The accuracy of the clinical prediction model determines its extrapolation and application value. When the prediction model is applied to a new setting, the differences between the new population and the initially modeled population in terms of study time, population characteristics, region, and other factors could lead to a reduction in its predictive performance. Calibrating or updating the prediction model with appropriate statistical methods is important to improve the accuracy of the prediction model in new populations. The model updating methods mainly include regression coefficients updating, meta-model updating and dynamic model updating. However, due to the limitations of meta-model updating and dynamic model updating in practical applications, the regression coefficient updating method is still the most common method in model updating. This paper introducd several types of model updating methods, the regression coefficients updating methods for two common clinical prediction models based on Logistic regression and Cox regression, and provide corresponding R codes for reference of researchers.
ObjectivesTo review individual treatment effect (ITE) models developed from randomized controlled trials, with the aim of systematically summarizing the current state of model development and assessing the risk of bias. MethodPubMed and Embase databases were searched for studies published between 1990 and 14 June 2024. Data were extracted using the CHARMS inventory, and the PROBAST risk of bias tool was used to assess model quality. ResultsA total of 11 publications were included, containing 19 ITE models. The ITE modelling methods were regression models with interaction terms (n=8, 42.1%), dual-range models (n=5, 26.3%) and machine learning (n=6, 31.6%). The ITE models had a reporting rate of 78.9%, 73.2% and 10.5% for differentiation, calibration and clinical validity, respectively. Fourteen models were assessed as having a high risk of bias (73.7%), particularly in the area of statistical analysis, due to inappropriate handling of missing data (n=15, 78.9%), inappropriate consideration of model fit issues (n=5, 26.3%), etc. ConclusionCommon approaches to ITE model development include constructing interaction terms, dual procedure theory, and machine learning, but suffer from a low number of model developments, more complex modeling methods, and non-standardized reporting. In the future, emphasis should be placed on further exploration of ITE models, promoting diversified modeling methods and standardized reporting to improve the clinical promotion and practical application value of the models.
ObjectiveTo analyze the reason and prevention of late bioprosthetic heart valve thrombosis (LBVT). MethodsBioprosthetic heart valves were implanted in 580 patients between January 2001 and July 2013 in Changhai Hospital, and only found one case of LBVT (0.2%). Reoperation was performed for a 67-year-old male patient 3 years after bioprosthetic aortic valve replacement due to severe aortic valve stenosis. Retrospectively analyzed the clinical data and reviewed the literature between January 1989 and December 2013 in Pubmed. ResultsNo risk factor for thrombosis was revealed in this patient. Pathology revealed valve thrombosis and collagen and elastic fibers fragmentation and disruption in valve leaflets. In literature review, 20 articles and 47 cases were brought in. The morbidity of LBVT was 0.2%-1.0%. At least 18 patients had high risk factors, and 15 of the 18 patients discontinued anticoagulation after 3 months. However, at least 10 patients had no high risk factors. Forty-four of the 47 patients received stented porcine valve replacement, only one patient received stented bovine pericardial valve replacement. ConclusionsLBVT is a rare but serious complication after bioprosthetic valve replacement, the causes of which include the feature of the patients and the bioprosthetic valves. Bovine pericardial valves could be superior to porcine valves in preventing LBVT. Postoperative long-time aspirin therapy is recommended for patients without high risk factors. Patients with any high risk factors should prolong anticoagulation.
Research of generating real-world evidence using real world data has attracted considerable attention globally. Outcome research of treatment based on existing health and medical data or registries has become one of the most important topics. However, there exists certain confusions in this line of research on how to design and implement appropriate statistical analysis. Therefore, in the fourth chapter of the series technical guidance to develop real world evidence by China REal world data and studies Alliance (ChinaREAL), we aim to provide an guidance on statistical analysis in the study to assess therapeutic outcomes based on existing health and medical data or registries.In this chapter, we first emphasize the significance of pre-specified statistical analysis plan, recommending key components of the statistical analysis plan. We then summarize the issue of sample size calculation in this content and clarify the interpretation of statistical p-value. Secondly, we recommend procedures to be considered to tackle the issue related to the selection bias, information bias and most importantly, confounding bias. We discuss the multivariable regression analysis as well as the popular causal inference models. We also suggest that careful consideration should be made to deal with missing data in real-world databases. Finally, we list core content of the statistical report.
With the boom of information technology and data science, real-world evidence (RWE) which is produced using diverse real-world data (RWD) has become an important source for healthcare practice and policy decisions, such as regulatory and coverage decisions, guideline development, and disease management. The production of high-quality RWE requires not only complete, accurate and usable data, but also scientific and sound study designs and data analyses to enable the questions of interest to be reliably answered. In order to improve the quality of production and use of RWE, China REal world data and studies ALliance (ChinaREAL) has developed the first series of technical guidance for developing real-world data and subsequent studies. The efforts are ongoing which would ultimately inform better healthcare practice and policy decisions.