Cost-effectiveness of a telemonitoring programme in patients with cardiovascular diseases compared with standard of care.

OBJECTIVES: The main aim of this work was to analyse the cost-effectiveness of an integrated care concept (NICC) that combines telemonitoring with the support of a care centre in addition to guideline therapy for patients. Secondary aims were to compare health utility and health-related quality of life (QoL) between NICC and standard of care (SoC). METHODS: The randomised controlled CardioCare MV Trial compared NICC and SoC in patients from Mecklenburg-West Pomerania (Germany) with atrial fibrillation, heart failure or treatment-resistant hypertension. QoL was measured using the EQ-5D-5L at baseline, 6 months and 1 year follow-up. Quality-adjusted life years (QALYs), EQ5D utility scores, Visual Analogue Scale (VAS) Scores and VAS adjusted life years (VAS-AL) were calculated. Cost data were obtained from health insurance companies, and the payer perspective was taken in health economic analyses. Quantile regression was used with adjustments for stratification variables. RESULTS: The net benefit of NICC (QALY) was 0.031 (95% CI 0.012 to 0.050; p=0.001) in this trial involving 957 patients. EQ5D Index values, VAS-ALs and VAS were larger for NICC compared with SoC at 1 year follow-up (all p≤0.004). Direct cost per patient and year were €323 (CI €157 to €489) lower in the NICC group. When 2000 patients are served by the care centre, NICC is cost-effective if one is willing to pay €10 652 per QALY per year. CONCLUSION: NICC was associated with higher QoL and health utility. The programme is cost-effective if one is willing to pay approximately €11 000 per QALY per year.

Multi-organ assessment in mainly non-hospitalized individuals after SARS-CoV-2 infection: The Hamburg City Health Study COVID programme.

AIMS: Long-term sequelae may occur after SARS-CoV-2 infection. We comprehensively assessed organ-specific functions in individuals after mild to moderate SARS-CoV-2 infection compared with controls from the general population. METHODS AND RESULTS: Four hundred and forty-three mainly non-hospitalized individuals were examined in median 9.6 months after the first positive SARS-CoV-2 test and matched for age, sex, and education with 1328 controls from a population-based German cohort. We assessed pulmonary, cardiac, vascular, renal, and neurological status, as well as patient-related outcomes. Bodyplethysmography documented mildly lower total lung volume (regression coefficient -3.24, adjusted P = 0.014) and higher specific airway resistance (regression coefficient 8.11, adjusted P = 0.001) after SARS-CoV-2 infection. Cardiac assessment revealed slightly lower measures of left (regression coefficient for left ventricular ejection fraction on transthoracic echocardiography -0.93, adjusted P = 0.015) and right ventricular function and higher concentrations of cardiac biomarkers (factor 1.14 for high-sensitivity troponin, 1.41 for N-terminal pro-B-type natriuretic peptide, adjusted P ≤ 0.01) in post-SARS-CoV-2 patients compared with matched controls, but no significant differences in cardiac magnetic resonance imaging findings. Sonographically non-compressible femoral veins, suggesting deep vein thrombosis, were substantially more frequent after SARS-CoV-2 infection (odds ratio 2.68, adjusted P < 0.001). Glomerular filtration rate (regression coefficient -2.35, adjusted P = 0.019) was lower in post-SARS-CoV-2 cases. Relative brain volume, prevalence of cerebral microbleeds, and infarct residuals were similar, while the mean cortical thickness was higher in post-SARS-CoV-2 cases. Cognitive function was not impaired. Similarly, patient-related outcomes did not differ. CONCLUSION: Subjects who apparently recovered from mild to moderate SARS-CoV-2 infection show signs of subclinical multi-organ affection related to pulmonary, cardiac, thrombotic, and renal function without signs of structural brain damage, neurocognitive, or quality-of-life impairment. Respective screening may guide further patient management.

Improving the Affordability of Anticancer Medicines Demands Evidence-Based Policy Solutions.

