there is a dose range and schedule at which the drug can be shown to be simultaneously safe rate of occurrence (events per time units of observation),.
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European Medicines Agency 7 Westferry Circus, Canary Wharf, London, E14 4HB, UK Tel. (44-20) 74 18 85 75 Fax (44-20) 75 23 70 40 E-mail: EMEA 2006 Reproduction and/or distribution of this document is authorised for non commercial pur poses only provided the EMEA is acknowledged September 1998 CPMP/ICH/363/96 ICH Topic E 9 Statistical Principles for Clinical Trials Step 5 NOTE FOR GUIDANCE ON STATISTICAL PRINCIPLES FOR CLINICAL TRIALS (CPMP/ICH/363/96) TRANSMISSION TO CPMP February 1997 RELEASE FOR CONSULTATION February 1997 COMMENTS REQUESTED BEFORE June 1997 FINAL APPROVAL BY CPMP March 1998 DATE FOR COMING INTO OPERATION September 1998

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© EMEA 2006 2 STATISTICAL PRINCIPLES FOR CLINICAL TRIALS ICH Harmonised Tripartite Guideline Table of Contents I INTRODUCTION 4 1.1 Background and Purpose4 1.2 Scope and Direction.. 5 II CONSIDERATIONS FOR OVERALL CLINICAL DEVELOPMENT ..6 2.1 Trial Context.. ..6 2.1.1 Development Plan.6 2.1.2 Confirmatory Trial6 2.1.3 Exploratory Trial7 2.2 Scope of Trials .7 2.2.1 Population.7 2.2.2 Primary and Secondary Variables.7 2.2.3 Composite Variables8 2.2.4 Global Assessment Variables..9 2.2.5 Multiple Primary Variables..9 2.2.6 Surrogate Variables10 2.2.7 Categorised Variables..10 2.3 Design Techniques to Avoid Bias10 2.3.1 Blinding1 1 2.3.2 Randomisation.12 III TRIAL DESIGN CONSIDERATIONS 13 3.1 Design Configuration.13 3.1.1 Parallel Group Design..13 3.1.2 Crossover Design13 3.1.3 Factorial Designs.14 3.2 Multicentre Trials .15 3.3 Type of Comparison17 3.3.1 Trials to Show Superiority.17 3.3.2 Trials to Show Equivalence or Non-inferiority17 3.3.3 Trials to Show Dose-response Relationship.18 3.4 Group Sequential Designs19 3.5 Sample Size. 19 3.6 Data Capture and Processing.20 IV TRIAL CONDUCT CONSIDERATIONS .20 4.1 Trial Monitoring and Interim Analysis.20

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© EMEA 2006 3 4.2 Changes in Inclusion and Exclusion Criteria.21 4.3 Accrual Rates.. ..21 4.4 Sample Size Adjustment..21 4.5 Interim Analysis and Early Stopping.22 V DATA ANALYSIS CONSIDERATIONS ..23 5.1 Prespecification of the Analysis23 5.2 Analysis Sets.. 24 5.2.1 Full Analysis Set.24 5.2.2 Per Protocol Set26 5.2.3 Roles of the Different Analysis Sets.26 5.3 Missing Values and Outliers..26 5.4 Data Transformation..27 5.5 Estimation, Confidence Intervals and Hypothesis Testing.27 5.6 Adjustment of Significance and Confidence Levels..28 5.7 Subgroups, Interactions and Covariates28 5.8 Integrity of Data and Computer Software Validity.29 VI EVALUATION OF SAFETY AND TOLERABILITY .29 6.1 Scope of Evaluation29 6.2 Choice of Variables and Data Collection.29 6.3 Set of Subjects to be Evaluated and Presentation of Data30 6.4 Statistical Evaluation.. 31 6.5 Integrated Summary31 VII REPORTING 31 7.1 Evaluation and Reporting.32 7.2 Summarising the Clinical Database33 7.2.1 Efficacy Data.33 7.2.2 Safety Data.34 GLOSSARY . .35

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© EMEA 2006 4 I INTRODUCTION 1.1 Background and Purpose The efficacy and safety of medicinal products s hould be demonstrated by clinical trials which follow the guidance in ‘Good Clinical Practice: Consolidated Guideline’ (ICH E6) adopted by the ICH, 1 May 1996. The role of statistics in clinical trial design and analysis is acknowledged as essential in that ICH guideline. Th e proliferation of statis tical research in the area of clinical trials coupled with the critical role of clinical research in the drug approval process and health care in general necessitate a succinct document on statistical issues related to clinical trials. This guidance is written primarily to attempt to harmonise the principles of statistical methodology applied to clinical tr ials for marketing applications submitted in Europe, Japan and the United States. As a starting point, this guideline utilised the CPMP (Committee for Proprietary Medicinal Products) Note for Guidance entitled ‘Biostatistical Methodology in Clinical Trials in Applications for Marketing Authorisations fo r Medicinal Products’ (December, 1994). It was also influenced by ‘Guidelines on the Statistical Analysis of Clinical Studies’ (March, 1992) from the Japanese Ministry of Health and Welfare and the U.S. Food and Drug Administration document entitled ‘Guideline fo r the Format and Content of the Clinical and Statistical Sections of a New Drug Application’ (J uly, 1988). Some topics related to statistical principles and methodology are also embedded within other ICH guidelines, particularly those listed below. The specific guidance that contains related text will be identified in various sections of this document. 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 Managemen t: 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 Gr oup in Clinical Trials M1: Standardisation of Medical Te rminology for Regulatory Purposes M3: Non-Clinical Safety Studies for the Conduct of Human Clinical Trials for Pharmaceuticals. This guidance is intended to give direction to sponsors in the design, conduct, analysis, and evaluation of clinical trials of an investigationa l product in the context of its overall clinical development. The document will also assist scientific experts charged with preparing application summaries or assessing evidence of efficacy and safety, principally from clinical trials in later phases of development.

