Julian P T Higgins’s research while affiliated with University of Bristol and other places

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Publications (405)


A structural description of biases that generate immortal time
  • Article

November 2024

Epidemiology

Miguel A. Hernán

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Jonathan A.C. Sterne

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Julian P.T. Higgins

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[...]

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Sonia Hernández-Díaz

Immortal time arises when individuals in the analysis are either selected based on post-assignment eligibility criteria or assigned to treatment strategies based on post-eligibility information. Explicit target trial emulation prevents the introduction of immortal time in survival analyses of observational data because it synchronizes eligibility and treatment assignment at the start of follow-up. Describing the structure of the biases that generate immortal time is facilitated by specifying the target trial so that the procedures to determine eligibility and assignment can be appropriately evaluated. Selection based on eligibility criteria applied after treatment assignment at the start of follow-up results in immortal time when the analysis starts the follow-up at assignment. Misclassification of assignment to treatment strategies based on treatment received after the start of follow-up results in immortal time when the treatment strategies are not distinguishable at the start of follow-up. The above selection and misclassification can be represented using causal diagrams. We summarize analytic approaches that prevent immortal time when longitudinal data are available from the time of treatment assignment. The term “immortal time bias” suggests that the source of the bias is the immortal time, but it is selection or misclassification that generates the immortal time, leading to bias.


A Complex Meta-Regression Model to Identify Effective Features of Interventions From Multi-Arm, Multi-Follow-Up Trials

October 2024

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3 Reads

Statistics in Medicine

Network meta‐analysis (NMA) combines evidence from multiple trials to compare the effectiveness of a set of interventions. In many areas of research, interventions are often complex, made up of multiple components or features. This makes it difficult to define a common set of interventions on which to perform the analysis. One approach to this problem is component network meta‐analysis (CNMA) which uses a meta‐regression framework to define each intervention as a subset of components whose individual effects combine additively. In this article, we are motivated by a systematic review of complex interventions to prevent obesity in children. Due to considerable heterogeneity across the trials, these interventions cannot be expressed as a subset of components but instead are coded against a framework of characteristic features. To analyse these data, we develop a bespoke CNMA‐inspired model that allows us to identify the most important features of interventions. We define a meta‐regression model with covariates on three levels: intervention, study, and follow‐up time, as well as flexible interaction terms. By specifying different regression structures for trials with and without a control arm, we relax the assumption from previous CNMA models that a control arm is the absence of intervention components. Furthermore, we derive a correlation structure that accounts for trials with multiple intervention arms and multiple follow‐up times. Although, our model was developed for the specifics of the obesity data set, it has wider applicability to any set of complex interventions that can be coded according to a set of shared features.


Mapping between measurement scales in meta‐analysis, with application to measures of body mass index in children

October 2024

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2 Reads

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1 Citation

Research Synthesis Methods

Quantitative evidence synthesis methods aim to combine data from multiple medical trials to infer relative effects of different interventions. A challenge arises when trials report continuous outcomes on different measurement scales. To include all evidence in one coherent analysis, we require methods to “map” the outcomes onto a single scale. This is particularly challenging when trials report aggregate rather than individual data. We are motivated by a meta‐analysis of interventions to prevent obesity in children. Trials report aggregate measurements of body mass index (BMI) either expressed as raw values or standardized for age and sex. We develop three methods for mapping between aggregate BMI data using known or estimated relationships between measurements on different scales at the individual level. The first is an analytical method based on the mathematical definitions of z‐scores and percentiles. The other two approaches involve sampling individual participant data on which to perform the conversions. One method is a straightforward sampling routine, while the other involves optimization with respect to the reported outcomes. In contrast to the analytical approach, these methods also have wider applicability for mapping between any pair of measurement scales with known or estimable individual‐level relationships. We verify and contrast our methods using simulation studies and trials from our data set which report outcomes on multiple scales. We find that all methods recreate mean values with reasonable accuracy, but for standard deviations, optimization outperforms the other methods. However, the optimization method is more likely to underestimate standard deviations and is vulnerable to non‐convergence.


