Translational Methods in Nephrology: Individual Treatment Effect Modeling.
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ABSTRACT:
SUBMITTER: Wilson FP
PROVIDER: S-EPMC6218865 | biostudies-literature | 2018 Nov
REPOSITORIES: biostudies-literature
Publications
Translational Methods in Nephrology: Individual Treatment Effect Modeling.
Wilson F Perry FP Parikh Chirag R CR
Journal of the American Society of Nephrology : JASN 20181002 11
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Project description:Estimation of individual treatment effect in observational data is complicated due to the challenges of confounding and selection bias. A useful inferential framework to address this is the counterfactual (potential outcomes) model, which takes the hypothetical stance of asking what if an individual had received both treatments. Making use of random forests (RF) within the counterfactual framework we estimate individual treatment effects by directly modeling the response. We find that accurate estimation of individual treatment effects is possible even in complex heterogenous settings but that the type of RF approach plays an important role in accuracy. Methods designed to be adaptive to confounding, when used in parallel with out-of-sample estimation, do best. One method found to be especially promising is counterfactual synthetic forests. We illustrate this new methodology by applying it to a large comparative effectiveness trial, Project Aware, to explore the role drug use plays in sexual risk. The analysis reveals important connections between risky behavior, drug usage, and sexual risk.
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Project description:Effect modification occurs while the effect of the treatment is not homogeneous across the different strata of patient characteristics. When the effect of treatment may vary from individual to individual, precision medicine can be improved by identifying patient covariates to estimate the size and direction of the effect at the individual level. However, this task is statistically challenging and typically requires large amounts of data. Investigators may be interested in using the individual patient data from multiple studies to estimate these treatment effect models. Our data arise from a systematic review of observational studies contrasting different treatments for multidrug-resistant tuberculosis, where multiple antimicrobial agents are taken concurrently to cure the infection. We propose a marginal structural model for effect modification by different patient characteristics and co-medications in a meta-analysis of observational individual patient data. We develop, evaluate, and apply a targeted maximum likelihood estimator for the doubly robust estimation of the parameters of the proposed marginal structural model in this context. In particular, we allow for differential availability of treatments across studies, measured confounding within and across studies, and random effects by study.
| S-EPMC8961254 | biostudies-literature
Project description:Disappointing results of recent tuberculosis chemotherapy trials suggest that knowledge gained from preclinical investigations was not utilized to maximal effect. A mouse-to-human translational pharmacokinetics (PKs) - pharmacodynamics (PDs) model built on a rich mouse database may improve clinical trial outcome predictions. The model included Mycobacterium tuberculosis growth function in mice, adaptive immune response effect on bacterial growth, relationships among moxifloxacin, rifapentine, and rifampin concentrations accelerating bacterial death, clinical PK data, species-specific protein binding, drug-drug interactions, and patient-specific pathology. Simulations of recent trials testing 4-month regimens predicted 65% (95% confidence interval [CI], 55-74) relapse-free patients vs. 80% observed in the REMox-TB trial, and 79% (95% CI, 72-87) vs. 82% observed in the Rifaquin trial. Simulation of 6-month regimens predicted 97% (95% CI, 93-99) vs. 92% and 95% observed in 2RHZE/4RH control arms, and 100% predicted and observed in the 35 mg/kg rifampin arm of PanACEA MAMS. These results suggest that the model can inform regimen optimization and predict outcomes of ongoing trials.