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© EMEA 2006 5 1.2 Scope and Direction The focus of this guidance is on statistical princi ples. It does not address the use of specific statistical procedures or methods. Specific proc edural steps to ensure that principles are implemented properly are the responsibility of th e sponsor. Integration of data across clinical trials is discussed, but is not a primary focu s of this guidance. Selected principles and procedures related to data management or clin ical trial monitoring activities are covered in other ICH guidelines and are not addressed here. This guidance should be of interest to individual s from a broad range of scientific disciplines. However, it is assumed that the actual responsib ility for all statistical work associated with clinical trials will lie with an appropriately qualified and experienced statistician, as indicated in ICH E6. The role and responsibility of the tria l statistician (see Glossary), in collaboration with other clinical trial professionals, is to ensure that statistical principles are applied appropriately in clinical trials supporting drug de velopment. Thus, the trial statistician should have a combination of education/training and experience sufficient to implement the principles articulated in this guidance. For each clinical trial contributing to a market ing application, all important details of its design and conduct and the principal features of its proposed statistical analysis should be clearly specified in a protocol written before the trial begins. The extent to which the procedures in the protocol are followed and the primary analysis is planned a priori will contribute to the degree of confidence in the final results and conclusions of the trial. The protocol and subsequent amendments should be approved by the responsible personnel, including the trial statistician. The trial statis tician should ensure that the protocol and any amendments cover all relevant statistical issues clearly and accurately, using technical terminology as appropriate. The principles outlined in this guidance are primar ily relevant to clinical trials conducted in the later phases of development, many of which are confirmatory trials of efficacy. In addition to efficacy, confirmatory trials may have as th eir primary variable a safety variable (e.g. an adverse event, a clinical laboratory variab le or an electrocardiographic measure), a pharmacodynamic or a pharmacokinetic variable (as in a confirmatory bioequivalence trial). Furthermore, some confirmatory findings may be derived from data integrated across trials, and selected principles in this guidance are ap plicable in this situation. Finally, although the early phases of drug development consist mainly of clinical trials that are exploratory in nature, statistical principles are also relevant to these clinical trials. Hence, the substance of this document should be applied as far as possi ble to all phases of clinical development. Many of the principles delineated in this guida nce deal with minimising bias (see Glossary) and maximising precision. As used in this guid ance, the term ‘bias’ describes the systematic tendency of any factors associated with the desi gn, conduct, analysis and interpretation of the results of clinical trials to make the estimate of a treatment effect (see Glossary) deviate from its true value. It is important to identify potenti al sources of bias as completely as possible so that attempts to limit such bias may be made. The presence of bias may seriously compromise the ability to draw valid conclusions from clinical trials. Some sources of bias arise from the design of the trial, for example an assignment of treatments such that subjects at lower risk are systematically assigned to one treatment. Other sources of bias arise during the conduct and analys is of a clinical trial. For example, protocol violations and exclusion of subjects from analysis based upon knowledge of subject outcomes are possible sources of bias that may affect the accurate assessment of the treatment effect. Because bias can occur in subtle or unknown ways and its effect is not measurable directly, it is important to evaluate the robustness of the results and primary conclusions of the trial. Robustness is a concept that refers to the sensitivity of the overall conclusions to various limitations of the data, assumptions, and anal ytic approaches to data analysis. Robustness

