A systematic review of the methodological quality of economic evaluations in genetic screening and testing for monogenic disorders

PurposeUnderstanding the value of genetic screening and testing for monogenic disorders requires high-quality, methodologically robust economic evaluations. This systematic review sought to assess the methodological quality among such studies and examined opportunities for improvement.MethodsWe searched PubMed, Cochrane, Embase, and Web of Science for economic evaluations of genetic screening/testing (2013-2019). Methodological rigor and adherence to best practices were systematically assessed using the British Medical Journal checklist.ResultsAcross the 47 identified studies, there were substantial variations in modeling approaches, reporting detail, and sophistication. Models ranged from simple decision trees to individual-level microsimulations that compared between 2 and >20 alternative interventions. Many studies failed to report sufficient detail to enable replication or did not justify modeling assumptions, especially for costing methods and utility values. Meta-analyses, systematic reviews, or calibration were rarely used to derive parameter estimates. Nearly all studies conducted some sensitivity analysis, and more sophisticated studies implemented probabilistic sensitivity/uncertainty analysis, threshold analysis, and value of information analysis.ConclusionWe describe a heterogeneous body of work and present recommendations and exemplar studies across the methodological domains of (1) perspective, scope, and parameter selection; (2) use of uncertainty/sensitivity analyses; and (3) reporting transparency for improvement in the economic evaluation of genetic screening/testing.     

Living with the financial consequences of cancer: A life course perspective

Abstract

Purpose

Financial hardship can be a major cause of distress among persons with cancer, resulting in chronic stress and impacting physical and emotional health. This paper provides an analysis of the lived experience of cancer patients’ financial hardship from diagnosis to post-treatment.     

Can EGFR mutation status be reliably determined in pre‐operative needle biopsies from adenocarcinomas of the lung?

The identification of EGFR mutations in non‐small‐cell lung cancer is important for selecting patients, who may benefit from treatment with EGFR tyrosine kinase inhibitors. The analysis is usually performed on cytological aspirates and/or histological needle biopsies, representing a small fraction of the tumour volume. The aim of the present investigation was to evaluate the diagnostic performance of this molecular test. We retrospectively included 201 patients with primary adenocarcinoma of the lung. EGFR mutation status (exon 19 deletions and exon 21 L858R point mutation) was evaluated on both pre‐operative biopsies (131 histological and 70 cytological) and on the surgical specimens, using PCR. Samples with low tumour cell fraction were assigned to laser micro‐dissection (LMD). We found nine (4.5%) patients with EGFR mutation in the lung tumour resections, but failed to identify mutation in one of the corresponding pre‐operative, cytological specimens. Several (18.4%) analyses of the pre‐operative biopsies were inconclusive, especially in case of biopsies undergoing LMD and regarding exon 21 analysis. Discrepancy of mutation status in one patient may reflect intra‐tumoural heterogeneity or technical issues. Moreover, several inconclusive results in the diagnostic biopsies reveal that attention must be paid on the suitability of pre‐operative biopsies for EGFR mutation analysis.     

Population Genetics Identifies Challenges in Analyzing Rare Variants

Geneticists have, for years, understood the nature of genome‐wide association studies using common genomic variants. Recently, however, focus has shifted to the analysis of rare variants. This presents potential problems for researchers, as rare variants do not always behave in the same way common variants do, sometimes rendering decades of solid intuition moot. In this paper, we present examples of the differences between common and rare variants. We show why one must be significantly more careful about the origin of rare variants, and how failing to do so can lead to highly inflated type I error. We then explain how to best avoid such concerns with careful understanding and study design. Additionally, we demonstrate that a seemingly low error rate in next‐generation sequencing can dramatically impact the false‐positive rate for rare variants. This is due to the fact that rare variants are, by definition, seen infrequently, making it hard to distinguish between errors and real variants. Compounding this problem is the fact that the proportion of errors is likely to get worse, not better, with increasing sample size. One cannot simply scale their way up in order to solve this problem. Understanding these potential pitfalls is a key step in successfully identifying true associations between rare variants and diseases.