Covariates are factors which have significant impacts on the primary analysis prior to the subjects being treated. Covariates adjustment should be considered in the design and analysis stages of the clinical trial. Through controlling in the design stage is the optimal resolution; randomization, stratified randomization and restricted covariant values could be used to balance the covariates between comparative treatments. During data analysis stage, analysis of covariance, stratified analysis, linear or generalized linear model can be conducted for covariate adjustment according to different types of outcome and covariate. For confirmatory clinical trial, covariates should be defined in advance in the protocol and statistical analysis plan with the main statistical model.
Survival data include the occurrence and duration of an event. As most survival data are distributed irregularly, the Kaplan-Meier method is often used in survival analysis; however, studies usually only report the Kaplan-Meier curve and median survival time and do not provide the original survival data, which creates issues for subsequent secondary research. This study introduced a systematic method whereby image processing software and R software were used to process and extract survival data from published Kaplan-Meier curves. It also introduced the specific steps required to obtain survival data using an example to show the accuracy and feasibility of the extraction method and provided references for the effective secondary use of survival data.
In medical research, latent subgroups often emerge with characteristics or trends distinct from the general population, yet identifying them directly remain challenging. The latent variable mixture modeling, grounded in the idea that a population consists of a limited mixture of subgroups, assigns latent categories to individuals based on posterior probabilities. This model is suitable for both cross-sectional and longitudinal datasets. Approaching from a statistical perspective, this paper thoroughly explicates the foundational principles of four prevalent methods within the latent variable mixture modeling realm, outlining the essential modeling workflow. By integrating insights from previous cases and real-world data, we review the rational applications of these methods. The latent variable mixture modeling stands as a flexible classification tool for identifying and analyzing latent categories within research populations, further facilitating the in-depth exploration of predictors influencing these latent categories and their consequent effects on outcome variables.
ObjectiveTo review the characteristics of registered industry-sponsored clinical trials of pediatric drugs and vaccines in China and to provide references for promoting the development of new pediatric drugs. MethodsWe searched ClinicalTrials.gov and the Chinese Clinical Trial Registry for completed registered industry-sponsored clinical trials of pediatric drugs and vaccines from the database inception to September 11, 2022. Data including the date the trial was first posted, product type (drug or vaccine), sample size, and other information to describe the general characteristics of pediatric clinical trials were collected. The studies were divided into 2 phases based on the trial posted date, 2005―2010 and 2011―2022, reflecting the enactment of pediatric drug clinical trial policies in recent years. The quality of trial registration and the main characteristics of interventional trials in the 2 phases were then compared. Exploring the results attached to industry and non-industry sponsored clinical trials. ResultsData for 145 trials were collected, and the largest proportion (63.4%) involved vaccines. Randomized control trial (RCT) was the study type with the highest percentage (68.3%). The average report completion rate for registered interventional trials was 81.0%. Compared with 2005―2010, the percentage of average report completions, pediatric drug clinical studies, multicenter, RCTs, and double-blinded registered trials increased in 2011―2022. The proportion of positive outcomes in pediatric clinical trials sponsored by industries was higher than those sponsored by non-industry. ConclusionThe majority of completed pediatric clinical trials sponsored by industries are for vaccines, in line with the promotion of pediatric policies. The quality of trial registration has improved, but not significantly, and some characteristics of trial design have changed. The proportion of positive outcomes in pediatric clinical trials sponsored by industries is higher. And further promotion of pediatric clinical trials is needed.
Survival data were widely used in oncology clinical trials. The methods used, such as the log-rank test and Cox regression model, should meet the assumption of proportional hazards. However, the survival data with non-proportional hazard (NPH) are also quite usual, which will decrease the power of these methods and conceal the true treatment effect. Therefore, during the trial design, we need to test the proportional hazard assumption and plan different analysis methods for different testing results. This paper introduces some methods that are widely used for proportional hazard testing, and summarizes the application condition, advantages and disadvantages of analysis methods for non-proportional hazard survival data. When the non-proportional hazard occurs, we need to choose the suitable method case by case and to be cautious in the interpretation of the results.
Assessing the clinical value of pharmaceuticals is crucial for comprehensive evaluation in clinical practice and plays a vital role in supporting decision-making for drug supply assurance. Real-world data (RWD) offers valuable insights into the actual diagnosis and treatment processes, serving as a significant data source for evaluating the clinical demand, effectiveness, and safety of drugs. This technical guidance aims to elucidate the scope of application of RWD for the clinical value assessment of pharmaceuticals, as well as the key considerations for conducting value assessment research. These considerations include identifying the dimensions of clinical value that necessitate RWD and effectively utilizing RWD for evaluation purposes. Additionally, this guidance provides essential points for implementing pharmaceutical clinical value assessment based on real-world data, with a specific focus on study design and statistical analysis. By doing so, this guidance assists researchers in accurately comprehending and standardizing the utilization of real-world research in conducting pharmaceutical clinical research.