The high cost of many new anticancer medicines significantly impedes breakthrough discoveries from reaching patients. A commonly heard refrain is that high prices are necessary to compensate for the high costs of research and development (R&D). Yet, there are promising policy proposals aimed at improving affordability without compromising innovation. In seeking new policy solutions, we argue for a shift away from entrenched opinion toward an evidence-based discourse that is grounded in experiments and real-world pilot studies. We offer a novel perspective and practical recommendations on how empirical evidence could and should be gathered to inform evidence-based policy interventions that lead to sustainable medicine prices in oncology.See related article by Franzen et al. (Cancer Res Commun 2022;2:39-47).

Affordable Prices Without Threatening the Oncological R&D Pipeline-An Economic Experiment on Transparency in Price Negotiations.

The high prices of innovative medicines endanger access to care worldwide. Sustainable prices need to be affordable while sufficiently incentivizing research and development (R&D) investments. A proposed solution is increased transparency. Proponents argue that price and R&D cost confidentiality are drivers of high prices. On the contrary, supporters of confidentiality claim that confidentiality enables targeted discounts which make treatments affordable; moreover, pharmaceutical companies argue that R&D investments would suffer with more transparency. Despite the political relevance, limited empirical evidence exists on the effects of transparency regulations. We contribute to fill this gap with an experiment where we replicate the EU pharmaceutical market in a laboratory setting. In a randomized controlled study, we analyzed how participants, 400 students located in four European countries, negotiated in the current system of Price Secrecy in comparison with innovative bargaining settings where either prices only (Price Transparency) or prices and R&D costs (Full Transparency) were made transparent to buyers. We found that Price transparency had no statistically significant effect on average prices or number of patients treated and made R&D investments significantly smaller (-16.86%; P: 0.0024). On the other hand, Full Transparency reduced prices (-26%; P: 0.0004) and held the number of patients constant at the level of Price Secrecy. It produced price convergence between countries with low and high health budgets, and, despite lower prices, had no effect on R&D investments. Our findings provide novel evidence that combining price and R&D cost transparency could be an effective policy to contribute to sustainable medicine prices. See related article by Franzen et al. (Cancer Discov 2022;12:299-302).

Psychosocial benefits of insulin pump therapy in children with diabetes type 1 and their families: The pumpkin multicenter randomized controlled trial.

OBJECTIVE: Continuous subcutaneous insulin infusion (CSII) is on the rise among pediatric patients with type 1 diabetes mellitus. Metabolic effects alone cannot explain this rising popularity. From the patient's perspective, the main benefits of CSII may be found in subjective psychosocial health outcomes (patient-reported outcomes [PRO]). SUBJECTS AND METHODS: In a multicenter open randomized controlled trial, children and adolescents aged 6 to16 years currently treated with multiple daily injections (MDI) were randomized 1:1, stratified by center, to either starting with CSII immediately after the baseline interview or to continuing MDI while waiting 6 months for transmission to CSII. The primary outcomes were patient-reported diabetes-specific health-related quality of life (DHRQOL) and diabetes burden of the main caregiver. Secondary outcomes were caregiver stress, fear of hypoglycemia, satisfaction with treatment, and HbA1c. RESULTS: Two-hundred and eleven patients were randomized between February 2011 and October 2014, and 186 caregivers and 170 patients were analyzed using the intention-to-treat principle for primary outcomes. Children 8 to 11 years in the CSII group reported improved DHRQOL at follow-up compared to MDI (median difference [MD] 9.5, 95% confidence interval [CI] 3.6-16.7, P = 0.004). There were no treatment differences in the adolescent age-group 12 to 16 years (MD 2.7; 95% CI -3.2-9.5; P = 0.353). The main caregivers of the CSII group reported a significant decline of overall diabetes burden at follow-up compared to the MDI group (MD 0; 95% CI -1-0; P = 0.029). Secondary PROs also were in favor of CSII. CONCLUSIONS: CSII has substantial psychosocial benefits. PROs demonstrate these benefits. Registered as NCT01338922 at clinicaltrials.gov.

Genetic Testing for Autism Spectrum Disorder is Lacking Evidence of Cost-effectiveness. A Systematic Review.

