Francesca Ieva

Associate Professor

Phone:+39 02 2399 4578
Fax: +39 02 2399
Office: Dip. di matematica - VI floor
Email:
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Available MOX Reports
Theses
Thesis Proposals
MOX Projects

Performance indicators for survival outcome

Performance measures for institutions represent an important aspect concerning services quality. In the last years, there has been an increasing use of performance indicators in health care [1]. A performance indicator is a statistical measurement related to the quality of functioning of an institution. These measures concern different aspects and they must account for all indicators to have a summary outcome as plausible as possible. There is an ongoing debate about the best choice of indicator measures and their reliability. To establish which is the best performance indicator it is a difficult decision both for clinicians and statisticians. The problem is how well a specific indicator reflects the quality of institution and how to draw conclusions from the estimates obtained by modeling the data. In order to quantify these results, in clinical literature rankings and league tables are usually created.

Performance indicators for hospitals could be for example proportion of complications, observed or expected mortality rate, proportion of patients still alive at a specific time point. When such indicators are built, appropriate statistical methodology to illustrate the presence of uncertainty in the presentation of results and the adjustment for case-mix is required. All individuals’ characteristics, known as case-mix, such as age, sex, diagnosis, stage of disease, therapy administered etc, are generally taken into account in statistical analysis. A fair comparison between centers is made after adjusting for case-mix [2-4].

Funnel plots [2] are well known tool used to compare institutions when the outcome is binary but little research has been performed for survival outcomes. Quaresma et al. [3] provide funnel plots as graphical tools designed to display performance indicators for cancer survival. To compare performance between institutions fixed or random effects models can be used.

The aim of this thesis is to provide performance indicators for survival outcomes to compare hospitals.

The motivating example comes from data about patients that underwent a surgery for oesophageal cancer in a Dutch hospital. The presence of about 55% of patients with unknown surgery date, which is the starting time for the event of interest, requires an algorithm to estimate this quantity. In this thesis an algorithm to impute missing values will be investigated. A simulations study will be performed to study the performance of method proposed.

Marshall C. and Spiegelhalter D.J., Reliability of league tables of in vitro fertil- ization clinics: retrospective analysis of live birth rates. British Medical Journal, 1998;316:1701-1705.

Spiegelhalter D.J., Funnel plots for comparing institutional performance. Statis- ticsin Medicine, 2005; 24:1185-1202.

Quaresma M., Funnel plots for population-based cancer survival: principles, methods and applications. Statistics in Medicine, 2014; 33: 1070-80.

D. Henneman, A. C. M. van Bommel, A. S. Snijders, R.A. A. E. Tollenaar, M.W.J.M. Wouters, M. Fiocco. Ranking and rankability of hospital postoperative mortality rates in colorectal cancer surgery. Annals of Surgery 20 4 May;259(5):844-9

He Y., Normand S.-L. T., On the accuracy of classifying hospitals on their performance measures. Statistics in Medicine, 2014; 33:1081-1103.

Laird N. M. and Louis T. A., Empirical Bayes Ranking Methods. Journal of Educational Statistics, Spring 1989, 14: 29-46.

Applicazione di tecniche di Machine Learning per l'analisi di immagini in ambito radio oncomico
Il progresso della radioterapia nella cura delle lesioni tumorali pone sfide importanti alla modellistica di supporto della prognosi medica. L'analisi delle immagini provenienti dalle strumentazioni radiologiche richiede lo sviluppo di opportuni modelli di previsione per personalizzare la terapia del paziente. La tesi si inserisce nella linea di ricerca su Health Analytics.

Health Analytics for Myelodisplastic Syndromes
Il contesto clinico è quello di alcuni tumori del sangue che si chiamano sindromi mielodisplastiche. Si tratta di leucemie croniche rare, che riducono drasticamente l’aspettativa di vita dei pazienti, e per i quali l’unica terapia curativa è il trapianto di midollo. Purtroppo il trapianto fallisce in una percentuale elevata di casi o per recidiva della malattia o per tossicità della procedura di trapianto stessa. Circa 10 anni fa sono cominciati studi per definire le mutazioni geniche ricorrenti alla base della malattia. Le mutazioni sono importanti per predire la prognosi dei pazienti e anche per definire la probabilità di successo del trapianto.

