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Conditional Power and Model Selection Based Sample Size Reestimation with Type I Error Recalibration
No full textIntroduction The sample size estimation at study design depends on initial assumptions regarding the target power, treatment effect, accrual/follow-up duration and the underlying exponential distribution for time-to-event outcomes. However, observed data often deviate from these assumptions and the study may not progress as planned. Obtaining an updated sample size estimation after study initiation represents a valuable resource for monitoring, statistics and ethical considerations.
Objectives We introduce a methodological framework for sample size reestimation at interim stages of clinical trials with time-to-event endpoints using conditional power (CP) and model selection procedures. We developed an R function to compute the updated sample size and to recalibrate the type I error rate, based on the number of events required to achieve the target CP and the number of events observed at interim.
Methods The input data include design-stage parameters (type I error rate, hazard ratio, follow-up duration, target number of events and power), subject-level information (identifier, treatment arm, enrollment date, event status, event date, date of last observation), and user-defined updates (extended accrual and/or follow-up). Subjects are categorized by their follow-up status: lost to follow-up, event-free at the interim stage, or having experienced the event of interest. Time-to-event is computed in days for each subject and four parametric models (exponential, Weibull, log-normal and log-logistic) are fitted for each treatment group and compared using the Akaike Information Criterion (AIC) to identify the optimal arm-specific fits. Following the standardization of the chi-square statistic from the log-rank test, the interim CP is computed with Jennison and Turnbull’s equation [1]. The lower boundary of the interim CP acceptance region is derived using the Broberg‘s methodology [2]. If the observed CP is below this boundary, the function flags potential study futility and no sample size is updated. In the event the interim CP is greater than or equal to the target CP, the function confirms that the study is progressing as planned and the sample size remains unchanged. When the interim CP falls within the region, the required number of events to achieve the target CP is computed using the Newton–Raphson algorithm, the updated sample size is estimated via a generalized Schoenfeld formula based on Lachin and Foulkes’ framework and the type I error rate recalibration is performed using the technique proposed by Uemura, Matsuyama and Ohashi [3] [4] [5].
Results We applied our method at an interim stage of a phase III trial that evaluates the superiority in terms of Progression Free Survival (PFS) of an experimental treatment versus the control in metastatic colorectal cancer subjects. Starting from a hazard ratio of 0.58, a one-sided type I error of 5%, one-year follow-up and a planned enrollment of 140 subjects to observe 106 PFS events with a 80% target power, we updated the sample size and recalibrated the type I error assuming one additional year of accrual. At the interim analysis (three years after trial initiation), 18 PFS events and 44 enrolled subjects corresponded to an interim CP of 50.9%. For the experimental group, the exponential distribution provided the optimal fit for the time-to-event data, whereas the log-normal was identified as the best model for the control group. To achieve the target CP of 80%, the function increased the sample size to 269 subjects to observe 154 events. Consequently, the recalibrated one-sided type I error rate decreased to 3.1%, consistent with the slow accrual and low event rates observed at interim.
Conclusions This method enhances the clinical trials management effectively by providing the updated sample size at interim stages of clinical trials in a timely and methodologically sound manner. This function supports the operational and statistical aspects of clinical trials, contributing to their overall success
Comparison of Different Methodological Approaches to Simulate Geo-Referenced Populations to Be Used in a Cluster Analysis of Childhood Leukaemia Cases in Germany
No full textIntroductionA key methodological challenge in epidemiological studies using a cluster analysis approach is the choice of an appropriate set of controls. This challenge becomes particularly complex when cases are geo-referenced and the outcome is rare. In fact, in such situations, controls need to be sampled from a comprehensive primary base, where population is defined both geographically and temporally. Furthermore, if cases are geo-referenced, the controls need to be geo-referenced too. However, selecting and geo-referencing such controls can be highly resource-intensive, both in terms of time and cost.
ObjectiveThus, the main objective of our study is to use publicly available data and established geo-statistical techniques to simulate a geo-referenced population (GRP). This simulated geo-referenced population will be then used as the primary basis for the extraction of controls in a cluster analysis that will focus on childhood leukaemia incident cases in Germany.
