Design of clinical trials by bpk

Design of clinical trials: 

Design of clinical trials Bada Pragati Kumar

Clinical trail: 

Clinical trail A clinical trial evaluates the effect of a new drug (or device or procedure) on human volunteers. These trials can be used to evaluate the safety of a new drug in healthy human volunteers, or to assess treatment benefits in patients with a specific disease. Clinical trials can compare a new drug against existing drugs or against dummy medications (placebo) or they may not have a comparison arm.

Randomized clinical trial: 

Randomized clinical trial In a randomized clinical trial, patients and trial personnel are deliberately kept unaware of which patient is on the new drug. A study in which the participants are assigned by chance to separate groups that compare different treatments; neither the researchers nor the participants can choose which group. Using chance to assign people to groups means that the groups will be similar and that the treatments they receive can be compared objectively.

Randomized clinical trial cont..: 

Randomized clinical trial cont.. At the time of the trial, it is not known which treatment is best. It is the patient's choice to be in a randomized trial. This minimizes bias in the later evaluation so that the initial blind random allocation of patients to one or other treatment group is preserved throughout the trial.

Phases : 

Phases In Phase I, manufacturers usually test the effects of a new drug in healthy volunteers or patients unresponsive to usual therapies. They look at how the drug is handled in the human body (pharmacokinetics /pharmacodynamics) particularly with respect to the immediate short-term safety of higher doses.

Phases: 

Phases Phase II examine dose–response curves in patients and what benefits might be seen in a small group of patients with a particular disease. In Phase III , a new drug is tested in a controlled fashion in a large patient population against a placebo or standard therapy. This is a key phase, where a drug will either make or break its reputation with respect to safety and efficacy before marketing begins.

Phases : 

Phases A study in Phase IV is often called a post marketing study as the drug has already been granted regulatory approval/license. These studies are crucial for gathering additional safety information from a larger group of patients in order to understand the long-term safety of the drug and appreciate drug interactions.

Trial protocol: 

Trial protocol Component Details Protocol information page Study title, trial ID number, version, list of appendices, definition of abbreviations and terms Study summary or synopsis A summary of one or two pages in a table format Trial flow chart Describing flow of the trial Introduction Trial background and rationale Study objectives Primary and secondary objectives

Trial protocol: 

Trial protocol Component Details Investigational plan Overall study design and plan Trial design, study population (inclusion criteria, exclusion criteria), randomization, blinding, premature discontinuation criteria, record of study participants, study medication (description and labeling, storage, administration, dosing strategies, accountability, over-dosage, occupational safety). Study conduct Study schedule, study flow chart, written informed consent, participant numbering, screening entry, treatment phase (visit numbers), early withdrawal, concomitant medication, blood and urine sampling, processing of samples.

Trial protocol: 

Trial protocol Component Details Investigational plan Safety and efficacy evaluations Safety assessments, efficacy evaluations, unscheduled visits. Recording safety information Adverse events (glossary, adverse drug reactions, serious adverse events, reporting of overdose, pregnancy, breaking of study blinding by the investigator). Participant completion and discontinuation Premature withdrawal of participants from the study, procedure for handling withdrawals.

Trial protocol: 

Trial protocol Component Details Statistical issues Primary and secondary endpoints, sample size calculations, intention-to-treat population, per-protocol population, efficacy population, safety population, handling of dropouts and missing data, efficacy analyses (primary, secondary, tertiary), safety analysis, pharmacokinetic and pharmacodynamic analysis, adjusted analysis, subgroup analysis, statistical methods for various analyses Ethics Participant information sheet and informed consent, ethics approvals

Trial protocol: 

Trial protocol Regulatory requirements administrative issues, and monitoring Regulatory requirements, protocol amendments, investigator’s statement, trial monitoring (safety monitoring, quality control, auditing and inspecting), case report form, final reports, study documentation and publication of study results, financial agreement, termination of the study, study discontinuation by the sponsor and by the clinical investigator, insurance policy References Listing of references to justify the study rationale List of appendices Informed consent form, notable values for laboratories and/or vital signs, investigator’s statement, etc.

