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Randomized controlled trial: Going for the Gold

Techniques and methodology of randomized control trials

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Randomized controlled trial: Going for the Gold

  1. 1. Dr Gaurav Kamboj Junior Resident Community Medicine, PGIMS, Rohtak
  2. 2. Hierarchy of Evidence and Research designs
  3. 3. BACKGROUND Both in public health and in clinical practice, the main objective is to modify the natural history of disease so as to prevent or delay death or disability and to improve the health of patient or the population. The challenge is to select the best available preventive or therapeutic measure to achieve this goal. To do so, research is carried out that determine the value of these measures.
  4. 4. BACKGROUND  The first instance of random allocation of patients to experimental and control conditions is attributed to James Lind, a naval surgeon, in 1747.  Lind randomly assigned 12 sailors to 6 different treatments for scurvy. The two patients who were given lemons and oranges recovered most quickly, suggesting a beneficial effect of citrus.
  5. 5. BACKGROUND  Randomization was Contributed by statistician R.A. Fisher in agriculture in 1923.  Randomized plots of crops to receive different treatments
  6. 6. Background  The first RCT in medicine is credited to Sir A. Bradford Hill, an epidemiologist for England's Medical Research Council.  The trial, published in the British Medical Journal in 1948, tested whether streptomycin is effective in treating tuberculosis.  Because the drug was in short supply, he simply alternated the assignment of hospital admissions to drug versus control .Later he recognized it led to selection bias because the sequence was too easy to predict. That realization led to the use of a random numbers table to generate the numeric series by which patients would be assigned to conditions.
  7. 7. Experimental/ Intervention studies  Involve an active attempt  Types  With patients as unit of study CLINICAL TRIALS With healthy people as unit of study FIELD TRIALS/ COMMUNITY INTERVENTION STUDIES With communities as unit of study COMMUNITY TRIALS
  8. 8. Experimental studies  Types RANDOMIZED CONTROLLED TRIALS Those involving a process of ‘RANDOM ALLOCATION’ Non RANDOMIZED CONTROLLED TRIALS Those departing from ‘strict randomization’ for practical purposes
  9. 9. RANDOMIZED CONTROLLED TRIAL In a randomized controlled trial, Participants are assigned to treatment conditions at random (i.e., they have an equal probability of being assigned to any group). Procedures are controlled to ensure that all participants in all study groups are treated the same except for the factor that is unique to their group. The unique factor is the type of intervention they receive.
  10. 10. Goal of RCT Primary goal  to test whether an intervention works by comparing it to a control condition, usually either no intervention or an alternative intervention. Secondary goals may include:  identify factors that influence the effects of the intervention (i.e., moderators)  understand the processes through which an intervention influences change (i.e., mediators or change mechanisms that bring about the intervention effect)
  11. 11. Basic Design of Randomized Trial
  12. 12. Types of RCT study designs  Concurrent parallel study design  Cross-over type of study design  Factorial design  Cluster design
  13. 13. Concurrent parallel study design  Classical clinical trial approach  Two study groups
  14. 14. Cross-over type of study design  Will patient do better on drug A or drug B?  Removes “patient effect” reducing variability and increasing precision of estimation  Assumption of ‘no carryover effects’ is difficult to test  Needs to determine appropriate length of washout period.  “Period” effects : Progression of disease, Dropouts
  15. 15. The acceptability of the female and male condom: a randomized crossover trial. Kulczycki, A, Kim, D-jin, Duerr, A, Jamieson, DJ, Macaluso,M Perspect Sex Reprod Health 2004 May-Jun, vol. 36(3) A sample of 108 women in stable relationships recruited from an urban, reproductive health clinic were randomly assigned to use 10 male or female condoms, followed by use of 10 of the other type to analyze measures of the methods' relative acceptability.
  16. 16. A Walnut Diet Improves Endothelial Function in Hypercholesterolemic Subjects : A Randomized Crossover Trial Emilio Ros, Isabel Núñez, Ana Pérez-Heras, Merce Serra, Rosa Gilabert, Elena Casals and Ramón Deulofeu. Circulation 2004, 109:1609-1614 Randomized in a crossover design 21 hypercholesterolemic men and women to a cholesterol-lowering Mediterranean diet and a diet of similar energy and fat content in which walnuts replaced 32% of the energy from monounsaturated fat. Twelve participants followed the control diet first for 4 weeks and then switched to the walnut diet for 4 weeks; 9 subjects followed the same diets in reverse order. Because diet-induced lipoprotein changes stabilize in <4 weeks, they did not incorporate a washout period between diets.
