<p>The WHO database contains more than 2.8 million case reports of suspected adverse drug reactions reported from 70 countries worldwide since 1968. The Uppsala Monitoring Centre maintains and analyses this database for new signals on behalf of the WHO Programme for International Drug Monitoring. A goal of the Programme is to detect signals, where a signal is defined as "Reported information on a possible causal relationship between an adverse event and a drug, the relationship being unknown or incompletely documented previously."</p><p>The analysis of such a large amount of data on a case by case basis is impossible with the resources available. Therefore a quantitative, data mining procedure has been developed to improve the focus of the clinical signal detection process. The method used, is referred to as the BCPNN (Bayesian Confidence Propagation Neural Network). This not only assists in the early detection of adverse drug reactions (ADRs) but also further analysis of such signals. The method uses Bayesian statistical principles to quantify apparent dependencies in the data set. This quantifies the degree to which a specific drug- ADR combination is different from a background (in this case the WHO database). The measure of disproportionality used, is referred to as the Information Component (IC) because of its' origins in Information Theory. A confidence interval is calculated for the IC of each combination. A neural network approach allows all drug-ADR combinations in the database to be analysed in an automated manner. Evaluations of the effectiveness of the BCPNN in signal detection are described.</p><p>To compare how a drug association compares in unexpectedness to related drugs, which might be used for the same clinical indication, the method is extended to consideration of groups of drugs. The benefits and limitations of this approach are discussed with examples of known group effects (ACE inhibitors - coughing and antihistamines - heart rate and rhythm disorders.) An example of a clinically important, novel signal found using the BCPNN approach is also presented. The signal of antipsychotics linked with heart muscle disorder was detected using the BCPNN and reported.</p><p>The BCPNN is now routinely used in signal detection to search single drug - single ADR combinations. The extension of the BCPNN to discover 'unexpected' complex dependencies between groups of drugs and adverse reactions is described. A recurrent neural network method has been developed for finding complex patterns in incomplete and noisy data sets. The method is demonstrated on an artificial test set. Implementation on real data is demonstrated by examining the pattern of adverse reactions highlighted for the drug haloperidol. Clinically important, complex relationships in this kind of data are previously unexplored.</p><p>The BCPNN method has been shown and tested for use in routine signal detection, refining signals and in finding complex patterns. The usefulness of the output is influenced by the quality of the data in the database. Therefore, this method should be used to detect, rather than evaluate signals. The need for clinical analyses of case series remains crucial.</p>

<p>Spontaneous reporting systems (SRSs) for adverse drug reactions (ADRs) have been developed as a result of the thalidomide disaster, whereby thousands of children world-wide were born with birth defects. The Swedish Adverse Drug Reactions Advisory Committee was established in 1965. Since 1975, reporting has been compulsory for all suspected serious or new ADRs. International collaboration started in 1968 with countries contributing their ADR reports to an international database set up by the World Health Organization. </p><p>ADRs represent the negative side of the benefit-to-risk balance that in theory needs to be counteracted by perceived or established positive drug effects. All drugs are subject to preclinical and clinical testing prior to marketing authorization. However, these studies are insufficient to detect rare ADRs, ADRs that occur after long-term administration or with latency, ADRs that occur in special patient groups such as children, the elderly, patients with renal or hepatic insufficiency or patients on concomitant drug treatment, and ADRs that represent a modest increase in the risk of diseases (including mortality) that are prevalent in the study population. Postmarketing surveillance of drugs is therefore essential, and regulatory action may be needed on the basis of new ADR information. </p><p>SRSs are important sources of ADR information as exemplified here by the evaluation of peripheral sensory disturbances with fluoroquinolones, hyponatremia with antidepressants, blood dyscrasias with dipyrone, glucose intolerance with atypical antipsychotics, pulmonary embolism with combined oral contraceptives and extrapyramidal symptoms with selective serotonin reuptake inhibitors. SRSs can be used to study clinical manifestations of ADRs (that can give insights into potential ADR mechanisms), risk factors for the ADR or for specific outcomes of the ADR, and ADR reporting incidences when combined with sales data. Signals from SRSs may need to be studied further e.g., by use of large-scale epidemiologic studies based on record linkage between drug prescription databases and health databases. Owing to the rapid availability of information, however, SRSs are likely to remain of major importance for the post-marketing surveillance of drugs.</p>

