Just what the doctor ordered: An evaluation of provider preference-based Instrumental Variable methods in observational studies, with application for comparative effectiveness of type 2 diabetes therapy

Instrumental Variables provide a way of addressing bias due to unmeasured confounding when estimating treatment effects using observational data. As instrument prescription preference of individual healthcare providers has been proposed. Because prescription preference is hard to measure and often unobserved, a surrogate measure constructed from available data is often required for the analysis. Different construction methods for this surrogate measure are possible, such as simple rule-based methods which make use of the observed treatment patterns, or more complex model-based methods that employ formal statistical models to explain the treatment behaviour whilst considering measured confounders. The choice of construction method relies on aspects like data availability within provider, missing data in measured confounders, and possible changes in prescription preference over time. In this paper we conduct a comprehensive simulation study to evaluate different construction methods for surrogates of prescription preference under different data conditions, including: different provider sizes, missing covariate data, and change in preference. We also propose a novel model-based construction method to address between provider differences and change in prescription preference. All presented construction methods are exemplified in a case study of the relative glucose lowering effect of two type 2 diabetes treatments in observational data. Our study shows that preference-based Instrumental Variable methods can be a useful tool for causal inference from observational health data. The choice of construction method should be driven by the data condition at hand. Our proposed method is capable of estimating the causal treatment effect without bias in case of sufficient prescription data per provider, changing prescription preference over time and non-ignorable missingness in measured confounders.Comment: 44 pages, 11 figure

Le secteur des télécommunications surfe-t-il de bulle en bulle ?

Le secteur des télécommunications a connu un développement rapide qui s’est accéléré à partir de la deuxième moitié des années 1990, avec l’apparition du GSM et de l’Internet. Mais la croissance réelle du secteur s’est rapidement transformée en une gigantesque bulle financière qui a été à l’origine de l’une des pires crises sectorielles qu’aient connu les économies modernes. Dans cet article, nous essayons d’identifier les facteurs qui ont conduit à une telle valorisation financière des entreprises de télécommunications ainsi que ceux qui ont conduit au retournement des marchés financiers. Enfin, à la veille de la mise en place de l’UMTS, certains éléments nous amènent à penser qu’une nouvelle bulle pourrait se former dans les années à venir. En annexes, nous simulons la rentabilité financière de l’UMTS et évaluons l’impact macroéconomique de ce projet sur les composantes de la croissance française.The telecommunication sector has recently undergone a fast development which accelerated from the second half of the 1990s, with the rise of the GSM and the Internet. But the actual growth of the sector turned into a gigantic financial bubble which was at the origin of one of the worst sector-based crises that the modern economies had seen. In this article, we try to identify the factors driving such a financial valuation of the telecommunications companies as well as those leading to the reversal of financial markets. Finally, on the verge of the implementation of the UMTS in France, some elements let us think that a new bubble might appear in the coming years. In the appendices, we simulate the financial profitability of the UMTS and estimate the macroeconomic impact of this project on the constituents of French economic growth

Evaluating associations between the benefits and risks of drug therapy in type 2 diabetes:A joint modelling approach