Methods For the period 2000-2020, we used population counts of persons aged 0 to 14 years from the WorldPop’s (WP) project at the University of Southampton and available in 100×100m grid cells [1]. The WP project employs a top-down modelling approach and uses different types of variables (rural settlements, industrial areas, schools, etc.) to estimate age-specific (0, 1–4, 5–9, 10-14 years) and sex-specific counts of persons in a grid [2,3,4]. The observed population figures (RP) at the municipality level were provided by the German Childhood Cancer Registry and were used as constraint values. To simulate a georeferenced population, the WP was used as a probability distribution function for sampling, with replacement, a number of cells equal to the RP. Afterwards, a uniform distribution was applied to randomly sample inside each picked cell a number of points (coordinates) equal to the times the cell was extracted.
Here are shown results for three years: 2004, 2011 and 2019 and three simulated GRPs. Whereby the three simulations refer to the geographical level used to constrain the simulated population to the real population, i.e. the overall Childhood German population (S1), the childhood population at the state level (Bundesland) (S2), and the population at province level (Landkreis). For evaluation purposes, the percentage differences between the SP and the RP for all German municipalities were computed and summarized as the median and interquartile range (IQR). In addition, the root mean squared error (RMSE) was calculated.
ResultsIn Germany, the number of children between 0 and 14 years old was 12,045,019 in 2004, 10,832,081 in 2011, and 11,396,196 in 2019. The WP estimations for the same years were 12,178,611, 10,886,770, and 10,457,921, respectively. When using the overall childhood population of Germany as the constrain for the simulation (S1), in 2004 we observe an overestimation of the population in the eastern Germany and an underestimation elsewhere (Figure 1; a) (median percentage difference =-7.1; IQR: -13.3 – 7.8); RMSE of 17.8. Percentage differences decreases in the third simulation (S3: median= -3.5; IQR: -12.2 – 5.1; a RMSE=6.5). Simulations for 2011 show, in general, better results with S3 as best performance (median=-1.2; IQR: -10.1 – 7.3; RMSE 4.4). Generally, results observed in 2019 are similar to those observed in 2011.
ConclusionsDespite being computationally the most time-consuming, S3 shows the best performances in terms of narrower interquartile ranges and a more centered distribution. Thus, the simulated georeferenced population obtained using the RP at the province level as the constrain can be considered the optimal one. Further investigations are needed to shed light on the geographical differences observed in 2004. The inconsistencies between the WP and the RP must be considered as a limitation when interpreting our results. However, this is an innovative method which allows the future use of the overall georeferenced population or a selection of it for cluster analyses.
The application of this method to each year of interest and the cluster analysis itself are pending.
 
Identifying and Characterizing Shared and Ethnic Background Site-Specific Dietary Patterns by Hispanic/Latino Background and Site: The Use of Bayesian Multi-Study Factor Analysis in The Hispanic Community Health Study/Study of Latinos
No full textIntroduction
Dietary patterns (DPs) are combinations of dietary components intended to summarize key aspects of diet, while taking advantage of synergies between single components. A posteriori DPs are defined from the application of multivariate statistics, including principal component and factor analyses. New statistical methods like multi-study factor analysis have been recently used to distinguish subpopulation-specific DPs (i.e., study/country-specific features within an international consortium or subpopulation-specific features within a single study), as well as those shared among all groups in a population [1].
The Hispanic Community Health Study/Study of Latinos (HCHS/SOL), the most extensive and ongoing community-based cohort of Hispanic/Latino adults from 4 US sites to date, provides a unique opportunity to identify shared and subpopulation-specific a posteriori DPs.
Aims
The present work aims to: 1. identify shared and ethnic background-site (EBS)-specific (nutrient-based) DPs within the HCHS/SOL study and 2. characterize the identified DPs in terms of food-group consumption, an overall measure of diet quality, socio-demographic and lifestyle characteristics.
