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At least 127 records · Page 7Linked to original sources

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.

This paper deals with a conceptual discussion of a variety of statistical concepts, methods and strategies that are relevant to the quantitative assessment of risk derived from safety data collected during the pre- and post-marketing phase of a new drug's life cycle. A call is made for the use of more standard approaches to the analysis of safety data that are statistically and epidemiologically rigorous and for attempts to link the strategies for pre-market safety assessment with strategies for post-market safety evaluation. This link may be facilitated by recognizing the limitations and complementary roles played by pre- and post-market safety data collection schemes and by linking the quantitative analyses utilized for either exploratory or confirmatory purposes of risk assessment in each phase of safety data collection. Examples are provided of studies specifically designed to evaluate risk in a post approval setting and several available guidelines intended to improve the quality of these studies are discussed.

Data Collection↗

Tutorial in biostatistics. An introduction to hierarchical linear modelling.

Hierarchical linear models are useful for understanding relationships in hierarchical data structures, such as patients within hospitals or physicians within hospitals. In this tutorial we provide an introduction to the technique in general terms, and then specify model notation and assumptions in detail. We describe estimation techniques and hypothesis testing procedures for the three types of parameters involved in hierarchical linear models: fixed effects, covariance components, and random effects. We illustrate the application using an example from the Type II Diabetes Patient Outcomes Research Team (PORT) study and use two popular PC-based statistical computing packages, HLM/2L and SAS Proc Mixed, to perform two-level hierarchical analysis. We compare output from the two packages applied to our example data as well as to simulated data. We elaborate on model interpretation and provide guidelines for model checking.

Adult↗

Biostatistical assessment of mutagenicity studies by including the positive control.

Statistical analysis plays a fundamental part in the evaluation of mutagenicity experiments. However, a statistically significant or non-significant test result without incorporating the biological relevance cannot be a valid scientific criterion for concluding a positive or negative effect of the underlying compound (Hauschke et al., 1997). The classification of an experiment as being negative or positive should be based also on the magnitude of the responses in the positive control. We address the problem of determining the maximum safe dose by incorporating a biologically meaningful threshold value, which is expressed as a fraction of the difference between positive and vehicle control.

Biometry↗