  17. 17. Factorial design  Evaluates multiple factors simultaneously  Major concern: interaction of interventions  Patients must be willing and able to take any of the treatment combinations may be hard to determine
  18. 18. A Randomized Longitudinal Factorial Design to Assess Malaria Vector Control and Disease Management Interventions in Rural Tanzania Randall A. Kramer et al Int J Environ Res Public Health. 2014; 11(5): 5317–5332.
  19. 19. Cluster design  Groups or clusters randomly assigned, not individuals. E.g. : villages, classrooms , platoons
  20. 20. Home-based versus Mobile clinic HIV testing & counseling in rural Lesotho: A Cluster-Randomized Trial Labhardt ND et al PLoS Med. 2014 Dec 16;11(12):e1001768.
  21. 21. Types of RCT Clinical trials Preventive trials Risk factor trials Cessation experiments Trial of etiological agents Evaluation of health services
  22. 22. Basic steps in conducting RCT 1. Drawing up a protocol 2. Selecting reference and experimental populations 3. Allocation of study subjects: Randomization 4. Intervention / manipulation 5. Follow –Up 6. Assessment of Outcome
  23. 23. Drawing up protocol  One of the essential feature of RCT is that it is conducted under a strict Protocol.  Once protocol is evolved, it should be strictly adhered to throughout the study. Prevents bias and reduces source of errors in the study.  Preliminary or pilot test runs of protocol can be held so to see whether it contains any flaw.  Final version of protocol should be agreed upon by all concerned before the trial begins.
  24. 24. Selecting reference and study population I. Reference /Target Population:  It is the population to which findings of the trial ,if found successful , are applicable.  e.g. whole population, population of school children, population of a city, industrial workers or social groups II. Experimental /Study Population:  Derived from reference population.  Ideally, it should be chosen randomly from reference population so that it is representation of reference population. Otherwise it may not be possible to generalize the findings of the study to reference population.
  25. 25. Selection of study subjects  Inclusion & exclusion criteria : for determining who will or will not be included in the study must be spelled out with great precision, and in writing. To ensure the replicability by others, just as is the case with laboratory experiments
  26. 26. Allocation of study subjects to groups  To derive a causal inference regarding relationship of intervention and outcome, comparison is important.  Types of controls : Historical Simultaneous non-randomized controls Randomized controls
  27. 27. Historical controls  Comparison group from past.  We go back to records of patients who were treated before new treatment became available.  Simple Demerit:  Comparability can not be assured  Recall bias  Quality : Data for medical purpose not for research purpose  Changes in many factors over calendar time
  28. 28. Simultaneous non-random controls  Day of month of admission-odd/even days  Alternate assignment into study & control group Predictable by investigator- Selection bias No. TB deaths No. % Vaccinated 445 3 0.67 controls 545 18 3.30 No. TB deaths No. % Vaccinated 556 8 1.44 controls 528 8 1.52 Results of a trial of BCG vaccination : Am Rev Tuberculosis 53:517-532,1946
  29. 29. Randomized controls  Randomization - a statistical procedure by which the participants are allocated to “Study” and “Control” groups.  The critical element of randomization is the unpredictability of the next assignment.  It makes RCT the GOLD STANDARD design for performing clinical trials.
  30. 30. “RANDOMIZED, DOUBLE-BLIND, CONTROLLED TRIAL” is considered as research design par excellence and “GOLD STANDARD” amongst research designs with which results of other studies are often compared. Deviation from this standard has potential drawbacks
  31. 31. Sequence Generation Flipping a coin? Rolling dice? Shuffling cards? Table of random numbers Computer random number generators • Random • Reproducible Preferable & recommended • Random but tempt investigators toward non- randomness • Adequate methods but not optimal • Cannot be checked – no audit trail Not recommended
  32. 32. Allocation procedures the probability of being assigned to any intervention stays constant over the course of the trial the allocation probability changes in response to the balance, composition, or outcomes of the groups controversial because they allocate patients not purely at random Aim: increase the sample's probability of being assigned to the best treatment FIXED ALLOCATION PROCEDURES ADAPTIVE PROCEDURES
  33. 33. Allocation procedures 1. Simple(complete) randomization 2. Permuted block randomization 3. Balanced permuted block randomization 4. Stratified randomization 1. Minimization Adaptive Randomization i. Biased Coin Randomization ii.Urn Randomization 2. Response Adaptive Randomization i. "Play-the-winner" procedure FIXED ALLOCATION PROCEDURES ADAPTIVE PROCEDURES
  34. 34. Simple (Complete) Randomization Elementary form of randomization, in which, every time when there is an eligible participant, the investigator flips a coin to determine whether the participant goes into the intervention or control group. A limitation is that random assignment is truly random. A random process can result in the study winding up with different numbers of subjects in each group. This is more likely to happen if sample size is small
  35. 35. Table of Random Numbers 00–04 05–09 10–14 15–19 00 56348 01458 36236 07253 01 09372 27651 30103 37004 02 44782 54023 61355 71692 03 04383 90952 57204 57810 04 98190 89997 98839 76129 05 16263 35632 88105 59090 06 62032 90741 13468 02647 07 48457 78538 22759 12188 08 36782 06157 73084 48094 09 63302 55103 19703 74741
  36. 36.  Suppose we want to compare 2 treatments(A & B) Random Number Table can be used in a no. of ways: a) we will consider every odd number an assignment to A and every even number an assignment to B b) we could say that digits 0 to 4 would be treatment A, and digits 5 to 9 treatment B. c) If we are studying three groups, we could say that digits 1 to 3 are treatment A, digits 4 to 6 treatment B, digits 7 to 9 treatment C, and digit 0 would be ignored. d) Prepare a series of opaque envelopes that are numbered sequentially on the outside: 1, 2, 3, 4, 5, and so on.
