PEACE V - Salvage Treatment of OligoRecurrent nodal prostate cancer Metastases (STORM): a study protocol for a randomized controlled phase II trial.

BACKGROUND: Pelvic nodal recurrences are being increasingly diagnosed with the introduction of new molecular imaging techniques, like choline and PSMA PET-CT, in the restaging of recurrent prostate cancer (PCa). At this moment, there are no specific treatment recommendations for patients with limited nodal recurrences and different locoregional treatment approaches are currently being used, mostly by means of metastasis-directed therapies (MDT): salvage lymph node dissection (sLND) or stereotactic body radiotherapy (SBRT). Since the majority of patients treated with MDT relapse within 2 years in adjacent lymph node regions, with an estimated median time to progression of 12-18 months, combining MDT with whole pelvic radiotherapy (WPRT) may improve oncological outcomes in these patients. The aim of this prospective multicentre randomized controlled phase II trial is to assess the impact of the addition of WPRT to MDT and short-term androgen deprivation therapy (ADT) on metastasis-free survival (MFS) in the setting of oligorecurrent pelvic nodal recurrence. METHODS & DESIGN: Patients diagnosed with PET-detected pelvic nodal oligorecurrence (≤5 nodes) following radical local treatment for PCa, will be randomized in a 1:1 ratio between arm A: MDT and 6 months of ADT, or arm B: WPRT added to MDT and 6 months of ADT. Patients will be stratified by type of PET-tracer (choline, FACBC or PSMA) and by type of MDT (sLND or SBRT). The primary endpoint is MFS and the secondary endpoints include clinical and biochemical progression-free survival (PFS), prostate cancer specific survival, quality of life (QoL), toxicity and time to castration-resistant prostate cancer (CRPC) and to palliative ADT. Estimated study completion: December 31, 2023. DISCUSSION: This is the first prospective multicentre randomized phase II trial assessing the potential of combined WPRT and MDT as compared to MDT alone on MFS for patients with nodal oligorecurrent PCa. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT03569241, registered June 14, 2018, ; Identifier on Swiss National Clinical Trials Portal (SNCTP): SNCTP000002947, registered June 14, 2018.

[The use of the permanent sample (eps) to study the returnto- work after cancer. Challenges and opportunities for research]. / Utilisation de l'échantillon permanent (eps) pour l'étude du retour au travail après cancer. Défis et opportunités pour la recherche.

INTRODUCTION: The introduction of early cancer detection and the improvement in treatment efficacy have led to a significant increase in the survival and the prevalence of (ex) cancer patients. Approximately 40 % of new cancer cases are diagnosed every year in the working age population (20-64 years). Maintaining their quality of life results, among others, in their retain on the labour market. Even though it is necessary to realize the scale of the phenomenon and to plan interventions, no measure allows assessing the rate of return to work among of (ex) cancer patients in Belgium nowadays. METHODS: We observe during a five-year period the socio-professional status (inability, disability, unemployment or death) of 645 workers identified in the permanent sample (EPS), having had an oncological multidisciplinary consultation (MOC) in 2011. RESULTS: By the end of follow-up, 24 % of the workers were deceased. Among those who survived 26 % are unable to work, 12 % are unemployed and 63 % do not receive any social benefit. Women and young workers (20-44 years) seemed to have encountered difficulties the most. CONCLUSIONS: The results of this study allow giving a prudent first estimation of the return to work of socially insured Belgian citizens of almost 40 %, five years after the first MOC. Nevertheless, the structure of the EPS presents many limitations to the interpretation and reliability of results. We suggest some modifications of the EPS that might offer scientists better opportunities to improve the performance and reliability of such cohort studies.

