ICH guidelines
E1A: The Extent of Population Exposure to Assess Clinical Safety
E2A: Clinical Safety Data Management: Definitions and Standards for Expedited Reporting
E2B: Clinical Safety Data Management: Data Elements for Transmission of Individual Case Safety Reports
E2C: Clinical Safety Data Management: Periodic Safety Update Reports for Marketed Drugs
E3: Structure and Content of Clinical Study Reports
E4: Dose-Response Information to Support Drug Registration
E5: Ethnic Factors in the Acceptability of Foreign Clinical Data
E6: Good Clinical Practice: Consolidated Guideline
E7: Studies in Support of Special Populations: Geriatrics
E8: General Considerations for Clinical Trials
E10: Choice of Control Group in Clinical Trials
M1: Standardisation of Medical Terminology for Regulatory Purposes
M3: Non-Clinical Safety Studies for the Conduct of Human Clinical Trials for Pharmaceuticals.
III. TRIAL DESIGN CONSIDERATIONS
3.1 Design Configuration
3.1.1 Parallel Group Design
The most common clinical trial design for confirmatory trials is the parallel group design in which subjects are randomised to one of two or more arms, each arm being allocated a different treatment. These treatments will include the investigational product at one or more doses, and one or more control treatments, such as placebo and/or an active comparator. The assumptions underlying this design are less complex than for most other designs. However, as with other designs, there may be additional features of the trial that complicate the analysis and interpretation (e.g. covariates, repeated measurements over time, interactions between design factors, protocol violations, dropouts (see Glossary) and withdrawals).
3.1.2 Crossover Design
In the crossover design, each subject is randomised to a sequence of two or more treatments, and hence acts as his own control for treatment comparisons. This simple manoeuvre is attractive primarily because it reduces the number of subjects and usually the number of assessments needed to achieve a specific power, sometimes to a marked extent. In the simplest 2×2 crossover design each subject receives each of two treatments in randomised order in two successive treatment periods, often separated by a washout period. The most common extension of this entails comparing n(>2) treatments in n periods, each subject receiving all n treatments. Numerous variations exist, such as designs in which each subject receives a subset of n(>2) treatments, or ones in which treatments are repeated within a subject.
Crossover designs have a number of problems that can invalidate their results. The chief difficulty concerns carryover, that is, the residual influence of treatments in subsequent treatment periods. In an additive model the effect of unequal carryover will be to bias direct treatment comparisons. In the 2×2 design the carryover effect cannot be statistically distinguished from the interaction between treatment and period and the test for either of these effects lacks power because the corresponding contrast is 'between subject'. This problem is less acute in higher order designs, but cannot be entirely dismissed.
When the crossover design is used it is therefore important to avoid carryover. This is best done by selective and careful use of the design on the basis of adequate knowledge of both the disease area and the new medication. The disease under study should be chronic and stable. The relevant effects of the medication should develop fully within the treatment period. The washout periods should be sufficiently long for complete reversibility of drug effect. The fact that these conditions are likely to be met should be established in advance of the trial by means of prior information and data.
There are additional problems that need careful attention in crossover trials. The most notable of these are the complications of analysis and interpretation arising from the loss of subjects. Also, the potential for carryover leads to difficulties in assigning adverse events which occur in later treatment periods to the appropriate
A common, and generally satisfactory, use of the 2×2 crossover design is to demonstrate the bioequivalence of two formulations of the same medication. In this particular application in healthy volunteers, carryover effects on the relevant pharmacokinetic variable are most unlikely to occur if the wash-out time between the two periods is sufficiently long. However it is still important to check this assumption during analysis on the basis of the data obtained, for example by demonstrating that no drug is detectable at the start of each period.
3.5 Sample Size
The number of subjects in a clinical trial should always be large enough to provide a reliable answer to the questions addressed. This number is usually determined by the
16 Statistical Principles for Clinical Trials
primary objective of the trial. If the sample size is determined on some other basis, then this should be made clear and justified. For example, a trial sized on the basis of safety questions or requirements or important secondary objectives may need larger numbers of subjects than a trial sized on the basis of the primary efficacy question (see, for example, ICH E1a).
