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Meta-Analysis
Published in Trevor F. Cox, Medical Statistics for Cancer Studies, 2022
We finish this chapter with a short discussion on individual patient data (IPD), another name being individual participant data. If the original individual patient data are available for the studies that are going to be included in a meta-analysis, you can make a more accurate and deeper data synthesis and meta-analysis. This is obviously true, since you would have in your possession, so much more information than the summaries of aggregate data (AD) published in journals or elsewhere. You could easily re-create the aggregate data results for each study from the individual patient data for that study. You can go further, you can: (i) check the published results of each study; (ii) obtain the summary statistic of interest for a study if the study has published a different statistic; (iii) use baseline data as a covariate; (iv) carry out sub-group analyses and other pertinent analyses; and more besides.
Individual Participant Data Meta-Analysis
Published in Christopher H. Schmid, Theo Stijnen, Ian R. White, Handbook of Meta-Analysis, 2020
Systematic reviews with individual participant data (IPD) meta-analysis collect, validate, and re-analyze individual-level data recorded for each participant recruited in each included study—rather than using aggregate summary data, commonly obtained from journal articles or trial reports, as in most conventional systematic reviews. Underpinning principles and many supporting processes are the same. However, IPD meta-analyses can incorporate unreported data, allow standardization across studies, and offer considerable potential to carry out more nuanced and sophisticated analyses than are possible with aggregate data.
Cost-utility of azithromycin in patients with severe asthma
Published in Journal of Asthma, 2022
Jefferson Antonio Buendía, Diana Guerrero Patiño, John Edwin Feliciano-Alfonso
Multiple parameters were derived from published research and local data, which are presented in Table 1. A recent individual participant data meta-analysis of azithromycin in patients with severe asthma (15) was used to extract data on the relative risk (RR) of exacerbation rate. In this study, the relative risk of asthma exacerbations was 0.61 (CI 95% 0.49–0.78). Asthma exacerbations in this meta-analysis were defined as the use of systemic corticosteroid burst, (or SCS, outpatient visits with at least three days of high-dose oral corticosteroids (OCS), or a single SCS injection) emergency department (ED) or hospitalization. Two studies, AZISAST (16) and AMAZES (14), contributed data to the IPD meta-analyses of the primary exacerbation endpoint. In AZISAST, 70 patients (65%) were classified as non-eosinophilic and 38 (35%) as eosinophilic; all were using ICS. In AMAZES, 224 (53%) were non-eosinophilic (99.6% using ICS) and 196 (47%) were eosinophilic (100% using ICS).
Tuberculosis in children with severe acute malnutrition
Published in Expert Review of Respiratory Medicine, 2022
Bryan J Vonasek, Kendra K Radtke, Paula Vaz, W Chris Buck, Chishala Chabala, Eric D McCollum, Olivier Marcy, Elizabeth Fitzgerald, Alexander Kondwani, Anthony J Garcia-Prats
There are significant gaps in the literature regarding the impact of SAM on antituberculosis drug pharmacology in children. An improved understanding is unlikely with the typical pediatric study designs in TB, which are usually observational with small sample sizes (n < 100, but often <50) assessing one dosing schema across a wide range of ages (0–18 years) and body weights (4–50 kg). With this design, even if 50% of enrolled children have SAM, there will be insufficient power to reliably capture the influence of SAM due to the complex and dynamic factors driving pharmacokinetics and pharmacodynamics in children. One approach to overcome these challenges is to pool data from several clinical trials or observational studies and perform individual participant data meta-analysis with powerful quantitative analytical tools (e.g. nonlinear mixed-effects modeling). A large database of pharmacokinetics and treatment outcomes that includes diverse populations with substantial SAM prevalence and children living with HIV would be needed and is possible with currently published studies. Well-designed clinical trials including children with SAM and controls without SAM that evaluate pharmacokinetics and post-treatment outcomes are also necessary. Clearly, malnourished children have poorer TB outcomes, but the driving mechanism(s) and solutions remain unclear. Using high-quality models incorporating pharmacokinetics and outcomes data, we can describe these mechanisms and inform dosing approaches that may improve outcomes for children with SAM.
Emerging data on rifampicin pharmacokinetics and approaches to optimal dosing in children with tuberculosis
Published in Expert Review of Clinical Pharmacology, 2022
Kendra K. Radtke, Elin M. Svensson, Louvina E. van der Laan, Anneke C. Hesseling, Radojka M. Savic, Anthony J. Garcia-Prats
Understanding rifampicin pharmacokinetics in diverse populations and factors influencing pharmacokinetic parameters are essential to establishing safe and effective dosing for all children worldwide. Population pharmacokinetic models are useful for determining the optimal dose of a drug. However, when the models differ substantially, as is the case with rifampicin, so does the predicted optimal dose from each model (Figure 2). This is problematic, especially when the differences between models cannot be explained. Nevertheless, nearly all rifampicin pharmacokinetic models demonstrate that the current WHO weight band dosing is suboptimal and higher doses are needed to achieve target exposures in most children (Figure 2). Reconciling these differences through pooling available pharmacokinetic data and performing individual participant data meta-analysis would be a high-yield approach that can inform optimal rifampicin dosing practices for children. Furthermore, including pharmacokinetics data at doses > 20 mg/kg will facilitate characterizing instances of nonlinearity in absorption, bioavailability or clearance in pediatric populations [35].