Simulation-based sample size calculations of marginal proportional means models for recurrent events with competing risks.

In randomised controlled trials, the outcome of interest could be recurrent events, such as hospitalisations for heart failure. If mortality rates are non-negligible, both recurrent events and competing terminal events need to be addressed when formulating the estimand and statistical analysis is no longer trivial. In order to design future trials with primary recurrent event endpoints with competing risks, it is necessary to be able to perform power calculations to determine sample sizes. This paper introduces a simulation-based approach for power estimation based on a proportional means model for recurrent events and a proportional hazards model for terminal events. The simulation procedure is presented along with a discussion of what the user needs to specify to use the approach. The method is flexible and based on marginal quantities which are easy to specify. However, the method introduces a lack of a certain type of dependence. This is explored in a sensitivity analysis which suggests that the power is robust in spite of that. Data from a randomised controlled trial, LEADER, is used as the basis for generating data for a future trial. Finally, potential power gains of recurrent event methods as opposed to first event methods are discussed.

Charge-to-spin conversion in fully epitaxial Ru/Cu hybrid nanolayers with interface control.

The demonstration of the charge-to-spin conversion, especially with enhanced spin Hall conductivity, is crucial for the development of energy-efficient spintronic devices such as spin-orbit torque (SOT) based magnetoresistive random access memories. In this work, fully epitaxial Ru/Cu heterostructures were fabricated with interface engineering and nanolayer insertions consisting of Cu (1 nm)/Ru (1 nm) structures with different numbers of periods. The atomically controlled interface was confirmed by the high-resolution high-angle annular dark-field scanning transmission electron microscopy, and the epitaxial relationship persists even in the hybrid nanolayer insertion structures. The spin current generation was detected by the measurement of unidirectional spin Hall magnetoresistance, and the effective damping-like spin Hall efficiency (ξDL) was further quantitatively evaluated by the spin-torque ferromagnetic resonance with thickness dependence of the ferromagnetic layer. It is found that the sharp interface Ru/Cu film has a sizeableξDLof -2.2% and the insertion of Cu/Ru nanolayers at the interface can increase theξDLvalue to -3.7%. The former could be attributed to the interface spin-orbit filtering effect and the latter may be further understood by the intrinsic contribution from the local electronic structure tuning due to the lattice distortion near the interface. A large effective spin Hall conductivity is achieved to be (3∼5) × 105ℏ2eΩ-1m-1in the epitaxial Ru/Cu hybrid nanolayers, which is in the same range as that of platinum. This work indicates that the interfacial control with hybrid nanolayer structures can extend the SOT-based materials to highly conductive metals, even with weak spin-orbit interactions, toward high stability, low cost, and low energy consumption for spintronic applications.

Unbiased and efficient estimation of causal treatment effects in crossover trials.

We introduce causal inference reasoning to crossover trials, with a focus on thorough QT (TQT) studies. For such trials, we propose different sets of assumptions and consider their impact on the modeling strategy and estimation procedure. We show that unbiased estimates of a causal treatment effect are obtained by a g-computation approach in combination with weighted least squares predictions from a working regression model. Only a few natural requirements on the working regression and weighting matrix are needed for the result to hold. It follows that a large class of Gaussian linear mixed working models lead to unbiased estimates of a causal treatment effect, even if they do not capture the true data-generating mechanism. We compare a range of working regression models in a simulation study where data are simulated from a complex data-generating mechanism with input parameters estimated on a real TQT data set. In this setting, we find that for all practical purposes working models adjusting for baseline QTc measurements have comparable performance. Specifically, this is observed for working models that are by default too simplistic to capture the true data-generating mechanism. Crossover trials and particularly TQT studies can be analyzed efficiently using simple working regression models without biasing the estimates for the causal parameters of interest.

On logistic regression with right censored data, with or without competing risks, and its use for estimating treatment effects.

