Biostatistical considerations in pharmacovigilance and pharmacoepidemiology: linking quantitative risk assessment in pre-market licensure application safety data, post-market alert reports and formal epidemiological studies

Evidence Synthesis in Harm Assessment of Medicines Using the Example of Rosiglitazone and Myocardial Infarction

The current system of harm assessment of medicines has been criticized for relying on intuitive expert judgment. There is a call for more quantitative approaches and transparency in decision-making. Illustrated with the case of cardiovascular safety concerns for rosiglitazone, we aimed to explore a structured procedure for the collection, quality assessment, and statistical modeling of safety data from observational and randomized studies. We distinguished five stages in the synthesis process. In Stage I, the general research question, population and outcome, and general inclusion and exclusion criteria are defined and a systematic search is performed. Stage II focusses on the identification of sub-questions examined in the included studies and the classification of the studies into the different categories of sub-questions. In Stage III, the quality of the identified studies is assessed. Coding and data extraction are performed in Stage IV. Finally, meta-analyses on the study results per sub-question are performed in Stage V. A Pubmed search identified 30 randomized and 14 observational studies meeting our search criteria. From these studies, we identified 4 higher level sub-questions and 4 lower level sub-questions. We were able to categorize 29 individual treatment comparisons into one or more of the sub-question categories, and selected study duration as an important covariate. We extracted covariate, outcome, and sample size information at the treatment arm level of the studies. We extracted absolute numbers of myocardial infarctions from the randomized study, and adjusted risk estimates with 95% confidence intervals from the observational studies. Overall, few events were observed in the randomized studies that were frequently of relatively short duration. The large observational studies provided more information since these were often of longer duration. A Bayesian random effects meta-analysis on these data showed no significant increase in risk of rosiglitazone for any of the sub-questions. The proposed procedure can be of additional value for drug safety assessment because it provides a stepwise approach that guides the decision-making in increasing process transparency. The procedure allows for the inclusion of results from both randomized an observational studies, which is especially relevant for this type of research.

Graphical approaches to the analysis of safety data from clinical trials

Patient safety has always been a primary focus in the development of new pharmaceutical products. The predominant method for statistical evaluation and interpretation of safety data collected in a clinical trial is the tabular display of descriptive statistics. There is a great opportunity to enhance evaluation of drug safety through the use of graphical displays, which can convey multiple pieces of information concisely and more effectively than can tables. Graphs can be used in an exploratory setting to help identify emerging safety signals, or in a confirmatory setting as a tool to elucidate known safety issues. We developed several graphical displays for routine safety data collected during a clinical trial, covering a broad range of graphical techniques, and illustrate here 10 specific graphical designs, many of which display the data along with statistics derived from them. Two are simple plots, comparing distributions in the form of boxplots or cumulative plots, and four more display data and summaries over time, comparing information from two groups in terms of distribution (with boxplots), cumulative incidence, hazard, or simply means with error bars. The other four are multi-panel displays: one-dimensional and two-dimensional arrays of scatterplots, a trellis of individual profiles, and a paired dotplot displaying risk together with relative risk. The displays focus on key safety endpoints in clinical trials including the QT interval from electrocardiograms, laboratory measurements for detecting hepatotoxicity, and adverse events of special interest. We discuss in detail the statistical and graphical principles underlying the production and interpretation of the displays.

A note on post-marketing safety study design to characterise time-dependent adverse events

PURPOSE

To investigate a suitable post-marketing safety study design, that is number of patients and duration of exposure, to well characterise adverse events (AEs) profiles under limited resources, fixed patient-months.

METHODS

A simulation study is conducted to investigate a suitable study design that can appropriately characterise the shape of the hazard function of AEs using the Weibull model. The reliability of the estimates is evaluated by referring their bias and mean squared error (MSE).

RESULTS

In general, patients should be followed for a longer period even if the number of patients is relatively small for characterising delayed AEs. Patients' drop-out affects the estimation and deteriorates its reliability. For AEs that are likely to occur soon after the exposure, a study with relatively shorter duration and large number of patients is preferable.

CONCLUSIONS

It is important to evaluate statistically the appropriate study design in planning a safety study so that a good estimate of the hazard function would be obtained.

Observation of Time-Dependent Adverse Events and the Influence of Drop-Out Thereon in Long-Term Safety Studies—Simulation Study Under the Current Practice of Post-marketing Safety Evaluation in Japan

Safety assessment of a new drug should be continuously carried out in the premarketing phase as well as in the postmarketing phase. Considering the actual conditions and problems of postmarketing safety studies in Japan, i.e., the lack of attention to the extent of patients' exposure to the drug (duration and the number of patients), we simulated the number of adverse events to be observed after specified intervals of exposure. This was done by applying different sets of hazard functions for a Weibull distribution under the circumstances that a certain number of patients has dropped out, focusing on rare and delayed adverse events associated with chronically used drugs. By using the result of these simulations, we point out potential problems of underestimating adverse event rates in situations where the hazard rate of the event escalates over time. Patients drop-out from the study also deteriorates the ability to observe such time-dependent adverse events. The simulation can also serve as a useful tool to examine the necessary sample size and the duration of exposure in order to observe and characterize potentially expected adverse events. It is important to take the two key factors into consideration: the change of hazard function over time and the effect of drop-out in designing, analyzing, and evaluating safety studies for new drugs.

Data Quality Management in Pharmacovigilance

Pharmacovigilance relies on information gathered from the collection of individual case safety reports and other pharmacoepidemiological data. Even given the inherent limitations of spontaneous reports, the usefulness of this data source can be improved with good data quality management. Although under-reporting cannot be remedied this way, the negative impact of incomplete reports, which is another serious problem in pharmacovigilance, can be reduced. Quality management consists of quality planning, quality control, quality assurance and quality improvements. The pharmacovigilance data processing cycle starts with data collection and, in computerised systems, data entry; the next step is data storage and maintenance; followed by data selection, retrieval and manipulation. The resulting data output is analysed and assessed. Finally, conclusions are drawn and decisions made. The increased knowledge feeds back into the data processing cycle. Focussing on the first three steps of the data processing cycle, the different quality dimensions associated with these steps are described in this review, together with examples relevant to pharmacovigilance data. Functioning, well documented, and transparent quality management systems will benefit not only those involved in data collection, management and output production, but, ultimately, also the pharmacovigilance end users, the patients.