INTRODUCTION: L'introduction de la détection précoce des cancers et l'amélioration de l'efficacité des traitements ont mené à une augmentation significative de la prévalence d'(ex) patients. A peu près 40 % des nouveaux cancers sont diagnostiqués chaque année dans la population active (20-64 ans). Le maintien de leur qualité de vie passe, notamment, par leur maintien sur le marché du travail. Bien que nécessaire pour évaluer l'ampleur du phénomène et planifier des interventions spécifiques, aucune mesure ne permet actuellement d'établir avec précision le taux de réinsertion professionnelle des travailleurs atteints de cancer en Belgique. Matériel et Méthodes : Nous observons durant cinq ans le statut socioprofessionnel (incapacité de travail, invalidité, chômage ou décès) de 645 travailleurs identifiés dans l'échantillon permanent (EPS) ayant eu une première consultation oncologique multidisciplinaire (COM) en 2011. Résultats : Au terme du suivi, 24 % des travailleurs sont décédés. Parmi les travailleurs ayant survécu, 26 % sont en incapacité de travail, 12 % sont au chômage et 63 % ne bénéficient d'aucun revenu de remplacement. Les femmes et les jeunes travailleurs (20-44 ans) semblent rencontrer le plus de difficultés pour le retour au travail. CONCLUSIONS: Les résultats de cette étude permettent d'avancer une première estimation du retour au travail des assurés sociaux belges atteints de cancer à un peu moins de 40 %, cinq ans après la première COM. Toutefois, la structure et les données de l'EPS présentent de nombreuses limites pour l'interprétation et la fiabilité des résultats. Nous suggérons quelques modifications des données de l'EPS qui offriront aux scientifiques des opportunités pour améliorer la réalisation et la fiabilité de telles études de cohorte.

Enhanced analysis of real-time PCR data by using a variable efficiency model: FPK-PCR.

Current methodology in real-time Polymerase chain reaction (PCR) analysis performs well provided PCR efficiency remains constant over reactions. Yet, small changes in efficiency can lead to large quantification errors. Particularly in biological samples, the possible presence of inhibitors forms a challenge. We present a new approach to single reaction efficiency calculation, called Full Process Kinetics-PCR (FPK-PCR). It combines a kinetically more realistic model with flexible adaptation to the full range of data. By reconstructing the entire chain of cycle efficiencies, rather than restricting the focus on a 'window of application', one extracts additional information and loses a level of arbitrariness. The maximal efficiency estimates returned by the model are comparable in accuracy and precision to both the golden standard of serial dilution and other single reaction efficiency methods. The cycle-to-cycle changes in efficiency, as described by the FPK-PCR procedure, stay considerably closer to the data than those from other S-shaped models. The assessment of individual cycle efficiencies returns more information than other single efficiency methods. It allows in-depth interpretation of real-time PCR data and reconstruction of the fluorescence data, providing quality control. Finally, by implementing a global efficiency model, reproducibility is improved as the selection of a window of application is avoided.

Impact of allelic dropout on evidential value of forensic DNA profiles using RMNE.

MOTIVATION: Two methods are commonly used to report on evidence carried by forensic DNA profiles: the 'Random Man Not Excluded' (RMNE) approach and the likelihood ratio (LR) approach. It is often claimed a major advantage of the LR method that dropout can be assessed probabilistically. RESULTS: In this article, a new RMNE measure is proposed that like-wise accounts for allelic dropout in an observed forensic DNA profile. We discuss the necessary calculations, underline their simplicity and provide a tool for performing the calculations.

Optimal screening for promising genes in 2-stage designs.

Detecting genetic markers with biologically relevant effects remains a challenge due to multiple testing. Standard analysis methods focus on evidence against the null and protect primarily the type I error. On the other hand, the worthwhile alternative is specified for power calculations at the design stage. The balanced test as proposed by Moerkerke and others (2006) and Moerkerke and Goetghebeur (2006) incorporates this alternative directly in the decision criterion to achieve better power. Genetic markers are selected and ranked in order of the balance of evidence they contain against the null and the target alternative. In this paper, we build on this guiding principle to develop 2-stage designs for screening genetic markers when the cost of measurements is high. For a given marker, a first sample may already provide sufficient evidence for or against the alternative. If not, more data are gathered at the second stage which is then followed by a binary decision based on all available data. By optimizing parameters which determine the decision process over the 2 stages (such as the area of the "gray" zone which leads to the gathering of extra data), the expected cost per marker can be reduced substantially. We also demonstrate that, compared to 1-stage designs, 2-stage designs achieve a better balance between true negatives and positives for the same cost.

