27 research outputs found
Joint models for longitudinal and time-to-event data: an updated review of reporting quality with a view to meta-analysis.
No full textMaria Sudell, Catrin Tudur Smith, Ruwanthi Kolamunnage-Dona, Tom Spain. Joint models for longitudinal and time-to-event data: an updated review of reporting quality with a view to meta-analysis. PROSPERO 2025 CRD420251023469. Available from https://www.crd.york.ac.uk/PROSPERO/view/CRD420251023469
Methodology and software for joint modelling of time-to-event data and longitudinal outcomes across multiple studies
No full textThesis Title: Methodology and Software for Joint Modelling of Time-to-Event Data and Longitudinal Outcomes Across Multiple Studies Author: Maria Sudell Introduction and Aims: Univariate joint models for longitudinal and time-to-event data simultaneously model one outcome that is repeatedly measured over time, with another outcome which measures the time until the occurrence of an event. They have been increasingly used in the literature to account for dropout in longitudinal studies, to include time-varying covariates in time-to-event analyses, or to investigate links between longitudinal and time-to-event outcomes. Meta-analysis is the quantitative pooling of data from multiple studies. Such analyses can provide increased sample size and so detect small covariate effects. Modelling of multi-study data requires accounting for the clustering of individuals within studies and careful consideration of heterogeneity between studies. Research concerning methodology for modelling of joint longitudinal and time-to-event data in a multi-study or meta-analytic setting does not currently exist. This thesis develops novel methodologies and software for the modelling of multi-study joint longitudinal and time-to-event ... (continues
Joint or simultaneous modelling of related longitudinal and time-to-event data for a network of treatments across multiple data sources
No full textLongitudinal data is recorded repeatedly over time, allowing trends over time to examined as well as results at a particular timepoint (examples include monthly blood pressure measurements, repeated laboratory measurements or regular mental health assessments). Time-to-event or survival data records the time until an individual experiences a clinical event of interest or withdraws from the dataset for unrelated reasons (for example time until first stroke, or time until hospital discharge). Commonly in healthcare data, related longitudinal and time-to-event data exists (for example, blood pressure measured repeatedly over time might be related in some way or predictive of time to first stroke). Ignoring this relationship could lead to misleading or biased results in analyses, so joint models that simultaneously evaluate the longitudinal and time-to-event outcomes and their relationship are useful.
In healthcare, many treatments may exist for a particular condition, for a given group of patients (a population). To make properly informed decisions about how these treatments compare to each other, we need to be able to compare them simultaneously. This can be difficult, if different data sources only involve a subset of the possible treatments (for example, if many clinical trials have been conducted for a given condition, but they each examine subsets of the possible treatments).
Network Meta Analysis (NMA) provides an approach to pool data from multiple trials, each of which compare a subset of treatments from the complete set of possible treatments for a population (as long as a connected “network” of treatments can be drawn from the available data). Approaches for separate longitudinal NMA and separate time-to-event NMA currently exist, but not for NMA involving both longitudinal and time-to-event data. This research provides novel methodology and code linking joint modelling with NMA approaches, to better allow evaluation of all available treatment options and their effects on longitudinal and time-to-event outcomes of interest.
The proposed methodology and code is applied to a multi-study cardiovascular dataset, containing repeated blood pressure measurements, and the times until various cardiovascular events (stroke, myocardial infarction, death), for a network of anti-hypertensive treatments
Individual patient data meta-analyses compared with meta-analyses based on aggregate data. (Review)
No full textBackground
Meta‐analyses based on individual participant data (IPD‐MAs) allow more powerful and uniformly consistent analyses as well as better characterisation of subgroups and outcomes, compared to those which are based on aggregate data (AD‐MAs) extracted from published trial reports. However, IPD‐MAs are a larger undertaking requiring greater resources than AD‐MAs. Researchers have compared results from IPD‐MA against results obtained from AD‐MA and reported conflicting findings. We present a methodology review to summarise this empirical evidence .