  37. 37. Restricted Randomization Random assignment to achieve balance between study groups in size or baseline characteristics. Restricted randomizations guarantee balance 1. Permuted-block 2. Biased coin (Efron) 3. Urn design (LJ Wei) 542-04-#37
  38. 38. Blocked Randomization Blocked randomization reduces the risk that different numbers of people will be assigned to the treatment (T) and control (C) groups. Patients are randomized by blocks. The order is chosen randomly at the beginning of the block. In randomly permuted blocks, there are several block sizes (e.g., 4, 6, and 8), and the block size and specific order are chosen randomly at the beginning of each block.
  39. 39. Permuted-Block Randomization Example 0 Block size 2m = 4 2 Trts A,B }  4C2 = 6 possible 0 Write down all possible assignments 0 For each block, randomly choose one of the six possible arrangements 0 {AABB, ABAB, BAAB, BABA, BBAA, ABBA} ABAB BABA ...... Pts 1 2 3 4 5 6 7 8 9 10 11 12
  40. 40. Blocked randomization Advantage A balance in the number of cases assigned to T versus C at any point in the trial (which could be valuable if the trial needs to be stopped early). Disadvantage With fixed blocks, predictability of the group assignment of patients being randomized late in the block by research staff . Reduced by using method of randomly permuted blocks and blinding of research staff to the randomization process
  41. 41. Stratified randomization To ensure that the treatment and control groups are balanced on important prognostic factors that can influence the study outcome (e.g., gender, ethnicity, age, socioeconomic status). Before doing the trial, the investigator decides which strata are important and how many stratification variables can be considered given the proposed sample size. A separate simple or blocked randomization schedule is developed for each stratum. Large trials often use randomly permuted blocks within stratification groups.
  42. 42. Stratified Randomization
  43. 43. Minimization Method o Minimization corrects (minimizes) imbalances that arise over the course of the study in the numbers of people allocated to the treatment and control. o An attempt to resolve the problem of empty strata when trying to balance on many factors with a small number of subjects o Balances Trt assignment simultaneously over many strata. o Used when the number of strata is large relative to sample size as stratified randomization would yield sparse strata. o Logistically more complicated
  44. 44. Biased Coin Randomization o In this procedure, if the imbalance in treatment assignments passes some threshold, the allocation is changed from chance to a bias in favor of the under-represented group. o For example... If after 10 randomizations, there are 7 patients assigned to intervention and 3 assigned to control, the coin toss will become biased. o Then, rather than having 50/50 chance of being assigned to either condition, the next patient will be given a 2/3 chance of being assigned to the under-represented condition and a 1/3 chance of being assigned to the overrepresented one. o This procedure requires keeping track of imbalances throughout the trial. In smaller trials, imbalances can still result
  45. 45. Urn Randomization This procedure tries to correct imbalances after each allocation. For example... The investigator starts off with an urn containing a red ball and a blue ball to represent each condition. If the first draw pulls the red ball, then the red ball is replaced together with a blue ball, increasing the odds that blue will be chosen on the next draw. This continues, replacing the chosen ball and one of the opposite color on each draw. The procedure works best at preventing imbalance when final sample size will be small.
  46. 46. Timing of randomization  Actual randomization should be delayed until just prior to initiation of therapy after consenting.  This prevents randomizing participants who drop out before participating in any of the study.  This is important because everyone who gets randomized needs to be included in the study's analysis.