Methods
The HCHS/SOL
The HCHS/SOL is a population-based cohort study designed to identify disease prevalence rates and risk factors of Hispanic/Latino populations residing within 4 urban US communities (Bronx, Chicago, Miami, and San Diego) and representing individuals with 7 ethnicity backgrounds (Cuban, Dominican, Mexican, Puerto Rican, Central and South American, and mixed). Participants were selected using a probability sampling design [2].
Dietary habits at baseline (16,415 subjects from 2008 to 2011) were assessed using two 24-hr recalls, the first conducted in person and the second via telephone <=30 days after. The Nutrition Data System for Research software allowed for nutrient intake estimation [3].
Selection of subjects and variables
We excluded Hispanics and Latinos from other/mixed backgrounds, with unreliable dietary recalls, or providing extreme energy intake. We also excluded subpopulations <200 participants after previous exclusions. This gave a final sample size of 15,021 participants.
We selected 42 nutrients that well represent the overall diet for Hispanics/Latinos. For each participant, nutrient intakes were derived from either one available reliable recall or the mean of the two available reliable recalls.
Statistical analysis
Bayesian multi-study factor analysis (BMSFA) was carried out on the correlation matrices of the log-transformed nutrient intakes. The total number of factors to retain was selected using the spectral decomposition of the factors. After the singular value decomposition method used in the BMSFA for identifiability, the varimax rotation was applied to the shared factor-loading matrix to achieve a better-defined loading structure [4]. Characterization of DPs against selected food groups, a measure of diet quality, selected socio-demographic and lifestyle factors was based on survey-weighted regression models. Calculations were carried out using the R software [5].
Results
The selected model included 4 shared (62.5% total variance explained) and 12 EBS-specific DPs (variance around 10%), one for each of the 12 EBS combinations (Figure 1). Among shared DPs, the first, named Plant-based foods, loaded highly on vegetable protein, several minerals, vitamin B1, niacin, natural folate, soluble and insoluble fiber, the second, named Dairy products, loaded highly on short- and medium-chain saturated fatty acids and calcium, vitamins B2, B12, D, and retinol; the third shared factor, named Seafood, loaded highly on EPA, DPA, and DHA and the fourth, named Processed foods, loaded highly on several fats, including long-chain saturated and monounsaturated fatty acids, linoleic and linolenic acids, total trans fatty acids, and natural alpha-tocopherol. Most EBS-specific DPs were further grouped into overarching profiles: Animal vs. vegetable source, Animal source only, and Poultry vs. dairy products, to capture nuances within animal-based DPs. Puerto Rican background participants from Chicago expressed a strikingly different DP.
When interpreted in terms of food groups, the identified DPs confirmed the names based on nutrients. Higher overall diet quality was observed with increasing categories of Plant-based foods, Seafood, and the “Puerto Rican background–Chicago” EBS-specific DP, whereas increasing categories of Dairy products, Processed foods, and the remaining EBS-specific DPs were related to lower diet quality. Compared to non-US-born participants, US-born individuals exhibited lower adherence to the Plant-based foods and Dairy products DPs but higher adherence to Processed foods, Seafood, and 6 EBS-specific DPs.
Conclusions
In its first application in nutritional epidemiology, BMSFA succeeded in simultaneously estimating well-interpretable shared and EBS-specific DPs within 12 combinations of background and site
The Importance of Jointly Analyzing Quality of Life and Survival: Insights From a Simulation Study
No full textINTRODUCTION
Patient-Reported Outcomes (PROs) are a key innovation in clinical research, providing direct insights into patients’ perception of symptoms and quality of life (QoL).[1] Beyond their role in measuring well-being, PROs have also shown consistent associations with survival. Nonetheless, they are often analyzed separately from survival, which may lead to biased estimates of treatment effects and loss of clinical information. PROs in fact are only collected from surviving patients and early mortality among those with lower QoL can lead to an overestimation of average QoL, distorting perceived treatment effects. Moreover, survival models that rely only on baseline PRO values may fail to capture crucial changes in QoL that might correlate with prognosis.[2] Joint models (JMs), which combine the analysis of repeated measurements and time-to-event data, were developed to address these issues.[3] However, their use in practice remains limited.[4]
AIM
This simulation study aimed to explore the interplay between QoL and survival under different scenarios and to compare traditional approaches, such as Cox models, with and without time-dependent covariates, with the JM in terms of accuracy and robustness.
