Saturday, April 25, 2009

Acronym related to Clinical trials in EU countries

In order to conduct a clinical trial in the EC, the sponsor must first submit a valid request for authorisation to the Competent Authority of the Member State where they propose to conduct the trial. This request is known as the Clinical Trial Application (CTA). The content of this application will then be assessed by the competent authority and/or the Ethics Committee to ensure that the anticipated therapeutic benefits to the patient justify any foreseeable risks before a favourable opinion is issued to allow the trial to proceed.

The safety of subjects participating in a clinical trial is the main reason behind many of the changes brought about by the Directive and thus why the need for a common system of authorization has also come about. This requirement within the pharmaceutical industry was previously only applicable to commercial products. However, this change now means that all facilities used for the manufacture or import of Investigational Medicinal Products (IMPs) will be subject to an inspection by the competent authority.

This is to ensure that the principles of Good Manufacturing Practice (GMP) as led down in Annex 13 to the EU guide to Good Manufacturing Practice are being adhered to. On the basis of this inspection, they may become licensed by the competent authority. This authorisation takes the form of a Manufacturing Authorisation for IMPs or MA for IMPs.

Yet, one additional aspect must be fulfilled in order for a facility to be granted a licence. This is the need for the manufacturer or importer to have a Qualified Person (QP) permanently and continuously at their disposal. This person will be named on the licence and will be responsible for the release of batches of clinical trial material before they can be used in a clinical trial.

Several scenarios present themselves. The first one is when the IMP has been manufactured within Europe. This is no doubt the simplest case for the QP when discharging their duties. In order to release material of European origin, they must confirm that each batch has been manufactured and checked in compliance with GMP, the Product Specification File (PSF) and the request for authorisation to conduct the trial, i.e. the CTA.

Another scenario exists when a comparator product from outside the EU, with a marketing authorisation (MA) in that country is to be used as an IMP. Under such circumstances the QP can perform release, if documentation is available to certify its manufacture to standards at least equivalent to European GMP. However, in the absence of such documentation, the QP must ensure that each lot undergoes all relevant analyses, tests or checks to confirm its quality.

This can sometimes prove difficult and therefore it is important that the sponsor gives purchase of comparators due consideration. One piece of advice would be that, if possible, comparators should be sourced within Europe or from countries where Mutual Recognition Agreements are already in existence, such as Canada, Australia, New Zealand, Switzerland and Japan. These Mutual Recognition Agreements are based on trust and confidence and are therefore very useful when it comes to importing comparators, as they aim to remove barriers to trade and promote standardization of GMP.

MA: Marketing authorization

MAH: Marketing authorization holder

CA: Competent Authority

QP: Qualified Person

MRP: Mutual Recognition Process

MRA: Mutual Recognition Agreement

EMEA: European Medicines Agency

  • The European Medicines Agency (EMEA) is a decentralised body of the European Union with headquarters in London. Its main responsibility is the protection and promotion of public and animal health, through the evaluation and supervision of medicines for human and veterinary use.

BPWP: blood product working party

CHMP: The Committee for Medicinal Products

NfG: Notes for Guidance

PtC: Point to Consider

PEI: paul-Enrlich-Institut

The Paul-Ehrlich-Institut is an institution of the Federal Republic of Germany. It reports to the Bundesministerium für GesundheitSimilar to CBER of FDA.
(Federal Ministry of Health).

RMS: Reference Member State

Concerned Member States

Application for variation to a marketing authorisation = sNDA or sBLA

MHRA: Medicines and Healthcare products Regulatory Agency - An executive agency of the Department of Health in UK - simiar to FDA in US

SPC or SmPC: Summary of Product Characteristics - similar to Label or Package Insert in US.