This is the author accepted manuscript. The final version is available from Dove Medical Press via the DOI in this record.Data statement: No additional data are available from the authors although the individual participant data from the ADOPT trial used in this study are available from GlaxoSmithKline on application via www.clinicalstudydatarequest.comObjective: Precision medicine drug therapy seeks to maximise efficacy and minimise harm for individual patients. This will be difficult if drug response and side-effects are positively associated, meaning patients likely to respond best are at increased risk of side-effects. We applied joint longitudinal-survival models to evaluate associations between drug response (longitudinal outcome) and risk of side-effects (survival outcome) for patients initiating type 2 diabetes therapy. Study Design and Setting: Participants were randomised to metformin, sulfonylurea or thiazolidinedione therapy in the ADOPT drug-efficacy trial (n=4,351). Joint models were parameterised for: 1) current HbA1c response (change from baseline in HbA1c); 2) cumulative HbA1c response (total HbA1c change). Results: With metformin, greater HbA1c response did not increase risk of gastrointestinal events (Hazard ratio (HR) per 1% absolute greater current response 0.82 (95% confidence interval 0.67,1.01); HR per 1% higher cumulative response 0.90 (0.81,1.00)). With sulfonylureas, greater current response was associated with increased risk of hypoglycaemia (HR 1.41 (1.04,1.91)). With thiazolidinediones, greater response was associated with increased risk of oedema (current HR 1.45 (1.05,2.01); cumulative 1.22 (1.07,1.38)) but not fracture. Conclusion: Joint modelling provides a useful framework to evaluate the association between response to a drug and risk of developing side-effects. There may be great potential for widespread application of joint modelling to evaluate the risks and benefits of both new and established medications.This work was supported by the Medical Research Council (UK) (Grant MR/N00633X/1). ATH is a NIHR Senior Investigator and a Wellcome Trust Senior Investigator. ERP is a Wellcome Trust New Investigator (102820/Z/13/Z). AGJ is supported by an NIHR Clinician Scientist award. ATH and BMS are supported by the NIHR Exeter Clinical Research Facility. WEH received additional support from IQVIA and the National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care South West Peninsula (NIHR CLAHRC South West Peninsula)

Genetic variants in Apolipoprotein AV alter triglyceride concentrations in pregnancy

BACKGROUND: Triglyceride concentrations are raised in pregnancy and are considered a key fetal fuel. Several gene variants are known to alter triglyceride concentrations, including those in the Apolipoprotein E (ApoE), Lipoprotein Lipase (LPL), and most recently, the Apolipoprotein AV (ApoAV) gene. However, less is known about how variants in these genes alter triglyceride concentrations in pregnancy or affect fetal growth. We aimed to determine the effect of the recently identified ApoAV gene on triglycerides in pregnancy and fetal growth. We assessed the role of two ApoAV haplotypes, defined by the C and W alleles of the -1131T>C and S19W polymorphisms, in 483 pregnant women and their offspring from the Exeter Family Study of Childhood Health. RESULTS: The -1131T>C and S19W variants have rare allele frequencies of 6.7% and 4.9% and are present in 13.4% and 9.7% of subjects respectively. In carriers of the -1131C and 19W alleles triglyceride concentrations were raised by 11.0% (1.98 mmol/ l(1.92 – 2.04) to 2.20 mmol/l (2.01 – 2.42), p = 0.035; and 16.2% (1.97 mmol/l (1.91 – 2.03) to 2.29 mmol/l (2.12 – 2.48), p < 0.001 respectively. There is nominally significant evidence that the -1131T>C variant is having an effect on maternal height (164.9 cm (164.3 – 165.5) to 167.0 cm (165.2 – 168.8), p = 0.029). There was no evidence that ApoAV genotype alters any other anthropometric measurements or biochemistries such as High Density Lipoprotein Cholesterol (HDL-C) or Low Density Lipoprotein Cholesterol (LDL-C). There is nominally significant evidence that the presence of a maternal -1131C variant alters fetal birth length (50.2 cm (50.0 – 50.4) to 50.9 cm (50.3 – 51.4), p = 0.022), and fetal birth crown-rump length (34.0 cm (33.8 – 34.1) to 34.5 cm (34.1 – 35.0), p = 0.023). There is no evidence that ApoAV genotype alters fetal birth weight or other fetal growth measurements. CONCLUSION: In conclusion variation in the ApoAV gene raises triglyceride concentrations in pregnancy, as well as normolipaemic states and there is preliminary evidence that it alters fetal growth parameters

Development and validation of multivariable clinical diagnostic models to identify type 1 diabetes requiring rapid insulin therapy in adults aged 18-50 years