La/le tesi si focalizzerà/anno su:

1) Definire dei modelli predittivi innovativi, che comprendano anche le informazioni genomiche per predire la probabilità di successo del trapianto nel singolo paziente e quindi selezionare in modo ottimale i candidati a questa procedura.

2) Definire nel paziente candidato il momento ottimale (rispetto alla storia naturale della malattia) in cui eseguire la procedura di trapianto. Infatti la malattia ha diversi stati, e soprattutto per i pazienti diagnosticati in fase iniziale, puo’ passare molto tempo prima che la malattia intacchi lo stato di salute della persona. In questi soggetti, eseguire un trapianto subito al momento della diagnosi può risultare sconveniente, perché sarebbero sottoposti ai rischi del trapianto in una fase di malattia in cui l’impatto di salute è invece basso (rapporto rischi/beneficio sfavorevole)

Joint frailty modelling of time-to-event data to elicit the evolution pathway of events: a generalised linear mixed model approach.
La tesi ha risvolti metodologici e applicativi (ambito clinico ed epidemiologico). E' necessario un solido background in statistica applicata e una buona conoscenza di almeno uno dei seguenti software: R, Python.

Fattori di rischio cardiovascolari in soggetti con infezione da HIV
Sviluppo di algoritmi di algoritmi di Machine Learning per la valutazione dei fattori predittivi l’insorgenza di eventi cardiovascolari in soggetti con infezione da HIV con Follow Up di 10 anni.

Farb - Health And Education Systems Assessment,Politecnico Di Milano
The core interest of this project is in improving the quality of health and educational services at local, regional and national levels, through a program of applied research and close involvement with the Italian Ministry of Health, the Italian Institute for the Evaluation of Educational Systems (Invalsi) and other healthcare/educational organizations and institutions. Studying and monitoring outcomes and process indicators in healthcare and educational processes allow decision makers to improve the system performances with an optimized resource allocation; indeed, decision-makers involved in public administration (health/education) need information about efficiency and effectiveness of provided services. In this project, we propose an innovative knowledge discovery model that takes advantage by the massive use of administrative data that have been collected only with storage and control purposes. Administrative datasets are naturally updated, complete and available with a lower cost with respect to ad-hoc data collections. Even if their use has been questioned, the recent statistical literature points out that the risks of biased results can be minimized with suitable statistical and reporting techniques. Conditionally to a field of interest (pathology or pattern of care for health care students' achievement for education) a main outcome jointly with possibly quite secondary ones, are pointed out. To improve the outcomes is mandatory to understand the variables correlated, and eventually their causal impact on them. Specifically the variability affecting outcomes can be decomposed in many different sources. Due to the natural hierarchical structure of data (patients within divisions, divisions within hospitals, pupils within classes, classes within schools, �) they are affected by over-dispersion that can be catched and modeled by means of statistical �mixed effect models�. Despite the relevance of such research, funding is at moment very problematic. Public decision-makers need to see first results before committing in this direction. Private funding (e.g. biomedical companies interested to follow-up their technologies) need a commitment from the regional/national Institutions that allow them to proceed in this direction. Within this background, we think that FARB funding is a tremendous opportunity to seed this new research stream and create the first results useful to create the conditions for a next public and private funding context. The main objects of the project are then i) identification and study of the outcomes and process indicators for the problem of interest, ii) monitoring and analysis of these parameters, iii) supporting decisions in order to improve the process, iv) checking of the change effectiveness, or at least implementing a method for this and testing it; v) increasing the present awareness about the value of opening and using administrative databases for informing decision-making and promote both the public and the private funding of further researches in this direction. Within this background, during the three year FARB project we will start working in two specific contexts: a) the healthcare process for patients affected by heart failure, b) educational attainments of students at grades 2, 5 (primary education), 6 and 8 (lower secondary schooling). In both the cases, we have already been authorized to use the related administrative databases for this research.

Heart,Azienda Ospedaliera Niguarda Cà Granda
Utilization of regional health services databases for evaluating epidemiology, short-and medium-term out come, and process indexes in patients hospitalized for heart failure

Programma Strategico,Ministero Della Salute, Regione Lombardia
Exploitation, integration and study of current and future health databases in Lombardia for Acute Coronary Sindromes