ABSTRACT
OBJECTIVE: To assess the risk of preterm birth associated with nonsteroidal anti-inflammatory drugs (NSAIDs), focusing on early exposure in the period from conception to 22 weeks of gestation (WG). DESIGN: National population-based retrospective cohort study. SETTING: The French National Health Insurance Database that includes hospital discharge data and health claims data. POPULATION: Singleton pregnancies (2012-2014) with a live birth occurring after 22WG from women between 15 and 45 years old and insured the year before the first day of gestation and during pregnancy were included. We excluded pregnancies for which anti-inflammatory medications were dispensed after 22WG. METHODS: The association between exposure and risk of preterm birth was evaluated with GEE models, adjusting on a large number of covariables, socio-demographic variables, maternal comorbidities, prescription drugs and pregnancy complications. MAIN OUTCOME MEASURES: Prematurity, defined as a birth that occurred before 37WG. RESULTS: Among our 1 598 330 singleton pregnancies, early exposure to non-selective NSAIDs was associated with a significantly increased risk of preterm birth, regardless of the severity of prematurity: adjusted odds ratio (aOR) = 1.76 (95% CI 1.54-2.00) for extreme prematurity (95% CI 22-27WG), 1.28 (95% CI 1.17-1.40) for moderate prematurity (28-31WG) and 1.08 (95% CI 1.05-1.11) for late prematurity (32-36WG), with non-overlapping confidence intervals. We identified five NSAIDs for which the risk of premature birth was significantly increased: ketoprofen, flurbiprofen, nabumetone, etodolac and indomethacin: for the latter, aOR = 1.92 (95% CI 1.37-2.70) with aOR = 9.33 (95% CI 3.75-23.22) for extreme prematurity. CONCLUSION: Overall, non-selective NSAID use (delivered outside hospitals) during the first 22WG was found to be associated with an increased risk of prematurity. However, the association differs among NSAIDs. TWEETABLE ABSTRACT: French study for which early exposure to non-selective NSAIDs was associated with increased risk of prematurity.
Subject(s)
Anti-Inflammatory Agents, Non-Steroidal/adverse effects , Maternal Exposure/adverse effects , Pregnancy Complications/drug therapy , Premature Birth/epidemiology , Adolescent , Adult , Cohort Studies , Databases, Factual , Female , France/epidemiology , Humans , Middle Aged , Pregnancy , Premature Birth/chemically induced , Retrospective Studies , Young AdultABSTRACT
In this paper, we propose new methods for analyzing cases of vaccine adverse events spontaneously reported to a surveillance database. The methods use the self-controlled case series approach, extended in several ways with parametric and nonparametric assumptions to account for the specific features of the data (large amount of underreporting and variation of reporting with time since vaccination). This work was motivated by the documented risk of intussusception after RotaShield vaccination (Wyeth-Lederle Vaccines, Radnor, Pennsylvania) and used worldwide spontaneous reports of intussusception occurring after Rotarix vaccination (GlaxoSmithKline Biologics, Research Triangle Park, North Carolina) collected between January 2004 and February 2010. The estimated risk during the 3- to 7-day period after vaccination was approximately 5 times higher after dose 1 of Rotarix than after dose 2, which is similar to published findings on the same topic. We undertook a large simulation study to evaluate the performance of the method in different scenarios, including its robustness to different sample sizes and time-dependent reporting functions. The bias was generally small, the type I error rate was correctly controlled, and the power to detect a risk ratio of 4 was satisfactory, provided that the sample size was over 100. The proposed methods are an effective way to explore and quantify vaccine safety signals from spontaneous reports.