This is the final version. Available on open access from BMJ Publishing Group via the DOI in this recordObjective: To develop and validate multivariable clinical diagnostic models to assist distinguishing between type 1 and type 2 diabetes in adults aged 18 to 50. Design: Multivariable logistic regression analysis was used to develop classification models integrating five pre-specified predictor variables, including clinical features (age of diagnosis, BMI) and clinical biomarkers (GADA and Islet Antigen 2 islet autoantibodies, Type 1 Diabetes Genetic Risk Score), to identify type 1 diabetes with rapid insulin requirement using data from existing cohorts. Setting: United Kingdom cohorts recruited from primary and secondary care. Participants: 1,352 (model development) and 582 (external validation) participants diagnosed with diabetes between the age of 18 and 50 years of white European origin. Main outcome measures: Type 1 diabetes was defined by rapid insulin requirement (within 3 years of diagnosis) and severe endogenous insulin deficiency (C-peptide <200pmol/L). Type 2 diabetes was defined by either a lack of rapid insulin requirement or, where insulin treated within 3 years, retained endogenous insulin secretion (C-peptide >600pmol/L at ≥5 years diabetes duration). Model performance was assessed using area under the receiver operating characteristic curve (ROC AUC), and internal and external validation. 4 Results: Type 1 diabetes was present in 13% of participants in the development cohort. All five predictor variables were discriminative and independent predictors of type 1 diabetes (p<0.001 for all) with individual ROC AUC ranging from 0.82 to 0.85. Model performance was high: ROC AUC range 0.90 [95%CI 0.88, 0.93] (clinical features only) to 0.97 [0.96, 0.98] (all predictors) with low prediction error. Results were consistent in external validation (clinical features and GADA ROC AUC 0.93 [0.90, 0.96]). Conclusions: Clinical diagnostic models integrating clinical features with biomarkers have high accuracy for identifying type 1 diabetes with rapid insulin requirement, and could assist clinicians and researchers in accurately identifying patients with type 1 diabetes.National Institute for Health Research (NIHR)European Community FP7Oxford Hospitals Charitable FundWellcome TrustMedical Research Council (MRC

Improvements in Awareness and Testing Have Led to a Threefold Increase Over 10 Years in the Identification of Monogenic Diabetes in the U.K

This is the author accepted manuscript. The final version is available from the American Diabetes Association via the DOI in this recordAims/hypothesis: Maturity Onset Diabetes of the Young (MODY) is a rare monogenic form of diabetes. In 2009, >80% of UK cases were estimated to be misdiagnosed. Since then, there have been a number of initiatives to improve the awareness and detection of MODY including education initiatives (Genetic Diabetes Nurse (GDN) programme), the MODY probability calculator, and targeted next generation sequencing (tNGS). We aimed to examine how the estimated prevalence of MODY, and other forms of monogenic diabetes diagnosed outside the neonatal period, has changed over time and how the initiatives have impacted case finding. Research design and Methods: UK referrals for genetic testing for monogenic diabetes diagnosed >1y of age from 01/01/1996 to 31/12/2019 were examined. Positive-test rates were compared for referrals reporting involvement of the GDNs/MODY calculator with those that did not. Results: A diagnosis of monogenic diabetes was confirmed in 3860 individuals, >3-fold higher than 2009 (01/01/1996-28/02/2009; n=1177). Median age at diagnosis in probands was 21y. GDN involvement was reported in 21% of referrals; these referrals had a higher positive-test rate than those without GDN involvement (32% v 23%, p<0.001). MODY calculator usage was indicated on 74% of eligible referrals since 2014; these referrals had a higher positive-test rate than those not using the calculator (33% v 25%, p=0.001). 410 (10.6%) cases were identified through tNGS. Monogenic diabetes prevalence was estimated to be 248 cases/million (double that estimated in 2009 due to increased case-finding). 3 Conclusions: Since 2009, referral rates and case diagnosis have increased three-fold. This is likely to be the consequence of tNGS, GDN education and the MODY calculator

A common genetic variant in the 15q24 nicotinic acetylcholine receptor gene cluster (CHRNA5–CHRNA3–CHRNB4) is associated with a reduced ability of women to quit smoking in pregnancy