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© EMEA 2006 6 implies that the treatment effect and primary conclusions of the trial are not substantially affected when analyses are carried out based on alternative assumptions or analytic approaches. The interpretation of statistical meas ures of uncertainty of the treatment effect and treatment comparisons should involve consider ation of the potential contribution of bias to the p-value, confidence interval, or inference. Because the predominant approaches to the design and analysis of clinical trials have been based on frequentist statistical me thods, the guidance largely refers to the use of frequentist methods (see Glossary) when discussing hypothesi s testing and/or confidence intervals. This should not be taken to imply that other approaches are not appropriate: the use of Bayesian (see Glossary) and other approaches may be considered when the reasons for their use are clear and when the resulting conclusions are sufficiently robust. II CONSIDERATIONS FOR OVERALL CLINICAL DEVELOPMENT 2.1 Trial Context 2.1.1 Development Plan The broad aim of the process of clinical development of a new drug is to find out whether there is a dose range and schedule at which the drug can be shown to be simultaneously safe and effective, to the extent that the risk-benefit relationship is acceptable. The particular subjects who may benefit from the drug, and the specific indications for its use, also need to be defined. Satisfying these broad aims usually requires an ordered programme of clinical trials, each with its own specific objectives (see ICH E8). Th is should be specified in a clinical plan, or a series of plans, with appropriate decision po ints and flexibility to allow modification as knowledge accumulates. A marketing application sh ould clearly describe the main content of such plans, and the contribution made by each trial. Interpretation and assessment of the evidence from the total programme of trials involves synthesis of the evidence from the individual trials (see Section 7.2). This is facilitated by ensuring that common standards are adopted for a number of features of the trials su ch as dictionaries of medical terms, definition and timing of the main measurements, handling of protocol deviations and so on. A statistical summary, overview or meta-analysis (see Glos sary) may be informative when medical questions are addressed in more than one trial. Where possible this should be envisaged in the plan so that the relevant trials are clearly identified and any necessary common features of their designs are specified in advance. Other major statistical issues (if any) that are expected to affect a number of trials in a common plan should be addressed in that plan. 2.1.2 Confirmatory Trial A confirmatory trial is an adequately controlled trial in which the hypotheses are stated in advance and evaluated. As a rule, confirmatory trials are necessary to provide firm evidence of efficacy or safety. In such trials the key hypothesis of interest follows directly from the trial™s primary objective, is always pre-defined, and is the hypothesis that is subsequently tested when the trial is complete. In a confirmatory trial it is equally important to estimate with due precision the size of the effects attributable to the treatment of interest and to relate these effects to their clinical significance. Confirmatory trials are intended to provide firm evidence in support of claims and hence adherence to protocols and standard operati ng procedures is particularly important; unavoidable changes should be explained and documented, and their effect examined. A justification of the design of each such trial, and of other important statistical aspects such as

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© EMEA 2006 8 provide strong scientific evidence regarding e fficacy. Safety/tolerability may sometimes be the primary variable, and will always be an impo rtant consideration. Measurements relating to quality of life and health economics are furthe r potential primary variables. The selection of the primary variable should reflect the accepted norms and standards in the relevant field of research. The use of a reliable and validated variable with which experience has been gained either in earlier studies or in published literature is recommended. There should be sufficient evidence that the primary variable can provide a valid and reliable measure of some clinically relevant and important treatment benefit in the patient population described by the inclusion and exclusion criteria. The primary variable shou ld generally be the one used when estimating the sample size (see section 3.5). In many cases, the approach to assessing subject outcome may not be straightforward and should be carefully defined. For example, it is inadequate to specify mortality as a primary variable without further clarification; mortal ity may be assessed by comparing proportions alive at fixed points in time, or by comparing overall distributions of survival times over a specified interval. Another common example