Objectives
To review systematically empirical comparisons of meta‐analyses of randomised trials based on IPD with those based on AD extracted from published reports, to evaluate the level of agreement between IPD‐MA and AD‐MA and whether agreement is affected by differences in type of effect measure, trials and participants included within the IPD‐MA and AD‐MA, and whether analyses were undertaken to explore the main effect of treatment or a treatment effect modifier.
Search methods
An electronic search of the Cochrane Library (includes Cochrane Database of Systematic Reviews, Database of Abstracts of Reviews of Effectiveness, CENTRAL, Cochrane Methodology Register, HTA database, NHS Economic Evaluations Database), MEDLINE, and Embase was undertaken up to 7 January 2016. Potentially relevant articles that were known to any of the review authors and reference lists of retrieved articles were also checked.
Selection criteria
Studies reporting an empirical comparison of the results of meta‐analyses of randomised trials using IPD with those using AD. Studies were included if sufficient numerical data, comparing IPD‐MA and AD‐MA, were available in their reports.
Data collection and analysis
Two review authors screened the title and abstract of identified studies with full‐text publications retrieved for those identified as eligible or potentially eligible. A ‘quality’ assessment was done and data were extracted independently by two review authors with disagreements resolved by involving a third author. Data were summarised descriptively for comparisons where an estimate of effect measure and corresponding precision have been provided both for IPD‐MA and for AD‐MA in the study report. Comparisons have been classified according to whether identical effect measures, identical trials and patients had been used in the IPD‐MA and the AD‐MA, and whether the analyses were undertaken to explore the main effect of treatment, or to explore a potential treatment effect modifier.
Effect measures were transformed to a standardised scale (z scores) and scatter plots generated to allow visual comparisons. For each comparison, we compared the statistical significance (at the 5% two‐sided level) of an IPD‐MA compared to the corresponding AD‐MA and calculated the number of discrepancies. We examined discrepancies by type of analysis (main effect or modifier) and according to whether identical trials, patients and effect measures had been used by the IPD‐MA and AD‐MA. We calculated the average of differences between IPD‐MA and AD‐MA (z scores, ratio effect estimates and standard errors (of ratio effects)) and 95% limits of agreement.
Main results
From the 9330 reports found by our searches, 39 studies were eligible for this review with effect estimate and measure of precision extracted for 190 comparisons of IPD‐MA and AD‐MA. We classified the quality of studies as ‘no important flaws’ (29 (74%) studies) or ‘possibly important flaws’ (10 (26%) studies).
A median of 4 (interquartile range (IQR): 2 to 6) comparisons were made per study, with 6 (IQR 4 to 11) trials and 1225 (542 to 2641) participants in IPD‐MAs and 7 (4 to 11) and 1225 (705 to 2541) for the AD‐MAs. One hundred and forty‐four (76%) comparisons were made on the main treatment effect meta‐analysis and 46 (24%) made using results from analyses to explore treatment effect modifiers.
There is agreement in statistical significance between the IPD‐MA and AD‐MA for 152 (80%) comparisons, 23 of which disagreed in direction of effect. There is disagreement in statistical significance for 38 (20%) comparisons with an excess proportion of IPD‐MA detecting a statistically significant result that was not confirmed with AD‐MA (28 (15%)), compared with 10 (5%) comparisons with a statistically significant AD‐MA that was not confirmed by IPD‐MA. This pattern of disagreement is consistent for the 144 main effect analyses but not for the 46 comparisons of treatment effect modifier analyses. Conclusions from some IPD‐MA and AD‐MA differed even when based on identical trials, participants (but not necessarily identical follow‐up) and treatment effect measures. The average difference between IPD‐MA and AD‐MA in z scores, ratio effect estimates and standard errors is small but limits of agreement are wide and include important differences in both directions. Discrepancies between IPD‐MA and AD‐MA do not appear to increase as the differences between trials and participants increase.