Simple logistic regression can be adapted to deal with right-censoring by inverse probability of censoring weighting (IPCW). We here compare two such IPCW approaches, one based on weighting the outcome, the other based on weighting the estimating equations. We study the large sample properties of the two approaches and show that which of the two weighting methods is the most efficient depends on the censoring distribution. We show by theoretical computations that the methods can be surprisingly different in realistic settings. We further show how to use the two weighting approaches for logistic regression to estimate causal treatment effects, for both observational studies and randomized clinical trials (RCT). Several estimators for observational studies are compared and we present an application to registry data. We also revisit interesting robustness properties of logistic regression in the context of RCTs, with a particular focus on the IPCW weighting. We find that these robustness properties still hold when the censoring weights are correctly specified, but not necessarily otherwise.

Sampling in the case-time-control design among drug users when outcome prevents further treatment.

PURPOSE: The objective of this article is to advocate a new way of sampling controls in the case-time-control design in a cohort of drug users when the studied outcome prevents further treatment. METHODS: Mathematically we demonstrate how a standard sampling of controls, where controls are sampled among all subjects without an event at end-of-study, leads to a biased effect estimate. We propose to add the requirement that controls initiate treatment before the calendar time of event of their matched case to circumvent this. The standard and proposed sampling methods are compared in a simulation study and in an empirical data example examining the effect of nonsteroidal anti-inflammatory drug usage on the risk of upper gastrointestinal bleeding. RESULTS: When the controls are sampled the standard way, the case-time-control design confers a bias because cases and controls have a different time-trend of exposure. The bias has been upwards in all the scenarios we have investigated. The requirement we add to be a potential control ensures that cases and controls have the same time-trend of exposure when treatment and outcome are independent. The simulation study confirms that the proposed sampling method removes the bias between treatment and outcome. The proposed sampling method lowered the odds-ratio estimate from 3.72 to 3.26 in the data example. CONCLUSION: The proposed sampling method makes it possible to use the case-time-control design in a cohort of subjects with registered use of a drug when outcome prevents further treatment.

Weight change during the first week of life and a new method for retrospective prediction of birthweight among exclusively breastfed newborns.

INTRODUCTION: Identification of low birthweight and small for gestational age is pivotal in clinical management and many research studies, but in low-income countries, birthweight is often unavailable within 24 h of birth. Newborn weights measured within days after birth and knowledge of the growth patterns in the first week of life can help estimate the weight at birth retrospectively. This study aimed to generate sex-specific prediction maps and weight reference charts for the retrospective estimation of birthweight for exclusively breastfed newborns in a low-resource setting. MATERIAL AND METHODS: This was a prospective cohort study nested in a clinical trial of intermittent preventive treatment in pregnancy for malaria with either dihydroartemisinin-piperaquine with/without azithromycin or sulfadoxine-pyrimethamine in Korogwe District, north-eastern Tanzania (Clinicaltrials.gov: NCT03208179). Newborns were weighed at birth or in the immediate hours after birth and then daily for 1 week. Reference charts, nadir, time to regain weight, and prediction maps were generated using nonlinear mixed-effects models fitted to the longitudinal data, incorporating interindividual variation as random effects. Predictions and prediction standard deviations were computed using a linear approximation approach. RESULTS: Between March and December 2019, 513 live newborns with birthweights measured within 24 h of delivery were weighed daily for 1 week. Complete datasets were available from 476 exclusively breastfed newborns. There was a rapid decline in weight shortly after delivery. The average weight loss, time of nadir, and time to regain weight were 4.3% (95% confidence interval [CI] 3.8-4.9) at 27 h (95% CI 24-30) and 105 h (95% CI 91-120) in boys and 4.9% (95% CI 4.2-5.6) at 28 h (95% CI 23-33) and 114 h (95% CI 93-136) in girls, respectively. The data were used to generate prediction maps with 1-h time intervals and 0.05 kg weight increments showing the predicted birthweights and weight-for-age and weight-change-for-age reference charts depicting variation in weight loss from <1 to >10%. CONCLUSIONS: The prediction maps and reference charts can be used by researchers in low-resource settings to retrospectively estimate birthweights using weights collected up to 168 h after delivery, thereby maximizing data utilization. Clinical practitioners can also use the prediction maps to retrospectively classify newborns as low birthweight or small for gestational age.

Testing equivalence of survival before but not after end of follow-up.