Treatment Effects in Randomized and Nonrandomized Studies of Pharmacological Interventions: A Meta-Analysis

September 2024

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24 Reads

JAMA Network Open

Importance Randomized clinical trials (RCTs) are widely regarded as the methodological benchmark for assessing clinical efficacy and safety of health interventions. There is growing interest in using nonrandomized studies to assess efficacy and safety of new drugs. Objective To determine how treatment effects for the same drug compare when evaluated in nonrandomized vs randomized studies. Data Sources Meta-analyses published between 2009 and 2018 were identified in MEDLINE via PubMed and the Cochrane Database of Systematic Reviews. Data analysis was conducted from October 2019 to July 2024. Study Selection Meta-analyses of pharmacological interventions were eligible for inclusion if both randomized and nonrandomized studies contributed to a single meta-analytic estimate. Data Extraction and Synthesis For this meta-analysis using a meta-epidemiological framework, separate summary effect size estimates were calculated for nonrandomized and randomized studies within each meta-analysis using a random-effects model and then these estimates were compared. The reporting of this study followed the Guidelines for Reporting Meta-Epidemiological Methodology Research and relevant portions of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting guideline. Main Outcome and Measures The primary outcome was discrepancies in treatment effects obtained from nonrandomized and randomized studies, as measured by the proportion of meta-analyses where the 2 study types disagreed about the direction or magnitude of effect, disagreed beyond chance about the effect size estimate, and the summary ratio of odds ratios (ROR) obtained from nonrandomized vs randomized studies combined across all meta-analyses. Results A total of 346 meta-analyses with 2746 studies were included. Statistical conclusions about drug benefits and harms were different for 130 of 346 meta-analyses (37.6%) when focusing solely on either nonrandomized or randomized studies. Disagreements were beyond chance for 54 meta-analyses (15.6%). Across all meta-analyses, there was no strong evidence of consistent differences in treatment effects obtained from nonrandomized vs randomized studies (summary ROR, 0.95; 95% credible interval [CrI], 0.89-1.02). Compared with experimental nonrandomized studies, randomized studies produced on average a 19% smaller treatment effect (ROR, 0.81; 95% CrI, 0.68-0.97). There was increased heterogeneity in effect size estimates obtained from nonrandomized compared with randomized studies. Conclusions and Relevance In this meta-analysis of treatment effects of pharmacological interventions obtained from randomized and nonrandomized studies, there was no overall difference in effect size estimates between study types on average, but nonrandomized studies both overestimated and underestimated treatment effects observed in randomized studies and introduced additional uncertainty. These findings suggest that relying on nonrandomized studies as substitutes for RCTs may introduce additional uncertainty about the therapeutic effects of new drugs.



Prevalence of problematic pharmaceutical opioid use in patients with chronic non‐cancer pain: A systematic review and meta‐analysis

August 2024

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19 Reads

Addiction

Background and aims Chronic non‐cancer pain (CNCP) is one of the most common causes of disability globally. Opioid prescribing to treat CNCP remains widespread, despite limited evidence of long‐term clinical benefit and evidence of harm such as problematic pharmaceutical opioid use (POU) and overdose. The study aimed to measure the prevalence of POU in CNCP patients treated with opioid analgesics. Method A comprehensive systematic literature review and meta‐analysis was undertaken using MEDLINE, Embase and PsycINFO databases from inception to 27 January 2021. We included studies from all settings with participants aged ≥ 12 with non‐cancer pain of ≥ 3 months duration, treated with opioid analgesics. We excluded case–control studies, as they cannot be used to generate prevalence estimates. POU was defined using four categories: dependence and opioid use disorder (D&OUD), signs and symptoms of D&OUD (S&S), aberrant behaviour (AB) and at risk of D&OUD. We used a random‐effects multi‐level meta‐analytical model. We evaluated inconsistency using the I ² statistic and explored heterogeneity using subgroup analyses and meta‐regressions. Results A total of 148 studies were included with > 4.3 million participants; 1% of studies were classified as high risk of bias. The pooled prevalence was 9.3% [95% confidence interval (CI) = 5.7–14.8%; I ² = 99.9%] for D&OUD, 29.6% (95% CI = 22.1–38.3%, I ² = 99.3%) for S&S and 22% (95% CI = 17.4–27.3%, I ² = 99.8%) for AB. The prevalence of those at risk of D&OUD was 12.4% (95% CI = 4.3–30.7%, I ² = 99.6%). Prevalence was affected by study setting, study design and diagnostic tool. Due to the high heterogeneity, the findings should be interpreted with caution. Conclusions Problematic pharmaceutical opioid use appears to be common in chronic pain patients treated with opioid analgesics, with nearly one in 10 experiencing dependence and opioid use disorder, one in three showing signs and symptoms of dependence and opioid use disorder and one in five showing aberrant behaviour.