The WHO database contains more than 2.8 million case reports of suspected adverse drug reactions reported from 70 countries worldwide since 1968. The Uppsala Monitoring Centre maintains and analyses this database for new signals on behalf of the WHO Programme for International Drug Monitoring. A goal of the Programme is to detect signals, where a signal is defined as "Reported information on a possible causal relationship between an adverse event and a drug, the relationship being unknown or incompletely documented previously." The analysis of such a large amount of data on a case by case basis is impossible with the resources available. Therefore a quantitative, data mining procedure has been developed to improve the focus of the clinical signal detection process. The method used, is referred to as the BCPNN (Bayesian Confidence Propagation Neural Network). This not only assists in the early detection of adverse drug reactions (ADRs) but also further analysis of such signals. The method uses Bayesian statistical principles to quantify apparent dependencies in the data set. This quantifies the degree to which a specific drug- ADR combination is different from a background (in this case the WHO database). The measure of disproportionality used, is referred to as the Information Component (IC) because of its' origins in Information Theory. A confidence interval is calculated for the IC of each combination. A neural network approach allows all drug-ADR combinations in the database to be analysed in an automated manner. Evaluations of the effectiveness of the BCPNN in signal detection are described. To compare how a drug association compares in unexpectedness to related drugs, which might be used for the same clinical indication, the method is extended to consideration of groups of drugs. The benefits and limitations of this approach are discussed with examples of known group effects (ACE inhibitors - coughing and antihistamines - heart rate and rhythm disorders.) An example of a clinically important, novel signal found using the BCPNN approach is also presented. The signal of antipsychotics linked with heart muscle disorder was detected using the BCPNN and reported. The BCPNN is now routinely used in signal detection to search single drug - single ADR combinations. The extension of the BCPNN to discover 'unexpected' complex dependencies between groups of drugs and adverse reactions is described. A recurrent neural network method has been developed for finding complex patterns in incomplete and noisy data sets. The method is demonstrated on an artificial test set. Implementation on real data is demonstrated by examining the pattern of adverse reactions highlighted for the drug haloperidol. Clinically important, complex relationships in this kind of data are previously unexplored. The BCPNN method has been shown and tested for use in routine signal detection, refining signals and in finding complex patterns. The usefulness of the output is influenced by the quality of the data in the database. Therefore, this method should be used to detect, rather than evaluate signals. The need for clinical analyses of case series remains crucial.

Spontaneous reporting systems (SRSs) for adverse drug reactions (ADRs) have been developed as a result of the thalidomide disaster, whereby thousands of children world-wide were born with birth defects. The Swedish Adverse Drug Reactions Advisory Committee was established in 1965. Since 1975, reporting has been compulsory for all suspected serious or new ADRs. International collaboration started in 1968 with countries contributing their ADR reports to an international database set up by the World Health Organization. ADRs represent the negative side of the benefit-to-risk balance that in theory needs to be counteracted by perceived or established positive drug effects. All drugs are subject to preclinical and clinical testing prior to marketing authorization. However, these studies are insufficient to detect rare ADRs, ADRs that occur after long-term administration or with latency, ADRs that occur in special patient groups such as children, the elderly, patients with renal or hepatic insufficiency or patients on concomitant drug treatment, and ADRs that represent a modest increase in the risk of diseases (including mortality) that are prevalent in the study population. Postmarketing surveillance of drugs is therefore essential, and regulatory action may be needed on the basis of new ADR information. SRSs are important sources of ADR information as exemplified here by the evaluation of peripheral sensory disturbances with fluoroquinolones, hyponatremia with antidepressants, blood dyscrasias with dipyrone, glucose intolerance with atypical antipsychotics, pulmonary embolism with combined oral contraceptives and extrapyramidal symptoms with selective serotonin reuptake inhibitors. SRSs can be used to study clinical manifestations of ADRs (that can give insights into potential ADR mechanisms), risk factors for the ADR or for specific outcomes of the ADR, and ADR reporting incidences when combined with sales data. Signals from SRSs may need to be studied further e.g., by use of large-scale epidemiologic studies based on record linkage between drug prescription databases and health databases. Owing to the rapid availability of information, however, SRSs are likely to remain of major importance for the post-marketing surveillance of drugs.