Subject(s)
Intussusception/etiology , Pharmacovigilance , Rotavirus Vaccines/adverse effects , Age Factors , Computer Simulation , Dose-Response Relationship, Drug , Humans , Infant , Infant, Newborn , Odds Ratio , Rotavirus Vaccines/administration & dosage , Time Factors , Vaccines/administration & dosage , Vaccines/adverse effectsABSTRACT
The early detection of adverse reactions caused by drugs that are already on the market is the prime concern of pharmacovigilance efforts; the methods in use for postmarketing surveillance are aimed at detecting signals pointing to potential safety concerns, on the basis of reports from health-care providers and from information available in various databases. Signal detection methods based on the estimation of false discovery rate (FDR) have recently been proposed. They address the limitation of arbitrary detection thresholds of the automatic methods in current use, including those last updated by the US Food and Drug Administration and the World Health Organization's Uppsala Monitoring Centre. We used two simulation procedures to compare the false-positive performances for three current methods: the reporting odds ratio (ROR), the information component (IC), the gamma Poisson shrinkage (GPS), and also for two FDR-based methods derived from the GPS model and Fisher's test. Large differences in FDR rates were associated with the signal-detection methods currently in use. These differences ranged from 0.01 to 12% in an analysis that was restricted to signals with at least three reports. The numbers of signals generated were also highly variable. Among fixed-size lists of signals, the FDR was lowered when the FDR-based approaches were used. Overall, the outcomes in both simulation studies suggest that improvement in effectiveness can be expected from use of the FDR-based GPS method.
Subject(s)
Computer Simulation/statistics & numerical data , Data Mining/methods , Models, Statistical , Product Surveillance, Postmarketing/methods , HumansABSTRACT
Pharmacovigilance systems aim at early detection of adverse effects of marketed drugs. They maintain large spontaneous reporting databases for which several automatic signaling methods have been developed. One limit of those methods is that the decision rules for the signal generation are based on arbitrary thresholds. In this article, we propose a new signal-generation procedure. The decision criterion is formulated in terms of a critical region for the P-values resulting from the reporting odds ratio method as well as from the Fisher's exact test. For the latter, we also study the use of mid-P-values. The critical region is defined by the false discovery rate, which can be estimated by adapting the P-values mixture model based procedures to one-sided tests. The methodology is mainly illustrated with the location-based estimator procedure. It is studied through a large simulation study and applied to the French pharmacovigilance database.
Subject(s)
Adverse Drug Reaction Reporting Systems/statistics & numerical data , Algorithms , Data Interpretation, Statistical , Decision Support Systems, Clinical , False Positive Reactions , Pattern Recognition, Automated/methods , HumansABSTRACT
The hypothesis that hepatitis B vaccination is a risk factor for multiple sclerosis has been discussed at length. The data from an earlier case-control study were reanalyzed using the self-controlled case series method. Using the matched cases from the case-control study, we found a relative incidence of 1.68, 95% CI (0.77-3.68) for the 0-60-day post-vaccination risk period; this compares to an odds ratio of 1.8, 95% CI (0.7-4.6). When an additional 53 unmatched cases not used in the case-control study were included, the relative incidence was 1.35, 95% CI (0.66-2.79). Our results throw further light on the methodological aspects of the case series method. We recommend that, when case-control studies of vaccination and adverse events are planned, case series analyses based on the cases are also undertaken when appropriate.
Subject(s)
Central Nervous System Diseases/chemically induced , Data Interpretation, Statistical , Demyelinating Diseases/chemically induced , Hepatitis B Vaccines/adverse effects , Case-Control Studies , Central Nervous System , Central Nervous System Diseases/epidemiology , Demyelinating Diseases/epidemiology , Hepatitis B/prevention & control , Hepatitis B Vaccines/administration & dosage , Humans , VaccinationABSTRACT
OBJECTIVE: The framework consists of cohort or case-series studies with intermittent exposure and two types of events. The aim is to define and estimate an association measure between the exposure and the occurrence of one type of event rather than the other. STUDY DESIGN AND SETTING: The model and the estimation method are obtained by extending Farrington's approach for one type of recurrent event. The proposed association measure "RR(c)" is the ratio of the relative risks pertaining to each type of event. The estimated RR(c) and its confidence interval are derived under the independence assumption between the counts of the two types of events. The data that are analyzed are part of the data of a study on antimicrobial resistance in children. RESULTS: An interpretation of the RR(c) is proposed in terms of an odds ratio, which parallels a similar association measure defined in cross-sectional studies ("OR(c)"). The estimated value of the RR(c) agrees with the OR(c) reported in previous studies. CONCLUSION: The RR(c) appears as a useful tool for evaluating the risk of colonization (or infection) with resistant rather than susceptible bacteria following a previous intake of a given antibiotic conditional on colonization (or infection) with any bacteria.