Using the usual method for determining the appropriate sample size, the following items should be specified: a primary variable, the test statistic, the null hypothesis, the alternative ('working') hypothesis at the chosen dose(s) (embodying consideration of the treatment difference to be detected or rejected at the dose and in the subject population selected), the probability of erroneously rejecting the null hypothesis (the type I error), and the probability of erroneously failing to reject the null hypothesis (the type II error), as well as the approach to dealing with treatment withdrawals and protocol violations. In some instances, the event rate is of primary interest for evaluating power, and assumptions should be made to extrapolate from the required number of events to the eventual sample size for the trial.
The method by which the sample size is calculated should be given in the protocol, together with the estimates of any quantities used in the calculations (such as variances, mean values, response rates, event rates, difference to be detected). The basis of these estimates should also be given. It is important to investigate the sensitivity of the sample size estimate to a variety of deviations from these assumptions and this may be facilitated by providing a range of sample sizes appropriate for a reasonable range of deviations from assumptions. In confirmatory trials, assumptions should normally be based on published data or on the results of earlier trials. The treatment difference to be detected may be based on a judgement concerning the minimal effect which has clinical relevance in the management of patients or on a judgement concerning the anticipated effect of the new treatment, where this is larger. Conventionally the probability of type I error is set at 5% or less or as dictated by any adjustments made necessary for multiplicity considerations; the precise choice may be influenced by the prior plausibility of the hypothesis under test and the desired impact of the results. The probability of type II error is conventionally set at 10% to 20%; it is in the sponsor’s interest to keep this figure as low as feasible especially in the case of trials that are difficult or impossible to repeat. Alternative values to the conventional levels of type I and type II error may be acceptable or even preferable in some cases.
Sample size calculations should refer to the number of subjects required for the primary analysis. If this is the 'full analysis set', estimates of the effect size may need to be reduced compared to the per protocol set (see Glossary). This is to allow for the dilution of the treatment effect arising from the inclusion of data from patients who have withdrawn from treatment or whose compliance is poor. The assumptions about variability may also need to be revised.
The sample size of an equivalence trial or a non-inferiority trial (see Section 3.3.2) should normally be based on the objective of obtaining a confidence interval for the treatment difference that shows that the treatments differ at most by a clinically acceptable difference. When the power of an equivalence trial is assessed at a true difference of zero, then the sample size necessary to achieve this power is underestimated if the true difference is not zero. When the power of a non-inferiority trial is assessed at a zero difference, then the sample size needed to achieve that power will be underestimated if the effect of the investigational product is less than that of the active control. The choice of a 'clinically acceptable’ difference needs justification with respect to its meaning for future patients, and may be smaller than the 'clinically relevant' difference referred to above in the context of superiority trials designed to establish that a difference exists.
Source: http://www.ich.org/LOB/media/MEDIA485.pdf
Prepare a protocol for the study
The study protocol is the blueprint that all researchers will follow.
A study protocol is a document that describes, in detail, the plan for conducting the clinical study. The study protocol explains the purpose and function of the study as well as how to carry it out. Some specific things included in the protocol are the reason for the study, the number of participants, eligibility and exclusion criteria, details of the intervention or therapy the participants will receive (such as frequency and dosages), what data will be gathered, what demographic information about the participants will be gathered, steps for clinical caregivers to carry out, and the study endpoints. A single standard protocol must be used without deviation to ensure that the resulting data will be significant and reliable.
The NICHD expects that each study protocol will include a monitoring plan that defines:
How the study will comply with regulatory requirements
The specific events and activities that will be monitored during the study
The roles and responsibilities for everyone on the team who is involved in monitoring
Who has responsibility for reporting (and who they report to)
A schedule for monitoring
The timing or number of events that would lead to a stop in study accrual, an assessment of eligibility, monitoring, intervention, and under what conditions study accrual would resume
Defining the study design for the protocolProtocols may outline one of several different types of study designs, some which are described below, that investigators may follow:
Case Study: This type of study relies on a literature review or uses a physician’s clinical cases to introduce a clinical abnormality, usually something rare, new, or something that presents in an unusual way.
Case-Control Study: This study compares the number of people who had a potential risk factor in the case group (those with the disease) with those who had the same potential risk factor in the control group (those without the disease). This type of study demonstrates association, but not cause and effect.
Cohort Study: Much like a case-control study, this type of study tracks and compares risk factors shared between the case group and the control group. This type of study can be prospective (following participants as time moves forward) or retrospective (looking back at events that have already happened to participants). This type of study also demonstrates association, but not cause and effect.