Authors' conclusions
IPD offers the potential to explore additional, more thorough, and potentially more appropriate analyses compared to those possible with AD. But in many cases, similar results and conclusions can be drawn from IPD‐MA and AD‐MA. Therefore, before embarking on a resource‐intensive IPD‐MA, an AD‐MA should initially be explored and researchers should carefully consider the potential added benefits of IPD
Mulierum amicus: or, The womans friend plainly discovering all those diseases that are incident to that sex only, and advising them to cure, either 1. By those receipts prescribed. Or, 2. By certain secret arcanums and specifical medicines. The author hereof living at the sign of the Golden Ball and Flower-Pot in Mark-Lane in Tower-street, and is lycensiate in physick, and student in chymistry; known commonly by the name of Nich. Sudell.
No full textDevelopment and Application of Joint Modelling of Longitudinal and Event-Time Data in Frequentist and Bayesian Settings: Addressing the Uncertainty of Association Structure Selection
No full textThesis title: Development and Application of Joint Modelling of Longitudinal and Event-Time Data in frequentist and Bayesian settings: Addressing the Uncertainty of Association Structure Selection
Author: Maha Abid Alsefri
Background and aims: Over the last decade, there has been an increasing interest in applying joint models to related longitudinal and time-to-event outcome data due to their ability to reduce bias in the estimated parameters and individual-level patients' risks predictions, and availability of user-friendly software. Joint models consist of two linked submodels; a longitudinal submodel and a time-to-event submodel. These two submodels are connected through an association structure, a function that represents the relationship between the two outcomes. The choice of the association is an important factor for specification of the joint model, and is usually based on the clinical information regarding the application area. However, the current research in this aspect is considerably limited. Often, a single best fit joint model is used to draw the inference from estimated parameters, which overlooks the issue of model uncertainty entirely. The aim of this thesis is to develop appropriate statistical methodologies to improve the inference of the joint model when background knowledge to support the selection of an association structure is unavailable.
Method: A comprehensive review of joint models in Bayesian framework is undertaken to understand the approaches of using background knowledge for joint modelling methodologies and limitations of the current approaches, and to identify future directions. Two novel weighted average (WA) approaches are developed to collate information from multiple joint models with different association structures. The first approach is based on the inverse-variance (IV) weighting and the second is on the Monte Carlo (MC) sampling technique. The proposed approaches are investigated through simulation studies in both frequentist and Bayesian settings of the joint model, and illustrated with real-world clinical data. The methods are applied to explore the prediction of longitudinal biomarkers for diagnosing early recurrence after liver resection for hepatocellular carcinoma (HCC).
Results: The simulation studies showed the proposed IV WA approach with an adjusted variance and MC WA approach perform well in estimating the parameters of interest close to the true value even when a model with a wrong association structure was included in the weighting process. Further, even when absence of the model with the true association structure, the two WA approaches were capable of estimating the parameters close to the true value. As observed with the illustrative data, the variability of the combined effect estimated from both WA approaches was consistent with the variability of separate model parameter estimates. The two approaches also showed an improved prediction of the biomarker values on risks of HCC recurrence.
