A microcosting and cost-consequence analysis of clinical genomic testing strategies in autism spectrum disorder.

PurposeWhole-exome (WES) and whole-genome sequencing (WGS) increase the diagnostic yield in autism spectrum disorder (ASD) compared to chromosomal microarray (CMA), but there have been no comprehensive cost analyses. The objective was to perform such an assessment of CMA, WES, and WGS and compare the incremental cost per additional positive finding in hypothetical testing scenarios.MethodsFive-year patient and program costs were estimated from an institutional perspective. WES and WGS estimates were based on HiSeq 2500 with an additional WGS estimate for HiSeq X platforms. Parameter uncertainty was assessed with probabilistic and deterministic sensitivity analysis.ResultsThe cost per ASD sample was CAD$1,655 (95% CI: 1,611; 1,699) for WES, CAD$2,851 (95% CI: 2,750; 2,956) for WGS on HiSeq X, and CAD$5,519 (95% CI: 5,244; 5,785) on HiSeq 2500, compared to CAD$744 (95% CI 714, 773) for CMA. The incremental cost was over CAD$25,000 per additional positive finding if CMA was replaced by newer technology.ConclusionWhile costs for WES and WGS remain high, future reductions in material and equipment costs, and increased understanding of newly discovered variants and variants of unknown significance will lead to improved value.

Cost-effectiveness of treating relapsed or refractory 3L+ follicular lymphoma with axicabtagene ciloleucel vs mosunetuzumab in the United States.

Introduction: Novel therapies for 3L+ relapsed/refractory (r/r) follicular lymphoma (FL) have been approved recently by the US Food and Drug Administration including anti-CD19 CAR-T therapies such as axicabtagene ciloleucel (axi-cel) and CD20 × CD3 T-cell-engaging bispecific monoclonal antibodies such as mosunetuzumab (mosun). The objective of this study was to assess the cost-effectiveness of axi-cel compared to mosun in 3L+ r/r FL patients from a US third-party payer perspective. Methods: A three-state (progression-free, progressed disease, and death) partitioned-survival model was used to compare two treatments over a lifetime horizon in a hypothetical cohort of US adults (age ≥18) receiving 3L+ treatment for r/r FL. ZUMA-5 and GO29781 trial data were used to inform progression-free survival (PFS) and overall survival (OS). Mosun survival was modeled via hazard ratios (HRs) applied to axi-cel survival curves. The PFS HR value was estimated via a matching-adjusted indirect comparison (MAIC) based on mosun pseudo-individual patient data and adjusted axi-cel data to account for trial populations differences. One-way sensitivity analysis (OWSA) and probabilistic sensitivity analyses (PSA) were conducted. Scenario analyses included: 1) the mosun HRs were applied to the weighted (adjusted) ZUMA-5 24-month data to most exactly reflect the MAIC, 2) mosun HR values were applied to axi-cel 48-month follow-up data, and 3) recent axi-cel health state utility values in diffuse large B-cell lymphoma patients. Results: The analysis estimated increases of 1.82 LY and 1.89 QALY for axi-cel compared to mosun. PFS for axi-cel patients was 6.42 LY vs. 1.60 LY for mosun. Increase of $257,113 in the progression-free state was driven by one-time axi-cel treatment costs. Total incremental costs for axi-cel were $204,377, resulting in an ICER of $108,307/QALY gained. The OWSA led to ICERs ranging from $240,255 to $75,624, with all but two parameters falling below $150,000/QALY. In the PSA, axi-cel had an 64% probability of being cost-effective across 5,000 iterations using a $150,000 willingness-to-pay threshold. Scenarios one and two resulted in ICERs of $105,353 and $102,695, respectively. Discussion: This study finds that axi-cel is cost-effective compared to mosun at the commonly cited $150,000/QALY US willingness-to-pay threshold, with robust results across a range of sensitivity analyses accounting for parameter uncertainty.

Noise injection for training artificial neural networks: a comparison with weight decay and early stopping.

The purpose of this study was to investigate the effect of a noise injection method on the "overfitting" problem of artificial neural networks (ANNs) in two-class classification tasks. The authors compared ANNs trained with noise injection to ANNs trained with two other methods for avoiding overfitting: weight decay and early stopping. They also evaluated an automatic algorithm for selecting the magnitude of the noise injection. They performed simulation studies of an exclusive-or classification task with training datasets of 50, 100, and 200 cases (half normal and half abnormal) and an independent testing dataset of 2000 cases. They also compared the methods using a breast ultrasound dataset of 1126 cases. For simulated training datasets of 50 cases, the area under the receiver operating characteristic curve (AUC) was greater (by 0.03) when training with noise injection than when training without any regularization, and the improvement was greater than those from weight decay and early stopping (both of 0.02). For training datasets of 100 cases, noise injection and weight decay yielded similar increases in the AUC (0.02), whereas early stopping produced a smaller increase (0.01). For training datasets of 200 cases, the increases in the AUC were negligibly small for all methods (0.005). For the ultrasound dataset, noise injection had a greater average AUC than ANNs trained without regularization and a slightly greater average AUC than ANNs trained with weight decay. These results indicate that training ANNs with noise injection can reduce overfitting to a greater degree than early stopping and to a similar degree as weight decay.

A microsimulation cost-utility analysis of alcohol screening and brief intervention to reduce heavy alcohol consumption in Canada.

