MAGE-A3 immunotherapeutic as adjuvant therapy for patients with resected, MAGE-A3-positive, stage III melanoma (DERMA): a double-blind, randomised, placebo-controlled, phase 3 trial.

BACKGROUND: Despite newly approved treatments, metastatic melanoma remains a life-threatening condition. We aimed to evaluate the efficacy of the MAGE-A3 immunotherapeutic in patients with stage IIIB or IIIC melanoma in the adjuvant setting. METHODS: DERMA was a phase 3, double-blind, randomised, placebo-controlled trial done in 31 countries and 263 centres. Eligible patients were 18 years or older and had histologically proven, completely resected, stage IIIB or IIIC, MAGE-A3-positive cutaneous melanoma with macroscopic lymph node involvement and an Eastern Cooperative Oncology Group performance score of 0 or 1. Randomisation and treatment allocation at the investigator sites were done centrally via the internet. We randomly assigned patients (2:1) to receive up to 13 intramuscular injections of recombinant MAGE-A3 with AS15 immunostimulant (MAGE-A3 immunotherapeutic; 300 µg MAGE-A3 antigen plus 420 µg CpG 7909 reconstituted in AS01B to a total volume of 0·5 mL), or placebo, over a 27-month period: five doses at 3-weekly intervals, followed by eight doses at 12-weekly intervals. The co-primary outcomes were disease-free survival in the overall population and in patients with a potentially predictive gene signature (GS-positive) identified previously and validated here via an adaptive signature design. The final analyses included all patients who had received at least one dose of study treatment; analyses for efficacy were in the as-randomised population and for safety were in the as-treated population. This trial is registered with ClinicalTrials.gov, number NCT00796445. FINDINGS: Between Dec 1, 2008, and Sept 19, 2011, 3914 patients were screened, 1391 randomly assigned, and 1345 started treatment (n=895 for MAGE-A3 and n=450 for placebo). At final analysis (data cutoff May 23, 2013), median follow-up was 28·0 months [IQR 23·3-35·5] in the MAGE-A3 group and 28·1 months [23·7-36·9] in the placebo group. Median disease-free survival was 11·0 months (95% CI 10·0-11·9) in the MAGE-A3 group and 11·2 months (8·6-14·1) in the placebo group (hazard ratio [HR] 1·01, 0·88-1·17, p=0·86). In the GS-positive population, median disease-free survival was 9·9 months (95% CI 5·7-17·6) in the MAGE-A3 group and 11·6 months (5·6-22·3) in the placebo group (HR 1·11, 0·83-1·49, p=0·48). Within the first 31 days of treatment, adverse events of grade 3 or worse were reported by 126 (14%) of 894 patients in the MAGE-A3 group and 56 (12%) of 450 patients in the placebo group, treatment-related adverse events of grade 3 or worse by 36 (4%) patients given MAGE-A3 vs six (1%) patients given placebo, and at least one serious adverse event by 14% of patients in both groups (129 patients given MAGE-A3 and 64 patients given placebo). The most common adverse events of grade 3 or worse were neoplasms (33 [4%] patients in the MAGE-A3 group vs 17 [4%] patients in the placebo group), general disorders and administration site conditions (25 [3%] for MAGE-A3 vs four [<1%] for placebo) and infections and infestations (17 [2%] for MAGE-A3 vs seven [2%] for placebo). No deaths were related to treatment. INTERPRETATION: An antigen-specific immunotherapeutic alone was not efficacious in this clinical setting. Based on these findings, development of the MAGE-A3 immunotherapeutic for use in melanoma has been stopped. FUNDING: GlaxoSmithKline Biologicals SA.

Testing interaction between treatment and high-dimensional covariates in randomized clinical trials.