is a recurring event; the measure of treatment effect may again be a simple dichotomous va riable (any occurrence during a specified interval), time to first occurrence, rate of occurrence (events per time units of observation), etc. The assessment of functional status over time in studying treatment for chronic disease presents other challenges in selection of the primary variable. There are many possible approaches, such as comparisons of the asse ssments done at the beginning and end of the interval of observation, comparisons of slopes calculated from all assessments throughout the interval, comparisons of the proportions of subjects exceeding or declining beyond a specified threshold, or comparisons based on methods for repeated measures data. To avoid multiplicity concerns arising from post hoc definitions, it is cr itical to specify in the protocol the precise definition of the primary variable as it will be used in the statistical analysis. In addition, the clinical relevance of the specific primary variable selected and the validity of the associated measurement procedures will generally need to be addressed and justified in the protocol. The primary variable should be specified in the protocol, along with the rationale for its selection. Redefinition of the primary variab le after unblinding will almost always be unacceptable, since the biases this introduces are difficult to assess. When the clinical effect defined by the primary objective is to be measured in more than one way, the protocol should identify one of the measurements as the primary variable on the basis of clinical relevance, importance, objectivity, and/or other relevant characteristics, whenever such selection is feasible. Secondary variables are either supportive measur ements related to the primary objective or measurements of effects related to the second ary objectives. Their pre-definition in the protocol is also important, as well as an explan ation of their relative importance and roles in interpretation of trial results. The number of secondary variables should be limited and should be related to the limited number of questions to be answered in the trial. 2.2.3 Composite Variables If a single primary variable cannot be selected from multiple measurements associated with the primary objective, another useful strategy is to integrate or combine the multiple measurements into a single or ‘composite’ vari able, using a pre-defined algorithm. Indeed, the primary variable sometimes arises as a combinat ion of multiple clinical measurements (e.g. the rating scales used in arthritis, psychiatric disorders and elsewhere). This approach addresses the multiplicity problem without requiring adjustment to the type I error. The method of combining the multiple measurements should be specified in the protocol, and an interpretation of the resulting scale should be provided in terms of the size of a clinically relevant benefit. When a composite variable is used as a primary variable, the components of this variable may sometimes be analysed separately, where clinically meaningful and

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© EMEA 2006 9 validated. When a rating scale is used as a pr imary variable, it is especially important to address such factors as content validity (see Glos sary), inter- and intra-rater reliability (see Glossary) and responsiveness for detecting changes in the severity of disease. 2.2.4 Global Assessment Variables In some cases, ‘global assessment’ variables (see Glossary) are developed to measure the overall safety, overall efficacy, and/or overall usef ulness of a treatment. This type of variable integrates objective variables and the investigat or™s overall impression about the state or change in the state of the subject, and is usua lly a scale of ordered ca tegorical ratings. Global assessments of overall efficacy are well established in some therapeutic areas, such as neurology and psychiatry. Global assessment variables generally have a subjective component. When a global assessment variable is used as a primary or secondary variable, fuller details of the scale should be included in the protocol with respect to: 1. the relevance of the scale to the primary objective of the trial; 2. the basis for the validity and reliability of the scale; 3. how to utilise the data collected on an individual subject to assign him/her to a unique category of the scale; 4. how to assign subjects with missing data to a unique category of the scale, or otherwise evaluate them. If objective variables are considered by the i nvestigator when making a global assessment, then those objective variables should be considered as additional primary, or at least important secondary, variables. Global assessment of usefulness integrates co mponents of both benefit and risk and reflects the decision making process of the treating phy sician, who must weigh benefit and risk in making product use decisions. A problem with gl obal usefulness variables is that their use could in some cases lead to the result of tw o products being declared equivalent despite having very different profiles of beneficial and adverse effects. For example, judging the global usefulness of a treatment as equivalent or superior to an alternative may mask the fact that it has little or no efficacy but fewer adverse effects. Therefore it is not advisable to use a global usefulness variable as a primary variable. If global usefulness is specified as primary, it is important to consider specific efficacy and sa fety outcomes separately as additional primary variables. 