BACKGROUND: Autism Spectrum Disorder (ASD) is a highly heritable neural development disorder characterized by social impairment. The earlier the diagnosis is made, the higher are the chances of obtaining relief of symptoms. A very early diagnosis uses molecular genetic tests, which are also offered commercially. OBJECTIVE: Systematic review of the economic impact of genetic tests in ASD. METHODS: We performed a systematic search of databases Pubmed, Medline, Cochrane, Econlit and the NHS Center for Reviews and Dissemination for articles in English and German from January 1, 2000 to December 31, 2015. Original articles published in peer-reviewed journals were screened in a two-step process. First, we focused our search on economic evaluations of genetic tests for ASD. Second, we searched for any economic evaluation (EE) of genetic tests. RESULTS: We identified 185 EE of genetic tests for various diseases. However, not a single EE of genetic tests has been found for ASD. The outcomes used in the EE of the genetic tests were heterogeneous, and results were generally not comparable. CONCLUSION: There is no evidence for cost-effectiveness of any genetic diagnostic test for ASD, although such genetic tests are available commercially. Cost-effectiveness analyses for genetic diagnostic tests for ASD are urgently required. There is a clear lack in research for EE of genetic tests.

Unbiased split variable selection for random survival forests using maximally selected rank statistics.

The most popular approach for analyzing survival data is the Cox regression model. The Cox model may, however, be misspecified, and its proportionality assumption may not always be fulfilled. An alternative approach for survival prediction is random forests for survival outcomes. The standard split criterion for random survival forests is the log-rank test statistic, which favors splitting variables with many possible split points. Conditional inference forests avoid this split variable selection bias. However, linear rank statistics are utilized by default in conditional inference forests to select the optimal splitting variable, which cannot detect non-linear effects in the independent variables. An alternative is to use maximally selected rank statistics for the split point selection. As in conditional inference forests, splitting variables are compared on the p-value scale. However, instead of the conditional Monte-Carlo approach used in conditional inference forests, p-value approximations are employed. We describe several p-value approximations and the implementation of the proposed random forest approach. A simulation study demonstrates that unbiased split variable selection is possible. However, there is a trade-off between unbiased split variable selection and runtime. In benchmark studies of prediction performance on simulated and real datasets, the new method performs better than random survival forests if informative dichotomous variables are combined with uninformative variables with more categories and better than conditional inference forests if non-linear covariate effects are included. In a runtime comparison, the method proves to be computationally faster than both alternatives, if a simple p-value approximation is used. Copyright © 2017 John Wiley & Sons, Ltd.

Adaptive linear rank tests for eQTL studies.

Expression quantitative trait loci (eQTL) studies are performed to identify single-nucleotide polymorphisms that modify average expression values of genes, proteins, or metabolites, depending on the genotype. As expression values are often not normally distributed, statistical methods for eQTL studies should be valid and powerful in these situations. Adaptive tests are promising alternatives to standard approaches, such as the analysis of variance or the Kruskal-Wallis test. In a two-stage procedure, skewness and tail length of the distributions are estimated and used to select one of several linear rank tests. In this study, we compare two adaptive tests that were proposed in the literature using extensive Monte Carlo simulations of a wide range of different symmetric and skewed distributions. We derive a new adaptive test that combines the advantages of both literature-based approaches. The new test does not require the user to specify a distribution. It is slightly less powerful than the locally most powerful rank test for the correct distribution and at least as powerful as the maximin efficiency robust rank test. We illustrate the application of all tests using two examples from different eQTL studies.

A confidence-limit-based approach to the assessment of Hardy-Weinberg equilibrium.

The classical chi(2)-procedure for the assessment of Hardy-Weinberg equilibrium (HWE) is tailored for detecting violations of HWE. However, many applications in genetic epidemiology require approximate compatibility with HWE. In a previous contribution to the field (Wellek, S. (2004). Biometrics, 60, 694-703), the methodology of statistical equivalence testing was exploited for the construction of tests for problems in which the assumption of approximate compatibility of a given genotype distribution with HWE plays the role of the alternative hypothesis one aims to establish. In this article, we propose a procedure serving the same purpose but relying on confidence limits rather than critical bounds of a significance test. Interval estimation relates to essentially the same parametric function that was previously chosen as the target parameter for constructing an exact conditional UMPU test for equivalence with a HWE conforming genotype distribution. This population parameter is shown to have a direct genetic interpretation as a measure of relative excess heterozygosity. Confidence limits are constructed using both asymptotic and exact methods. The new approach is illustrated by reanalyzing genotype distributions obtained from published genetic association studies, and detailed guidance for choosing the equivalence margin is provided. The methods have been implemented in freely available SAS macros.