Subject(s)
Data Interpretation, Statistical , Risk Assessment/methods , Anti-Bacterial Agents/pharmacology , Case-Control Studies , Child , Child, Preschool , Cohort Studies , Confidence Intervals , Drug Resistance, Bacterial , Humans , Odds Ratio , Oropharynx/microbiology , Penicillin G/pharmacology , Streptococcus pneumoniae/drug effectsABSTRACT
In recent years randomized trials designed to establish non-inferiority of a new treatment as compared to a standard one have been more widely used. Two-sample statistics have been proposed for this equivalence testing problem. However, they are not suited to situations where a long-term survivor fraction is expected. In this paper we propose a score test designed for establishing non-inferiority for the new treatment as compared to the standard one while assuming identical long-term survivor rates. Simulations results show that the proposed statistic has satisfactory size and power as long as certain restricting conditions are verified. A breast cancer trial is analysed as an example.
Subject(s)
Models, Biological , Models, Statistical , Therapeutic Equivalency , Breast Neoplasms/radiotherapy , Breast Neoplasms/surgery , Computer Simulation , Female , Humans , Lymph Nodes/surgery , Randomized Controlled Trials as Topic/methods , Survival AnalysisABSTRACT
In the two-sample comparison of survival times with long-term survivors, the overall difference between the two distributions reflects differences occurring in early follow-up for susceptible subjects and in long-term follow-up for nonsusceptible subjects. In this setting, we propose statistics for testing (i) no overall, (ii) no short-term, and (iii) no long-term difference between the two distributions to be compared. The statistics are derived as follows. A semiparametric model is defined that characterizes a short-term effect and a long-term effect. By approximating this model about no difference in early survival, a time-dependent proportional hazards model is obtained. The statistics are obtained from this working model. The asymptotic distributions of the statistics for testing no overall or no short-term effects are ascertained, while that of the statistic for testing no long-term effect is valid only when the short-term effect is small. Simulation studies investigate the power properties of the proposed tests for different configurations. The results show the interesting behavior of the proposed tests for situations where a short-term effect is expected. An example investigating the impact of progesterone receptors status on local tumor relapse for patients with early breast cancer illustrates the use of the proposed tests.
Subject(s)
Biometry , Survival Analysis , Breast Neoplasms/metabolism , Disease-Free Survival , Female , Humans , Neoplasm Recurrence, Local/metabolism , Proportional Hazards Models , Prospective Studies , Receptors, Progesterone/metabolism , Statistical DistributionsABSTRACT
Treatment comparisons in clinical trials often involve several endpoints. For example, one might wish to demonstrate that a new treatment is superior to the current standard for some components of the multivariate response vector and is not inferior, modulo biologically unimportant difference to the standard treatment for all other components. We introduce a new approach to multiple-endpoint testing that incorporates the essential univariate and multivariate features of the treatment effects. This approach is compared with existing methods in a simulation study and applied to data on rheumatoid arthritis patients receiving one of two treatments.