For equivalence trials with survival outcomes, a popular testing approach is the elegant test for equivalence of two survival functions suggested by Wellek (Biometrics 49: 877-881, 1993). This test evaluates whether or not the difference between the true survival curves is practically irrelevant by specifying an equivalence margin on the hazard ratio under the proportional hazards assumption. However, this approach is based on extrapolating the behavior of the survival curves to the whole time axis, whereas in practice survival times are only observed until the end of follow-up. We propose a modification of Welleks test that only addresses equivalence until end of follow-up and derive the large sample properties of this test. Another issue is the proportional hazards assumption which may not be realistic. If this assumption is violated, one may severely misjudge the actual treatment effect with a hazard ratio quantification and wrongly declare equivalence. We suggest a non-parametric test for assessing survival equivalence within the follow-up period. We derive the large sample properties of this test and provide an approximation to the limiting distribution under some mild assumptions on the functional form of the difference between the two survival curves. Both suggestions are investigated by simulation and applied to a clinical trial on survival of gastric cancer patients.

Modeling the cumulative incidence function of multivariate competing risks data allowing for within-cluster dependence of risk and timing.

We propose to model the cause-specific cumulative incidence function of multivariate competing risks data using a random effects model that allows for within-cluster dependence of both risk and timing. The model contains parameters that makes it possible to assess how the two are connected, e.g. if high-risk is related to early onset. Under the proposed model, the cumulative incidences of all failure causes are modeled and all cause-specific and cross-cause associations specified. Consequently, left-truncation and right-censoring are easily dealt with. The proposed model is assessed using simulation studies and applied in analysis of Danish register-based family data on breast cancer.

Variation in the HLA-F gene locus with functional impact is associated with pregnancy success and time-to-pregnancy after fertility treatment.

STUDY QUESTION: The aim of this study was to investigate a possible influence of three single nucleotide polymorphisms (SNPs) in the HLA-F gene locus on time-to-pregnancy and pregnancy success after fertility treatment. SUMMARY ANSWER: HLA-F SNP genotypes and HLA-F diplotypes are associated with the number of fertility treatment cycles needed to achieve pregnancy and live birth. WHAT IS KNOWN ALREADY: HLA class Ib molecules, including HLA-F, which are known to be expressed by extra-villous trophoblast cells have immunomodulatory properties and play a role at the feto-maternal interface. However, a few recent studies suggest that HLA-F expressed in the mid-luteal endometrium may play a part in the establishment of pregnancy as well. Three genetic polymorphisms in the HLA-F gene locus influence the expression of HLA-F in the mid-luteal endometrium and are associated with time-to-pregnancy in healthy women. STUDY DESIGN, SIZE, DURATION: The current study included 102 female patients and 91 male patients attending for ART treatment and recruited between 2009 and 2014 at fertility clinics in a University Hospital setting, and 78 fertile female controls recruited in 2017 and 2018 at a department of Obstetrics and Gynaecology in a University Hospital. All women in the control group conceived naturally, and no other clinical data for the controls were retrieved. PARTICIPANTS/MATERIALS, SETTING, METHODS: Genotyping of genomic DNA from blood samples was performed with Sanger sequencing for the three SNPs of interest in the HLA-F gene locus: rs1362126 (G/A), rs2523405 (T/G) and rs2523393 (A/G). Furthermore, clinical data were collected for the couples in fertility treatment. MAIN RESULTS AND THE ROLE OF CHANCE: There were no significant differences in the distributions of the three HLA-F SNP genotypes and alleles between the female fertile control group and the female infertility group. We considered if the number of treatment cycles was related to the HLA-F SNP genotypes and HLA-F diplotypes in a discrete time to event analyses. A significant association with longer time-to-pregnancy, measured as number of fertility treatment cycles, was observed for women in the ART group who carried the HLA-F genotypes that are associated with a lower amount of HLA-F mRNA expressed in mid-luteal endometrium. For the rs1362126 AA genotype relative to the GG genotype, the odds ratio (OR) was 0.30 (95% CI = 0.10-0.87, P = 0.02); for the rs2523405 GG genotype relative to the TT genotype, the OR was 0.40 (95% CI = 0.15-1.04, P = 0.06); and for the rs2523393 GG genotype relative to the AA genotype, the OR was 0.27 (95% CI = 0.09-0.78, P = 0.01). In addition to comparing the HLA-F genotypes by a standard likelihood-ratio test, a trend test based on the number of G or A alleles were also performed. The HLA-F genotypes associated with longer time-to-pregnancy in these tests were as follows: number of A alleles at rs1362126 (P = 0.01), the OR was 0.56 per A allele (95% CI = 0.35-0.89); number of G alleles at rs2523405 (P = 0.05), OR was 0.65 per G allele (95% CI = 0.42-1.00); and number of G alleles at rs2523393 (P = 0.01), OR was 0.56 per G allele (95% CI = 0.36-0.86). On average, for the rs1362126 SNP, 2.1 more treatment cycles for a woman who carried the AA genotype were needed to achieve pregnancy within the first eight treatment cycles compared with a woman who carried the GG genotype. Likewise, for the rs2523405 SNP, 1.8 more cycles for the GG genotype compared with the TT genotype were needed, and for the rs2523393 SNP, 2.2 more treatment cycles for a woman who carried the GG genotype compared with a woman who carried the AA genotype were needed. Adjustments for the covariates BMI, female age, IVF (yes/no for each cycle), ICSI (yes/no for each cycle), female factor (yes/no) and male factor (yes/no), were also performed modeling the cycle-specific probabilities and the genotypes remained significant and almost unchanged. LIMITATIONS, REASONS FOR CAUTION: Specific types of ART will be chosen from the start of treatment, which means that the chances of achieving pregnancy could differ between the women solely due to their first line of treatment. However, multivariate analyses are performed to adjust for type of ART treatment. WIDER IMPLICATIONS OF THE FINDINGS: To our knowledge, this is the first study that shows associations between, and implications of, HLA-F gene locus variation and time-to-pregnancy and pregnancy success in a clinical setting for fertility treatment/ART. STUDY FUNDING/COMPETING INTEREST(S): Supported by the Region Zealand Health Sciences Research foundation and by Zealand University Hospital through the ReproHealth Research Consortium ZUH. The authors declare no conflict of interest.