  • Pescription medications are regulated by governmental bodies to assure quality and appropriate use. In the US, the FDA regulates medications, and requires "labels" to be approved. "Package inserts" are written for health care providers. They contain very detailed information about different drugs. Frequently, there are also official documents for patients, called Patient Information leaflets. The manufacturers prepare this information, and the FDA approves it (sometimes after considerable discussions and negotiations!).
  • In Europe, a similar process is used, with the "label" called the Summary of Product Characteristics (SPC, or SmPC). The patient-oriented document is called a "Package Leaflet" or "Patient Information Leaflet" (PILs)

IPMD: The Investigational Medicinal Product Dossier (IMPD)- similar to IND submission in US. IMPD needs to be submitted to the concerned competent authority (CA) in order to obtain the authorization of conducting the clinical trial.

CTA: Clinical Trial Application - similar to IND (investigational new drug)

NICE: The National Institute for Health and Clinical Excellence a counterpart in US is the Agency for Healthcare Research and Quality.

  • NICE is a special health aurhority of the National Health Service (NHS) in England and Wales. It was set up as the National Institute for Clinical Excellence in 1999, and on 1 April 2005 joined with the Health Development Agency to become the new National Institute for Health and Clinical Excellence (still abbreviated as NICE).
  • NICE publishes clinical appraisals of whether particular treatments should be considered worthwhile by the NHS. These appraisals are based primarily on cost-effectiveness.
For further reading:

Sunday, April 19, 2009

Risk management, pharmacoepidemiology, and pharmacovigilence

Risk Management:
Risk management is the overall and continuing process of minimizing risks throughout a product's lifecycle to optimize its benefit/risk balance. Risk information emerges continuously throughout a product's lifecycle, during both the investigation and marketing phases through both labeled and off-label uses. FDA considers risk management to be a continuous process of (1) learning about and interpreting a product's benefits and risks, (2) designing and implementing interventions to minimize a product's risks, (3) evaluating interventions in light of new knowledge that is acquired over time, and (4) revising interventions when appropriate.

pharmacoepidemiology is the study of the utilization and effects of drugs in large numbers of patients. It can be viewed as an epidemiological discipline with particular focus on drugs.The process of identifying and responding to safety issues about drugs.

Pharmacovigilance (PVG):
Pharmacovigilance is generally regarded as all postapproval scientific and data gathering activities relating to the detection, assessment, understanding, and prevention of adverse events or any other product-related problems. This includes the use of pharmacoepidemiologic studies.

Patient registry:
The term "registry" as used in pharmacovigilance and pharmacoepidemiology is often given different meanings. For the purpose of this concept paper, we are defining a registry as a systematic collection of defined events or product exposures in a defined patient population for a defined period of time. Through the creation of registries, a sponsor can monitor for safety signals identified from spontaneous case reports, literature reports, or other sources, and evaluate factors that affect the risk of adverse outcomes, such as dose, timing of exposure, or other patient characteristics.

REMS: Risk Evaluation and Mitigation Strategy

A Risk Evaluation and Mitigation Strategy (REMS) is a strategy to manage a known or potential serious risk associated with a drug or biological product. A REMS will be required if FDA finds that a REMS is necessary to ensure that the benefits of the drug or biological product outweigh the risks of the product, and FDA notifies the sponsor. A REMS can include a Medication Guide, Patient Package Insert, a communication plan, elements to assure safe use, and an implementation system, and must include a timetable for assessment of the REMS. Some drug and biological products that previously were approved/licensed with risk minimization action plans (RiskMAPs) will now be deemed to have REMS.

For more information, see FDA's website about FDAAA.

also, the following website may be useful:

Sunday, April 12, 2009

Effect Size

Effect size (ES) is a name given to a family of indices that measure the magnitude of a treatment effect. Unlike significance tests, these indices are independent of sample size. Effect size has been frequently linked to the power analysis (or sample size calculation) and Meta analysis.

The concept of effect size seems to come from Cohen's book "Statistical Power Analysis for the Behavioral Sciences". Effect size is not just for the continuous variable, it could also be for rates and proportions, and other type of data.