METHODS
Six scenarios with 500 samples of 1,500 patients (750 treated, 750 controls) were simulated by combining three different treatment impacts on QoL (worsening, no change, improvement over time) with two survival settings (a halving of mortality vs. no direct survival benefit). The follow-up period was set at 5 years and divided into monthly intervals. In each interval, a linear mixed effect model (LMM) was used to generate QoL score for each patient. The probability of death within each interval was simulated considering the treatment arm and the current QoL score, assigning the same risk to everyone with the same profile. Informative censoring was introduced by modeling a lower probability of being observed during intervals when subjects had lower QoL scores. Additional scenarios without informative censoring were also simulated to assess how models’ performances were affected by the QoL–survival association independently of observation bias. Three statistical approaches were applied: a univariate Cox model with only the treatment variable, an extended Cox model with QoL as a time-dependent covariate, and a JM with a Weibull survival component and a LMM for QoL. Performances were compared through mean estimates of treatment and QoL effects on survival in terms of hazard ratio (HR), bias, standard error, and 95% coverage probability (CP).
RESULTS
Even a modest association between QoL and survival (e.g., HR QoL=0.96) significantly influenced mortality patterns. Mortality increased when treatment had negative impacts on QoL even in the context of a direct survival benefit. Conversely, positive effects on QoL further amplified survival benefits. In the main scenarios with informative censoring, the univariate Cox model, while accurate if treatment had no impact on QoL, tended to overestimate the protective effect of treatment on survival when it was positively associated with QoL, sometimes even indicating a benefit where none existed. Conversely, when treatment negatively affected QoL, the model either underestimated its protective effect on survival or suggested harm where there was none. This pattern highlighted the potential for misinterpretation in clinical settings, where changes in QoL might be mistakenly attributed to treatment effects on survival. The extended Cox model showed mild improvement in certain scenarios but consistently failed to accurately estimate the protective effect of QoL on survival, leading to substantial bias. In contrast, JM consistently produced accurate, unbiased estimates with CPs near 95%, reflecting its robustness in all scenarios, even with a simplified structure that included only a random intercept (Table1). In the absence of informative censoring, the extended Cox model was able to accurately estimate the effect of QoL on survival when treatment positively influenced QoL and was better than the JM in terms of CP. However, it continued to perform poorly in estimating the treatment effect on survival, showing consistent bias and low CP across most scenarios. The JM remained the most reliable approach, although its performance slightly decreased, likely due to a less clearly defined QoL–survival association.
CONCLUSIONS
JMs offer a more accurate and comprehensive approach for analyzing PROs and survival, capturing both direct and indirect treatment effects. Their capacity to integrate multiple dimensions of patient data make them valuable for analyzing chronic conditions where PROs and survival are linked. Even modest interdependencies can meaningfully influence outcomes and ignoring them may lead to misleading conclusions. Their use should be prioritized in both randomized and observational studies to ensure a valid inference and a deeper understanding of treatment effects
Exploring the Genetic Link between Clonal Hematopoiesis and Dilated Cardiomyopathy: Insights from a Polygenic Risk Score Analysis
No full textIntroduction
Clonal hematopoiesis (CH) refers to the expansion of a blood stem cell and its descendants, driven by somatic driver mutations, and includes clinically relevant subsets such as clonal hematopoiesis of indeterminate potential (CHIP). CHIP, in particular, is increasingly recognized for its role in lymphoid malignancies. Although CH is a relatively common phenomenon—affecting over one-third of individuals and becoming more prevalent with age—it is linked to a heightened risk of hematological cancers, various non-hematological conditions and inflammation. Inflammation responses play a central role in cardiovascular diseases and heart failure and recent studies suggested CH as an important trigger for dilated cardiomyopathy (DCM) [1].
