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biostatists presentation

  1. ANIL KUMAR M.Sc. PHE Ist (2015-17)
  2. “ A clinical trial is a research activity that involves administration of a test treatment to some experimental unit in order to evaluate the treatment” by Meinert(1986).  Experimental unit: It is referred as the subject from the target population under the study.  Treatment: It can be new drug with increased pharmacokinetic activity, specificity or new therapy or new diagnostic techniques or new surgical methods.  Evaluation: A clinical trial is done on the basis of effectiveness and safety of the test treatment. It ensures quality of life to the target population. It also keeps maintains the pharmacoeconimic status of the pharma companies.
  3. Pre clinical phase Clinical phase Launc h Life cycle of clinical trial :  Rational based drug design  Chemical synthesis and purification  Animal trials  Preliminary drug action, toxicity and pharmacokinetic information Phase I • <100 healthy human volunteers • Pharmacokinetic and pharmacodynamics study • Determine safe drug dosage and metabolic path followed by drug Phase II • <500 patients •Evaluate effectiveness and look for side effects Phase III •<4000 patients •Confirm effectiveness, monitor adverse effect for long term •Check point before drug approval Phase IV  After launch 10,000-30,000 patients  Additional post-marketing test for drug risk, benefit and optimal use
  4. CONTENTS  TECHNIQUES IN CLINICAL DESIGN  CLINICAL DESIGN  TYPES OF CLINICAL TRIAL  ANALYSIS  CASE STUDY
  5. TECHNIQUES USED IN CLINICAL DESIGN:  RANDOMIZATION Controlling bias and variability in clinical trials is very much important to ensure the integrity of clinical trials. Thus, few techniques were introduced in clinical designs to minimize these. o The principle of randomization was implemented in clinical trial in 1948 by British Medial Research Council under Sir Austin Bradford Hill. o During subsequent analysis of the trial data, it provides a sound statistical basis for the quantitative evaluation of the evidence relating to treatment effects. o It also tends to produce treatment groups in which the distribution of prognostic factors, known and unknown, are similar. o Some ethical considerations may also arise as some subjects receive the treatment under study while the remaining receive the standard treatment or the placebo. o Randomization consist of - random selection of subjects from the targeted patient population - random assignment of patients in order to study the medicines
  6. There are different randomization procedures like 1. Simple randomization 2. Restricted randomization 3. Adaptive randomization Simple randomization: • It is a process of drawing a sample from a population whereby the sample participants are not known in advance. • A simple random sample of size k is a sample determined by chance whereby each individual in the population has the same chance of being selected. ex- Consider tossing a coin for each subject that enters a trial, such as Head= Active and tail= Placebo • However, it shows imbalance randomization with lowering statistical power when the sample size is less Restricted randomization: Restricted randomization Blocking Unequal allocation Stratified randomization contd. TECHNIQUES USED IN CLINICAL DESIGN:
  7. o Blocking contd. TECHNIQUES USED IN CLINICAL DESIGN:  Block randomization ensures equal treatment numbers at certain equally spaced points in the sequence of patient assignments.  Each random digit specifies what treatment is given to the next block of patients o Unequal Allocation  Sometimes more participants are kept in one group compared to other.  It leads to minimize cost  Data can give a good statistical inference  It can be used for learning purpose. o Stratified randomization  It refers to the situation in which strata are constructed based on values of prognostic variables and a randomization scheme is performed separately within each stratum.  The objective of stratified randomization is to ensure balance of the treatment groups with respect to the various combinations of the prognostic variables.  Ex- if there are two prognostic variables, age and gender, such that two strata are constructed:Gender, Age Treatment A Treatment B Male, age <18 12 12 Male, age ≥18 13 12 Female, age <18 17 16 Female, age ≥18 35 37
  8. contd. TECHNIQUES USED IN CLINICAL DESIGN: Adaptive randomization: • Stratification is not applicable where too many prognostic factors are present in the data here comes the Adaptive randomization. • It refers to any scheme in which the probability of treatment assignment changes according to assigned treatments of patients already in the trial. • Randomization is adjusted dynamically to achieve the balance. ex- if there is one “A” ball and one “B” ball in an urn and the objective of the trial is equal allocation between treatments A and B. Suppose that an “A” ball is blindly selected, so that the first patient is assigned treatment A. Then the original “A” ball and another “B” are placed in the urn so that the second patient has a 1/3 chance of receiving treatment A and a 2/3 chance of receiving treatment b. At any point in time with nA, “A ” balls and nB “B” balls in the urn, the probability of being treatment A is nA/(nA+nB).