2.2.5 Multiple Primary Variables It may sometimes be desirable to use more than one primary variable, each of which (or a subset of which) could be sufficient to cover the range of effects of the therapies. The planned manner of interpretation of this type of eviden ce should be carefully spelled out. It should be clear whether an impact on any of the variable s, some minimum number of them, or all of them, would be considered necessary to achieve the trial objectives. The primary hypothesis or hypotheses and parameters of interest (e .g. mean, percentage, distribution) should be clearly stated with respect to the primary variab les identified, and the approach to statistical inference described. The effect on the type I error should be explained because of the potential for multiplicity problems (see Section 5.6); the method of controlling type I error should be given in the protocol. The extent of intercorrelation among the proposed primary variables may be considered in evaluating the impa ct on type I error. If the purpose of the trial is to demonstrate effects on all of the designated primary variables, then there is no need for

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© EMEA 2006 10 adjustment of the type I error, but the impact on type II error and sample size should be carefully considered. 2.2.6 Surrogate Variables When direct assessment of the clinical benef it to the subject through observing actual clinical efficacy is not practical, indirect criteria (surrogate variables – see Glossary) may be considered. Commonly accepted surrogate variables are used in a number of indications where they are believed to be reliable predicto rs of clinical benefit. There are two principal concerns with the introduction of any proposed surrogate variable. First, it may not be a true predictor of the clinical outcome of interest. For example it may measure treatment activity associated with one specific pharmacological mechanism, but may not provide full information on the range of actions and ultimate effects of the treatment, whether positive or negative. There have been many instances where treatments showing a highly positive effect on a proposed surrogate have ultimately been shown to be detrimental to the subjects’ clinical outcome; conversely, there are cases of treatments conferring clinical benefit without measurable impact on proposed surrogates. Secondly, proposed surrogate variables may not yield a quantitative measure of clinical benefit that can be weighed directly against adverse effects. Statistical criteria for validating su rrogate variables have been proposed but the experience with their use is relatively limited. In practice, the strength of the evidence for surrogacy depends upon (i) the biological plau sibility of the relationship, (ii) the demonstration in epidemiological studies of the prognostic value of the surrogate for the clinical outcome and (iii) evidence from clinical trials that treatment effects on the surrogate correspond to effects on the clinical outcome. Relationships between clinical and surrogate variables for one product do not necessarily apply to a product with a different mode of action for treating the same disease. 2.2.7 Categorised Variables Dichotomisation or other categorisation of cont inuous or ordinal variables may sometimes be desirable. Criteria of ‘success’ and ‘response’ are common examples of dichotomies which require precise specification in terms of, for example, a minimum percentage improvement (relative to baseline) in a continuous variable, or a ranking categorised as at or above some threshold level (e.g., ‘good’) on an ordinal rating scale. The reduction of diastolic blood pressure below 90mmHg is a common dichotomisat ion. Categorisations are most useful when they have clear clinical relevance. The crite ria for categorisation s hould be pre-defined and specified in the protocol, as knowledge of trial results could easily bias the choice of such criteria. Because categorisation normally implies a loss of information, a consequence will be a loss of power in the analysis; this should be accounted for in the sample size calculation. 2.3 Design Techniques to Avoid Bias The most important design techniques for avoiding bias in clinical trials are blinding and randomisation, and these should be normal features of most controlled clinical trials intended to be included in a marketing application. Most such trials follow a double-blind approach in which treatments are pre-packed in accordance with a suitable randomisation schedule, and supplied to the trial centre(s) labelled only with the subject number and the treatment period so that no one involved in the conduct of the trial is aware of the specific treatment allocated to any particular subject, not even as a code letter. This approach will be assumed in Section 2.3.1 and most of Section 2.3.2, exceptions being considered at the end. Bias can also be reduced at the design stage by specifying procedures in the protocol aimed at minimising any anticipated irregularities in tria l conduct that might impair a satisfactory analysis, including various types of protocol violations, withdrawals and missing values. The