Randomization: 

Randomization Randomization is the unpredictable allocation of a patient to a particular treatment strategy in a clinical trial. A fundamental assumption that forms the basis of the RCT is that patients in different groups are similar for characteristics such as age, gender, social class, time of year of presentation, country of presentation, and type of hospital.

Minimizing bias: 

Minimizing bias A further requirement of randomization is that it must not be predictable by the person assigning patients to the treatment strategies, otherwise there is a chance that the groups will contain bias. To prevent this, certain methods of ‘blinding’ or ‘masking’ are used so that patients and staff are not aware whether treatment A or B is the new treatment, or even which group patients are in (active or placebo/standard treatment), until the end of the trial.

How should the randomization code be determined?: 

How should the randomization code be determined? A randomization code is a list of which treatment a subject should receive. It is usually determined by a statistician using computer-generated random numbers or a random-number table.

Which are the common randomization methods?: 

Which are the common randomization methods? simple randomization block randomization stratified randomization minimization or adaptive randomization

Simple randomization: 

Simple randomization The most common form of randomization, referred to as simple or complete randomization, is a procedure that makes each new treatment allocation without regard to those already made. The principle of this method for a trial with two treatments can be demonstrated by deciding treatment assignment by tossing an unbiased coin, e.g.: heads for treatment A and tails for treatment B.

Example of simple randomization.: 

Example of simple randomization. Subject Treatment Treatment 1 A 2 B 3 A 4 A 5 B 6 B 7 B 8 B 9 A 10 A 11 B 12 B Consider an example trial with 12 patients. While there is an equal chance of being allocated treatment A or treatment B, the number of subjects randomly assigned to each treatment ends up being 5 and 7, respectively. This imbalance in the initial allocation will result in significant difficulties in the statistics and possibly a lower power for detecting differences between the treatments. Therefore, in cases where there are few patients, there is a need for other methods of randomization.

Block randomization: 

Block randomization A block randomization method can be used to periodically enforce a balance in the number of patients assigned to each treatment. The size of each block of allocations must be an integer multiple of the number of treatment groups, so with two treatment strategies the block size can be either 2, 4, 6, and so on.

Block randomization: 

Block randomization A block randomization can be implemented in three steps: Step 1: Choose the block size and the number of blocks needed to cover the number of patients in the study. Step 2: List all possible permutations of treatments in a block. Step 3: Generate a randomization code for the order in which to select each block.

Example of block randomization using a block size of 4: 

Example of block randomization using a block size of 4 Block Permutation Subject Treatment 1 6 1,2,3,4 BBAA 2 4 5,6,7,8 BAAB 3 3 9,10,11,12 ABBA 4 1 13,14,15,16 AABB 5 2 17,18,19,20 ABAB 6 5 21,22,23,24 BABA

Stratified randomization: 

Stratified randomization Not only are the numbers with treatments A and B balanced periodically, but a balance is also constantly maintained for a set of predetermined important factors that may impact on the prognosis of the patient, such as age, gender, diabetes, severity of illness, or geography.

Stratified randomization: 

Stratified randomization Step 1: Choose the prognostic factors that could impact on the primary endpoint. Step 2: Determine the number of strata for each factor. Step 3: Generate randomization codes. Stratum number can be calculated as follows: Atrophy -2 types; FEV – 3 types; Age – 2 types 2x3x2=12 strata are required.

Example Stratified randomization: 

Example Stratified randomization Stratum Atrophy FEV1 (%) Age (years) Randomization 1 Positive 40–60 <17 ABAB, BABA, AABB... 2 Positive 40–60 ≥17 3 Positive 61–80 <17 4 Positive 61–80 ≥17 5 Positive 81–100 <17 6 Positive 81–100 ≥17 7 Negative 40–60 <17 8 Negative 40–60 ≥17 9 Negative 61–80 <17 10 Negative 61–80 ≥17 11 Negative 81–100 <17 12 Negative 81–100 ≥17 FEV1 = forced expiratory volume within 1 second.