Flowchart of the study selection
Methodological review of NMA bias concepts provides groundwork for the development of a list of concepts for potential inclusion in a new risk of bias tool for network meta-analysis (RoB NMA Tool)
  • Article
  • Full-text available

January 2024

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145 Reads

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4 Citations

Systematic Reviews

Introduction Network meta-analyses (NMAs) have gained popularity and grown in number due to their ability to provide estimates of the comparative effectiveness of multiple treatments for the same condition. The aim of this study is to conduct a methodological review to compile a preliminary list of concepts related to bias in NMAs. Methods and analysis We included papers that present items related to bias, reporting or methodological quality, papers assessing the quality of NMAs, or method papers. We searched MEDLINE, the Cochrane Library and unpublished literature (up to July 2020). We extracted items related to bias in NMAs. An item was excluded if it related to general systematic review quality or bias and was included in currently available tools such as ROBIS or AMSTAR 2. We reworded items, typically structured as questions, into concepts (i.e. general notions). Results One hundred eighty-one articles were assessed in full text and 58 were included. Of these articles, 12 were tools, checklists or journal standards; 13 were guidance documents for NMAs; 27 were studies related to bias or NMA methods; and 6 were papers assessing the quality of NMAs. These studies yielded 99 items of which the majority related to general systematic review quality and biases and were therefore excluded. The 22 items we included were reworded into concepts specific to bias in NMAs. Conclusions A list of 22 concepts was included. This list is not intended to be used to assess biases in NMAs, but to inform the development of items to be included in our tool.

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Meta-regression of genome-wide association studies to estimate age-varying genetic effects

January 2024

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16 Reads

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2 Citations

European Journal of Epidemiology

Fixed-effect meta-analysis has been used to summarize genetic effects on a phenotype across multiple Genome-Wide Association Studies (GWAS) assuming a common underlying genetic effect. Genetic effects may vary with age (or other characteristics), and not allowing for this in a GWAS might lead to bias. Meta-regression models between study heterogeneity and allows effect modification of the genetic effects to be explored. The aim of this study was to explore the use of meta-analysis and meta-regression for estimating age-varying genetic effects on phenotypes. With simulations we compared the performance of meta-regression to fixed-effect and random -effects meta-analyses in estimating (i) main genetic effects and (ii) age-varying genetic effects (SNP by age interactions) from multiple GWAS studies under a range of scenarios. We applied meta-regression on publicly available summary data to estimate the main and age-varying genetic effects of the FTO SNP rs9939609 on Body Mass Index (BMI). Fixed-effect and random-effects meta-analyses accurately estimated genetic effects when these did not change with age. Meta-regression accurately estimated both main genetic effects and age-varying genetic effects. When the number of studies or the age-diversity between studies was low, meta-regression had limited power. In the applied example, each additional minor allele (A) of rs9939609 was inversely associated with BMI at ages 0 to 3, and positively associated at ages 5.5 to 13. Our findings challenge the assumption that genetic effects are consistent across all ages and provide a method for exploring this. GWAS consortia should be encouraged to use meta-regression to explore age-varying genetic effects.


Citations (71)


... Specific tools are available to appraise RoB relevant to different study designs (e.g. NTP OHAT CAT (OHAT-NTP, 2015), Cochrane RoB-2 (Sterne et al., 2019), Cochrane ROBINS-E (Higgins et al., 2024), NESR RoB-NObs (NESR, 2019). Such tools facilitate the formulation of RoB judgements on RoB domains identified as critical for each study design. ...

Reference:

Guidance for establishing and applying tolerable upper intake levels for vitamins and essential minerals
A tool to assess risk of bias in non-randomized follow-up studies of exposure effects (ROBINS-E)
  • Citing Article
  • March 2024

Environment International

... Recognising the limitations of NMA, particularly regarding transitivity and potential biases such as publication and selection bias, we propose exploring future research directions to enhance its robustness. One promising avenue lies in strategically integrating alternative quantitative methodologies alongside NMA (Lunny et al. 2024). While bias adjustment techniques can be valuable for addressing inconsistencies within the existing data used for NMA, trial sequential analysis (TSA) plays a crucial role in informing the design of future trials. ...

