnosis and prognosis. DESIGN: Development of PRISMA-IPD followed the EQUATOR Network framework guidance and used the existing standard PRISMA Statement as a starting point to draft additional relevant material. A web-based survey informed discussion at an international workshop that included researchers, clinicians, methodologists experienced in conducting systematic reviews and meta-analyses of IPD, and journal editors. The statement was drafted and iterative refinements were made by the project, advisory, and development groups. The PRISMA-IPD Development Group reached agreement on the PRISMA-IPD checklist and flow diagram by consensus. FINDINGS: Compared with standard PRISMA, the PRISMA-IPD checklist includes 3 new items that address (1) methods of checking the integrity of the IPD (such as pattern of randomization, data consistency, baseline imbalance, and missing data), (2) reporting any important issues that emerge, and (3) exploring variation (such as whether certain types of individual benefit more from the intervention than others). A further additional item was created by reorganization of standard PRISMA items relating to interpreting results. Wording was modified in 23 items to reflect the IPD approach. CONCLUSIONS AND RELEVANCE: PRISMA-IPD provides guidelines for reporting systematic reviews and meta-analyses of IPD.
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