Pharmacovigilance in healthcare is essential for safe drug treatment. Spontaneous reporting is the most common source of information in the context of implementing label changes and taking a drug off the market. However, underreporting is found to be very prevalent. One way to decrease underreporting is to include different categories of healthcare professionals in such reporting and to investigate attitudes towards and incentives for reporting adverse drug reaction (ADR)s. As nurses form the largest group of health professionals, a sample of nurses were allowed and encouraged to report ADR during a 12 month period after they had received training in pharmacovigilance. A questionnaire posted to physicians and nurses investigated their knowledge and attitudes towards reporting. Spontaneous reports of torsade de pointes (TdP) and erectile dysfunction (ED) were scrutinized with respect to the reported drugs, risk factors and if the reaction was listed in the summary of product characteristics (SPC). After training, the nurses produced relevant reports and three years after the introduction of nurses in the reporting scheme, more than half of the responding nurses were aware of their role as reporters. Both nurses and physicians stated that the most important factor for reporting a suspected ADR was the severity of the ADR and an ADR arising in response to a newly approved drug. A web-based reporting system was deemed to facilitate the reporting. In spontaneous reports of TdP, citalopram was reported as a suspected drug. However, neither QT prolongations, nor TdP, were labelled in the SPC. ED was reported for all antihypertensive drugs including angiotensin II type I blockers. A positive information component (IC), assessing the disproportionality between the observed and the expected number of reports, was found indicating that ED was reported more often in association with antihypertensive drug classes, except for angiotensinconverting enzyme inhibitors. This thesis demonstrates the importance of pharmacoviglilance in healthcare in terms of capturing new signals. By including nurses as reporters, the overall safety of drugs might improve. Information and education are needed to secure safe treatment when applying drugs.

Adverse drug reactions (ADRs) constitute a major problem in society and in drug therapy. They are a common cause of short-term hospitalization, prolonged hospitalization and death. Spontaneous reporting of ADRs remains one the most effective methods for detecting new and serious drug reactions. In Sweden physicians are legally required to report fatal and serious ADRs. We know from previous studies that there is a substantial degree of under-reporting of ADRs also in Sweden. Attitudes towards reporting of ADRs among physicians in the northern region of Sweden were investigated using a questionnaire. The most important factor for not reporting ADRs among physicians and general practioners in our region was that the reaction was considered to be well known. However, their attitudes could also allow for a considerable rate of under-reporting. The effect on the reporting rate when nurses received instruction and were encouraged to report ADRs was studied. During a 12-month study period, 18 ADR reports with a total number of 22 ADRs were sent in by the nurses participating in the study to test nurses as reporters of ADRs. Using the Swedish ADR database, we calculated the risk of agranulocytosis associated with the use of metamizole by using consumption data from the case records of scrutinized patients’ and stored prescriptions. Over the period from 1996 to 1999, ten cases of agranulocytosis during treatment with metamizole were reported to SADRAC. Metamizole was prescribed to 666 (19%) inpatients during the 3-month study period and 112 prescriptions were identified at the participating pharmacies. Thirty-eight percent of them indicated treatment for more than 15 days. Making certain assumptions, the calculated risk of agranulocytosis was one out of every 31 000 inpatients and one out of every 1400 outpatients. The degree of under-reporting of serious ADRs was studied in five hospitals. More than 1300 case records were scrutinized and among these we found 107 cases that according to current rules for ADR reporting, should have been reported. Only fifteen of these were found in the SADRAC database, indicating a under-reporting rate of 86%.The effect on the reporting rate of ADRs was studied in an intervention study in which a small economical inducement was given to those who reported ADRs. The effect of a small economical stimulation to increase the reporting rate was studied. From the intervention area we received 62 suspected ADRs compared with 50 from the control area. The increase in the number of reports was 59% compared with an unchanged reporting rate from the control area. The physicians in northern Sweden have a relatively good knowledge of the existing rules for ADR reporting. Nurses could play an important role in detecting and reporting suspected ADRs. The risk of developing an metamizole induced agranulocytosis is considerably increased if metamizole is given to patients for a longer time than recommended. The rate of reported ADRs is very low, also for serious and fatal reactions. An increase in the reporting rate of suspected ADRs was observed during study period.