  9. RANDOMIZED CLINICAL TRIAL  In most clinical trials the group of subjects (or sample) who participate is just a small portion of a heterogeneous patient population with the intended disease.  A well-controlled randomized clinical trial is necessary to provide an unbiased and valid assessment of the study medicine.  A well-controlled randomized trial is conducted under well-controlled experimental conditions, usually it varies from a physician’s best clinical practice.  Therefore it is a concern whether the clinical results observed from the well-controlled randomized clinical trial can be applied on the patient population with the disease.  Feasibility and generalization of well-controlled randomized trials are important issue in public health. COMPLICATIONS IN RCT RCT NON COMPILANCE MISSING OUTCOMES
  10. contd. RCT  One potential solution to this problem is a statistical concept called Intention-To Treat(ITT) analysis.  ITT analysis includes every subject who is randomized according to randomized treatment assignment.  It ignores noncompliance, protocol deviations, withdrawal and anything that happens after randomization.  Principle: “Once Randomized, Always Randomized”
  11. contd. TECHNIQUES USED IN CLINICAL DESIGN:  BLINDING/MASKING • Blinding is defined as an experimental condition in which various groups of the individuals involved with the trial are withheld from the knowledge of the treatments assigned to patients and corresponding relevant information. • The purpose of blinding is to eliminate bias in subjective judgment due to knowledge of the treatment. • Since the subjective and judgmental bias is directly or indirectly related to treatment, it can seriously distort statistical inference on the treatment effect. • Therefore, it is important to remove such type of bias. BLINDING OPEN LABEL SINGLE BLINDING DOUBLE BLINDING TRIPLE BLINDIN G
  12. contd. TECHNIQUES USED IN CLINICAL DESIGN: o Open label: • In this technique no blinding is employed. • Ethical consideration is always an important factor, and thus a trial study is conducted in an open label fashion. Ex- phase I dose-escalating studies for determination of the maximum tolerable dose of drugs in treating terminally ill cancer patients are usually open labeled, evaluation of the effectiveness and safety of new surgical procedure is also open labeled o Single blinding: • It is a technique in which either the patient or investigator is unaware of the assigned treatment • As compared with open-label trials, single-blind studies offer a certain degree of control and the assurance of the validity of clinical trials. • However, it retains the chances of bias as the investigator is mostly exposed to the treatment assigned to the subject.
  13. contd. TECHNIQUES USED IN CLINICAL DESIGN: o Double blinding: • A double-blind trial is a trial in which neither the patients nor the investigator is aware of patient’s treatment assignment. • This is done to minimize the bias compared to single blinding. o Triple blinding: • A triple-blind study with respect to blindness can provide the highest degree for the validity of a controlled clinical trial. • Hence it provides the most conclusive unbiased evidence for the evaluation of the effectiveness and safety of the therapeutic intervention under investigation.  Carefully chosen study design with appropriate randomization method  A proper control should be given according to the study  A sufficient statistical power should come  Patient compliance should be cared  Appropriate statistical methods for data analysis
  14. STUDY DESIGN Primary objective Secondary objective Objective  All medical questions should be formulated  Based on same subject number, events, duration of events, check point of each event, end point of each event and evaluation should be done  It is achieved by drawing a comparison between new drug/therapy and current drug/therapy  It can be demonstrated by using parallel or randomized group trial  It decides the conclusion of clinical trial  It shows survival rate, response time, response rate, dosages and toxicity  The selection of patients for trials should be done according to the study. They should actually represent the targeted population  Each trial should be unbiased with minimum or no variability
  15. contd. study design  PARALLEL GROUP DESIGN:  It is a complete randomized design in which subjects are distributed randomly within two or more groups and each group should receive only one type of treatment parallel to the other groups  Each group should contain equal number of subjects  Most commonly used design  Before a patient undergo any clinical trial he must undergo placebo effect(Run in), so that the investigator can have baseline data to compare with the end of clinical trial for analysis of the research study R U N I N
  16. contd. study design  CROSSOVER DESIGN:  It is a modified randomized block design in which each design in which each block receives more than one treatment at different dosing periods.  A block can be a patient or a group of patients.  Patients in each group receive different sequences of treatment  *Each block under the investigation should receive all treatment.  It is not necessary that the number of treatments in each sequence be greater than or equal to the number of treatments to be compared.  It allows a within patient comparison between treatments, since each patient serves as his or her own treatment.  It removes the interpatient variability from the comparison between treatments  With a proper randomization of patients to the treatment sequences, it provides the best unbiased estimates for differences between treatments Applicable  Objective measures and interpretable data for both efficacy and safety are obtained  Chronic disease are under study  Relatively short treatment periods are considered  Baseline and washout periods are feasible Patient s Block A Rando mization Block B PERIOD W A S H O U T BLOCK A BLOCK B TEST CONTROL CONTR OL TEST (ex- two period cross over design)
  17. contd. study design  TITRATION DESIGN:For phase I safety and tolerance studies, Rodda et al. (1998) classify traditional designs as follows: 1. Rising single-dose design 2. Rising single-dose crossover design 3. Alternative- panel rising single-dose design 4. Alternative-panel rising single-dose crossover design 5. Parallel-panel rising multiple-dose design 6. Alternative-panel rising multiple-dose design  CLUSTER RANDOMIZED DESIGNS: The randomization unit is same as the analysis unit as the experimental unit for statistical unit(Fisher;1947).  It needs minimum sample size and generates highest power, hence it becomes most efficient  Units such as patients of a disease, athletes, hospitals , communities etc. act as cluster  Randomization is done at cluster level instead of subject level  However, clinical trial needs inference at subject level, hence, the standard sample size calculation and data analysis considering subject as analysis unit is not appropriate  It is important for the analysis of intracluster, intercluster and ICC variations.