Selecting "significant" differentially expressed genes from the combined perspective of the null and the alternative.

In the search for genes associated with disease, statistical analysis yields a key towards reproducible results. To avoid a plethora of type I errors, classical gene selection procedures strike a balance between magnitude and precision of observed effects in terms of p-values. Protecting false discovery rates recovers some power but still ranks genes according to classical p-values. In contrast, we propose a selection procedure driven by the concern to detect well-specified important alternatives. By summarizing evidence from the perspective of both the null and such an alternative hypothesis, genes line up in a substantially different order with different genes yielding powerful signals. A cutoff point for a measure of relative evidence which balances the standard p-value, p0, with its counterpart, p1, derived from the perspective of the target alternative, determines our gene selection. We find the cutoff point that maximizes an expected specific gain. This yields an optimal decision which exploits gene-specific variances and thus involves different type I and type II errors across genes. We show the dramatic impact of this alternative perspective on the detection of differentially expressed genes in hereditary breast cancer. Our analysis does not rely on parametric assumptions on the data.

Ten-year trends in CD4 cell counts at HIV and AIDS diagnosis in a London HIV clinic.

OBJECTIVE: To examine temporal trends (1986-1996) in the CD4 cell count at first HIV-1 positive test and initial AIDS diagnosis, and the influence of selected patient characteristics and treatment factors on these trends. DESIGN: A retrospective clinic-based study. SETTING: Three hospital-based clinics in West London. PATIENTS: A group of 5921 adult HIV-1-seropositive persons and 2835 reported patients with AIDS over a 10-year period from 1 January 1986 to 1 October 1996. METHODS: The CD4 cell count at HIV diagnosis (CD4HIV) was defined as the nearest CD4 cell count to within 2 months of HIV diagnosis; and the CD4 cell count at AIDS diagnosis (CD4AIDS) as the last CD4 cell count in the two months prior to the development of AIDS. Simple and multiple linear regression analysis were used to examine the influence of selected covariates on CD4HIV and CD4AIDS. RESULTS: The percentage of patients with an available CD4HIV and CD4AIDS increased from less than 5% in 1987 to 53% and 40%, respectively, in 1990, and 79% and 48%, respectively, in 1996. Patients with a missing CD4HIV or CD4AIDS were younger and less likely to have received antiretroviral therapy or prophylaxis for Pneumocystis carinii pneumonia (PCP). There was no significant change in CD4HIV over a 10-year period (median 334 x 10(6) cells/l), but a lower CD4HIV was associated with older age at presentation and injecting drug use. There was a delay in the onset of clinical AIDS, with a fall in the median CD4AIDS value from 99 x 10(6) cells/l prior to 1987, to 58 x 10(6) cells/l in 1990, 68 x 10(6) cells/l in 1994 and 60 x 10(6) cells/l in 1996; this decline in onset was seen for PCP as well as for cytomegalovirus and atypical mycobacterial infections. At all time periods, a lower CD4AIDS was associated with combined use of antiretroviral therapy and PCP prophylaxis. After adjustment for use of antiretroviral therapy and PCP prophylaxis prior to AIDS diagnosis, year of diagnosis was no longer associated with CD4AIDS. There was a significant trend towards an improved survival following AIDS diagnosis from 20.1 months prior to 1988, to 20.3 months (1989-1990), 21.0 months (1991-1992) and 22.1 (1993-1994) (P < 0.0005). CONCLUSIONS: The observed decline in CD4AIDS value was related to the introduction of antiretroviral therapy in 1988, and PCP prophylaxis in 1989. Temporal changes in the CD4 cell count at HIV and AIDS diagnosis among different demographic groups can provide insights into the changing natural history of the HIV epidemic and access to medical care. We recommend monitoring of the CD4 cell count at new HIV and AIDS diagnosis and at initiation of antiretroviral therapy as additional measures in national HIV/AIDS surveillance.

