Machine-Learning Assisted Screening of Correlated Covariates: Application to Clinical Data of Desipramine.

Stepwise covariate modeling (SCM) has a high computational burden and can select the wrong covariates. Machine learning (ML) has been proposed as a screening tool to improve the efficiency of covariate selection, but little is known about how to apply ML on actual clinical data. First, we simulated datasets based on clinical data to compare the performance of various ML and traditional pharmacometrics (PMX) techniques with and without accounting for highly-correlated covariates. This simulation step identified the ML algorithm and the number of top covariates to select when using the actual clinical data. A previously developed desipramine population-pharmacokinetic model was used to simulate virtual subjects. Fifteen covariates were considered with four having an effect included. Based on the F1 score (an accuracy measure), ridge regression was the most accurate ML technique on 200 simulated datasets (F1 score = 0.475 ± 0.231), a performance which almost doubled when highly-correlated covariates were accounted for (F1 score = 0.860 ± 0.158). These performances were better than forwards selection with SCM (F1 score = 0.251 ± 0.274 and 0.499 ± 0.381 without/with correlations respectively). In terms of computational cost, ridge regression (0.42 ± 0.07 seconds/simulated dataset, 1 thread) was ~20,000 times faster than SCM (2.30 ± 2.29 hours, 15 threads). On the clinical dataset, prescreening with the selected ML algorithm reduced SCM runtime by 42.86% (from 1.75 to 1.00 days) and produced the same final model as SCM only. In conclusion, we have demonstrated that accounting for highly-correlated covariates improves ML prescreening accuracy. The choice of ML method and the proportion of important covariates (unknown a priori) can be guided by simulations.

Genetic factors associated with tuberculosis-related clinical outcomes in HIV-infected Black African patients: a systematic review and meta-analysis.

Aim: To evaluate the genetic factors influencing tuberculosis (TB) clinical outcomes in HIV-infected Black African patients. Materials & methods: We systematically searched and identified eligible publications from >550 databases indexed through February 2021. Results: Eighteen studies were included in the qualitative synthesis. Only two cohorts from one study were included in quantitative synthesis of which the low expression MIF-794 CATT5,6 (5/5 + 5/6 + 6/6) genotypes were not associated with TB infectivity in HIV-infected patients (OR: 1.31, 95% CI: 0.46-3.79). Other TB clinical outcomes observed in HIV/TB co-infected patients included: drug-induced liver injury, peripheral neuropathy, mortality, lung function and TB cure. Conclusion: This review finds inconclusive evidence that genetic factors are associated with TB clinical outcomes among HIV-infected patients in sub-Saharan Africa.

Publication proportions for registered breast cancer trials: before and following the introduction of the ClinicalTrials.gov results database.

BACKGROUND: To limit selective and incomplete publication of the results of clinical trials, registries including ClinicalTrials.gov were introduced. The ClinicalTrials.gov registry added a results database in 2008 to enable researchers to post the results of their trials as stipulated by the Food and Drug Administration Amendment Act of 2007. This study aimed to determine the direction and magnitude of any change in publication proportions of registered breast cancer trials that occurred since the inception of the ClinicalTrials.gov results database. METHODS: A cross-sectional study design was employed using ClinicalTrials.gov, a publicly available registry/results database as the primary data source. Registry contents under the subcategories 'Breast Neoplasms' and 'Breast Neoplasms, Male' were downloaded on 1 August 2015. A literature search for included trials was afterwards conducted using MEDLINE and DISCOVER databases to determine publication status of the registered breast cancer trials. RESULTS: Nearly half (168/340) of the listed trials had been published, with a median time to publication of 24 months (Q1 = 14 months, Q3 = 42 months). Only 86 trials were published within 24 months of completion. There was no significant increase in publication proportions of trials that were completed before the introduction of the results database compared to those completed after (OR = 1.00, 95 % CI = .61 to 1.63; adjusted OR = 0.84, 95 % CI = .51 to 1.39). Characteristics associated with publication included trial type (observational versus interventional adjusted OR = .28, 95 % CI = .10 to .74) and completion/termination status (terminated versus completed adjusted OR = .22, 95 % CI = .09 to .51). CONCLUSIONS: Less than a half of breast cancer trials registered in ClinicalTrials.gov are published in peer-reviewed journals.