Aims
Thanks to recent findings of a genome-wide association study (GWAS) that identified 42 independent genetic variants associated with the risk of developing CH [2], we conducted a study aimed at evaluating whether a polygenic risk score (PRS) for CH risk is related to the diagnosis and prognosis of DCM.
Methods
The study analyzed a DCM cohort of 315 patients recruited in the Heart Muscle Disease Registry of Trieste (IT) and 718 healthy individuals from the same region. A PRS was derived based on 27 GWAS loci was calculated using imputed SNP-array data. PRS standardized levels were compared across groups using a generalized linear mixed-model that included a genomic relatedness matrix as random effect to account for familial relationships. As for the analysis of disease progression in the DCM cohort, two primary outcomes were investigated: (1) life-threatening arrhythmic events, and (2) heart failure–related events. Time-to-event analysis was performed using cause-specific Cox mixed-models.
Results
The PRS was significantly higher in healthy individuals compared to DCM patients (OR=0.82 95% CI [0.69, 0.97] per SD increase, p=0.005). When differentiating between DCM patients who were carriers and non-carriers of pathogenic/likely pathogenic variants, the observed difference was primarily driven by the carrier group (mean difference=-0.22, 95% CI [-0.41, -0.04]). During a median follow-up of 109 months (IQR=[24,194]), 80 (25%) individuals experienced life-threatening arrhythmic events, 43 (14%) experienced heart failure–related events and 57 (18%) died. No association was observed between PRS and either arrhythmic outcome, neither with heart failure outcome.
Conclusions
These findings contribute to expanding the knowledge on the relationship between clonal hematopoiesis (CH) and cardiovascular diseases, specifically dilated cardiomyopathy (DCM), where current understanding remains limited. The observed association between lower CH PRS levels and higher DCM risk was unexpected. Further studies are needed to confirm these results and clarify their implications.
 
Vending Machines and Youth Access to Cigarettes in Ireland: A Cross-sectional Study
No full textTobacco-related morbidity and mortality significantly adversely impact public health and well-being on a global scale. Most smokers start smoking before being legally of age to smoke. Cigarette vending machines are an acknowledged access route for underage smokers to access cigarettes. Using a convenience sample, this research uses an online survey to explore the willingness of adults to purchase a vending machine token for underage smokers. Over 12% of adults reported that they would buy such a token for a 17-year-old, while another 8.6% of respondents were unsure. Analysis revealed that smoking history and age were significant factors in predicting willingness to purchase a cigarette vending machine token for an underage smoker. As cigarette vending machines remain an access route for youths to cigarettes, this research supports the forthcoming legislation banning such machines in Ireland
A semiotic framework for the reception system: a taxonomy between care, control and agency
No full textThe Italian reception system for immigrants is shaped by tensions between integration-oriented frameworks and securitarian emergency-driven approaches. This article presents a model – a 2x2 matrix that intersects the axes of agency/control and reception/integration – providing a multidimensional analytical tool for analysis, training, and empirical research. The matrix allows for the classification of facilities and services, the examination of migratory trajectories, and identification of the specific connotations of each centre. When applied to regulatory developments, the matrix has highlighted an alternation between integration-oriented provisions and restrictive measures, with uneven and harmful impacts on immigrants’ rights. The use of this semiotic framework has simplified the interpretation of normative complexities, proving to be a valuable tool for critically understanding and explaining migration law as well as for enabling a systematic analysis of migration phenomena.Il sistema italiano di accoglienza per persone immigrate risente di tensioni tra logiche di integrazione e approcci emergenziali securitari. Questo articolo presenta un modello, una matrice 2x2 che incrocia gli assi agency/controllo e accoglienza/integrazione, offrendo uno strumento analitico multidimensionale utile per l’analisi, la formazione e la ricerca empirica. La matrice consente di classificare le strutture e i servizi offerti, analizzare le traiettorie migratorie ed individuare le connotazioni specifiche di ogni centro. Applicata all’evoluzione normativa, la matrice ha rivelato un’alternanza tra disposizioni volte all’integrazione e misure restrittive, con impatti disomogenei e dannosi sui diritti delle persone immigrate. L’uso del quadro semiotico ha semplificato l’interpretazione delle complessità normative, dimostrandosi un valido strumento per comprendere e spiegare criticamente il diritto delle migrazioni e supportare una lettura sistematica dei fenomeni migratori