Vaginal lavage with chlorhexidine during labour to reduce mother-to-child HIV transmission: clinical trial in Mombasa, Kenya.

OBJECTIVES: To evaluate the effect of vaginal lavage with diluted chlorhexidine on mother-to child transmission of HIV (MTCT) in a breastfeeding population. METHODS: This prospective clinical trial was conducted in a governmental hospital in Mombasa, Kenya. On alternating weeks, women were allocated to non-intervention or to intervention consisting of vaginal lavage with 120 ml 0.2% chlorhexidine, later increased to 0.4%, repeated every 3 h from admission to delivery. Infants were tested for HIV by DNA polymerase chain reaction within 48 h and at 6 and 14 weeks of life. RESULTS: Enrolment and follow-up data were available for 297 and 309 HIV-positive women, respectively, in the non-lavage and the lavage groups. There was no evidence of a difference in intrapartum MTCT (17.2 versus 15.9%, OR 0.9, 95% CI 0.6-1.4) between the groups. Lavage solely before rupture of the membranes tended towards lower MTCT with chlorhexidine 0.2% (OR 0.6, 95% CI 0.3-1.1), and even more with chlorhexidine 0.4% (OR 0.1, 95% CI 0.0-0.9). CONCLUSION: The need remains for interventions reducing MTCT without HIV testing, often unavailable in countries with a high prevalence of HIV. Vaginal lavage with diluted chlorhexidine during delivery did not show a global effect on MTCT in our study. However, the data suggest that lavage before the membranes are ruptured might be associated with a reduction of MTCT, especially with higher concentrations of chlorhexidine.

The impact of compliance in pharmacokinetic studies.

In population pharmacokinetic (PK) studies, one observes just a few concentration measures spread out in time, on a sizable sample of the target population. Common-sense dictates that for estimation of a drug exposure-plasma concentration relationship, one needs accurate information on drug intake history besides the concentration measures. The population PK literature is well aware of this. Studies of simulated compliance behaviour have helped quantify the problem with naive compliance estimators and pointed towards a solution. In this paper we look at actually observed compliance patterns recorded via electronic monitoring. We simulate a documented pharmacokinetic model from the hypertensive literature on top of these and come to some interesting findings. In this clinical trial the problem of noncompliance is much more dramatic than simulated compliance patterns suggested so far. The systematic errors made by compliance naive estimators can be corrected when using timing explicit hierarchical nonlinear models and accurate information on a number of previous dose timings. When it is possible to observe irregular drug intake times in a well-controlled study, a substantial amount of precision is retrieved from the same number of data points. In general, the estimators of PK parameters benefit greatly from information that enters through greater variation in the drug-exposure process. Here we find support for the claim that noncompliance as a rich natural experiment of dosing variation can be a blessing rather than a curse from the information/learning point of view.

Diagnostic test analyses in search of their gold standard: latent class analyses with random effects.

We review methods for analysing the performance of several diagnostic tests when patients must be classified as having a disease or not, when no gold standard is available. For latent class analysis (LCA) to provide consistent estimates of sensitivity, specificity and prevalence, traditionally 'independent errors conditional on disease status' have been assumed. Recent approaches derive estimators under more flexible assumptions. However, all likelihood-based approaches suffer from the sparseness of tables generated by this type of data; an issue which is often ignored. In light of this, we examine the potential and limitations of LCAs of diagnostic tests. We are guided by a data set of visceral leishmaniasis tests. In the example, LCA estimates suggest that the traditional reference test, parasitology, has poor sensitivity and underestimates prevalence. From a technical standpoint, including more test results in one analysis yields increasing degrees of sparseness in the table which are seen to lead to discordant values of asymptotically equivalent test statistics and eventually lack of convergence of the LCA algorithm. We suggest some strategies to cope with this.

Sense and sensitivity when correcting for observed exposures in randomized clinical trials.