Maternal smoking during pregnancy is associated with low birth weight and adverse pregnancy outcomes. Women are more likely to quit smoking during pregnancy than at any other time in their lives, but some pregnant women continue to smoke. A recent genome-wide association study demonstrated an association between a common polymorphism (rs1051730) in the nicotinic acetylcholine receptor gene cluster (CHRNA5–CHRNA3–CHRNB4) and both smoking quantity and nicotine dependence. We aimed to test whether the same polymorphism that predisposes to greater cigarette consumption would also reduce the likelihood of smoking cessation in pregnancy. We studied 7845 pregnant women of European descent from the South-West of England. Using 2474 women who smoked regularly immediately pre-pregnancy, we analysed the association between the rs1051730 risk allele and both smoking cessation during pregnancy and smoking quantity. Each additional copy of the risk allele was associated with a 1.27-fold higher odds (95% CI 1.11–1.45) of continued smoking during pregnancy (P = 0.0006). Adjustment for pre-pregnancy smoking quantity weakened, but did not remove this association [odds ratio (OR) 1.20 (95% CI 1.03–1.39); P = 0.018]. The same risk allele was also associated with heavier smoking before pregnancy and in the first, but not the last, trimester [OR for smoking 10+ cigarettes/day versus 1–9/day in first trimester = 1.30 (95% CI 1.13–1.50); P = 0.0003]. To conclude, we have found strong evidence of association between the rs1051730 variant and an increased likelihood of continued smoking in pregnancy and have confirmed the previously observed association with smoking quantity. Our data support the role of genetic factors in influencing smoking cessation during pregnancy

Patient preference for second- and third-line therapies in type 2 diabetes:a prespecified secondary endpoint of the TriMaster study

Patient preference is very important for medication selection in chronic medical conditions, like type 2 diabetes, where there are many different drugs available. Patient preference balances potential efficacy with potential side effects. As both aspects of drug response can vary markedly between individuals, this decision could be informed by the patient personally experiencing the alternative medications, as occurs in a crossover trial. In the TriMaster (NCT02653209, ISRCTN12039221), randomized double-blind, three-way crossover trial patients received three different second- or third-line once-daily type 2 diabetes glucose-lowering drugs (pioglitazone 30 mg, sitagliptin 100 mg and canagliflozin 100 mg). As part of a prespecified secondary endpoint, we examined patients’ drug preference after they had tried all three drugs. In total, 448 participants were treated with all three drugs which overall showed similar glycemic control (HbA1c on pioglitazone 59.5 sitagliptin 59.9, canagliflozin 60.5 mmol mol−1, P = 0.19). In total, 115 patients (25%) preferred pioglitazone, 158 patients (35%) sitagliptin and 175 patients (38%) canagliflozin. The drug preferred by individual patients was associated with a lower HbA1c (mean: 4.6; 95% CI: 3.9, 5.3) mmol mol−1 lower versus nonpreferred) and fewer side effects (mean: 0.50; 95% CI: 0.35, 0.64) fewer side effects versus nonpreferred). Allocating therapy based on the individually preferred drugs, rather than allocating all patients the overall most preferred drug (canagliflozin), would result in more patients achieving the lowest HbA1c for them (70% versus 30%) and the fewest side effects (67% versus 50%). When precision approaches do not predict a clear optimal therapy for an individual, allowing patients to try potential suitable medications before they choose long-term therapy could be a practical alternative to optimizing treatment for type 2 diabetes

Population-Based Assessment of a Biomarker-Based Screening Pathway to Aid Diagnosis of Monogenic Diabetes in Young-Onset Patients