Assessment of transmission distortion on chromosome 6p in healthy individuals using tagSNPs.

The best-documented example for transmission distortion (TD) to normal offspring are the t haplotypes on mouse chromosome 17. In healthy humans, TD has been described for whole chromosomes and for particular loci, but multiple comparisons have presented a statistical obstacle in wide-ranging analyses. Here we provide six high-resolution TD maps of the short arm of human chromosome 6 (Hsa6p), based on single-nucleotide polymorphism (SNP) data from 60 trio families belonging to two ethnicities that are available through the International HapMap Project. We tested all approximately 70,000 previously genotyped SNPs within Hsa6p by the transmission disequilibrium test. TagSNP selection followed by permutation testing was performed to adjust for multiple testing. A statistically significant evidence for TD was observed among male parents of European ancestry, due to strong and wide-ranging skewed segregation in a 730 kb long region containing the transcription factor-encoding genes SUPT3H and RUNX2, as well as the microRNA locus MIRN586. We also observed that this chromosomal segment coincides with pronounced linkage disequilibrium (LD), suggesting a relationship between TD and LD. The fact that TD may be taking place in samples not selected for a genetic disease implies that linkage studies must be assessed with particular caution in chromosomal segments with evidence of TD.

A general approach for sample size and power calculations based on the Haseman-Elston method.

Risch and Zhang (1995; Science 268: 1584-9) reported a simple sample size and power calculation approach for the Haseman-Elston method and based their computations on the null hypothesis of no genetic effect. We argue that the more reasonable null hypothesis is that of no recombination. For this null hypothesis, we provide a general approach for sample size and power calculations within the Haseman-Elston framework. We demonstrate the validity of our approach in a Monte-Carlo simulation study and illustrate the differences using data from published segregation analyses on body weight and heritability estimates on carotid artery artherosclerotic lesions.

Multiple test procedures using an upper bound of the number of true hypotheses and their use for evaluating high-dimensional EEG data.

Frequency analyses of EEG data yield large data sets, which are high-dimensional and have to be evaluated statistically without a large number of false positive statements. There exist several methods to deal with this problem in multiple comparisons. Knowing the number of true hypotheses increases the power of some multiple test procedures, however the number of true hypotheses is unknown, in general, and must be estimated. In this paper, we derive two new multiple test procedures by using an upper bound for the number of true hypotheses. Our first procedure controls the generalized family-wise error rate, and thus is an improvement of the step-down procedure of Hommel and Hoffmann [Hommel G., Hoffmann T. Controlled uncertainty. In: Bauer P. Hommel G. Sonnemann E., editors. Multiple Hypotheses Testing, Heidelberg: Springer 1987;ISBN 3540505598:p. 154-61]. The second new procedure controls the false discovery proportion and improves upon the approach of Lehmann and Romano [Lehmann E.L., Romano J.P. Generalizations of the familywise error rate. Ann. Stat. 2005;33:1138-54]. By Monte-Carlo simulations, we show how the gain in power depends upon the accuracy of the estimate of the number of true hypotheses. The gain in power of our procedures is demonstrated in an example using EEG data on the processing of memorized lexical items.

Haplotypes and haplotype-tagging single-nucleotide polymorphism: presentation Group 8 of Genetic Analysis Workshop 14.

Moderately dense maps of single-nucleotide polymorphism (SNP) markers across the human genome for both the simulated data set and data from the Collaborative Study of the Genetics of Alcoholism were available at Genetic Analysis Workshop 14 for the first time. This allowed examination of various novel and existing methods for haplotype analyses. Three contributors applied Mantel statistics in different ways for both linkage and association analysis by using the shared length between two haplotypes at a marker locus as a measure of genetic similarity. The results indicate that haplotype-sharing based on Mantel statistics can be a powerful approach and needs further methodological evaluation. Four contributors investigated haplotype-tagging SNP (htSNP) selection procedures, two contributors examined the use of multilocus haplotypes compared to single loci in association tests, and two contributors compared the accuracy of various methods for reconstructing haplotypes and estimating haplotype frequencies for both pedigree data and data from unrelated individuals. For all three different tasks, software packages and procedures gave similar results in regions of high linkage disequilibrium (LD). However, they were not as consistent in regions of moderate to low LD. One coalescence-based approach for estimating haplotype frequencies, coupled with a Markov chain Monte Carlo technique, outperformed the other haplotype frequency estimation methods in regions of low LD. In conclusion, regardless of the task, results were similar in chromosomal regions of high LD. However, based on the differing results observed here, methodological improvements are required for chromosomal regions of low to moderate LD.

