Subgroup identification for treatment selection in biomarker adaptive design

A systematic review of subgroup analyses in randomised clinical trials in cardiovascular disease

Background:

Subgroup analyses are frequently used to assess heterogeneity of treatment effects in randomised clinical trials. Inconsistent, improper and incomplete implementation, reporting and interpretation have been identified as ongoing challenges. Further, subgroup analyses were frequently criticised because of unreliable or potentially misleading results. More recently, recommendations and guidelines have been provided to improve the reporting of data in this regard.

Methods:

This systematic review was based on a literature search within the digital archives of three selected medical journals, The New England Journal of Medicine, The Lancet and Circulation. We reviewed articles of randomised clinical trials in the domain of cardiovascular disease which were published in 2015 and 2016. We screened and evaluated the selected articles for the mode of implementation and reporting of subgroup analyses.

Results:

We were able to identify a total of 130 eligible publications of randomised clinical trials. In 89/130 (68%) articles, results of at least one subgroup analysis were presented. This was dependent on the considered journal (p < 0.001), the number of included patients (p < 0.001) and the lack of statistical significance of a trial’s primary analysis (p < 0.001). The number of reported subgroup analyses ranged from 1 to 101 (median = 13). We were able to comprehend the specification time of reported subgroup analyses for 71/89 (80%) articles, with 55/89 (62%) articles presenting exclusively pre-specified analyses. This information was not always traceable on the basis of provided trial protocols and often did not include the pre-definition of cut-off values for the categorization of subgroups. The use of interaction tests was reported in 84/89 (94%) articles, with 36/89 (40%) articles reporting heterogeneity of the treatment effect for at least one primary or secondary trial outcome. Subgroup analyses were reported more frequently for larger randomised clinical trials, and if primary analyses did not reach statistical significance. Information about the implementation of subgroup analyses was reported most consistently for articles from The New England Journal of Medicine, since it was also traceable on the basis of provided trial protocols. We were able to comprehend whether subgroup analyses were pre-specified in a majority of the reviewed publications. Even though results of multiple subgroup analyses were reported for most published trials, a corresponding adjustment for multiple testing was rarely considered.

Conclusion:

Compared to previous reviews in this context, we observed improvements in the reporting of subgroup analyses of cardiovascular randomised clinical trials. Nonetheless, critical shortcomings, such as inconsistent reporting of the implementation and insufficient pre-specification, persist.

[Subgroup identification based on the Logistic model]

OBJECTIVE

We propose a subgroup identification method based on the Logistic model for data from a two-arm clinical trial with dichotomous outcome variables.In this method, binary Logistic regression models are established for each group to calculate the outcome probabilities of each patient for comparison.According to the established rules, the patients are classified into their corresponding subgroups to establish a multinomial Logistic regression model.We simulated the false rate, correct judgment rate, coincidence rate and model correct judgment rate for different sample sizes and carried out an example analysis.The results of simulation showed that for different sample sizes, the false rates of this method were below 0.07 and the correct judgment rates were all above 0.75 with adequate coincidence rates and model correct judgment rates, demonstrating the effectiveness and reliability of the proposed method for subgroup identification.

Competence Classification of Cumulus and Granulosa Cell Transcriptome in Embryos Matched by Morphology and Female Age

Objective

By focussing on differences in the mural granulosa cell (MGC) and cumulus cell (CC) transcriptomes from follicles resulting in competent (live birth) and non-competent (no pregnancy) oocytes the study aims on defining a competence classifier expression profile in the two cellular compartments. Design: A case-control study. Setting: University based facilities for clinical services and research. Patients: MGC and CC samples from 60 women undergoing IVF treatment following the long GnRH-agonist protocol were collected. Samples from 16 oocytes where live birth was achieved and 16 age- and embryo morphology matched incompetent oocytes were included in the study.

Methods

MGC and CC were isolated immediately after oocyte retrieval. From the 16 competent and non-competent follicles, mRNA was extracted and expression profile generated on the Human Gene 1.0 ST Affymetrix array. Live birth prediction analysis using machine learning algorithms (support vector machines) with performance estimation by leave-one-out cross validation and independent validation on an external data set.

Results

We defined a signature of 30 genes expressed in CC predictive of live birth. This live birth prediction model had an accuracy of 81%, a sensitivity of 0.83, a specificity of 0.80, a positive predictive value of 0.77, and a negative predictive value of 0.86. Receiver operating characteristic analysis found an area under the curve of 0.86, significantly greater than random chance. When applied on 3 external data sets with the end-point outcome measure of blastocyst formation, the signature resulted in 62%, 75% and 88% accuracy, respectively. The genes in the classifier are primarily connected to apoptosis and involvement in formation of extracellular matrix. We were not able to define a robust MGC classifier signature that could classify live birth with accuracy above random chance level.

Conclusion

We have developed a cumulus cell classifier, which showed a promising performance on external data. This suggests that the gene signature at least partly include genes that relates to competence in the developing blastocyst.