Subject(s)
Clinical Trials as Topic/statistics & numerical data , Arthritis, Rheumatoid/drug therapy , Arthritis, Rheumatoid/physiopathology , Biometry , Disability Evaluation , Humans , Models, Statistical , Multivariate AnalysisABSTRACT
Equivalence testing has been widely discussed and is commonly used in pharmacokinetics (bioequivalence) and clinical trials (therapeutic equivalence). It can also be applied to pharmacoepidemiology, where the aim may be to test with a known risk (one-group design) or with another drug (two-group design). Whether the approach is two-sided or one-sided, predefined equivalence limits are required. The definition of the equivalence region can be based on either risk difference or risk ratio. Risk equivalence testing is complicated by the binary nature of the outcome, its low frequency, and by the absence of commonly defined equivalence limits for differences or ratios. In this context, we consider usable formulae for sample sizes. In most cases, at least when the risk studied is large enough (above 1/1,000), it appears that these formulae result in sample sizes that may be acceptable for practical purposes. For example, demonstrating equivalence with a known risk of 0.01, a 20% maximal risk difference, and a one-sided test (alpha = 0.05 and beta = 0.2) requires: under the one-group design (known risk), 15,309 patients; and under the two-group design, 30,617 patients per group. This approach is the appropriate way to conclude equivalence, rather than the commonly used approach of difference testing and concluding equivalence when the null hypothesis of equality is not rejected.
Subject(s)
Clinical Trials as Topic/statistics & numerical data , Drugs, Investigational/adverse effects , Drugs, Investigational/pharmacokinetics , Humans , Pharmacoepidemiology , Risk , Sample Size , Therapeutic EquivalencyABSTRACT
A survey among Bordeaux pharmacovigilance centre 'users' and 'non-users' was conducted in Aquitaine, France. Two hundred physicians having reported to the centre at least one adverse drug reaction (ADR) during the past 3 years were matched to a randomly selected sample of 400 physicians who did not report. They were asked to anonymously fill out a postal questionnaire collecting data on their individual characteristics, including their practice mode, and on ADRs that they observed and reported during the past 12 months. The number of questionnaires returned was 151 (25%), of which 76 were from users (38%) and 75 from non-users (19%). The two groups had very close individual characteristics. All but three responders had observed at least one ADR during the past 12 months. For the different types of ADRs defined in terms of seriousness and labelling, more users had seen ADRs than non-users but among those who observed them, the numbers of ADRs seen were similar in both groups. In any case, the more recent the drug, the more prone to report were the physicians.
ABSTRACT
Spontaneous reporting remains the most frequently used technique in post-marketing surveillance. Decision-making usually depends on comparisons between the number of adverse drug reactions (ADRs) reported for two drugs on the basis of an equivalent number of prescriptions. The validity of such comparisons is expected to be jeopardized by probable underreporting ADR cases. This problem is accentuated when it cannot be assumed that the magnitude of underreporting is the same for the both drugs. Differences in reporting ratios can overemphasize, cancel, or reverse the conclusions of a statistical comparison based on the number of reports. We propose a single method for (1) calculating confidence intervals for relative risks estimated in the context of spontaneous reporting and (2) deriving the range of reporting ratios for which the conclusion of the statistical comparison remains statistically valid.
Subject(s)
Adverse Drug Reaction Reporting Systems , Anti-Inflammatory Agents, Non-Steroidal/adverse effects , Diclofenac/adverse effects , Piroxicam/adverse effects , Humans , Pharmacoepidemiology/methods , Poisson Distribution , Product Surveillance, PostmarketingABSTRACT
Medical studies often involve comparing the toxicity and efficacy of drugs. Separately evaluating toxicity and efficacy, the usual practice, does not correspond to how doctors manage patients and does not use the information provided in their bivariate relationship. This paper presents methods for analysing the bivariate data. One method is based on assessing the benefit for patient values to lie in different regions of the toxicity-efficacy plane. A second method includes patient thresholds for tolerating drugs. We propose dividing the toxicity-efficacy plane into regions where patients are likely to tolerate the drug. Several statistics are defined on these regions for measuring the toxic-therapeutic relationship, and the bootstrap is proposed for estimating their variances. We illustrate with treatment information available on rheumatoid arthritis patients.