Ce travail de thèse porte sur l'estimation paramétrique du maximum de vraisemblance pour des données de survie tronquées à droite, lorsque les délais de troncature sont considérés déterministes. Il a été motivé par le problème de la modélisation des délais de survenue des effets indésirables médicamenteux à partir des bases de données de pharmacovigilance, constituées des cas notifiés. Les distributions exponentielle, de Weibull et log-logistique ont été explorées.Parfois le caractère tronqué à droite des données est ignoré et un estimateur naïf est utilisé à la place de l'estimateur pertinent. Une première étude de simulations a montré que, bien que ces deux estimateurs - naïf et basé sur la troncature à droite - puissent être positivement biaisés, le biais de l'estimateur basé sur la troncature est bien moindre que celui de l'estimateur naïf et il en va de même pour l'erreur quadratique moyenne. De plus, le biais et l'erreur quadratique moyenne de l'estimateur basé sur la troncature à droite diminuent nettement avec l'augmentation de la taille d'échantillon, ce qui n'est pas le cas de l'estimateur naïf. Les propriétés asymptotiques de l'estimateur paramétrique du maximum de vraisemblance ont été étudiées. Sous certaines conditions, suffisantes, cet estimateur est consistant et asymptotiquement normal. La matrice de covariance asymptotique a été détaillée. Quand le délai de survenue est modélisé par la loi exponentielle, une condition d'existence de l'estimation du maximum de vraisemblance, assurant ces conditions suffisantes, a été obtenue. Pour les deux autres lois, une condition d'existence de l'estimation du maximum de vraisemblance a été conjecturée.A partir des propriétés asymptotiques de cet estimateur paramétrique, les intervalles de confiance de type Wald et de la vraisemblance profilée ont été calculés. Une seconde étude de simulations a montré que la couverture des intervalles de confiance de type Wald pouvait être bien moindre que le niveau attendu en raison du biais de l'estimateur du paramètre de la distribution, d'un écart à la normalité et d'un biais de l'estimateur de la variance asymptotique. Dans ces cas-là, la couverture des intervalles de la vraisemblance profilée est meilleure.Quelques procédures d'adéquation adaptées aux données tronquées à droite ont été présentées. On distingue des procédures graphiques et des tests d'adéquation. Ces procédures permettent de vérifier l'adéquation des données aux différents modèles envisagés.Enfin, un jeu de données réelles constitué de 64 cas de lymphomes consécutifs à un traitement anti TNF-α issus de la base de pharmacovigilance française a été analysé, illustrant ainsi l'intérêt des méthodes développées. Bien que ces travaux aient été menés dans le cadre de la pharmacovigilance, les développements théoriques et les résultats des simulations peuvent être utilisés pour toute analyse rétrospective réalisée à partir d'un registre de cas, où les données sur un délai de survenue sont aussi tronquées à droite. / This work investigates the parametric maximum likelihood estimation for right-truncated survival data when the truncation times are considered deterministic. It was motivated by the modeling problem of the adverse drug reactions time-to-onset from spontaneous reporting databases. The families of the exponential, Weibull and log-logistic distributions were explored.Sometimes, right-truncation features of spontaneous reports are not taken into account and a naive estimator is used instead of the truncation-based estimator. Even if the naive and truncation-based estimators may be positively biased, a first simulation study showed that the bias of the truncation-based estimator is always smaller than the naive one and this is also true for the mean squared error. Furthermore, when the sample size increases, the bias and the mean squared error are almost constant for the naive estimator while they decrease clearly for the truncation-based estimator.Asymptotic properties of the truncation-based estimator were studied. Under sufficient conditions, this parametric truncation-based estimator is consistent and asymptotically normally distributed. The covariance matrix was detailed. When the time-to-onset is exponentially distributed, these sufficient conditions are checked as soon as a condition for the maximum likelihood estimation existence is satisfied. When the time-to-onset is Weibull or log-logistic distributed, a condition for the maximum likelihood estimation existence was conjectured.The asymptotic distribution of the maximum likelihood estimator makes it possible to derive Wald-type and profile likelihood confidence intervals for the distribution parameters. A second simulation study showed that the estimated coverage probability of the Wald-type confidence intervals could be far from the expected level because of a bias of the parametric maximum likelihood estimator, a gap from the gaussian distribution and a bias of the asymptotic variance estimator. In these cases, the profile likelihood confidence intervals perform better.Some goodness-of-fit procedures adapted to right-truncated data are presented. Graphical procedures and goodness-of-fit tests may be distinguished. These procedures make it possible to check the fit of different parametric families to the data.Illustrating the developed methods, a real dataset of 64 cases of lymphoma, that occurred after anti TNF-α treatment and that were reported to the French pharmacovigilance, was finally analyzed. Whilst an application to pharmacovigilance was led, the theoretical developments and the results of the simulation study may be used for any retrospective analysis from case registries where data are right-truncated.