In this paper, we considered different methods to test the interaction between treatment and a potentially large number (p) of covariates in randomized clinical trials. The simplest approach was to fit univariate (marginal) models and to combine the univariate statistics or p-values (e.g., minimum p-value). Another possibility was to reduce the dimension of the covariates using the principal components (PCs) and to test the interaction between treatment and PCs. Finally, we considered the Goeman global test applied to the high-dimensional interaction matrix, adjusted for the main (treatment and covariates) effects. These tests can be used for personalized medicine to test if a large set of biomarkers can be useful to identify a subset of patients who may be more responsive to treatment. We evaluated the performance of these methods on simulated data and we applied them on data from two early phases oncology clinical trials.

Dynamic frailty models based on compound birth-death processes.

Frailty models are used in survival analysis to model unobserved heterogeneity. They accommodate such heterogeneity by the inclusion of a random term, the frailty, which is assumed to multiply the hazard of a subject (individual frailty) or the hazards of all subjects in a cluster (shared frailty). Typically, the frailty term is assumed to be constant over time. This is a restrictive assumption and extensions to allow for time-varying or dynamic frailties are of interest. In this paper, we extend the auto-correlated frailty models of Henderson and Shimakura and of Fiocco, Putter and van Houwelingen, developed for longitudinal count data and discrete survival data, to continuous survival data. We present a rigorous construction of the frailty processes in continuous time based on compound birth-death processes. When the frailty processes are used as mixtures in models for survival data, we derive the marginal hazards and survival functions and the marginal bivariate survival functions and cross-ratio function. We derive distributional properties of the processes, conditional on observed data, and show how to obtain the maximum likelihood estimators of the parameters of the model using a (stochastic) expectation-maximization algorithm. The methods are applied to a publicly available data set.

Comparison of stopped Cox regression with direct methods such as pseudo-values and binomial regression.

By far the most popular model to obtain survival predictions for individual patients is the Cox model. The Cox model does not make any assumptions on the underlying hazard, but it relies heavily on the proportional hazards assumption. The most common ways to circumvent this robustness problem are 1) to categorize patients based on their prognostic risk score and to base predictions on Kaplan-Meier curves for the risk categories, or 2) to include interactions with the covariates and suitable functions of time. Robust estimators of the t(0)-year survival probabilities can also be obtained from a "stopped Cox" regression model, in which all observations are administratively censored at t(0). Other recent approaches to solve this robustness problem, originally proposed in the context of competing risks, are pseudo-values and direct binomial regression, based on unbiased estimating equations. In this paper stopped Cox regression is compared with these direct approaches. This is done by means of a simulation study to assess the biases of the different approaches and an analysis of breast cancer data to get some feeling for the performance in practice. The tentative conclusion is that stopped Cox and direct models agree well if the follow-up is not too long. There are larger differences for long-term follow-up data. There stopped Cox might be more efficient, but less robust.

Frailties in multi-state models: Are they identifiable? Do we need them?

The inclusion of latent frailties in survival models can serve two purposes: (1) the modelling of dependence in clustered data, (2) explaining lack of fit of univariate survival models, like deviation from the proportional hazards assumption. Multi-state models are somewhere between univariate data and clustered data. Frailty models can help in understanding the dependence in sequential transitions (like in clustered data) and can be useful in explaining some strange phenomena in the effect of covariates in competing risks models (like in univariate data). The (im)possibilities of frailty models will be exemplified on a data set of breast cancer patients with death as absorbing state and local recurrence and distant metastasis as intermediate events.

From model building to validation and back: a plea for robustness.

This paper is the written version of the President's invited lecture speaker at the International Society for Clinical Biostatistics conference in Munich in 2013. The paper takes the stand of clinician and patient who are in need of a reliable prognostic model for the planning of treatment and patient care during the follow-up after the initial treatment. The paper discusses (i) the need for grouping of data; (ii) the lack of robustness of the Cox model; (iii) the robust approach to repeated measures; and (iv) the robust handling of time-dependent covariates (biomarkers) in dynamic survival analysis.

Validation, calibration and refitting of a familial breast cancer model in sisterships: a case study in the Swedish sisters data.