Methodological review of NMA bias concepts provides groundwork for the development of a list of concepts for potential inclusion in a new risk of bias tool for network meta-analysis (RoB NMA Tool)

Systematic Reviews

... The copyright holder for this preprint this version posted June 10, 2023. ; https://doi.org/10.1101/2023.06.07.23291014 doi: medRxiv preprint currently being developed [59]). If genetic instruments for such changes could be identified, this would be a step towards enabling the exploration of sleep traits effects that vary with age or following a cancer diagnosis. ...

Meta-regression of genome-wide association studies to estimate age-varying genetic effects

European Journal of Epidemiology

... ROB2 encompasses bias arising from the randomization process, deviations from intended interventions, missing outcome data, measurement of the outcome, selection of the reported result, and the overall risk of bias. Responses were assessed as low, some concerns, and high risk of bias for each item [14,15]. RoBANS 2 evaluated low, unclear, and high risk of bias for eight dimensions: selection bias due to the selection of inappropriate comparison target group, selection bias due to inappropriate intervention or inappropriate selection of exposure group or patient group, selection bias due to inappropriate confounder confirmation and consideration, performance bias due to inappropriate intervention or inappropriate exposure measurement, detection bias due to inappropriate blinding of assessors, and detection bias due to inappropriate outcome assessment methods [16]. ...

Ten tips for successful assessment of risk of bias in randomized trials using the RoB 2 tool: Early lessons from Cochrane

... 3,36 Recent reviews reported that the impact of heatwaves on respiratory mortality varies globally-compared to the more pronounced association between heatwaves and cardiovascular mortality. 37,38 While heatwaves were significantly associated with increased respiratory mortality in China, the results from Korea are inconclusive and tend to have wider confidence intervals. 37,38 Studies show that over the last two decades, there has been a decline in heat-related respiratory mortality in three Northeast Asian countries-Japan, Taiwan, and Korea. ...

Heat impacts on human health in the Western Pacific Region: an umbrella review
  • Citing Article
  • November 2023

The Lancet Regional Health - Western Pacific

... There was a significant difference between the number of patients getting tested for molecular sequencing during 2008-2015 compared to 2016-2022 (p < 0.0001). The integration of molecular characterization into the treatment planning process is essential, as evidenced by the distinct outcomes associated with specific mutations [30,31]. The identification of actionable mutations, such as EGFR, ALK, PD-L1, and ROS-1, has transformed the therapeutic landscape, allowing for more personalized and effective treatment strategies [32][33][34][35]. ...

Genomic Landscape and Actionable Mutations of Brain Metastases derived from Non-Small Cell Lung Cancer: a systematic review

Neuro-Oncology Advances

... To provide a complete summary of KT, we will use the Risk of Bias due to Missing Evidence tool to assess the meta-analysis results. 87 Briefly, three steps will be conducted following the tool: select meta-analyses related to outcome (step 1), determine which eligible studies have missing results (step 2) and consider potential for missing studies (step 3). Response options for the signal questions are categorised as 'yes', 'probably yes', 'probably no', 'no', 'no information' or 'not applicable'. ...

ROB-ME: a tool for assessing risk of bias due to missing evidence in systematic reviews with meta-analysis
  • Citing Article
  • November 2023

The BMJ

... It is essential to consider these impacts when designing hydraulic structures for potential flooding [27]. Climate change can also affect ecological health by modifying aquatic environments, agricultural production, and food security [28][29][30][31]. Therefore, studying climate change in key geographical areas is crucial for decision-makers to address challenges in water resource management [32,33]. ...

The influence of climate change on mental health in populations of the western Pacific region: An umbrella scoping review
  • Citing Article
  • November 2023

Heliyon

... Historically, natural language processing (NLP) techniques [13] such as Latent Dirichlet Allocation (LDA), Conditional Random Fields (CRFs), and support vector machines (SVM) were used for text mining unstructured publication text. [14][15][16][17][18] However, these models required extensively annotated datasets for training, consuming significant time and resources. The advent of transformer-based, pre-trained large language models (LLMs) has revolutionized text generation and processing with natural language instructions. ...

Data extraction methods for systematic review (semi)automation: Update of a living systematic review
  • Citing Article
  • October 2023

... Evaluating the methodological rigor of studies is essential for ensuring the reliability and validity of the review findings. AI systems can automate the risk of bias assessment process, providing consistent and objective evaluations (Schmidt et al., 2023). ...

A narrative review of recent tools and innovations toward automating living systematic reviews and evidence syntheses
  • Citing Article
  • August 2023