Are remitted affective disorders and familial risk of affective disorders associated with metabolic syndrome, inflammation and oxidative stress? - a monozygotic twin study.

BACKGROUND: Metabolic syndrome (MetS) is associated with reduced life expectancy in patients with affective disorders, however, whether MetS also plays a role before the onset of affective disorder is unknown. We aimed to investigate whether MetS, inflammatory markers or oxidative stress act as risk factors for affective disorders, and whether MetS is associated with increased inflammation and oxidative stress. METHODS: We conducted a high-risk study including 204 monozygotic (MZ) twins with unipolar or bipolar disorder in remission or partial remission (affected), their unaffected co-twins (high-risk) and twins with no personal or family history of affective disorder (low-risk). Metabolic Syndrome was ascertained according to the International Diabetes Federation (IDF) criteria. Inflammatory markers and markers of oxidative stress were analyzed from fasting blood and urine samples, respectively. RESULTS: The affected and the high-risk group had a significantly higher prevalence of MetS compared to the low-risk group (20% v. 15% v. 2.5%, p = 0.0006), even after adjusting for sex, age, smoking and alcohol consumption. No differences in inflammatory and oxidative markers were seen between the three groups. Further, MetS was associated with alterations in inflammatory markers, and oxidative stress was modestly correlated with inflammation. CONCLUSION: Metabolic syndrome is associated with low-grade inflammation and may act as a risk factor and a trait marker for affective disorders. If confirmed in longitudinal studies, this suggests the importance of early intervention and preventive approaches targeted towards unhealthy lifestyle factors that may contribute to later psychopathology.

Excess cumulative incidence estimation for matched cohort survival studies.

We suggest a regression approach to estimate the excess cumulative incidence function (CIF) when matched data are available. In a competing risk setting, we define the excess risk as the difference between the CIF in the exposed group and the background CIF observed in the unexposed group. We show that the excess risk can be estimated through an extended binomial regression model that actively uses the matched structure of the data, avoiding further estimation of both the exposed and the unexposed CIFs. The method naturally deals with two time scales, age and time since exposure and simplifies how to deal with the left truncation on the age time-scale. The model makes it easy to predict individual excess risk scenarios and allows for a direct interpretation of the covariate effects on the cumulative incidence scale. After introducing the model and some theory to justify the approach, we show via simulations that our model works well in practice. We conclude by applying the excess risk model to data from the ALiCCS study to investigate the excess risk of late events in childhood cancer survivors.