In Sweden, a unique data set has been compiled with breast cancer incidence in all sisterships with at least two sisters born between 1932 and 2001, and the effect of family history has been analyzed by standard epidemiological methods. Such data are ideal to explore the validity of existing models for familial breast cancer. This paper explores the validity of the Jonker model that adds a hypothetical gene to the well-known BRCA1 and BRCA2 genes. The validity of the model for the Swedish data is checked by using a calibration model for breast cancer incidence given the (retrospective) family history as assessed at the end of the study period. This enables the validity of the overall incidence and the effect of family history to be assessed in the same model. The conclusion is that the existing model does reasonably well for the effect of family history but is seriously wrong for the early incidence rate. Therefore, the model is refitted in the Swedish data. Finally, the calibration of the refitted model is checked when using current family history as used in the epidemiological studies. The refitted Jonker model fits the data well and shows good agreement with the epidemiological findings.

Sensitivity analysis of state-transition models: how to deal with a large number of inputs.

State-transition models are employed to project future prevalence rates of risk factors and diseases within populations. Sensitivity analysis should be performed to assess the reliability of the results but often the number of inputs of the model is so huge, and running the model is so time-consuming, that not all methods of sensitivity analysis are practically available. Screening methods detect which inputs have a major influence on the outputs. We briefly review the available screening methods, and discuss one in particular, Morris' OAT Design. We applied the method under different assumptions to a module of the RIVM Chronic Diseases Model, where we projected the rates of never smokers, former smokers and current smokers in time up to the year 2050, based on smoking rates, start, stop and quit rates from 2003 and information on selective mortality in smokers from the literature. Different assumptions with regard to the interval of the inputs used for screeing led to different conclusions, especially with regard to the importance of quit and relapse rates versus initial prevalence rates. This should not to be read as a lack of validity of the method, but it shows that any sensitivity method cannot be automated in a form that runs without expert guidance on the ranges.

Denial and social and emotional outcomes in lung cancer patients: the protective effect of denial.

Denial is a well-known phenomenon in clinical oncology practice. Yet whether the impact of denial on patient well-being is beneficial or harmful remains unknown. The purpose of the current study is to investigate the relationship between denial and social and emotional outcomes in a large sample of lung cancer patients over an extended time period. Denial and social and emotional outcomes were measured in 195 newly diagnosed lung cancer patients. Four assessments were conducted over 8 months. The level of denial was measured using the Denial of Cancer Interview. Patient-reported social and emotional outcomes were measured using the EORTC-QLQ-30 and the HADS. Patients with a moderate or increasing level of denial over time reported better social outcomes (role functioning: p = 0.0036, social functioning: p = 0.027) and less anxiety (p = 0.0001) and depression (p = 0.0019) than patients with a low level of denial. The overall quality of life was better among lung cancer patients who displayed either moderate or increasing levels of denial compared with those who displayed low levels of denial (p < 0.0001). A certain level of denial in lung cancer patients can have a protective effect on social and emotional outcomes. Clinicians should take this into account when providing information about the illness and its prognosis.

Vertical modeling: a pattern mixture approach for competing risks modeling.

We study an alternative approach for estimation in the competing risks framework, called vertical modeling. It is motivated by a decomposition of the joint distribution of time and cause of failure. The two elements of this decomposition are (1) the time of failure and (2) the cause of failure condition on time of failure. Both elements of the model are based on observable quantities, namely the total hazard and the relative cause-specific hazards. The model can be implemented using the standard software. The relative cause-specific hazards are flexibly estimated using multinomial logistic regression and smoothing splines. We show estimates of cumulative incidences from vertical modeling to be more efficient statistically than those obtained from the standard nonparametric model. We illustrate our methods using data of 8966 leukemia patients from the European Group for Blood and Marrow Transplantation.

Denial and physical outcomes in lung cancer patients, a longitudinal study.