Standard intent-to-treat analyses of randomized clinical trials can yield biased estimates of treatment efficacy and toxicity when not all patients comply with their assigned treatment. Flexible methods have been proposed which correct for this by modelling expected contrasts between an individual's observed outcome and his/her potential outcome in the absence of exposure. Because such comparisons often require untestable assumptions, a sensitivity analysis is warranted. We show how this can be performed in a meaningful and practically useful way. Following the approach of Molenberghs, Kenward and Goetghebeur in a missing data context, we evaluate the separate contributions of structural uninformativeness and sampling variation to uncertainty about the population parameters. This leads us to consider Honestly Estimated Ignorance Regions (HEIRs) and Estimated Uncertainty RegiOns (EUROs), respectively. We use the results to estimate the causal effect of observed exposure on successful blood pressure reduction in a randomized controlled clinical trial with partial non-compliance.

Causal proportional hazards models and time-constant exposure in randomized clinical trials.

The last decade saw enormous progress in the development of causal inference tools to account for noncompliance in randomized clinical trials. With survival outcomes, structural accelerated failure time (SAFT) models enable causal estimation of effects of observed treatments without making direct assumptions on the compliance selection mechanism. The traditional proportional hazards model has however rarely been used for causal inference. The estimator proposed by Loeys and Goetghebeur (2003, Biometrics vol. 59 pp. 100-105) is limited to the setting of all or nothing exposure. In this paper, we propose an estimation procedure for more general causal proportional hazards models linking the distribution of potential treatment-free survival times to the distribution of observed survival times via observed (time-constant) exposures. Specifically, we first build models for observed exposure-specific survival times. Next, using the proposed causal proportional hazards model, the exposure-specific survival distributions are backtransformed to their treatment-free counterparts, to obtain - after proper mixing - the unconditional treatment-free survival distribution. Estimation of the parameter(s) in the causal model is then based on minimizing a test statistic for equality in backtransformed survival distributions between randomized arms.

Semiparametric regression analysis of interval-censored data.

We propose a semiparametric approach to the proportional hazards regression analysis of interval-censored data. An EM algorithm based on an approximate likelihood leads to an M-step that involves maximizing a standard Cox partial likelihood to estimate regression coefficients and then using the Breslow estimator for the unknown baseline hazards. The E-step takes a particularly simple form because all incomplete data appear as linear terms in the complete-data log likelihood. The algorithm of Turnbull (1976, Journal of the Royal Statistical Society, Series B 38, 290-295) is used to determine times at which the hazard can take positive mass. We found multiple imputation to yield an easily computed variance estimate that appears to be more reliable than asymptotic methods with small to moderately sized data sets. In the right-censored survival setting, the approach reduces to the standard Cox proportional hazards analysis, while the algorithm reduces to the one suggested by Clayton and Cuzick (1985, Applied Statistics 34, 148-156). The method is illustrated on data from the breast cancer cosmetics trial, previously analyzed by Finkelstein (1986, Biometrics 42, 845-854) and several subsequent authors.

A causal proportional hazards estimator for the effect of treatment actually received in a randomized trial with all-or-nothing compliance.

Survival data from randomized trials are most often analyzed in a proportional hazards (PH) framework that follows the intention-to-treat (ITT) principle. When not all the patients on the experimental arm actually receive the assigned treatment, the ITT-estimator mixes its effect on treatment compliers with its absence of effect on noncompliers. The structural accelerated failure time (SAFT) models of Robins and Tsiatis are designed to consistently estimate causal effects on the treated, without direct assumptions about the compliance selection mechanism. The traditional PH-model, however, has not yet led to such causal interpretation. In this article, we examine a PH-model of treatment effect on the treated subgroup. While potential treatment compliance is unobserved in the control arm, we derive an estimating equation for the Compliers PROPortional Hazards Effect of Treatment (C-PROPHET). The jackknife is used for bias correction and variance estimation. The method is applied to data from a recently finished clinical trial in cancer patients with liver metastases.

Comparing compliance patterns between randomized treatments.