Risk of late health effects after soft-tissue sarcomas in childhood - a population-based cohort study within the Adult Life after Childhood Cancer in Scandinavia research programme.

BACKGROUND: In the 1960s only 1/3 of children with soft-tissue sarcomas survived, however with improved treatments survival today has reached 70%. Given the previous poor survival and the rarity of soft-tissue sarcomas, the risk of somatic late effects in a large cohort of Nordic soft-tissue sarcoma survivors has not yet been assessed. METHODS: In this population-based cohort study we identified 985 five-year soft-tissue sarcoma survivors in Nordic nationwide cancer registries and late effects in national hospital registries covering the period 1964-2012. Information on tumour site and radiotherapy was available for Danish and Finnish survivors (N = 531). Using disease-specific rates of first-time hospital contacts for somatic diseases in survivors and in 4,830 matched comparisons we calculated relative rates (RR) and rate differences (RD). RESULTS: Survivors had a RR of 1.5 (95% CI 1.4-1.7) and an absolute RD of 23.5 (17.7-29.2) for a first hospital contact per 1,000 person-years. The highest risks in both relative and absolute terms were of endocrine disorders (RR = 2.5; RD = 7.6), and diseases of the nervous system (RR = 1.9; RD = 6.6), digestive organs (RR = 1.7; RD = 5.4) and urinary system (RR = 1.7; RD = 5.6). By tumour site, excess risk was lower after extremity tumours. Irradiated survivors had a 2.6 (1.2-5.9) times higher risk than non-irradiated. CONCLUSIONS: Soft-tissue sarcoma survivors have an increased risk of somatic late effects in 5 out of 10 main diagnostic groups of diseases, and the risk remains increased up to 40 years after cancer diagnosis. Risks were slightly lower for those treated for tumours in the extremities, and radiotherapy increased the risk by more than two-fold.

Antibody response to vaccination after haematopoietic cell transplantation in children using a reduced dose schedule-A retrospective cohort study.

Children receiving HCT loose protective immunity to vaccines received pre-HCT. Therefore, revaccination post-HCT is of major importance. In Denmark, a vaccination schedule with fewer doses post-HCT has been used, including two doses for diphtheria, tetanus, polio, measles, mumps, and rubella, and one dose only for Haemophilus influenzae type B. The background for this was the presumption that post-HCT immunization constituted booster vaccination of donor immunity. Our objective was to evaluate the proportion of children protected after the scheduled vaccination programme. A nationwide retrospective cohort study of all children who have received an HCT in Denmark during 1994-2012. Antibody levels were analysed in blood samples drawn before and after vaccination, and the probability of achieving protection after the scheduled immunization programme was estimated. A total of 198 children were included. The protection post-immunization was as follows: diphtheria 75.3%, tetanus 89.1%, polio 97.7%, and Haemophilus influenzae type B 94.8%. For diphtheria and tetanus, the probability of achieving protection increased to 93.8% and 97.3%, respectively, after a third dose. For measles, mumps, and rubella, the probability of achieving protection was 89.4%, 80.9%, and 94.2%, respectively. In conclusion, our findings support a more extensive vaccination schedule including three doses for diphtheria and tetanus which are in line with current international guidelines.

On the estimation of average treatment effects with right-censored time to event outcome and competing risks.

We are interested in the estimation of average treatment effects based on right-censored data of an observational study. We focus on causal inference of differences between t-year absolute event risks in a situation with competing risks. We derive doubly robust estimation equations and implement estimators for the nuisance parameters based on working regression models for the outcome, censoring, and treatment distribution conditional on auxiliary baseline covariates. We use the functional delta method to show that these estimators are regular asymptotically linear estimators and estimate their variances based on estimates of their influence functions. In empirical studies, we assess the robustness of the estimators and the coverage of confidence intervals. The methods are further illustrated using data from a Danish registry study.

The Role of Comorbidity in Mortality After Hip Fracture: A Nationwide Norwegian Study of 38,126 Women With Hip Fracture Matched to a General-Population Comparison Cohort.