Although denial in cancer patients is often seen in clinical practice, studies relating denial to physical outcomes are lacking. The present study aims to investigate patterns of denial among lung cancer patients and connect these to their physical outcomes. Denial was measured longitudinally in 195 consecutive newly diagnosed lung cancer patients. Four assessments were conducted over an 8-month period. Patient-reported physical outcomes were measured with a generic and disease-specific quality of life measure. Medical data were provided by the patients' chest physicians. Three patterns of denial over time were identified in lung cancer patients: patients displayed either low, moderate or increasing denial. Male lung cancer patients were found to deny at a moderate level more often. A moderate or increasing level of denial was consistently related to improved patient-rated physical outcomes. Lung cancer patients displaying more denial reported a better overall perception of health and better physical functioning. They complained less about fatigue, nausea and vomiting, appetite loss, dysphagia and pain in arm and shoulder than low deniers. Other symptoms did not differ among denial classes. Denial in lung cancer patients may well be an adaptive mechanism and have to be respected in clinical practice.

A simple method for co-segregation analysis to evaluate the pathogenicity of unclassified variants; BRCA1 and BRCA2 as an example.

BACKGROUND: Assessment of the clinical significance of unclassified variants (UVs) identified in BRCA1 and BRCA2 is very important for genetic counselling. The analysis of co-segregation of the variant with the disease in families is a powerful tool for the classification of these variants. Statistical methods have been described in literature but these methods are not always easy to apply in a diagnostic setting. METHODS: We have developed an easy to use method which calculates the likelihood ratio (LR) of an UV being deleterious, with penetrance as a function of age of onset, thereby avoiding the use of liability classes. The application of this algorithm is publicly available http://www.msbi.nl/cosegregation. It can easily be used in a diagnostic setting since it requires only information on gender, genotype, present age and/or age of onset for breast and/or ovarian cancer. RESULTS: We have used the algorithm to calculate the likelihood ratio in favour of causality for 3 UVs in BRCA1 (p.M18T, p.S1655F and p.R1699Q) and 5 in BRCA2 (p.E462G p.Y2660D, p.R2784Q, p.R3052W and p.R3052Q). Likelihood ratios varied from 0.097 (BRCA2, p.E462G) to 230.69 (BRCA2, p.Y2660D). Typing distantly related individuals with extreme phenotypes (i.e. very early onset cancer or old healthy individuals) are most informative and give the strongest likelihood ratios for or against causality. CONCLUSION: Although co-segregation analysis on itself is in most cases insufficient to prove pathogenicity of an UV, this method simplifies the use of co-segregation as one of the key features in a multifactorial approach considerably.

Denial in lung cancer patients: a longitudinal study.

BACKGROUND: Although denial in cancer patients is well known clinically, few studies investigating the prevalence of denial over time have been conducted. The objectives of this study are to investigate the level of denial in lung cancer patients over time and the impact of socio-demographic and illness-related variables on denial in these patients. METHODS: The level of denial was measured in 195 consecutive newly diagnosed lung cancer patients, using the Denial of Cancer Interview. Four assessments were conducted in eight months. Socio-demographic data were collected during the interviews. Medical data were provided by the chest physicians. RESULTS: Most patients (86.6%) displayed a low or moderate level of denial at baseline. A small number (3%) showed a high level of denial. The mean level of denial was lowest at baseline and increased over time. Male lung cancer patients exhibited more denial than did female ones, and younger patients showed less denial than did the elderly. Shortly after diagnosis, patients with a lower level of education denied stronger than higher educated patients, and during the course of illness, both groups showed the same level of denial. CONCLUSION: A certain level of denial has to be considered a normal phenomenon in lung cancer patients, part of the illness process that they undergo. Whether the level of denial is related to adaptive or maladaptive coping remains to be investigated.

Dynamic predicting by landmarking as an alternative for multi-state modeling: an application to acute lymphoid leukemia data.

This paper considers the problem of obtaining a dynamic prediction for 5-year failure free survival after bone marrow transplantation in ALL patients using data from the EBMT, the European Group for Blood and Marrow Transplantation. The paper compares the new landmark methodology as developed by the first author and the established multi-state modeling as described in a recent Tutorial in Biostatistics in Statistics in Medicine by the second author and colleagues. As expected the two approaches give similar results. The landmark methodology does not need complex modeling and leads to easy prediction rules. On the other hand, it does not give the insight in the biological processes as obtained for the multi-state model.