Randomized Controlled Trial (RCT): In this type of study, participants are randomly assigned, using a computer or matrix, into the control group or the investigational group. The control group receives the typically used or approved treatment; the investigational group receives the treatment or intervention being studied. This study type is generally considered the most rigorous study design.
Blind and Double-Blind Studies: When a study is “blinded,” participants do not know which group they are randomly assigned to. A double-blind study means that neither the study personnel who interact with participants, nor the participants know who is assigned to which group. Another variation of this design is called a “double-dummy” study, meaning participants receive a mixture of active and inactive product. These study designs are meant to eliminate possible bias by the participant or caregiver toward or against a therapy or a placebo. Some study personnel who don’t interact with the participants keep track of who is in which group.
Meta-analysis: This type of study involves analysis of multiple similar studies (already completed) that is performed according to a protocol that outlines the methodology to be used.
Resources for designing a protocol for a study
Protocols for a product studied as an Investigational New Drug (IND) must follow the format outlined in the IND Content and Format, item 6 “Protocols” section, of the FDA Code of Federal Regulations.
The International Conference on Harmonization of Technical Requirements for Registration of Pharmaceuticals for Human Use (ICH) issues General Considerations for Clinical Trials.
Section 6 of the document on Good Clinical Practice provides guidelines on clinical study protocols.
The ICH General Considerations, together with Section 6 of the document on Good Clinical Practice, form an internationally accepted basis for preparing a study protocol.
For additional guidance from the ICH, please consult Choice of Comparator and Statistical Considerations for Clinical Studies.
NIH Protocol Guidelines outline what is required for a protocol funded by the NIH.
The National Cancer Institute offers a description of the Clinical Trials Process in its Cancer Clinical Trials Basic Workbook.
Planning a Clinical Trial (University College of London)
A 6-month Process for Planning Multinational Clinical Trials (Applied Clinical Trials, 2003)
Planning of Clinical Trials (Journal of Internal Medicine, 2004)
Planning a clinical trial with allowance for cost and patient recruitment rate (Computer Programs in Biomedicine, 1984)
E1A: The Extent of Population Exposure to Assess Clinical Safety
E2A: Clinical Safety Data Management: Definitions and Standards for Expedited Reporting
E2B: Clinical Safety Data Management: Data Elements for Transmission of Individual Case Safety Reports
E2C: Clinical Safety Data Management: Periodic Safety Update Reports for Marketed Drugs
E3: Structure and Content of Clinical Study Reports
E4: Dose-Response Information to Support Drug Registration
E5: Ethnic Factors in the Acceptability of Foreign Clinical Data
E6: Good Clinical Practice: Consolidated Guideline
E7: Studies in Support of Special Populations: Geriatrics
E8: General Considerations for Clinical Trials
E10: Choice of Control Group in Clinical Trials
M1: Standardisation of Medical Terminology for Regulatory Purposes
M3: Non-Clinical Safety Studies for the Conduct of Human Clinical Trials for Pharmaceuticals.
III. TRIAL DESIGN CONSIDERATIONS
3.1 Design Configuration
3.1.1 Parallel Group Design
The most common clinical trial design for confirmatory trials is the parallel group design in which subjects are randomised to one of two or more arms, each arm being allocated a different treatment. These treatments will include the investigational product at one or more doses, and one or more control treatments, such as placebo and/or an active comparator. The assumptions underlying this design are less complex than for most other designs. However, as with other designs, there may be additional features of the trial that complicate the analysis and interpretation (e.g. covariates, repeated measurements over time, interactions between design factors, protocol violations, dropouts (see Glossary) and withdrawals).
3.1.2 Crossover Design
In the crossover design, each subject is randomised to a sequence of two or more treatments, and hence acts as his own control for treatment comparisons. This simple manoeuvre is attractive primarily because it reduces the number of subjects and usually the number of assessments needed to achieve a specific power, sometimes to a marked extent. In the simplest 2×2 crossover design each subject receives each of two treatments in randomised order in two successive treatment periods, often separated by a washout period. The most common extension of this entails comparing n(>2) treatments in n periods, each subject receiving all n treatments. Numerous variations exist, such as designs in which each subject receives a subset of n(>2) treatments, or ones in which treatments are repeated within a subject.