On confidence intervals for genotype relative risks and attributable risks from case parent trio designs for candidate-gene studies.

Scherag et al. [Hum Hered 2002;54:210-217] recently proposed point estimates and asymptotic as well as exact confidence intervals for genotype relative risks (GRRs) and the attributable risk (AR) in case parent trio designs using single nucleotide polymorphism (SNP) data. The aim of this study was the investigation of coverage probabilities and bias in estimates if the marker locus is not identical to the disease locus. Using a variety of parameter constellations, including marker allele frequencies identical to and different from the SNP at the disease locus, we performed an analytical study to quantify the bias and a Monte-Carlo simulation study for quantifying both bias and coverage probabilities. No bias was observed if marker and trait locus coincided. Two parameters had a strong impact on coverage probabilities of confidence intervals and bias in point estimates if they did not coincide: the linkage disequilibrium (LD) parameter delta and the allele frequency at the marker SNP. If marker allele frequencies were different from the allele frequencies at the functional SNP, substantial biases occurred. Further, if delta between the marker and the disease locus was lower than the maximum possible delta, estimates were also biased. In general, biases were towards the null hypothesis for both GRRs and AR. If one GRR was not increased, as e.g. in a recessive genetic model, biases away from the null could be observed. If both GRRs were in identical directions and if both were substantially larger than 1, the bias always was towards the null. When applying point estimates and confidence intervals for GRRs and AR in candidate gene studies, great care is needed. Effect estimates are substantially biased towards the null if either the allele frequencies at the marker SNP and the true disease locus are different or if the LD between the marker SNP and the disease locus is not at its maximum. A bias away from the null occurs only in uncommon study situations; it is small and can therefore be ignored for applications.

Weighting affected sib pairs by marker informativity.

For the analysis of affected sib pairs (ASPs), a variety of test statistics is applied in genomewide scans with microsatellite markers. Even in multipoint analyses, these statistics might not fully exploit the power of a given sample, because they do not account for incomplete informativity of an ASP. For meta-analyses of linkage and association studies, it has been shown recently that weighting by informativity increases statistical power. With this idea in mind, the first aim of this article was to introduce a new class of tests for ASPs that are based on the mean test. To take into account how much informativity an ASP contributes, we weighted families inversely proportional to their marker informativity. The weighting scheme is obtained by use of the de Finetti representation of the distribution of identity-by-descent values. We derive the limiting distribution of the weighted mean test and demonstrate the validity of the proposed test. We show that it can be much more powerful than the classical mean test in the case of low marker informativity. In the second part of the article, we propose a Monte Carlo simulation approach for evaluating significance among ASPs. We demonstrate the validity of the simulation approach for both the classical and the weighted mean test. Finally, we illustrate the use of the weighted mean test by reanalyzing two published data sets. In both applications, the maximum LOD score of the weighted mean test is 0.6 higher than that of the classical mean test.

Assessment of 3 xeroderma pigmentosum group C gene polymorphisms and risk of cutaneous melanoma: a case-control study.