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© EMEA 2006 11 protocol should consider ways both to reduce the frequency of such problems, and also to handle the problems that do occur in the analysis of data. 2.3.1 Blinding Blinding or masking is intended to limit the occurrence of conscious and unconscious bias in the conduct and interpretation of a clinical trial arising from the influence which the knowledge of treatment may have on the recruitment and allocation of subjects, their subsequent care, the attitudes of subjects to th e treatments, the assessment of end-points, the handling of withdrawals, the exclusion of data from analysis, and so on. The essential aim is to prevent identification of the treatments until all such opportunities for bias have passed. A double-blind trial is one in which neither the s ubject nor any of the investigator or sponsor staff who are involved in the treatment or clinical evaluation of the subjects are aware of the treatment received. This includes anyone determ ining subject eligibility, evaluating endpoints, or assessing compliance with the protocol. This level of blinding is maintained throughout the conduct of the trial, and only when the data are cleaned to an acceptable level of quality will appropriate personnel be unblinded. If any of the sponsor staff who are not involved in the treatment or clinical evaluation of the subject s are required to be unblinded to the treatment code (e.g. bioanalytical scientists, auditors, t hose involved in serious adverse event reporting), the sponsor should have adequate standard operating procedures to guard against inappropriate dissemination of treatment codes. In a single-blind trial the investigator and/or his staff are aware of the treatment but the subjec t is not, or vice versa. In an open-label trial the identity of treatment is known to all. The do uble-blind trial is the optimal approach. This requires that the treatments to be applied during the trial cannot be distinguished (appearance, taste, etc.) either before or during administration, and that the blind is maintained appropriately during the whole trial. Difficulties in achieving the double-blind ideal can arise: the treatments may be of a completely different nature, for example, surgery and drug therapy; two drugs may have different formulations and, although they could be made indistinguishable by the use of capsules, changing the formulation might also change the pharmacokinetic and/or pharmacodynamic properties and hence require th at bioequivalence of the formulations be established; the daily pattern of administration of two treatments may differ. One way of achieving double-blind conditions under these ci rcumstances is to use a ‘double-dummy’ (see Glossary) technique. This technique may sometimes force an administration scheme that is sufficiently unusual to influence adversely th e motivation and compliance of the subjects. Ethical difficulties may also interfere with its use when, for example, it entails dummy operative procedures. Nevertheless, extensive efforts should be made to overcome these difficulties. The double-blind nature of some clinical trials may be partially compromised by apparent treatment induced effects. In such cases, bli nding may be improved by blinding investigators and relevant sponsor staff to certain test result s (e.g. selected clinical laboratory measures). Similar approaches (see below) to minimising bias in open-label trials should be considered in trials where unique or specific treatment effe cts may lead to unblinding individual patients. If a double-blind trial is not feasible, then the single-blind option should be considered. In some cases only an open-label trial is practically or ethically possible. Single-blind and open- label trials provide additional flexibility, but it is particularly important that the investigator’s knowledge of the next treatment should not influence the decision to enter the subject; this decision should precede knowledge of the randomised treatment. For these trials, consideration should be given to the use of a centralised randomisation method, such as telephone randomisation, to administer the assign ment of randomised treatment. In addition, clinical assessments should be made by medical staff who are not involved in treating the

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