Minimization: 

Minimization Minimization – also called an adaptive randomization procedure – takes the approach of assigning subjects to treatments in order to minimize the differences between the treatment groups on selected prognostic factors. This method starts with a simple randomization method (the first of our examples) for the first several subjects, and then adjusts the chance of allocating a new patient to a particular treatment based on existing imbalances in those prognostic factors.

Crossover Trials: 

Crossover Trials Crossover trials are designed so that each recruited patient receives both active and control treatments in either order for a specified duration, with a ‘washout’ period between treatments when no treatment is administered. In such trials, patients act as their own controls, therefore fewer patients are required to evaluate the effects of different therapies than in a trial with a parallel design.

What is a crossover trial?: 

What is a crossover trial? In a parallel study design, each subject is randomized to one and only one treatment. On the other hand, in a crossover trial, each subject receives more than one treatment in a specified sequence. In other words, a crossover trial is a study that compares two or more treatments or interventions in which subjects, on completion of a course of one treatment, are switched to another.

Crossover Trials: 

Crossover Trials A standard two-sequence, two-period crossover design

Factorial Design: 

Factorial Design In a clinical trial, a situation may arise where the nature of the study calls for the evaluation of more than one treatment for safety and/or efficacy compared to a control. By using a factorial design, it is possible to evaluate individual treatment effects for more than one treatment within the same trial.

What is a factorial study?: 

What is a factorial study? In a factorial design clinical trial with a 2 × 2 format, individuals are randomly assigned to two separate interventions (e.g., interventions A and B) and these interventions are each compared with their corresponding control(s).

Factorial Design: 

Factorial Design In a balanced 2 × 2 factorial design, this would mean that from a total of N individuals, N / 2 are randomly allocated to receive intervention A and N / 2 are randomly allocated not to receive intervention A. Correspondingly, N / 2 individuals are allocated to receive intervention B or to not receive intervention B.

Factorial Design: 

Factorial Design Overall: • N / 4 individuals are allocated to no treatment (control group). • N / 4 individuals are allocated to intervention A only. • N / 4 individuals are allocated to intervention B only. • N / 4 individuals are allocated to the combination of A + B simultaneously.

Advantages of Factorial Design: 

Advantages of Factorial Design The usual method is to compare individuals who are randomized to intervention A (i.e., those who receive A and those who receive A + B) with those who are not randomized to A (i.e., those receiving either intervention B or no treatment at all). Similarly, individuals who are randomized to intervention B are compared with those who are not randomized to B. In a factorial design, it is usual to assume A and B to have independent effects from each other, i.e., that there is no interaction between treatment A and B.

Example for Factorial Design: 

Example for Factorial Design In a 2 × 2 factorial trial set up to investigate the effects of multivitamins excluding vitamin A (factor 1) and including vitamin A (factor 2) on birth outcomes of HIV-1 infected women, each woman was allocated once to each treatment. Therefore, every mother was randomized twice overall, resulting in the following four treatment groups

Example for Factorial Design: 

Example for Factorial Design • Women who received vitamin A only. • Women who received multivitamins but no vitamin A. • Women who received both multivitamins and vitamin A. • Women who received neither.

Example for Factorial Design: 

Example for Factorial Design By using a factorial trial, it is possible to perform two comparisons simultaneously at the cost of one experiment. In this example, it is possible to compare birth outcomes for mothers who received vitamin A with those who did not by comparing column margins. Similarly, by comparing row totals, it is possible to evaluate the effect of using multivitamins during pregnancy

Example for Factorial Design: 

Example for Factorial Design

PowerPoint Presentation: 

The end