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Summarising and synthesising regression coefficients through systematic review and meta-analysis for improving hypertension prediction using metamodelling: protocol.

ABSTRACT: INTRODUCTION:Hypertension is one of the most common medical conditions and represents a major risk factor for heart attack, stroke, kidney disease and mortality. The risk of progression to hypertension depends on several factors, and combining these risk factors into a multivariable model for risk stratification would help to identify high-risk individuals who should be targeted for healthy behavioural changes and/or medical treatment to prevent the development of hypertension. The risk prediction models can be further improved in terms of accuracy by using a metamodel updating technique where existing hypertension prediction models can be updated by combining information available in existing models with new data. A systematic review and meta-analysis will be performed of hypertension prediction models in order to identify known risk factors for high blood pressure and to summarise the magnitude of their association with hypertension. METHODS AND ANALYSIS:MEDLINE, Embase, Web of Science, Scopus and grey literature will be systematically searched for studies predicting the risk of hypertension among the general population. The search will be based on two key concepts: hypertension and risk prediction. The summary statistics from the individual studies will be the regression coefficients of the hypertension risk prediction models, and random-effect meta-analysis will be used to obtain pooled estimates. Heterogeneity and publication bias will be assessed, along with study quality, which will be assessed using the Prediction Model Risk of Bias Assessment Tool checklist. ETHICS AND DISSEMINATION:Ethics approval is not required for this systematic review and meta-analysis. We plan to disseminate the results of our review through journal publications and presentations at applicable platforms.

SUBMITTER: Chowdhury MZI

PROVIDER: S-EPMC7170633 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Summarising and synthesising regression coefficients through systematic review and meta-analysis for improving hypertension prediction using metamodelling: protocol.

Chowdhury Mohammad Ziaul Islam MZI Naeem Iffat I Quan Hude H Leung Alexander A AA Sikdar Khokan C KC O'Beirne Maeve M Turin Tanvir C TC

BMJ open 20200409 4

<h4>Introduction</h4>Hypertension is one of the most common medical conditions and represents a major risk factor for heart attack, stroke, kidney disease and mortality. The risk of progression to hypertension depends on several factors, and combining these risk factors into a multivariable model for risk stratification would help to identify high-risk individuals who should be targeted for healthy behavioural changes and/or medical treatment to prevent the development of hypertension. The risk ...[more]

PMID: 32276958

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Project description:BackgroundSystematic reviews have developed into a powerful method for summarising and synthesising evidence. The rise in systematic reviews creates a methodological opportunity and associated challenges and this is seen in the development of overviews, or reviews of systematic reviews. One of these challenges is how to summarise evidence from systematic reviews of complex interventions for inclusion in an overview. Interventions for communicating with and involving consumers in their care are frequently complex. In this article we outline a method for preparing data integration tables to enable review-level synthesis of the evidence on interventions for communication and participation in health.Methods and resultsSystematic reviews published by the Cochrane Consumers and Communication Review Group were utilised as the basis from which to develop linked steps for data extraction, evidence assessment and synthesis. The resulting output is called a data integration table. Four steps were undertaken in designing the data integration tables: first, relevant information for a comprehensive picture of the characteristics of the review was identified from each review, extracted and summarised. Second, results for the outcomes of the review were assessed and translated to standardised evidence statements. Third, outcomes and evidence statements were mapped into an outcome taxonomy that we developed, using language specific to the field of interventions for communication and participation. Fourth, the implications of the review were assessed after the mapping step clarified the level of evidence available for each intervention.ConclusionThe data integration tables represent building blocks for constructing overviews of review-level evidence and for the conduct of meta-synthesis. Individually, each table aims to improve the consistency of reporting on the features and effects of interventions for communication and participation; provides a broad assessment of the strength of evidence derived from different methods of analysis; indicates a degree of certainty with results; and reports outcomes and gaps in the evidence in a consistent and coherent way. In addition, individual tables can serve as a valuable tool for accurate dissemination of large amounts of complex information on communication and participation to professionals as well as to members of the public.

Project description:ObjectiveOutcome variables gauging the frequency of specific disordered eating behaviors (e.g., binge eating, vomiting) are common in the study of eating and health behaviors. The nature of such data presents several analytical challenges, which may be best addressed through the application of underutilized statistical approaches. While zero-sensitive models are well-supported by methodologists, application of these models has yet to gain traction among a widespread audience of researchers who study eating-related behaviors. The current study examined several approaches to predicting count-based behaviors, including zero-sensitive (i.e., zero-inflated and hurdle) regression models.MethodExploration of alternative models to predict eating-related behaviors occurred in two parts. In Part 1, participants (N = 524; 54% female) completed the Eating Disorder Examination-Questionnaire and Daily Stress Inventory. We considered the theoretical basis and practical utility of several alternative approaches for predicting the frequency of binge eating and compensatory behaviors, including ordinary least squares (OLS), logistic, Poisson, negative binomial, and zero-sensitive models. In Part 2, we completed Monte Carlo simulations comparing negative binomial, zero-inflated negative binomial, and negative binomial hurdle models to further explore when these models are most useful.ResultsTraditional OLS regression models were generally a poor fit for the data structure. Zero-sensitive models, which are not limited to traditional distribution assumptions, were preferable for predicting count-based outcomes. In the data presented, zero-sensitive models were useful in modeling behaviors that were relatively rare (laxative use and vomiting, 9.7% endorsed) along with those that were somewhat common (binge eating, 33.4% endorsed; driven exercise, 40.7% endorsed). Simulations indicated missing data, sample size, and the number of zeros may impact model fit.DiscussionZero-sensitive approaches hold promise for answering key questions about the presence and frequency of common eating-related behaviors and improving the specificity of relevant statistical models. The current manuscript provides practical guidance to aid the use of these models when studying eating-related behaviors.

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Summarising and synthesising regression coefficients through systematic review and meta-analysis for improving hypertension prediction using metamodelling: protocol.

Publications

Summarising and synthesising regression coefficients through systematic review and meta-analysis for improving hypertension prediction using metamodelling: protocol.

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