Hip fracture patients often have comorbid conditions. We investigated whether the combination of comorbidity and hip fracture could explain the previously observed excess mortality among hip fracture patients as compared with the general population. Using a population-based matched study design with 38,126 Norwegian women who suffered a hip fracture during the period 2009-2015 and the same number of women in a matched comparison cohort, we matched participants on prefracture comorbidity, age, and education. We estimated relative survival and additive and multiplicative comorbidity-hip fracture interactions. An additive comorbidity-hip fracture interaction of 4 or 9 additional deaths per 100 patients, depending on Charlson Comorbidity Index (CCI) score, was observed 1 year after hip fracture. Among women with a CCI score of ≥3, 15 additional deaths per 100 patients were observed; of these, 9 deaths could be attributed to the interaction and 6 to the hip fracture per se. On the relative scale, we observed increasing heterogeneity in survival by comorbidity over time; survival was reduced by 39% after 6 years among patients with a CCI score of ≥3, while among women with no comorbidity, survival was reduced by 17% (hip fracture vs. no hip fracture). In summary, prefracture comorbidity was associated with short-term absolute excess mortality and long-term relative excess mortality.

Analysis of Generalized Semiparametric Regression Models for Cumulative Incidence Functions with Missing Covariates.

The cumulative incidence function quantifies the probability of failure over time due to a specific cause for competing risks data. The generalized semiparametric regression models for the cumulative incidence functions with missing covariates are investigated. The effects of some covariates are modeled as non-parametric functions of time while others are modeled as parametric functions of time. Different link functions can be selected to add flexibility in modeling the cumulative incidence functions. The estimation procedures based on the direct binomial regression and the inverse probability weighting of complete cases are developed. This approach modifies the full data weighted least squares equations by weighting the contributions of observed members through the inverses of estimated sampling probabilities which depend on the censoring status and the event types among other subject characteristics. The asymptotic properties of the proposed estimators are established. The finite-sample performances of the proposed estimators and their relative efficiencies under different two-phase sampling designs are examined in simulations. The methods are applied to analyze data from the RV144 vaccine efficacy trial to investigate the associations of immune response biomarkers with the cumulative incidence of HIV-1 infection.

Light-load resistance exercise increases muscle protein synthesis and hypertrophy signaling in elderly men.

The present study investigated whether well-tolerated light-load resistance exercise (LL-RE) affects skeletal muscle fractional synthetic rate (FSR) and anabolic intracellular signaling as a way to counteract age-related loss of muscle mass. Untrained healthy elderly (>65-yr-old) men were subjected to 13 h of supine rest. After 2.5 h of rest, unilateral LL-RE, consisting of leg extensions (10 sets, 36 repetitions) at 16% of 1 repetition maximum (RM), was conducted. Subsequently, the subjects were randomized to oral intake of 4 g of whey protein per hour (PULSE, n = 10), 28 g of whey protein at 0 h and 12 g of whey protein at 7 h postexercise (BOLUS, n = 10), or 4 g of maltodextrin per hour (placebo, n = 10). Quadriceps muscle biopsies were taken at 0, 3, 7, and 10 h postexercise from the resting and the exercised leg of each subject. Myofibrillar FSR and activity of select targets from the mechanistic target of rapamycin complex 1-signaling cascade were analyzed from the biopsies. LL-RE increased myofibrillar FSR compared with the resting leg throughout the 10-h postexercise period. Phosphorylated (T308) AKT expression increased in the exercised leg immediately after exercise. This increase persisted in the placebo group only. Levels of phosphorylated (T37/46) eukaryotic translation initiation factor 4E-binding protein 1 increased throughout the postexercise period in the exercised leg in the placebo and BOLUS groups and peaked at 7 h. In all three groups, phosphorylated (T56) eukaryotic elongation factor 2 decreased in response to LL-RE. We conclude that resistance exercise at only 16% of 1 RM increased myofibrillar FSR, irrespective of nutrient type and feeding pattern, which indicates an anabolic effect of LL-RE in elderly individuals. This finding was supported by increased signaling for translation initiation and translation elongation in response to LL-RE.

HLA class Ia and Ib molecules and FOXP3+ TILs in relation to the prognosis of malignant melanoma patients.