| S-EPMC5593171 | biostudies-literature
Project description:MotivationEstimating the effects of interventions on patient outcome is one of the key aspects of personalized medicine. Their inference is often challenged by the fact that the training data comprises only the outcome for the administered treatment, and not for alternative treatments (the so-called counterfactual outcomes). Several methods were suggested for this scenario based on observational data, i.e. data where the intervention was not applied randomly, for both continuous and binary outcome variables. However, patient outcome is often recorded in terms of time-to-event data, comprising right-censored event times if an event does not occur within the observation period. Albeit their enormous importance, time-to-event data are rarely used for treatment optimization. We suggest an approach named BITES (Balanced Individual Treatment Effect for Survival data), which combines a treatment-specific semi-parametric Cox loss with a treatment-balanced deep neural network; i.e. we regularize differences between treated and non-treated patients using Integral Probability Metrics (IPM).ResultsWe show in simulation studies that this approach outperforms the state of the art. Furthermore, we demonstrate in an application to a cohort of breast cancer patients that hormone treatment can be optimized based on six routine parameters. We successfully validated this finding in an independent cohort.Availability and implementationWe provide BITES as an easy-to-use python implementation including scheduled hyper-parameter optimization (https://github.com/sschrod/BITES). The data underlying this article are available in the CRAN repository at https://rdrr.io/cran/survival/man/gbsg.html and https://rdrr.io/cran/survival/man/rotterdam.html.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC9235492 | biostudies-literature
Project description:INTRODUCTION:The global nephrology workforce is shrinking and, in many countries, is unable to meet healthcare needs. Accurate data pertaining to human resources in nephrology in South Africa is lacking. This data is critical for the planning and delivery of renal services and the training of nephrologists in South Africa to meet the challenge of the growing burden of chronic kidney disease. METHODS:A cross-sectional study of adult and paediatric nephrologists currently delivering nephrology services in South Africa was conducted. Participants were identified using various data sources, including the register of the Health Professions Council of South Africa. This cohort of doctors was described in terms of their demographics and distribution. A survey was then conducted among these nephrologists to collect additional information on their training, scope of practice, job satisfaction, challenges and future plans. Finally, two focus group interviews were conducted to probe themes identified from the survey data. RESULTS:A total of 120 adult nephrologists and 22 paediatric nephrologists were identified (an overall density of 2.5 per million population). There is a male predominance (66%) and the median age is 45 years. The bulk of the workforce (128 nephrologists, 92%) is distributed in three of the nine South African provinces, and two provinces have no nephrologist at all. The survey was completed by 57% of the nephrologists. Most reported positive attitudes to their chosen profession; however, 35 nephrologists (43%) reported an excessive workload, 9 (11%) were planning emigration and 15 (19%) were planning early retirement. A higher frequency of dissatisfaction regarding remuneration (39% vs. 15%) and unsatisfactory work conditions (35% vs. 13%) was observed amongst nephrologists working in the public sector compared to the private sector. A total of 13 nephrologists participated in the focus group interviews. The themes which were identified included that of a rewarding profession, an overall shortage of nephrologists, poor career planning, a need for changes to nephrologists' training, excessive workloads with inadequate remuneration, and challenging work environments. CONCLUSION:There are insufficient numbers of nephrologists in South Africa, with a markedly uneven distribution amongst the provinces and healthcare sectors. Qualitative data indicate that South African nephrologists are faced with the challenges of a high workload, obstructive policies and unsatisfactory remuneration. In the public sector, a chronic lack of nephrologist posts and other resources are additional challenges. A substantial proportion of the workforce is contemplating emigration.
| S-EPMC7018074 | biostudies-literature
Project description:BackgroundJohns Hopkins was an early adopter of an in-house nephrology fellowship night float to improve work-life balance. Our study aimed to elucidate attitudes to guide fellowship structuring.MethodsWe performed a mixed-methods study surveying Johns Hopkins fellows, alumni, and faculty and conducting one focus group of current fellows. Surveys were developed through literature review, queried on a five-point Likert scale, and analyzed with t and ANOVA tests. The focus group transcript was analyzed by two independent reviewers.ResultsSurvey response rates were 14 (100%) fellows, 32 (91%) alumni, and 17 (94%) faculty. All groups felt quality of patient care was good to excellent with no significant differences among groups (range of means [SD], 4.1 [0.7]-4.6 [0.7]; P=0.12), although fellows had a statistically significantly more positive view than faculty on autonomy (4.6 [0.5] versus 4.1 [0.3]; P=0.006). Fellows perceived a positive effect across all domains of night float on the day team experience (range, 4.2 [0.8]-4.6 [0.6]; P<0.001 compared with neutral effect). Focus group themes included patient care, care continuity, professional development, wellness, and structural components. One fellow said, "…my bias is that every program would switch to a night float system if they could." All groups were satisfied with night float with 4.7 [0.5], 4.2 [0.8], and 4.0 [0.9] for fellows, faculty, and alumni, respectively; fellows were most enthusiastic (P=0.03). All three groups preferred night float, and fellows did so unanimously.ConclusionsNight float was well liked and enhanced the perceived daytime fellow experience. Alumni and faculty were positive about night float, although less so, possibly due to concerns for adequate preparation to handle overnight calls after graduation. Night float implementation at other nephrology programs should be considered based on program resources; such changes should be assessed by similar methods.
| S-EPMC8815554 | biostudies-literature
Project description:Genomic analyses often involve scanning for potential transcription-factor (TF) binding sites using models of the sequence specificity of DNA binding proteins. Many approaches have been developed to model and learn a protein’s binding specificity by representing sequence motifs, including the gaps and dependencies between binding-site residues, but these methods have not been systematically compared. Here we applied 26 such approaches to in vitro protein binding microarray data for 66 mouse TFs belonging to various families. For 9 TFs, we also scored the resulting motif models on in vivo data, and found that the best in vitro–derived motifs performed similarly to motifs derived from in vivo data. Our results indicate that simple models based on mononucleotide position weight matrices learned by the best methods perform similarly to more complex models for most TFs examined, but fall short in specific cases. In addition, the best-performing motifs typically have relatively low information content, consistent with widespread degeneracy in eukaryotic TF sequence preferences. Protein binding microarray (PBM) experiments were performed for a set of 86 mouse transcription factors. Briefly, the PBMs involved binding GST-tagged DNA-binding proteins to two double-stranded 44K Agilent microarrays, each containing a different DeBruijn sequence design, in order to determine their sequence preferences. Details of the PBM protocol are described in Berger et al., Nature Biotechnology 2006.