The following weblinks provide good summaries on effect size:

Recently, I came across a paper that described the use of effect size as Benchmarks for Interpreting Change - one of many ways to determine the sensitivity of the measurement and subsequently the minimal clinically important difference (MCID) or minimal important difference (MID).

In a paper by Kazis LE, Anderson JJ, Meenan RF (Effect sizes for interpreting changes in health status. Med Care 1989;27:S178-S189), they described the following:

"Effect size as used in this study is calcu-lated by taking the difference between the means before treatment and after treatment and dividing it by the standard deviation of the same measure before treatment. This method of calculating effect sizes can be expressed mathematically as ES = (mi - m2)/sl, where m, is the pretreatment mean, m2 the posttreatment mean, and s, the pretreatment standard deviation. In this instance the before-treatment scores are used as a proxy for control group scores. This approach treats the effect size as a standard measure of change in a "before and after study" context. We are interested in the magnitude or size of the change rather than statistical significance, so we use the standard deviation at baseline rather than the standard deviation of the differ-ence between the means.8 Effect sizes can be used to translate changes in health status into a standard unit of measurement that will provide a clearer interpretation of the results. This can be ac-complished by using effect sizes as bench-marks for measuring changes or as a means for taking comparisons between measures in the same study or across studies. "

Here the effect size is not to compare the two treatmetn groups, rather compare the differences pre and post. The formula for effect size can be explicitly rewritten to represent the mean change from pre treatment to the post treatment divided by the standard deviation of the baseline measures (effect size = (mui - mu0/SDmu0; mui = mean value of the post-baseline measure; mu0 = mean value at baseline).

If we calculate the effect size for both treatment group and placebo group, we should expect a very small effect size for Placebo group and a rather large effect size for treatment group - an indicator of a good measurement.

Sunday, April 05, 2009

Least squares means (marginal means) vs. means

If you work with SAS, you probably heard and used the term 'least squares means' very often. Least squares means (LS Means) are actually a sort of SAS jargon. Least square means is actually referred to as marginal means (or sometimes EMM - estimated marginal means). In an analysis of covariance model, they are the group means after having controlled for a covariate (i.e. holding it constant at some typical value of the
covariate, such as its mean value).

I often find that it is neccessary to use a very simple example to illulatrate the difference between LS Means and Means to my non-statistician colleagues. I made up the data in Table 1 above. There are two treatment groups (treatment A and treatment B) that are measured at two centers (Center 1 and Center 2).

The mean value for Treatment A is simply the summation of all measures divided by the total number of observations (Mean for treatment A = 24/5 = 4.8); similarly the Mean for treatment B = 26/5 = 5.2. Mean for treatmeng A > Mean for treatment B.

Table 2 shows the calculation of least squares means. First step is to calculate the means for each cell of treatment and center combination. The mean 9/3=3 for treatment A and center 1 combination; 7.5 for treatment A and center 2 combination; 5.5 for treatment B and center 1 combination; and 5 for treatment B and center 2 combination.

After the mean for each cell is calculated, the least squares means are simply the average of these means. For treatment A, the LS mean is (3+7.5)/2 = 5.25; for treatment B, it is (5.5+5)/2=5.25. The LS Mean for both treatment groups are identical.

It is easy to show the simple calculation of means and LS means in the above table with two factors. In clinical trials, the statistical model often needs to be adjusted for multiple factors including both categorical (treatment, center, gender) and continuous covariates (baseline measures). The calculation of LS mean is not easy to demonstrate. However, the LS mean should be used when the inferential comparison needs to be made. Typically, the means and LS means should point to the same direction (while with different values) for treatment comparison. Occasionally, they could point to the different directions (treatment A better than treatment B according to mean values; treatment B better than treatment A according to LS Mean).

SAS procedure GLM has a nice discussion about the comparison of Least Square Means vs. Means. A small article "Means vs LS Means and Type I vs Type III Sum of Squares"by Dan may also help.