BACKGROUND AND AIMS: Screening and brief intervention (SBI) is a public health intervention that has been shown to be effective in reducing heavy alcohol consumption. The aim of this study is to estimate the cost-effectiveness of implementing universal alcohol SBI in primary care in Canada. DESIGN: We developed a microsimulation model of alcohol consumption and its effects on 18 alcohol-related causes of death. SETTING: The model simulates a Canadian population. PARTICIPANTS: The model simulates individuals and their alcohol consumption on a continuous scale starting from age 17 years to death. INTERVENTIONS: The reference case assumes no SBI in Canada. The base case assumes screening was conducted using the Alcohol Use Disorders Identification Test (AUDIT) at a threshold score of 8. Additional analyses included evaluating SBI using the AUDIT at threshold scores between 4 and 8 or the Derived Alcohol Use Disorders Identification Test (AUDIT-C) at threshold scores between 3 and 7. MEASUREMENTS: The model estimates the direct health-care costs, life years gained and quality-adjusted life years (QALY) gained, which are then used to estimate the incremental cost-effectiveness ratio (ICER) of SBI versus no SBI. FINDINGS: SBI with AUDIT (at a threshold score of 8) had an ICER of $8729/QALY. Our results suggest that using AUDIT thresholds between 8 and 4, inclusive, would be cost-effective for the whole population, as well as for men and women individually. Our results suggest that the AUDIT-C would be cost-effective at thresholds of 7 to 3, inclusive, for men, women and the whole population. CONCLUSIONS: In Canada, screening and brief intervention via Alcohol Use Disorders Identification Test (AUDIT) and Derived Alcohol Use Disorders Identification Test (AUDIT-C) to reduce heavy alcohol consumption appears to be cost-effective for men and women at Alcohol Use Disorders Identification Test (AUDIT) thresholds of 8 and lower and at Derived Alcohol Use Disorders Identification Test (AUDIT-C) thresholds of 7 and lower.

Thiopurine S-methyltransferase testing for averting drug toxicity in patients receiving thiopurines: a systematic review.

AIM: Thiopurine S-methyltransferase (TPMT) testing is used in patients receiving thiopurines to identify enzyme deficiencies and risk for adverse drug reactions. It is uncertain whether genotyping is superior to phenotyping. The objectives were to conduct a systematic review of TPMT-test performance studies. MATERIALS & METHODS: Electronic and grey literature sources were searched for studies reporting test performance compared with a reference standard. Sixty-six eligible studies were appraised for quality. RESULTS: Thirty phenotype-genotype and six phenotype-phenotype comparisons were of high quality. The calculated sensitivity and specificity for genotyping to identify a homozygous mutation ranged from 0.0-100.0% and from 97.8-100.0%, respectively. CONCLUSION: Clinical decision-makers require high-quality evidence of clinical validity and clinical utility of TPMT genotyping to ensure appropriate use in patients.

Estimating screening-mammography receiver operating characteristic (ROC) curves from stratified random samples of screening mammograms: a simulation study.

RATIONALE AND OBJECTIVES: To evaluate stratified random sampling (SRS) of screening mammograms by (1) Breast Imaging Reporting and Data System (BI-RADS) assessment categories, and (2) the presence of breast cancer in mammograms, for estimation of screening-mammography receiver operating characteristic (ROC) curves in retrospective observer studies. MATERIALS AND METHODS: We compared observer study case sets constructed by (1) random sampling (RS); (2) SRS with proportional allocation (SRS-P) with BI-RADS 1 and 2 noncancer cases accounting for 90.6% of all noncancer cases; (3) SRS with disproportional allocation (SRS-D) with BI-RADS 1 and 2 noncancer cases accounting for 10%-80%; and (4) SRS-D and multiple imputation (SRS-D + MI) with missing BI-RADS 1 and 2 noncancer cases imputed to recover the 90.6% proportion. Monte Carlo simulated case sets were drawn from a large case population modeled after published Digital Mammography Imaging Screening Trial data. We compared the bias, root-mean-square error, and coverage of 95% confidence intervals of area under the ROC curve (AUC) estimates from the sampling methods (200-2000 cases, of which 25% were cancer cases) versus from the large case population. RESULTS: AUC estimates were unbiased from RS, SRS-P, and SRS-D + MI, but biased from SRS-D. AUC estimates from SRS-P and SRS-D + MI had 10% smaller root-mean-square error than RS. CONCLUSIONS: Both SRS-P and SRS-D + MI can be used to obtain unbiased and 10% more efficient estimate of screening-mammography ROC curves.

The effect of two priors on Bayesian estimation of "Proper" binormal ROC curves from common and degenerate datasets.

RATIONALE AND OBJECTIVES: We showed previously that maximum-likelihood (ML) and Bayesian (with a flat prior on a common parameterization of the model) estimates of "proper" binormal receiver operating characteristic (ROC) curves produce similar results. We propose a new prior that is flat over the area under the ROC curve (AUC) and investigate its effect on the Bayesian estimates. MATERIALS AND METHODS: In two simulation studies, we compared Bayesian estimation of the AUC with the two prior probability distributions against ML estimation in terms of root mean squared error (RMSE) and the coverage of 95% confidence (or credible) intervals (both abbreviated CIs). In the first study, we simulated categorical data that tend to be "well-behaved" and produce ROC curve estimates that most would consider reasonable. In the second study, we simulated coarsely discretized categorical data that often included so-called degenerate datasets that cause the ML estimate to be the perfect ROC curve. RESULTS: For the well-behaved datasets, all three AUC estimates were similar in terms of RMSE and 95% CI coverage. For the coarsely discretized datasets, the RMSE of ML was consistently greater than that of Bayesian estimation and the 95% CI coverage of ML estimation was consistently below nominal, whereas the 95% CI coverage of Bayesian estimation was consistently equal to, or greater than, nominal. CONCLUSION: Bayesian estimation with a flat prior on the AUC can provide reasonable inference from datasets with coarsely categorized data that are prone to be degenerate and produce results similar to other estimation methods on well-behaved datasets.