This is the author accepted manuscript. The final version is available from the American Diabetes Association via the DOI in this record.Objective: Monogenic diabetes, a young-onset form of diabetes, is often misdiagnosed as Type 1 diabetes, resulting in unnecessary treatment with insulin. A screening approach for monogenic diabetes is needed to accurately select suitable patients for expensive diagnostic genetic testing. We used C-peptide and islet autoantibodies, highly sensitive and specific biomarkers for discriminating Type 1 from non-Type 1 diabetes, in a biomarker screening pathway for monogenic diabetes. Research Design and Methods: We studied patients diagnosed ≤30y, currently <50y, in two UK regions with existing high detection of monogenic diabetes. The biomarker screening pathway comprised 3 stages: 1) Assessment of endogenous insulin secretion using urinary C-peptide/creatinine ratio (UCPCR); 2) If UCPCR≥0.2nmol/mmol, measurement of GAD and IA2 islet autoantibodies; 3) If negative for both autoantibodies, molecular genetic diagnostic testing for 35 monogenic diabetes subtypes. Results: 1407 patients participated (1365 no known genetic cause, 34 monogenic diabetes, 8 cystic-fibrosis-related diabetes). 386/1365(28%) had UCPCR≥0.2nmol/mmol. 216/386(56%) of these patients were negative for GAD and IA2 and underwent molecular genetic testing. 17 new cases of monogenic diabetes were diagnosed (8 common MODY (Sanger sequencing), 9 rarer causes (next generation sequencing)) in addition to the 34 known cases (estimated prevalence of 3.6% (51/1407) (95%CI: 2.7-4.7%)). The positive predictive value was 20%, suggesting a 1-in-5 detection rate for the pathway. The negative predictive value was 99.9%. Conclusions: The biomarker screening pathway for monogenic diabetes is an effective, cheap, and easily implemented approach to systematically screening all young-onset patients. The minimum prevalence of monogenic diabetes is 3.6% of patients diagnosed ≤30y.This study was funded by the Department of Health and Wellcome Trust Health Innovation Challenge Award (HICF-1009-041; WT-091985). ATH and SE are Wellcome Trust Senior Investigators. ATH is an NIHR Senior Investigator. BS, ATH, MH, SE, and BK are core members of the NIHR Exeter Clinical Research Facility. EP is a Wellcome Trust New Investigator. TM is supported by NIHR CSO Fellowship. JP is partly funded by the NIHR Collaboration for Leadership in Applied Health Research and Care for the South West (PenCLAHRC)

Time trends in prescribing of type 2 diabetes drugs, glycaemic response and risk factors:a retrospective analysis of primary care data, 2010-2017

This is the author accepted manuscript. The final version is available on open access from Wiley via the DOI in this recordAim: Prescribing in type 2 diabetes has changed markedly in recent years, with increasing use of newer, more expensive glucose-lowering drugs. We aimed to describe population-level time trends in both prescribing patterns and short-term patient outcomes (HbA1c, weight, blood pressure, hypoglycemia and treatment discontinuation) after initiating new therapy. Materials and methods: We studied 81,532 UK patients with type 2 diabetes initiating a first to fourth line drug in primary care between 2010-2017 inclusive (Clinical Practice Research Datalink). Trends in new prescriptions and subsequent six and twelve-month adjusted changes in glycemic response (reduction in HbA1c), weight, blood pressure, and rates of hypoglycemia and treatment discontinuation were examined. Results: DPP4-inhibitor use second-line near doubled (41% of new prescriptions in 2017 vs. 22% 2010), replacing sulfonylureas as the most common second-line drug (29% 2017 vs. 53% 2010). SGLT2-inhibitors, introduced in 2013, comprised 17% of new first-fourth line prescriptions by 2017. First-line use of metformin remained stable (91% of new prescriptions in 2017 vs. 91% 2010). Over the study period there was little change in average glycemic response and treatment discontinuation. There was a modest reduction in weight second and third-line (second line 2017 vs. 2010: -1.5 kg (95%CI -1.9;-1.1), p<0.001), and a slight reduction in systolic blood pressure first to third-line (2017 vs. 2010 difference range -1.7 to -2.1 mmHg, all p<0.001). Hypoglycemia rates decreased second-line (incidence rate ratio 0.94 per-year (95%CI 0.88;1.00, p=0.04)), mirroring the decline in use of sulfonylureas. 4 Conclusions: Recent changes in prescribing of therapy in type 2 diabetes have not led to a change in glycemic response and have resulted in modest improvements in other population-level short-term patient outcomes.Medical Research Council (MRC)National Institute for Health Research (NIHR)Wellcome Trus