Crossover designs have a number of problems that can invalidate their results. The chief difficulty concerns carryover, that is, the residual influence of treatments in subsequent treatment periods. In an additive model the effect of unequal carryover will be to bias direct treatment comparisons. In the 2×2 design the carryover effect cannot be statistically distinguished from the interaction between treatment and period and the test for either of these effects lacks power because the corresponding contrast is 'between subject'. This problem is less acute in higher order designs, but cannot be entirely dismissed.
When the crossover design is used it is therefore important to avoid carryover. This is best done by selective and careful use of the design on the basis of adequate knowledge of both the disease area and the new medication. The disease under study should be chronic and stable. The relevant effects of the medication should develop fully within the treatment period. The washout periods should be sufficiently long for complete reversibility of drug effect. The fact that these conditions are likely to be met should be established in advance of the trial by means of prior information and data.
There are additional problems that need careful attention in crossover trials. The most notable of these are the complications of analysis and interpretation arising from the loss of subjects. Also, the potential for carryover leads to difficulties in assigning adverse events which occur in later treatment periods to the appropriate
A common, and generally satisfactory, use of the 2×2 crossover design is to demonstrate the bioequivalence of two formulations of the same medication. In this particular application in healthy volunteers, carryover effects on the relevant pharmacokinetic variable are most unlikely to occur if the wash-out time between the two periods is sufficiently long. However it is still important to check this assumption during analysis on the basis of the data obtained, for example by demonstrating that no drug is detectable at the start of each period.
3.5 Sample Size
The number of subjects in a clinical trial should always be large enough to provide a reliable answer to the questions addressed. This number is usually determined by the
16 Statistical Principles for Clinical Trials
primary objective of the trial. If the sample size is determined on some other basis, then this should be made clear and justified. For example, a trial sized on the basis of safety questions or requirements or important secondary objectives may need larger numbers of subjects than a trial sized on the basis of the primary efficacy question (see, for example, ICH E1a).
Using the usual method for determining the appropriate sample size, the following items should be specified: a primary variable, the test statistic, the null hypothesis, the alternative ('working') hypothesis at the chosen dose(s) (embodying consideration of the treatment difference to be detected or rejected at the dose and in the subject population selected), the probability of erroneously rejecting the null hypothesis (the type I error), and the probability of erroneously failing to reject the null hypothesis (the type II error), as well as the approach to dealing with treatment withdrawals and protocol violations. In some instances, the event rate is of primary interest for evaluating power, and assumptions should be made to extrapolate from the required number of events to the eventual sample size for the trial.
The method by which the sample size is calculated should be given in the protocol, together with the estimates of any quantities used in the calculations (such as variances, mean values, response rates, event rates, difference to be detected). The basis of these estimates should also be given. It is important to investigate the sensitivity of the sample size estimate to a variety of deviations from these assumptions and this may be facilitated by providing a range of sample sizes appropriate for a reasonable range of deviations from assumptions. In confirmatory trials, assumptions should normally be based on published data or on the results of earlier trials. The treatment difference to be detected may be based on a judgement concerning the minimal effect which has clinical relevance in the management of patients or on a judgement concerning the anticipated effect of the new treatment, where this is larger. Conventionally the probability of type I error is set at 5% or less or as dictated by any adjustments made necessary for multiplicity considerations; the precise choice may be influenced by the prior plausibility of the hypothesis under test and the desired impact of the results. The probability of type II error is conventionally set at 10% to 20%; it is in the sponsor’s interest to keep this figure as low as feasible especially in the case of trials that are difficult or impossible to repeat. Alternative values to the conventional levels of type I and type II error may be acceptable or even preferable in some cases.
Sample size calculations should refer to the number of subjects required for the primary analysis. If this is the 'full analysis set', estimates of the effect size may need to be reduced compared to the per protocol set (see Glossary). This is to allow for the dilution of the treatment effect arising from the inclusion of data from patients who have withdrawn from treatment or whose compliance is poor. The assumptions about variability may also need to be revised.
