Calculating the power to examine treatment-covariate interactions when planning an individual participant data meta-analysis of randomized trials with a binary outcome.

Before embarking on an individual participant data meta-analysis (IPDMA) project, researchers and funders need assurance it is worth their time and cost. This should include consideration of how many studies are promising their IPD and, given the characteristics of these studies, the power of an IPDMA including them. Here, we show how to estimate the power of a planned IPDMA of randomized trials to examine treatment-covariate interactions at the participant level (ie, treatment effect modifiers). We focus on a binary outcome with binary or continuous covariates, and propose a three-step approach, which assumes the true interaction size is common to all trials. In step one, the user must specify a minimally important interaction size and, for each trial separately (eg, as obtained from trial publications), the following aggregate data: the number of participants and events in control and treatment groups, the mean and SD for each continuous covariate, and the proportion of participants in each category for each binary covariate. This allows the variance of the interaction estimate to be calculated for each trial, using an analytic solution for Fisher's information matrix from a logistic regression model. Step 2 calculates the variance of the summary interaction estimate from the planned IPDMA (equal to the inverse of the sum of the inverse trial variances from step 1), and step 3 calculates the corresponding power based on a two-sided Wald test. Stata and R code are provided, and two examples given for illustration. Extension to allow for between-study heterogeneity is also considered

Multivariate meta-analysis of multiple outcomes: characteristics and predictors of borrowing of strength from Cochrane reviews.

OBJECTIVES: Multivariate meta-analysis allows the joint synthesis of multiple outcomes accounting for their correlation. This enables borrowing of strength (BoS) across outcomes, which may lead to greater efficiency and even different conclusions compared to separate univariate meta-analyses. However, multivariate meta-analysis is complex to apply, so guidance is needed to flag (in advance of analysis) when the approach is most useful. STUDY DESIGN AND SETTING: We use 43 Cochrane intervention reviews to empirically investigate the characteristics of meta-analysis datasets that are associated with a larger BoS statistic (from 0 to 100%) when applying a bivariate meta-analysis of binary outcomes. RESULTS: Four characteristics were identified as strongly associated with BoS: the total number of studies, the number of studies with the outcome of interest, the percentage of studies missing the outcome of interest, and the largest absolute within-study correlation. Using these characteristics, we then develop a model for predicting BoS in a new dataset, which is shown to have good performance (an adjusted R2 of 50%). Applied examples are used to illustrate the use of the BoS prediction model. CONCLUSIONS: Cochrane reviewers mainly use univariate meta-analysis methods, but the identified characteristics associated with BoS and our subsequent prediction model for BoS help to flag when a multivariate meta-analysis may also be beneficial in Cochrane reviews with multiple binary outcomes. Extension to non-Cochrane reviews and other outcome types is still required

Development and external validation of the eFalls tool:a multivariable prediction model for the risk of ED attendance or hospitalisation with a fall or fracture in older adults

BackgroundFalls are common in older adults and can devastate personal independence through injury such as fracture and fear of future falls. Methods to identify people for falls prevention interventions are currently limited, with high risks of bias in published prediction models. We have developed and externally validated the eFalls prediction model using routinely collected primary care electronic health records (EHR) to predict risk of emergency department attendance/hospitalisation with fall or fracture within 1 year.MethodsData comprised two independent, retrospective cohorts of adults aged ≥65 years: the population of Wales, from the Secure Anonymised Information Linkage Databank (model development); the population of Bradford and Airedale, England, from Connected Bradford (external validation). Predictors included electronic frailty index components, supplemented with variables informed by literature reviews and clinical expertise. Fall/fracture risk was modelled using multivariable logistic regression with a Least Absolute Shrinkage and Selection Operator penalty. Predictive performance was assessed through calibration, discrimination and clinical utility. Apparent, internal–external cross-validation and external validation performance were assessed across general practices and in clinically relevant subgroups.ResultsThe model’s discrimination performance (c-statistic) was 0.72 (95% confidence interval, CI: 0.68 to 0.76) on internal–external cross-validation and 0.82 (95% CI: 0.80 to 0.83) on external validation. Calibration was variable across practices, with some over-prediction in the validation population (calibration-in-the-large, −0.87; 95% CI: −0.96 to −0.78). Clinical utility on external validation was improved after recalibration.ConclusionThe eFalls prediction model shows good performance and could support proactive stratification for falls prevention services if appropriately embedded into primary care EHR systems

Calculating the power of a planned individual participant data meta-analysis of randomised trials to examine a treatment-covariate interaction with a time-to-event outcome

Before embarking on an individual participant data meta-analysis (IPDMA) project, researchers should consider the power of their planned IPDMA conditional on the studies promising their IPD and their characteristics. Such power estimates help inform whether the IPDMA project is worth the time and funding investment, before IPD are collected. Here, we suggest how to estimate the power of a planned IPDMA of randomised trials aiming to examine treatment-covariate interactions at the participant-level (i.e., treatment effect modifiers). We focus on a time-to-event (survival) outcome with a binary or continuous covariate, and propose an approximate analytic power calculation that conditions on the actual characteristics of trials, for example, in terms of sample sizes and covariate distributions. The proposed method has five steps: (i) extracting the following aggregate data for each group in each trial—the number of participants and events, the mean and SD for each continuous covariate, and the proportion of participants in each category for each binary covariate; (ii) specifying a minimally important interaction size; (iii) deriving an approximate estimate of Fisher's information matrix for each trial and the corresponding variance of the interaction estimate per trial, based on assuming an exponential survival distribution; (iv) deriving the estimated variance of the summary interaction estimate from the planned IPDMA, under a common-effect assumption, and (v) calculating the power of the IPDMA based on a two-sided Wald test. Stata and R code are provided and a real example provided for illustration. Further evaluation in real examples and simulations is needed