Conclusion: The weighted average approaches developed within this thesis provide readily accessible methods for joint models when background knowledge to support the selection of a single association structure is unavailable. Incorporating an accurate association structure is a key factor of the joint model specification. The proposed methods may facilitate greater use of the joint models in health research making more transparent estimation of covariate effects
The analysis and reporting of time to event data in randomised controlled trials : impact on evidence synthesis
No full textIntroduction and aims: The most commonly used approaches for the analysis of time-to-event (TTE) outcomes impose an assumption of proportional hazards (PH), such that the hazard ratio (HR) is assumed to be constant over time. Meta-analysis of TTE data is most commonly based on extracting or estimating the HR from individual trials, and so again assumes PH. Methods are available for assessing the validity of the PH assumption, however, the assumption is not always checked or reported for validity. This is a problem for meta-analysis, where different assumptions may have been made in the analysis of each included study. The aim of this thesis is to investigate how often the PH assumption is assessed within Randomised Controlled Trials (RCTs) and meta-analysis, including understanding the impact of non-PH on meta-analyses. This is of particular importance as current research has focused on alternative methodology, without knowing what impact non-PH may have on results. Methods: The thesis summarises the results from a novel systematic review (SR) of the reporting of meta-analysis of TTE outcomes that have assumed PH, and how often the results of the PH assumption were reported. Two further SRs of PH assumption ... (continues
Exploring the potential of spinal registry data for the use in clinical trials
No full textBackground: Sciatica describes the symptoms of low-back and leg pain most commonly due to a herniated disc that presses on the sciatic nerve. If persistent, invasive methods such as surgical microdiscectomy are required. Although being a surgery with relatively small incisions, it bears some risks of adverse events (AEs), e.g. durotomy, wound infection or in rare cases even nerve root damage. Observational registries allow for continuous data collection over indefinite time for numerous patients. One can therefore gain additional insight in subgroup demographics of the patient population and rare events. Furthermore, large numbers of patients and observations can improve the performance of prediction models.
Purpose: The aims of this study are to: (1) provide a comprehensive overview of the collected dataset from the Spine Tango registry; (2) determine the best method for imputing missing data in this routinely collected registry data set; (3) assess the predictive values of patient characteristics on patient-reported outcome measures (PROMs) and complications during surgery; and (4) examine the utilization of registries in both clinical trials and observational studies and identify strategies to increase their impact on clinical trials.
Methods: To understand the patient population and potential relationships among collected variables, thorough descriptive statistics were performed. Simulation studies were conducted to determine the best approach for imputing PROM items and scores, including the examination of missingness percentages, mechanisms, and cut-off point score calculations. The focus of prediction modeling was the routinely collected Core Outcome Measurement Index (COMI) and complications. Patients with sciatica were identified in collaboration with the Spine Tango committee, and various model approaches were compared for goodness of fit and prediction accuracy, including regression and mixed models. A literature review of both randomised controlled trials and observational studies was conducted, comparing differences in missing data, collected outcomes, study length, number of patients, and registry use. Case studies of successful registry utilization in other clinical areas were analyzed to identify potential for implementation in the present clinical focus.
Results: The international nature of the Spine Tango registry led to variability in documentation and data collection across countries. The simulation studies showed that item-based imputation was superior to score-based imputation in most scenarios. Mixed models with random intercepts and slopes, as well as non-linear time terms, performed best in terms of model fit. Logistic regression models that defined complications as outcome were able to identify risk factors, such as prior surgery, level of spine of physical status. The utilization of registries in the field of this clinical population is underutilized, and studies from other areas demonstrate that registry use can reduce trial costs by facilitating patient identification, data collection, and event detection, as well as reducing trial-specific patient visits and improving patient retention.
Conclusion: The potential of routinely collected registry data remains under-utilized within the sciatica-affected patient population. The noteworthy resemblances observed between observational data and randomized controlled trial data, both in descriptive statistics and prognostic factors, underscore the comparability of these sources and advocate for the integration of registry data in this domain. While the integration of a registry into a trial presents complexities, successful endeavors in related fields point to an innovative trial design that harmonizes these two research approaches
Correction to: joint models for longitudinal and time-to-event data: a review of reporting quality with a view to meta-analysis
No full textFollowing publication of the original article [1] the authors reported that reference 15 (Cella et al.) had been incorrectly replaced with a duplicate of Brombin et al. during publication
Addressing the Selection Uncertainty in Association Structure in the Joint Modelling Framework
No full textInvited presentation given at the World Statistics Conference in The Hague, Netherlands, in October 202
