Earlier, individual patient or participant data (IPD) meta-analyses\n(Pooled analyses) were commonly used to generate a summary estimate such as to\nevaluate the effect of treatment of CVD, cancer or diabetes etc. However, now IPD is more used in other\nhealthcare areas such as to examine the effect of treatments of HIV, hernia,\nAlzheimer\u2019s disease and epilepsy. IPD is more powerful and considered as the\ngold standard as the approach can improve the quality of both the data, as well\nas the analyses. This approach allows us to include more trials, participants,\nand outcomes along with standardisation of outcomes across trials. IPD provides\ngreater scope and flexibility in the analysis, including the ability to\ninvestigate whether an intervention is more effective, or less effective for\ndifferent types of participants. However, in terms of its disadvantages it\ntakes more time to complete (Several years), cost significantly more (data\nhousing, data cleaning and harmonisation) and can face challenges while obtaining\nthe data than MA of AD. Table 1 show the main differences between IPD-MA of\ntreatment intervention studies and multivariable prediction modeling studies. <\/p>\n\n\n\n
Table 1. The main differences between IPD-MA of treatment intervention studies and multivariable prediction modelling studies.<\/p>\n\n\n\n Abbreviations: CHARMS, Checklist for Critical Appraisal and Data Extraction for Systematic Reviews <\/p>\n\n\n\n While conducting IPD-MA, the following steps need to\nbe carried out<\/p>\n\n\n\n Contact us for performing IPD-MA that adherence to the\nguidelines of PRISMA IPD statement released in a special communication in JAMA\n2015<\/p>\n","protected":false},"excerpt":{"rendered":" Earlier, individual patient or participant data (IPD) meta-analyses (Pooled analyses) were commonly used to generate a summary estimate such as to evaluate the effect of treatment of CVD, cancer or diabetes etc. However, now IPD is more used in other healthcare areas such as to examine the effect of treatments of HIV, hernia, Alzheimer\u2019s disease and epilepsy. IPD is more […]<\/p>\n","protected":false},"author":1,"featured_media":918,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[53],"tags":[],"class_list":["post-472","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog"],"_links":{"self":[{"href":"https:\/\/guires.uk\/newsroom\/wp-json\/wp\/v2\/posts\/472","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/guires.uk\/newsroom\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/guires.uk\/newsroom\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/guires.uk\/newsroom\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/guires.uk\/newsroom\/wp-json\/wp\/v2\/comments?post=472"}],"version-history":[{"count":5,"href":"https:\/\/guires.uk\/newsroom\/wp-json\/wp\/v2\/posts\/472\/revisions"}],"predecessor-version":[{"id":919,"href":"https:\/\/guires.uk\/newsroom\/wp-json\/wp\/v2\/posts\/472\/revisions\/919"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/guires.uk\/newsroom\/wp-json\/wp\/v2\/media\/918"}],"wp:attachment":[{"href":"https:\/\/guires.uk\/newsroom\/wp-json\/wp\/v2\/media?parent=472"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/guires.uk\/newsroom\/wp-json\/wp\/v2\/categories?post=472"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/guires.uk\/newsroom\/wp-json\/wp\/v2\/tags?post=472"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}<\/td> \n Intervention Research\n <\/td> \n Diagnostic\/Prognostic Modeling Research\n <\/td><\/tr> \n General Issues<\/strong>\n <\/td><\/tr> \n Primary aim\n <\/td> \n Estimation of the therapeutic effect of a specific\n treatment\n <\/td> \n Estimation of the probability of the presence\n (diagnosis) or future occurrence (prognosis) based on combinations of two or\n more predictors\n <\/td><\/tr> \n Secondary aims\n <\/td> \n Treatment effect in study subgroups\n <\/td> \n Evaluate the accuracy of model predictions across\n subgroups, settings, or countries\n <\/td><\/tr> \n Estimates of interest\n <\/td> \n (Adjusted) treatment-outcome associations\n <\/td> \n (Distribution of) individual outcome\n probabilities\/risks; discrimination and calibration of estimated model\n probabilities\n <\/td><\/tr> \n Association measures\n <\/td> \n Relative risk estimates: risk ratio, hazard ratio,\n risk difference, and odds ratio\n <\/td> \n Absolute probability or risk estimates of the\n outcome at interest\n <\/td><\/tr> \n Study design\n <\/td> \n Randomised studies\n <\/td> \n Observational research (randomised study data\n sometimes also used)\n <\/td><\/tr> \n Data retrieval<\/strong>\n <\/td><\/tr> \n Study registration\n <\/td> \n Clinical Trials registry (http:\/\/clinicaltrials.gov\/<\/a>)\n <\/td> \n No such registry\n <\/td><\/tr> \n Developing search query\n <\/td> \n Extensive Cochrane Collaboration guidance, including\n search filters\n <\/td> \n Recent but less evolved guidance (by Cochrane\n Collaboration)\n <\/td><\/tr> \n Assessing the risk of bias\n <\/td> \n Risk of Bias tool (Cochrane Collaboration)\n <\/td> \n CHARMS tool (Cochrane Collaboration)\n <\/td><\/tr> \n Data analysis<\/strong>\n <\/td><\/tr> \n Statistical model\n <\/td> \n Models yielding valid estimates of relative\n treatment effects\n <\/td> \n Models yielding absolute outcome probabilities\n <\/td><\/tr> \n Relevance of covariates\n <\/td> \n Covariates may be included to adjust for baseline\n imbalance and to investigate potential effect modifiers\n <\/td> \n Covariates (other predictors) are explicitly\n included to increase the model\u2019s predictive accuracy\n <\/td><\/tr> \n Dealing with between-study heterogeneity\n <\/td> \n Random-effects modelling of treatment effect, the inclusion\n of treatment-covariate interactions, meta-regression, and subgroup analysis\n <\/td> \n Stratification of baseline risk across studies focus\n on homogeneous and weakly heterogeneous predictors and inclusion of\n interaction terms and nonlinear predictor effects\n <\/td><\/tr> \n Validation of research findings\n <\/td> \n Comparison of model fit, sensitivity analyses, and\n recursive cumulative meta-analysis\n <\/td> \n Evaluation of model discrimination and calibration;\n internal, internal-external, and external validation\n <\/td><\/tr> \n Measures of precision\n <\/td> \n Standard error, p<\/em>-value, confidence\n intervals of (relative) treatment effect, and prediction intervals\n <\/td> \n Confidence and prediction intervals of model discrimination\n and calibration\n <\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n