Development and application of multivariate meta-analysis in medical research: borrowing strength across multiple correlated outcomes

Multivariate meta-analysis methods combine effect estimates for multiple correlated outcomes (such as systolic and diastolic blood pressure) from independent studies, utilising their between-study and within-study correlations. In contrast, a univariate meta-analysis pools effect estimates for each outcome independently. By using the multivariate over the univariate approach, there is a potential gain in information toward summary meta-analysis results, quantified by the Borrowing of Strength (BoS) statistic, a percentage reduction in the variance for the summary effect between the two approaches. This thesis examines BoS and multivariate meta-analysis applications in detail. Firstly, multivariate meta-analysis is applied to an individual participant data metaanalysis examining the effect of diet and exercise interventions during pregnancy. This shows results from the univariate and multivariate meta-analyses are similar. However, a review of 43 Cochrane reviews concludes that although results between the univariate and multivariate are often similar, a few multivariate meta-analyses do give important differences to results from univariate meta analyses, and these are shown to have a larger magnitude of BoS. This motivates research to identify predictors of BoS and to develop a model to predict BoS (in advance of analysis) to flag when researchers should consider multivariate meta-analysis. Additionally, an interactive tool is developed to investigate the relationship between the various characteristics and the magnitude of BoS. The magnitude of BoS is shown mathematically to be approximately bounded by the percentage of missing data for the outcome of interest. A novel application of bivariate meta-analysis is then proposed for trials with continuous outcomes analysed with final score or ANCOVA models, with examination in real examples and a simulation study. In conclusion, multivariate meta-analysis may provide important differences to univariate meta-analysis when BoS is large, and so researchers should consider the approach when BoS is expected to be large

PRIME-IPD SERIES Part 2. Retrieving, checking, and harmonizing data are underappreciated challenges in individual participant data meta-analyses.

No abstrac

Studies of the Mechanisms of TFIIH and Noncoding RNAs in Eukaryotic Transcription

The control of eukaryotic transcription is carefully orchestrated and involves many types of regulatory factors. Transcription is the underlying mechanism that controls all cellular processes and when left unchecked results in diseased cell states and cell death. Understanding the detailed mechanisms and processes of eukaryotic transcription is the goal of these studies. Inspired by our previous eukaryotic transcription kinetic studies, Chapter 2 describes identifying a factor that accelerates the rate of promoter escape. Spiking in vitro transcription assays with a nuclear extract resulted in an increase in the rate of in vitro transcription from the adenovirus major late promoter. With the understanding that many factors are involved in transcriptional regulation, we hypothesized that a factor could function to enhance the rate of transcription after being recruited to promoters. I set out to purify, identify, and characterize this factor. I developed a rate assay to monitor purification of the factor over several columns. The purified rate-accelerating factor was identified to be the general transcription factor TFIIH. Comparing my purified TFIIH to two standard TFIIH purifications revealed that high concentrations of TFIIH accelerated the rate of early transcription. Recent studies have identified thousands of long noncoding RNAs (lncRNAs) with the potential to regulate gene expression, some on a single gene level and others potentially regulating multiple genes through mechanisms controlling chromatin structure. At the time this work was started, there were no genome-wide methods to determine whether these lncRNAs interact directly with chromatin, and if so, where. I developed a method named ChOP-seq to identify the genomic regions with which the lncRNA HOTAIR associates. I was ultimately able to show RNA-dependent enrichment of specific genomic regions using the ChOP technique, identifying a diverse set of genes that may be regulated by HOTAIR. We are positioned to apply our new knowledge of ChOP assays to other ncRNAs. This method has the potential to extend our understanding of the mechanisms that contribute to epigenetic programming

Free surface flows emerging from beneath a semi-infinite plate with constant vorticity

The free surface flow past a semi-infinite horizontal plate in a finite-depth fluid is considered. It is assumed that the fluid is incompressible and inviscid and that the flow approaches a uniform shear flow downstream. Exact relations are derived using conservation of mass and momentum for the case where the downstream free surface is flat. The complete nonlinear problem is solved numerically using a boundary integral method and these waveless solutions are shown to exist only when the height of the plate above the bottom is greater than the height of the uniform shear flow. Interesting results are found for various values of the constant vorticity. Solutions with downstream surface waves are also considered, and nonlinear results of this type are compared with linear results found previously. These solutions can be used to model the flow near the stern of a (two-dimensional) ship

Two-stage or not two-stage? That is the question for IPD meta-analysis projects

Individual participant data meta-analysis (IPDMA) projects obtain, check, harmonise and synthesise raw data from multiple studies. When undertaking the meta-analysis, researchers must decide between a two-stage or a one-stage approach. In a two-stage approach, the IPD are first analysed separately within each study to obtain aggregate data (e.g., treatment effect estimates and standard errors); then, in the second stage, these aggregate data are combined in a standard meta-analysis model (e.g., common-effect or random-effects). In a one-stage approach, the IPD from all studies are analysed in a single step using an appropriate model that accounts for clustering of participants within studies and, potentially, between-study heterogeneity (e.g., a general or generalised linear mixed model). The best approach to take is debated in the literature, and so here we provide clearer guidance for a broad audience. Both approaches are important tools for IPDMA researchers and neither are a panacea. If most studies in the IPDMA are small (few participants or events), a one-stage approach is recommended due to using a more exact likelihood. However, in other situations, researchers can choose either approach, carefully following best practice. Some previous claims recommending to always use a one-stage approach are misleading, and the two-stage approach will often suffice for most researchers. When differences do arise between the two approaches, often it is caused by researchers using different modelling assumptions or estimation methods, rather than using one or two stages per se