When two equally efficacious drugs enter the market, the one with the better compliance is likely to be more widely used. Special management of the delivery may produce increased compliance. In this paper we analyze a trial of a single drug dosing prescription with patients randomized to either daily self monitoring of the outcome (blood pressure) or not. The study used Medication Event Monitoring Systems (MEMS) to record each exact time and date when a patient opened the pill container. No established method is available for comparing these high-dimensional compliance patterns between groups. This paper investigates several summary measures that highlight different dimensions of the pattern and the drug context in which they may be meaningful. Further, we examine conditional and marginal models that enable comparisons of the full pattern of daily dosing indicators for subjects between the groups. We found no simple difference in average compliance levels, but we found an interesting interaction between treatment and time: similar compliance existed initially among patients in both randomized groups, with a stronger decline over time for patients who did not monitor their blood pressure. We discuss how a balance between simplicity of interpretation and efficiency of data use may be sought in this case.

Baseline information in structural failure time estimators for the effect of observed treatment compliance.

Structural accelerated failure time models allow expression of the effect of treatment actually received in placebo-controlled randomized trials with non-compliance. Without further assumptions, the structural parameter is typically estimated via a series of auxiliary logrank tests, searching for the structural parameter that back-transforms treated survival times to latent treatment-free survival times which are equally distributed between randomized arms. In this paper we investigate to what extent score tests involving baseline covariates provide more powerful auxiliary tests and lead to more precise estimates of the structural parameter without compromising the alpha-level. We propose a set of estimating equations which combines score components for covariate effects based on the control arm only, with a log-likelihood score for treatment effect based on both arms. Analytic results for exponential models as well as simulation studies for the semi-parametric approach indicate that in many practical situations this incorporation of baseline covariates leads to more precise estimators of the structural effect. Relative efficiency is shown to depend on the selective nature of compliance. In a leukaemia trial we find the length of the 95 per cent confidence interval for the structural parameter is reduced to two-thirds of the original length by incorporating baseline covariates in this way.

Regression models for disease prevalence with diagnostic tests on pools of serum samples.

Whether the aim is to diagnose individuals or estimate prevalence, many epidemiological studies have demonstrated the successful use of tests on pooled sera. These tests detect whether at least one sample in the pool is positive. Although originally designed to reduce diagnostic costs, testing pools also lowers false positive and negative rates in low prevalence settings and yields more precise prevalence estimates. Current methods are aimed at estimating the average population risk from diagnostic tests on pools. In this article, we extend the original class of risk estimators to adjust for covariates recorded on individual pool members. Maximum likelihood theory provides a flexible estimation method that handles different covariate values in the pool, different pool sizes, and errors in test results. In special cases, software for generalized linear models can be used. Pool design has a strong impact on precision and cost efficiency, with covariate-homogeneous pools carrying the largest amount of information. We perform joint pool and sample size calculations using information from individual contributors to the pool and show that a good design can severely reduce cost and yet increase precision. The methods are illustrated using data from a Kenyan surveillance study of HIV. Compared to individual testing, age-homogeneous, optimal-sized pools of average size seven reduce cost to 44% of the original price with virtually no loss in precision.

Analysing non-compliance in clinical trials: ethical imperative or mission impossible?

Assuming that a drug is active and different from placebo, a patient's gain or loss is likely to depend on how much of the drug is taken and when. The present paper motivates the ethical imperative of statistical compliance analysis by considering important clinical questions, the advent of more precise compliance measuring instruments and new statistical efforts towards well understood analyses that seek to preserve scientific integrity. An extension of Efron and Feldman's approach is developed which exploits the randomization assumption in combination with structural models. It also generates more promising designs and alternative statistical approaches.

Missing cause of death information in the analysis of survival data.

Goetghebeur and Ryan proposed a method for proportional hazards analyses of competing risks failure-time data when the failure type is missing for some cases. This paper evaluates the properties of the method using data from a clinical trial in Hodgkin's disease. We generated several patterns of missingness in the cause of death in 'pseudo-studies' derived from the study database. We found that the proposed method provided regression coefficients and inferences that were less biased than those from other methods over an increasing percentage of missingness in the failure type when missingness is random, when it depends on an important covariate, when it depends on failure type, and when it depends on follow-up time. We present suggestions for study design with planned missingness in the failure type.