The sample size of an equivalence trial or a non-inferiority trial (see Section 3.3.2) should normally be based on the objective of obtaining a confidence interval for the treatment difference that shows that the treatments differ at most by a clinically acceptable difference. When the power of an equivalence trial is assessed at a true difference of zero, then the sample size necessary to achieve this power is underestimated if the true difference is not zero. When the power of a non-inferiority trial is assessed at a zero difference, then the sample size needed to achieve that power will be underestimated if the effect of the investigational product is less than that of the active control. The choice of a 'clinically acceptable’ difference needs justification with respect to its meaning for future patients, and may be smaller than the 'clinically relevant' difference referred to above in the context of superiority trials designed to establish that a difference exists.
Source: http://www.ich.org/LOB/media/MEDIA485.pdf
Prepare a protocol for the study
The study protocol is the blueprint that all researchers will follow.
A study protocol is a document that describes, in detail, the plan for conducting the clinical study. The study protocol explains the purpose and function of the study as well as how to carry it out. Some specific things included in the protocol are the reason for the study, the number of participants, eligibility and exclusion criteria, details of the intervention or therapy the participants will receive (such as frequency and dosages), what data will be gathered, what demographic information about the participants will be gathered, steps for clinical caregivers to carry out, and the study endpoints. A single standard protocol must be used without deviation to ensure that the resulting data will be significant and reliable.
The NICHD expects that each study protocol will include a monitoring plan that defines:
How the study will comply with regulatory requirements
The specific events and activities that will be monitored during the study
The roles and responsibilities for everyone on the team who is involved in monitoring
Who has responsibility for reporting (and who they report to)
A schedule for monitoring
The timing or number of events that would lead to a stop in study accrual, an assessment of eligibility, monitoring, intervention, and under what conditions study accrual would resume
Defining the study design for the protocolProtocols may outline one of several different types of study designs, some which are described below, that investigators may follow:
Case Study: This type of study relies on a literature review or uses a physician’s clinical cases to introduce a clinical abnormality, usually something rare, new, or something that presents in an unusual way.
Case-Control Study: This study compares the number of people who had a potential risk factor in the case group (those with the disease) with those who had the same potential risk factor in the control group (those without the disease). This type of study demonstrates association, but not cause and effect.
Cohort Study: Much like a case-control study, this type of study tracks and compares risk factors shared between the case group and the control group. This type of study can be prospective (following participants as time moves forward) or retrospective (looking back at events that have already happened to participants). This type of study also demonstrates association, but not cause and effect.
Randomized Controlled Trial (RCT): In this type of study, participants are randomly assigned, using a computer or matrix, into the control group or the investigational group. The control group receives the typically used or approved treatment; the investigational group receives the treatment or intervention being studied. This study type is generally considered the most rigorous study design.
Blind and Double-Blind Studies: When a study is “blinded,” participants do not know which group they are randomly assigned to. A double-blind study means that neither the study personnel who interact with participants, nor the participants know who is assigned to which group. Another variation of this design is called a “double-dummy” study, meaning participants receive a mixture of active and inactive product. These study designs are meant to eliminate possible bias by the participant or caregiver toward or against a therapy or a placebo. Some study personnel who don’t interact with the participants keep track of who is in which group.
Meta-analysis: This type of study involves analysis of multiple similar studies (already completed) that is performed according to a protocol that outlines the methodology to be used.
Resources for designing a protocol for a study
Protocols for a product studied as an Investigational New Drug (IND) must follow the format outlined in the IND Content and Format, item 6 “Protocols” section, of the FDA Code of Federal Regulations.
The International Conference on Harmonization of Technical Requirements for Registration of Pharmaceuticals for Human Use (ICH) issues General Considerations for Clinical Trials.
Section 6 of the document on Good Clinical Practice provides guidelines on clinical study protocols.
The ICH General Considerations, together with Section 6 of the document on Good Clinical Practice, form an internationally accepted basis for preparing a study protocol.
For additional guidance from the ICH, please consult Choice of Comparator and Statistical Considerations for Clinical Studies.
NIH Protocol Guidelines outline what is required for a protocol funded by the NIH.
The National Cancer Institute offers a description of the Clinical Trials Process in its Cancer Clinical Trials Basic Workbook.
Planning a Clinical Trial (University College of London)
A 6-month Process for Planning Multinational Clinical Trials (Applied Clinical Trials, 2003)
Planning of Clinical Trials (Journal of Internal Medicine, 2004)
Planning a clinical trial with allowance for cost and patient recruitment rate (Computer Programs in Biomedicine, 1984)
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