Individuals with the rare DNA repair deficiency syndrome xeroderma pigmentosum (XP) are sensitive to the sun and exhibit a 1000-fold increased risk for developing skin cancers, including cutaneous melanoma. Inherited polymorphisms of XP genes may contribute to subtle variations in DNA repair capacity and genetic susceptibility to melanoma. We investigated the role of three polymorphic alleles of the DNA repair gene XPC in a hospital-based case-control study of 294 Caucasian patients from Germany who had cutaneous melanoma and 375 healthy cancer-free sex-matched Caucasian control subjects from the same area. We confirmed that the XPC intron 9 PAT+, intron 11 -6A, and the exon 15 2920C polymorphisms are in a linkage disequilibrium. Only 1.6% of the 669 donors genotyped were discordant for these three polymorphisms. The allele frequencies (cases: controls) were for intron 9 PAT+ 41.7%:36.9%, for intron 11 -6A 41.8%:37.0% and for exon 15 2920C 41.3%:37.3%. Using multivariate logistic regression analyses to control for age, skin type and number of nevi, the three polymorphisms were significantly associated with increased risks of melanoma: OR 1.87 (95% CI: 1.10-3.19; P = 0.022), OR 1.83 (95% CI: 1.07-3.11; P = 0.026), and OR 1.82 (95% CI: 1.07-3.08; P = 0.026), respectively. Exploratory multivariate analyses of distinct subgroups revealed that these polymorphisms were associated with increased risks for the development of multiple primary melanomas (n = 28). The results of our case-control study support the hypothesis that the intron 9 PAT+, intron 11 -6A and exon 15 2920C haplotype may contribute to the risk of developing cutaneous melanoma by increasing the rate of an alternatively spliced XPC mRNA isoform that skips exon 12 and leads to reduced DNA repair. Our results should be validated in independent samples in order to guard against false positive findings.

Reducing sample sizes in genome scans: group sequential study designs with futility stops.

Group sequential study designs can greatly facilitate analyses of genetic linkage in complex traits. We recently proposed designs allowing stopping investigations early if the result is significant (König et al. [2001] Am. J. Hum. Genet. 69:590-600), thereby decreasing average sample sizes under the alternative hypothesis. However, average sample sizes were slightly increased under the null hypothesis. We now present designs where the analysis of markers is additionally stopped in case of futility, i.e., if the probability for significant results is sufficiently low. These sequential designs are applied to linkage analyses of single loci. We calculated sample sizes, time points, and critical boundaries for all analyses for 2- and 3-stage designs at an overall significance level of 0.0001. To confirm the validity of asymptotic approximations, Monte Carlo simulations were performed. The utility is demonstrated analyzing genome scan data provided for the Genetic Analysis Workshop 12. Application of the novel sequential designs yields tremendous decreases in average sample sizes, regardless of the size of the underlying genetic effect at investigated loci. Depending on the applied design, almost half of the sample size is spared on average. These enormous savings are expected to have a special impact on costs and time of large-scale studies such as genome scans.

Group sequential study designs in genetic-epidemiological case-control studies.

OBJECTIVE: In past years, the focus of genetic-epidemiological studies has shifted to analyzing complex diseases. Here, single genes often contribute only little to the manifestation of traits so that many probands have to be included in a study to reliably detect small effects. To reduce the number of required phenotypings and genotypings and thus facilitate analyzing complex traits, sequential study designs can be applied. METHODS: For sequential analyses of complex diseases in association studies, we compare the procedure by Sobell et al. (Am J Med Genet 1993;48:28-35) with the adaptation of formal group sequential study designs by Pampallona and Tsiatis (J Stat Plan Inf 1994;42:19-35). Error rates and average sample sizes are investigated by Monte-Carlo simulations. RESULTS: Formal sequential designs have a higher power regardless of underlying genetic effects. In addition, compared with conventional designs with fixed samples, average sample sizes are reduced considerably; under the null hypothesis of no association, up to 50% of the required sample size can be spared. CONCLUSIONS: To increase the efficiency of genetic-epidemiological case-control studies, we recommend using formal group sequential study designs. The tremendous savings in average sample sizes are expected to affect both cost and time spent on large-scale studies.

Analysis of pregnancy and other factors on detection of human papilloma virus (HPV) infection using weighted estimating equations for follow-up data.

Generalized estimating equations have been well established to draw inference for the marginal mean from follow-up data. Many studies suffer from missing data that may result in biased parameter estimates if the data are not missing completely at random. Robins and co-workers proposed using weighted estimating equations (WEE) in estimating the mean structure if drop-out occurs missing at random. We illustrate the differences between the WEE and the commonly applied available case analysis in a simulation study. We apply the WEE and reanalyse data of a longitudinal study of pregnancy and human papilloma virus (HPV) infection. We estimate the response probabilities and demonstrate that the data are not missing completely at random. Upon use of the WEE, we are able to show that pregnant women have an increased odds for an HPV infection compared with non-pregnant women after delivery (p=0.027). We conclude that the WEE are useful for dealing with monotone missing data due to drop-outs in follow-up data.