  18. contd. study design  FACTORIAL DESIGN:• Two or more treatments are evaluated simultaneously through the use of varying combinations of the treatments. • The simplest example is the 2×2 factorial design in which subjects are randomly allocated to one of the four possible combinations of two treatments, A and B say. These are: A alone; B alone; both A and B; neither A nor B. • In many cases this design is used for the specific purpose of examining the interaction of A and B. • The statistical test of interaction may lack power to detect an interaction if the sample size was calculated based on the test for main effects. • This consideration is important when this design is used for examining the joint effects of A and B, in particular, if the treatments are likely to be used together
  19. MULTICENTRE TRIAL SUPERIORITY TRIAL NONINFERIORITY TRIAL DOSE RESPONSE TRIAL COMBINATION TRIAL BRIDGING TRIAL VACCINE CLINICAL TRIAL CLINICAL TRIAL
  20. ANALYSIS When designing a clinical trial the principle features of the eventual statistical analysis of the data should be described in the statistical section of the protocol. This section should include all the principle features of the proposed confirmatory analysis of the primary variable(s) and the way in which analysis problems will be handled. The important considerations for Analysis of the study data are as follows: Analysis sets: If all subjects randomized into a clinical trial satisfied all entry criteria, followed all trial procedures perfectly with no losses to follow-up, and provided complete data records, then the set of subjects to be included in the analysis would be self-evident. The design and conduct of a trial should aim to approach this ideal as closely as possible, but, in practice, it is doubtful if it can ever be fully achieved. Hence, the statistical section of the protocol should address anticipated problems prospectively in terms of how these affect the subjects and data to be analyzed. Missing Values and Outliers : Missing values represent a potential source of bias in a clinical trial. Hence, every effort should be undertaken to fulfill all the requirements of the protocol concerning the collection and management of data. In reality, however, there will almost always be some missing data. A trial may be regarded as valid, provided the methods of dealing with missing values are sensible, and particularly if those methods are pre-defined in the protocol. Data Transformation: The decision to transform key variables prior to which analysis is made should be done based on similar design of the trial from data of earlier clinical trials.
  21. contd. ANALYSISEstimation, Confidence Intervals and Hypothesis Testing: The statistical section of the protocol should specify the hypothesis that are to be tested and/or the treatment effects which are to be estimated in order to satisfy the primary objectives of the trial. The statistical methods to be used to accomplish these tasks should be described for the primary (and preferably the secondary) variables, and the underlying statistical model should be made clear. Estimates of treatment effects should be accompanied by confidence intervals, whenever possible, and the way in which these will be calculated should be identified. Adjustment of Significance and Confidence Levels: When multiplicity is present, the usual approach to the analysis of clinical trial data may necessitate an adjustment to the type I error. Multiplicity may arise, for example, from multiple primary variables, multiple comparisons of treatments, repeated evaluation over time and/or interim analysis. Subgroups, Interactions and Covariates: The primary variable(s) is often systematically related to other influences apart from treatment. For example, there may be relationships between two covariates such as age and sex, or there may be differences between specific subgroups of subjects such as those treated at the different centers of a multicenter trial. The treatment effect itself may also vary with subgroup or covariate - for example, the effect may decrease with age or may be larger in a particular diagnostic category of subjects. Pre-trial deliberations should identify those covariates and factors expected to have an important influence on the primary variable(s), and should consider how to account for these in the analysis in order to improve precision and to compensate for any lack of balance between treatment groups.
  22. contd. ANALYSIS contd. Analysis The different methods for calculating data analysis are as follows:  Graphical Data Analysis: The technical basis of graphical data analysis is simultaneous display of both magnitudes and frequencies of individual data values in order to characterize data distribution.  Data Analysis with Summary Measures: Bar charts are good for showing magnitudes, and line-scatter plots are good for showing trends. Commonly used summary measures are the number of observations, mean, median, standard deviation, average deviation, and standard error.  Analysis of Variance (ANOVA): The analysis of variance summarizes data with the mean, and the quality of summarization is measured with the standard error. The major use is simultaneous evaluation of multiple interrelated factors. The basic operation is grouping and curve fitting.  Nonparametric Analysis: The word, nonparametric, really means no involvement of mathematical distributions. The most commonly performed “nonparametric” analyses are essentially the traditional analysis of variance on transformed data.  Statistical Sampling and Estimation: Sometimes the size of the entire population to be studied is so large that measuring a particular parameter (say population mean X) becomes to time consuming and costly. To deal with this problem researchers can draw a sample of the population at random and then calculate the mean X of that population. This parameter becomes an estimate of the population parameter that needs to be measured.  Statistical Tests of Significance: Tests of significance try to find out whether there is a real relation between two events or the relation appears by chance only. One of
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  24. DISCUSSION

Hinweis der Redaktion

  1. Clinical: from Greek word “klinike” means practice of caring to sick,Tiral: from Anglo-French word “trier” means to try
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