Reduced-rank proportional hazards regression and simulation-based prediction for multi-state models.

In this paper we address two issues arising in multi-state models with covariates. The first issue deals with how to obtain parsimony in the modeling of the effect of covariates. The standard way of incorporating covariates in multi-state models is by considering the transitions as separate building blocks, and modeling the effect of covariates for each transition separately, usually through a proportional hazards model for the transition hazard. This typically leads to a large number of regression coefficients to be estimated, and there is a real danger of over-fitting, especially when transitions with few events are present. We extend the reduced-rank ideas, proposed earlier in the context of competing risks, to multi-state models, in order to deal with this issue. The second issue addressed in this paper was motivated by the wish to obtain standard errors of the regression coefficients of the reduced-rank model. We propose a model-based resampling technique, based on repeatedly sampling trajectories through the multi-state model. The same ideas are also used for the estimation of prediction probabilities in general multi-state models and associated standard errors. We use data from the European Group for Blood and Marrow Transplantation to illustrate our techniques.

Interobserver agreement of scoring of histopathological characteristics and classification of lupus nephritis.

BACKGROUND: Assessing renal biopsies from patients with lupus nephritis (LN) is a difficult task and it is subject to interobserver variability. In this study the interobserver agreement amongst five nephropathologists was analysed. METHODS: Five specialized nephropathologists scored 126 biopsies, comprising 87 first and 39 repeat biopsies from 87 patients with biopsy-proven proliferative LN, included in a randomized controlled trial. The interobserver agreement [expressed as intraclass correlation coefficients (ICC)] of the scored histopathological items was calculated. Also, the WHO1995 and ISN/RPS2003 classification systems for LN were compared, with extra attention being given to the comparison between patients with diffuse proliferative LN with either segmental (IV-S) or global (IV-G) lesions. RESULTS: There was a wide range of agreement. A good interobserver agreement (ICC>0.6) was present in 15%, and a moderate interobserver agreement (ICC 0.4-0.6) in 31% of the scored items. The activity index for LN showed a good (ICC 0.716) and the chronicity index a moderate (ICC 0.494) interobserver agreement. Both classification systems showed low agreement, although consensus was easily reached. Patients classified as IV-S (n=15) had more favorable clinical parameters at study entry than those with class IV-G (n=57). Although suggested by others, we found no differences in outcome between these two subclasses. CONCLUSIONS: This study shows that, although definitions were agreed upon beforehand, even specialized on nephropathologists have difficulties with scoring histopathological characteristics of LN, particularly with SLE the classification systems.

Surgery versus prolonged conservative treatment for sciatica.

BACKGROUND: Lumbar-disk surgery often is performed in patients who have sciatica that does not resolve within 6 weeks, but the optimal timing of surgery is not known. METHODS: We randomly assigned 283 patients who had had severe sciatica for 6 to 12 weeks to early surgery or to prolonged conservative treatment with surgery if needed. The primary outcomes were the score on the Roland Disability Questionnaire, the score on the visual-analogue scale for leg pain, and the patient's report of perceived recovery during the first year after randomization. Repeated-measures analysis according to the intention-to-treat principle was used to estimate the outcome curves for both groups. RESULTS: Of 141 patients assigned to undergo early surgery, 125 (89%) underwent microdiskectomy after a mean of 2.2 weeks. Of 142 patients designated for conservative treatment, 55 (39%) were treated surgically after a mean of 18.7 weeks. There was no significant overall difference in disability scores during the first year (P=0.13). Relief of leg pain was faster for patients assigned to early surgery (P<0.001). Patients assigned to early surgery also reported a faster rate of perceived recovery (hazard ratio, 1.97; 95% confidence interval, 1.72 to 2.22; P<0.001). In both groups, however, the probability of perceived recovery after 1 year of follow-up was 95%. CONCLUSIONS: The 1-year outcomes were similar for patients assigned to early surgery and those assigned to conservative treatment with eventual surgery if needed, but the rates of pain relief and of perceived recovery were faster for those assigned to early surgery. (Current Controlled Trials number, ISRCTN26872154 [controlled-trials.com].).