Subject(s)
Clinical Trials as Topic/statistics & numerical data , Data Interpretation, Statistical , Drug Evaluation/statistics & numerical data , Multivariate Analysis , Arthritis, Rheumatoid/drug therapy , Decision Making, Computer-Assisted , Gold/therapeutic use , Humans , Methotrexate/therapeutic useABSTRACT
We examined the relation between overall 1-year exposure to diet and drugs prescribed for hyperlipidemia and the occurrence of medically certified absence from work with depression during the year of exposure (N = 289). The 17,244 persons studied are middle-aged employees of a national company who volunteered as cohort participants. Depression was more prevalent among those exposed to an antihyperlipidemic diet (N = 1,614) than among those unexposed. After stratification by sex and professional status, we found a prevalence ratio (PR) of 1.83 [95% confidence interval (CI) = 1.30-2.58]. Exposure to simvastatin (N = 376) produced comparable results, with a prevalence ratio of 2.18 (95% CI = 1.18-4.03). For subjects who were not cases in the year of exposure assessment, the hypolipidemic treatments are not associated with depression-induced absenteeism the following year. Our results point to a possible role of prescribed diet and simvastatin in depression-related absenteeism.
Subject(s)
Absenteeism , Depression/etiology , Hyperlipidemias/drug therapy , Hypolipidemic Agents/adverse effects , Adult , Cohort Studies , Depression/chemically induced , Diet, Fat-Restricted/adverse effects , Female , France , Humans , Hyperlipidemias/diet therapy , Hypolipidemic Agents/therapeutic use , Lovastatin/adverse effects , Lovastatin/analogs & derivatives , Lovastatin/therapeutic use , Male , Middle Aged , Occupations , Pharmacoepidemiology , Prevalence , Risk Factors , Simvastatin , Surveys and QuestionnairesABSTRACT
Spontaneous reporting remains the most used and, undoubtedly, the most cost-effective approach for the identification of adverse drug reactions (ADRs). Most of the limitations of this method are well recognised but the possibility of receiving false-positive reports of coincidental drug-event associations has received little attention. In this paper we propose a method based on the Poisson distribution for computing the maximum number of reports of an ADR that could be expected to be reported coincidentally. Three parameters are required: (i) the background risk of the event in the reference population, (ii) the total number of patients treated with the drug considered and, (iii) the proportion of cases that have been reported to the pharmacovigilance system. For most empirical situations occurring in the post-marketing surveillance setting, the expected number remains low and only a maximum of one to three cases could be accepted as possibly coincidental. For rare adverse events such as agranulocytosis or toxic epidermal necrolysis, coincidental associations are so unlikely that a number of reports greater than three constitutes a strong warning and requires further investigation. These findings suggest that for rare events, reports of coincidental drug-event associations are too unlikely to be considered as an important limitation of spontaneous reporting.
Subject(s)
Drug-Related Side Effects and Adverse Reactions , False Positive Reactions , Product Surveillance, Postmarketing/standards , Humans , Models, Theoretical , Poisson Distribution , ProbabilityABSTRACT
In pharmacoepidemiology, single group cohort is the most frequently proposed design to determine if the incidence rate of an adverse drug reaction among the exposed differs from a reference value. In many situations, the number of events expected in the cohort is too small to conduct sample size calculations based on the normal distribution. This paper proposes, for a single group cohort study, calculations and tables derived from the Poisson distribution. The results are based on a one-sided test with a 0.05 significance level and a power of 0.9 and 0.8. Two parameters have to be specified a priori: the expected incidence of the event under the null hypothesis and the minimum risk ratio to be detected. The required sample size and the critical number of events to reject the null hypothesis are directly derived from the tables. Results show that the normal approximation may lead to an underestimation of the required sample size.