2012-12-13 | E-GEOD-42864 | biostudies-arrayexpress
Project description:Many approaches have been proposed to segment high uptake objects in 18F-fluoro-deoxy-glucose positron emission tomography images but none provides consistent performance across the large variety of imaging situations. This study investigates the use of two methods of combining individual segmentation methods to reduce the impact of inconsistent performance of the individual methods: simple majority voting and probabilistic estimation.The National Electrical Manufacturers Association image quality phantom containing five glass spheres with diameters 13-37 mm and two irregularly shaped volumes (16 and 32 cc) formed by deforming high-density polyethylene bottles in a hot water bath were filled with 18-fluoro-deoxyglucose and iodine contrast agent. Repeated 5-min positron emission tomography (PET) images were acquired at 4:1 and 8:1 object-to-background contrasts for spherical objects and 4.5:1 and 9:1 for irregular objects. Five individual methods were used to segment each object: 40% thresholding, adaptive thresholding, k-means clustering, seeded region-growing, and a gradient based method. Volumes were combined using a majority vote (MJV) or Simultaneous Truth And Performance Level Estimate (STAPLE) method. Accuracy of segmentations relative to CT ground truth volumes were assessed using the Dice similarity coefficient (DSC) and the symmetric mean absolute surface distances (SMASDs).MJV had median DSC values of 0.886 and 0.875; and SMASD of 0.52 and 0.71 mm for spheres and irregular shapes, respectively. STAPLE provided similar results with median DSC of 0.886 and 0.871; and median SMASD of 0.50 and 0.72 mm for spheres and irregular shapes, respectively. STAPLE had significantly higher DSC and lower SMASD values than MJV for spheres (DSC, p < 0.0001; SMASD, p = 0.0101) but MJV had significantly higher DSC and lower SMASD values compared to STAPLE for irregular shapes (DSC, p < 0.0001; SMASD, p = 0.0027). DSC was not significantly different between 128 × 128 and 256 × 256 grid sizes for either method (MJV, p = 0.0519; STAPLE, p = 0.5672) but was for SMASD values (MJV, p < 0.0001; STAPLE, p = 0.0164). The best individual method varied depending on object characteristics. However, both MJV and STAPLE provided essentially equivalent accuracy to using the best independent method in every situation, with mean differences in DSC of 0.01-0.03, and 0.05-0.12 mm for SMASD.Combining segmentations offers a robust approach to object segmentation in PET. Both MJV and STAPLE improved accuracy and were robust against the widely varying performance of individual segmentation methods. Differences between MJV and STAPLE are such that either offers good performance when combining volumes. Neither method requires a training dataset but MJV is simpler to interpret, easy to implement and fast.
| S-EPMC3612113 | biostudies-other
Project description:Recent developments in pre-clinical screening tools, that more reliably predict the clinical effects and adverse events of candidate therapeutic agents, has ushered in a new era of drug development and screening. However, given the rapid pace with which these models have emerged, the individual merits of these translational research tools warrant careful evaluation in order to furnish clinical researchers with appropriate information to conduct pre-clinical screening in an accelerated and rational manner. This review assesses the predictive utility of both well-established and emerging pre-clinical methods in terms of their suitability as a screening platform for treatment response, ability to represent pharmacodynamic and pharmacokinetic drug properties, and lastly debates the translational limitations and benefits of these models. To this end, we will describe the current literature on cell culture, organoids, in vivo mouse models, and in silico computational approaches. Particular focus will be devoted to discussing gaps and unmet needs in the literature as well as current advancements and innovations achieved in the field, such as co-clinical trials and future avenues for refinement.
| S-EPMC7897192 | biostudies-literature
Project description:Genomic analyses often involve scanning for potential transcription-factor (TF) binding sites using models of the sequence specificity of DNA binding proteins. Many approaches have been developed to model and learn a protein’s binding specificity by representing sequence motifs, including the gaps and dependencies between binding-site residues, but these methods have not been systematically compared. Here we applied 26 such approaches to in vitro protein binding microarray data for 66 mouse TFs belonging to various families. For 9 TFs, we also scored the resulting motif models on in vivo data, and found that the best in vitro–derived motifs performed similarly to motifs derived from in vivo data. Our results indicate that simple models based on mononucleotide position weight matrices learned by the best methods perform similarly to more complex models for most TFs examined, but fall short in specific cases. In addition, the best-performing motifs typically have relatively low information content, consistent with widespread degeneracy in eukaryotic TF sequence preferences.
2012-12-13 | GSE42864 | GEO