The denial of cancer interview: development and first assessment of psychometric properties in lung cancer patients.

OBJECTIVE: Based on Weissman and Hackett's comprehensive definition of denial, a semi-structured interview was developed to measure denial in cancer patients. The denial in cancer interview (DCI) covers both the patients' recount of their illness experience and the expert's impression of the level of denial in the patient story. This paper describes the development and first psychometric analyses of the instrument. METHOD: The development of the DCI was based on clinical observation, the expert opinion of eight specialised psychiatrist as well as three small pilot studies to assess feasibility. The DCI is composed of two parts: a semi-structured interview consisting of nine specific items to be answered by the patient and two items covering the interviewer's clinical impression of the patient's type and level of denial. Follow-up interviews were held at 8, 16 and 32 weeks after the baseline assessment (T2-4). To measure the inter-rater reliability, interviews were recorded and rated independently by one interviewer and one of the study's co-workers. RESULTS: One hundred and ninety-five consecutive newly diagnosed lung cancer patients were interviewed. The internal consistency of the DCI (Cronbach's alpha) was 0.84 at first interview and 0.85, 0.82 and 0.83 at T2-4, respectively. The inter-rater agreement was good for the DCI overall and the patient's assessment scale, and satisfactory for the clinical impression items. Content validity was supported by clinical observation, in depth open interviewing and expert opinion. CONCLUSION: The DCI proved to be a feasible and reliable instrument for measuring denial in lung cancer patients. Further testing in other oncology settings will provide insight in wider applicability. PRACTICE IMPLICATIONS: The DCI can be used in future studies concerning denial in cancer patients. Insight in denial and its background will help us to adequately address denial in patients and communicate with them.

Treatment with cyclophosphamide delays the progression of chronic lesions more effectively than does treatment with azathioprine plus methylprednisolone in patients with proliferative lupus nephritis.

OBJECTIVE: To analyze the effect of treatment with either pulse cyclophosphamide (CYC) or azathioprine (AZA) combined with methylprednisolone (MP), on serial biopsy results in patients with proliferative lupus nephritis, and to evaluate the predictive value of various histopathologic and clinical parameters with regard to disease outcome. METHODS: Biopsy specimens from patients with proliferative lupus nephritis, obtained at study entry and after 2 years of therapy, were scored according to a standardized method, and results assessed in relation to disease outcome. RESULTS: Of the 87 patients originally enrolled, 39 underwent repeat biopsy. These patients were representative of the overall group, both at entry and at 2-year followup. The median activity index changed from 8.0 to 2.7 (no differences between the treatment groups). In the group treated with AZA plus MP (AZA group), the increase in the median chronicity index (from 2.7 to 3.8) was larger than that in the CYC group (from 2.7 to 3.0) (P = 0.050). In multivariate analyses, renal function at enrollment and after 2 years was the best predictor of renal function at the last visit, while none of the histopathologic variables (either at entry or at 2 years) added to this prediction. Comparing patients whose disease transitioned to class II with those who had persistent proliferative lupus nephritis revealed no differences between the treatment groups at either time point, and no clinical differences at 2 years. However, a higher serum creatinine level at entry and greater proteinuria at last visit were characteristic of patients who still had proliferative lupus nephritis at 2 years. CONCLUSION: These results indicate that, although both CYC and AZA are effective in reducing active lesions in lupus nephritis, progression of chronic lesions is more effectively halted by CYC. Variables assessed by repeat biopsy do not predict clinical outcome.

Approaches in modelling long-term survival: an application to breast cancer.

Several modelling techniques have been proposed for non-proportional hazards. In this work we consider different models which can be classified into three wide categories: models with time-varying effects of the covariates; frailty models and cure rate models. We present those different extensions of the proportional hazards model on an application of 2433 breast cancer patients with a long follow-up. We comment on the differences and similarities among the models and evaluate their performance using survival and hazard plots, Brier scores and pseudo-observations.