Subgroup identification based on an accelerated failure time model combined with adaptive elastic net / 南方医科大学学报

OBJECTIVE@#We propose a strategy for identifying subgroups with the treatment effect from the survival data of a randomized clinical trial based on accelerated failure time (AFT) model.@*METHODS@#We applied adaptive elastic net to the AFT model (designated as the penalized model) and identified the candidate covariates based on covariate-treatment interactions. To classify the patient subgroups, we utilized a likelihood-based change-point algorithm to determine the threshold cutoff point. A two-stage adaptive design was adopted to verify if the treatment effect existed within the identified subgroups.@*RESULTS@#The penalized model with the main effect of the covariates considerably outperformed the univariate model without the main effect for the trial data with a small sample size, a high censoring rate, a small subgroup size, or a sample size that did not exceed the number of covariates; in other scenarios, the latter model showed better performances. Compared with the traditional design, the adaptive design improved the power for detecting the treatment effect where subgroup effect exists with a well-controlled type Ⅰ error.@*CONCLUSIONS@#The penalized AFT model with the main effect of the covariates has advantages in subgroup identification from the survival data of clinical trials. Compared with the traditional design, the two-stage adaptive design has better performance in evaluation of the treatment effect when a subgroup effect exists.

A Research on Methods of Balance Evaluation among the Covariates of Multiple Groups / 中国卫生统计

Objective To construct a new index(abbreviate FQ statistic) for testing the balance of covariates among 3 groups;to compare the power of hypothesis testing,standardized difference and FQ statistics to test the balance of covariates among 3 groups.Methods Using pooled variance to build FQ Statistic;Calculating propensity score for each individual by using ordinal logistic regression and multinomial logistic regression;Comparing the power of hypothesis testing,standardized difference and FQ statistics to test the balance of covariates among 3 groups by Monte Carlo simulation.Results The distribution of a covariate can be considered balanced among the 3 groups if FQ statisticsis less than 0.2.The power of hypothesis test is affected by sample size but FQ statistics and standardized difference.The power of FQ statistics and standardized difference to test the balance of covariates among 3 groups are higher than hypothesis testing,and both highly consistent.Conclusion FQ statistics and standardized differences are valid methods to test the balance of covariates among 3 groups.With more convenient calculating step than standardized difference,FQ statistic has more advantages in applications.

Propensity score matching in SPSS / 南方医科大学学报

<p><b>OBJECTIVE</b>To realize propensity score matching in PS Matching module of SPSS and interpret the analysis results.</p><p><b>METHODS</b>The R software and plug-in that could link with the corresponding versions of SPSS and propensity score matching package were installed. A PS matching module was added in the SPSS interface, and its use was demonstrated with test data.</p><p><b>RESULTS</b>Score estimation and nearest neighbor matching was achieved with the PS matching module, and the results of qualitative and quantitative statistical description and evaluation were presented in the form of a graph matching.</p><p><b>CONCLUSION</b>Propensity score matching can be accomplished conveniently using SPSS software.</p>

Chemical proteomics: terra incognita for novel drug target profiling / 癌症

The growing demand for new therapeutic strategies in the medical and pharmaceutic fields has resulted in a pressing need for novel druggable targets. Paradoxically, however, the targets of certain drugs that are already widely used in clinical practice have largely not been annotated. Because the pharmacologic effects of a drug can only be appreciated when its interactions with cellular components are clearly delineated, an integrated deconvolution of drug-target interactions for each drug is necessary. The emerging field of chemical proteomics represents a powerful mass spectrometry (MS)-based affinity chromatography approach for identifying proteome-wide small molecule-protein interactions and mapping these interactions to signaling and metabolic pathways. This technique could comprehensively characterize drug targets, profile the toxicity of known drugs, and identify possible off-target activities. With the use of this technique, candidate drug molecules could be optimized, and predictable side effects might consequently be avoided. Herein, we provide a holistic overview of the major chemical proteomic approaches and highlight recent advances in this area as well as its potential applications in drug discovery.