HLA class Ia (HLA-ABC) and HLA class Ib (HLA-E, -F and -G) molecules and FOXP3+ tumor-infiltrating lymphocytes (TILs) are often reported as relevant factors of tumor immune regulation. We investigated their expression as prognostic factors in 200 patients with primary cutaneous melanoma (PCM). In our cohort, patients with tumors showing upregulation of HLA-ABC molecules had significantly thicker tumors (32% vs 7%, P<0.001), frequent ulceration (20% vs 6%, P=0.007) and frequent nodular melanomas (20% vs 4%, P=0.001). Additionally, high expression of HLA-G in the tumor was a sign of bad prognosis for the patients, being associated with thick tumors (30% vs 12%, P=0.017), ulceration (24% vs 5%, P<0.001) and positive sentinel node (13% vs 6%, P=0.015). HLA-E, HLA-F and FOXP3+ TILs were not indicative of the prognosis in PCM. High HLA-ABC and HLA-G were associated with tumor aggressiveness and could be relevant predictive markers for effective immunotherapy of melanoma tumors.

Lung cancer, genetic predisposition and smoking: the Nordic Twin Study of Cancer.

BACKGROUND: We aimed to disentangle genetic and environmental causes in lung cancer while considering smoking status. METHODS: Four Nordic twin cohorts (43 512 monozygotic (MZ) and 71 895 same sex dizygotic (DZ) twin individuals) had smoking data before cancer diagnosis. We used time-to-event analyses accounting for censoring and competing risk of death to estimate incidence, concordance risk and heritability of liability to develop lung cancer by smoking status. RESULTS: During a median of 28.5 years of follow-up, we recorded 1508 incident lung cancers. Of the 30 MZ and 28 DZ pairs concordant for lung cancer, nearly all were current smokers at baseline and only one concordant pair was seen among never smokers. Among ever smokers, the case-wise concordance of lung cancer, that is the risk before a certain age conditional on lung cancer in the co-twin before that age, was significantly increased compared with the cumulative incidence for both MZ and DZ pairs. This ratio, the relative recurrence risk, significantly decreased by age for MZ but was constant for DZ pairs. Heritability of lung cancer was 0.41 (95% CI 0.26 to 0.56) for currently smoking and 0.37 (95% CI 0.25 to 0.49) for ever smoking pairs. Among smoking discordant pairs, the pairwise HR for lung cancer of the ever smoker twin compared to the never smoker co-twin was 5.4 (95% CI 2.1 to 14.0) in MZ pairs and 5.0 (95% CI 3.2 to 7.9) in DZ pairs. CONCLUSIONS: The contribution of familial effects appears to decrease by age. The discordant pair analysis confirms that smoking causes lung cancer.

Penalized estimation for competing risks regression with applications to high-dimensional covariates.

High-dimensional regression has become an increasingly important topic for many research fields. For example, biomedical research generates an increasing amount of data to characterize patients' bio-profiles (e.g. from a genomic high-throughput assay). The increasing complexity in the characterization of patients' bio-profiles is added to the complexity related to the prolonged follow-up of patients with the registration of the occurrence of possible adverse events. This information may offer useful insight into disease dynamics and in identifying subset of patients with worse prognosis and better response to the therapy. Although in the last years the number of contributions for coping with high and ultra-high-dimensional data in standard survival analysis have increased (Witten and Tibshirani, 2010. Survival analysis with high-dimensional covariates. Statistical Methods in Medical Research 19: (1), 29-51), the research regarding competing risks is less developed (Binder and others, 2009. Boosting for high-dimensional time-to-event data with competing risks. Bioinformatics 25: (7), 890-896). The aim of this work is to consider how to do penalized regression in the presence of competing events. The direct binomial regression model of Scheike and others (2008. Predicting cumulative incidence probability by direct binomial regression. Biometrika 95: (1), 205-220) is reformulated in a penalized framework to possibly fit a sparse regression model. The developed approach is easily implementable using existing high-performance software to do penalized regression. Results from simulation studies are presented together with an application to genomic data when the endpoint is progression-free survival. An R function is provided to perform regularized competing risks regression according to the binomial model in the package timereg (Scheike and Martinussen, 2006. Dynamic Regression models for survival data New York: Springer), available through CRAN.