Child Rights Impact Assessment and Responsible Design in the Digital Transformation: Notes for a Preliminary Reflection
No full textLa trasformazione digitale sta modificando in profondità le esperienze dell’infanzia, aprendo nuove possibilità ma anche generando rischi rilevanti per i diritti dei minori. In questo contesto, il principio del superiore interesse del minore, riconosciuto dalla Convenzione ONU sui Diritti dell’Infanzia, richiede strumenti capaci di guidare concretamente le scelte progettuali. La valutazione d’impatto sui diritti dei minori o Child Rights Impact Assessment (CRIA) risponde a questa esigenza come metodologia di valutazione preventiva, utile a integrare la prospettiva dei diritti nei processi che portano alla realizzazione di tecnologie digitali. Il contributo esamina i limiti del paradigma del consenso informato e propone un cambio di approccio, fondato sulla responsabilità progettuale e sull’etica by design. Dopo aver ricostruito il quadro normativo e le principali esperienze internazionali in materia di impact assessment, si riflette sul ruolo che le imprese possono e devono assumere nel tutelare l’infanzia online. La CRIA viene così delineata come uno strumento capace di coniugare dimensione tecnica, giuridica e culturale. Il paper si chiude richiamando l’importanza di rendere sistematiche le valutazioni d’impatto sui diritti dei bambini, affinché la tutela dell’infanzia diventi parte integrante delle politiche pubbliche e delle strategie del settore privato.Digital transformation is profoundly reshaping childhood experiences, offering new opportunities but also posing significant risks to children’s rights. Against this backfrop, the principle of the best interests of the child, as established by the UN Convention on the Rights of the Child, must be translated into tools capable of concretely guiding design choices. The Child Rights Impact Assessment (CRIA) meets this need by providing a preventive evaluation method that embeds a child-rights perspective into the development of digital technologies. This paper critiques the limitations of the informed-consent model and advocates for a shift toward a responsibility-driven, ethics-by-design approach. After outlining the relevant legal framework and reviewing key international experiences with impact assessments, the paper examines the role businesses can play in protecting children’s rights in digital environments. CRIA is presented as a method that bridges technical, legal, and cultural dimensions. The conclusion emphasizes the urgency of institutionalizing child rights impact assessments, ensuring that the protection of children becomes an integral part of both public policy and corporate strategy
Recensione a Serena Mocci, Donne e impero nell’Ottocento americano. La cultura politica di Lydia Maria Child e Margaret Fuller, Roma, Viella, 2023, 318 p.
No full textRecensione al volume di Serena Mocci, Donne e impero nell’Ottocento americano. La cultura politica di Lydia Maria Child e Margaret Fuller, Roma, Viella, 2023, 318 p
In search for an epistemology for the sciences of built environments
No full textThis article is a call for increased attention to the epistemological challenges in the scientific study of built environments. These issues are acknowledged, yet only superficially discussed, in the philosophy of architecture. While the philosophy of science has recently examined how environments shape human health, the specific problems raised by built spaces remain largely overlooked. However, it is crucial to address the epistemological foundations that underlie the ethical, aesthetic, social, and political dimensions of architecture. Evidence-Based Design illustrates this need: it aims to enhance scientific rigour in building design and improve performance. Based on a systematic review of design research, I argue that its methodological inspiration (Evidence-Based Medicine) is ill-suited to architectural contexts. A constructive update would incorporate insights from philosophy of science on experimentation, pluralism, and the role of theory in practical sciences. This case study exemplifies philosophical engagement with built environments and design research