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P Tubert-Bitter

Publications and source records attributed to P Tubert-Bitter.

11 recordsLinked to original sources
PubMed2003

A score test for establishing non-inferiority with respect to short-term survival in two-sample comparisons with identical proportions of long-term survivors.

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.

Breast Neoplasms↗
PubMed2001

A semiparametric approach for the two-sample comparison of survival times with long-term survivors.

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.

Biometry↗
PubMed2001

One-sided tests in clinical trials with multiple endpoints.

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.

Arthritis, Rheumatoid↗
PubMed2000

Sample size calculations for risk equivalence testing in pharmacoepidemiology.

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.

Clinical Trials as Topic↗
PubMed1998

Spontaneous reporting of adverse drug reactions: who reports and what?

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.

Journal Article↗
PubMed1996

Comparing the toxicity of two drugs in the framework of spontaneous reporting: a confidence interval approach.

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.

Adverse Drug Reaction Reporting Systems↗
PubMed1995

Depression-induced absenteeism in relation to antihyperlipidemic treatment: a study using GAZEL cohort data.

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.

Absenteeism↗
PubMed1994

Sample size calculations for single group post-marketing cohort studies.

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.

Cohort Studies↗
PubMed1994

False-positives in spontaneous reporting: should we worry about them?

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.

Drug-Related Side Effects and Adverse Reactions↗
PubMedDate not supplied

Comparing the bivariate effects of toxicity and efficacy of treatments.

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.

Arthritis, Rheumatoid↗