  47. 47. Operational mechanics of randomization 1. Sequenced sealed envelopes (prone to tampering!) 2. Sequenced bottles/ packets 3. Phone call to central location - Live response - Voice Response System 4. One site PC system 5. Web based Best plans can easily be messed up in the implementation
  48. 48. Multi-institutional Trials Often in multi-institutional trials, there is a marked institution effect on outcome measures. In multi-site trials, randomization usually occurs at a centralized location. Using permuted blocks within strata, adding institution as yet another stratification factor will probably lead to sparse cells (and potentially more cells than patients!) Use permuted block randomization balanced within institutions Or use the minimization method, using institution as a stratification factor
  49. 49. Allocation Concealment Allocation concealment means that the person who generates the random assignment remains blind to what condition the person will enter. Preferably, randomization should be completed by someone who has no other responsibilities in the study. Often, the study statistician assumes responsibility for performing the randomization. If allocation is not concealed, research staff is prone to assign "better" patients to intervention rather than control, which can bias the treatment effect upward by 20- 30%
  50. 50. Follow-up An adverse event (AE) is an undesirable health occurrence that occurs during the trial and that may or may not have a causal relationship to the treatment. A serious adverse event (SAE) is defined as something life-threatening, requiring or prolonging hospitalization and/or creating significant disability. E.g. A suicide attempt -- SAE in a study of any treatment. The SAE needs to be reported regardless of whether it bears any relationship to the treatment or the problem being studied Depending on the severity and frequency of adverse events, investigators and data safety monitors may have to decide to terminate the trial prematurely.
  51. 51. Attrition Rate of loss of participants from the study that differs between the intervention and control groups. Can compromise study findings by reducing the power of a study
  52. 52. Data Analysis and Results Which participants will be analyzed?  Intention to treat  Per protocol analysis Subgroup Analyses Statistical Power of study Data Analytic Techniques  Continuous Outcome Variables  Categorical Outcome Variables
  53. 53. Intention to treat (ITT) analysis  Basic Principle - “Analyze What is Randomized” All participants who were randomized and entered the trial need to be included in the analysis in the condition to which they were assigned, regardless of whether they completed the trial, or may even have switched over to receive the incorrect treatment. The ITT analysis addresses the question of whether the study treatment, if made available to the population, would be superior to an alternative intervention. For that reason, the disposition of the sample from the moment they learn their allocation is relevant in evaluating the treatment.
  54. 54. Per protocol analysis Opposite end of the spectrum from ITT analysis Includes in the analysis only those cases who completed treatment. Its results represent the best case treatment results that could be achieved if the study sample were retained and remained compliant with treatment. Should not be used alone/main analysis
  55. 55. Subgroup Analyses  Planned subgroup analyses. In a few instances, a study may have been designed and powered to test whether a treatment works better for one demographic group (e.g., females) than another (e.g., males). In that case, testing a hypothesized treatment by demographic group interaction would be a primary aim that definitely needs be tested.  Exploratory subgroup analyses. More often, many different treatment-by-subgroup interactions will be explored. Those analyses can support hypothesis generation. They are done in the context of discovery rather than confirmation. Any findings require replication in another trial.
  56. 56. Statistical power Power is ability to find a difference when a real difference exists. The power of a study is determined by three factors:  Alpha level  Sample size  Effect size:  Association between DV and IV  Separation of Means relative to error variance.
  57. 57. Power and effect size  As the separation of means increases, the power of study increases.  As the variability about a mean decreases power also increases
  58. 58. Effect size Effect size refers to the magnitude (i.e., size) of a difference when it is expressed on a standardized scale. An effect size is exactly equivalent to a 'Z-score' of a standard Normal distribution. For example, an effect size of 0.8 means that the score of the average person in the experimental group is 0.8 standard deviations above the average person in the control group.
  59. 59. Effect size Cohen’s d : expressed on a standardized scale that ranges from -3.00 to + 3.00 It is just the standardized mean difference between the two groups. In other words:
  60. 60. Effect size “Effect-size r,” which is simply the Pearson Correlation Coefficient (r) R is also expressed on a standardized scale, -1.00 to +1.00 R values can also be averaged while weighting the avg. to take into account varying sample size
  61. 61. Measures of effect size for ANOVA Measures of association  Eta-squared (2)- proportion of the total variance that is attributed to an effect  R-squared (R2)- proportion of variance explained by the model  Omega-squared (2)- estimate of the dependent variable population variability accounted for by the independent variable Measures of difference  Cohen’s f - averaged standardised difference between the 3 or more levels of the IV Small effect - f=0.10; Medium effect - f=0.25; Large effect - f=0.40
  62. 62. Maximizing Validity and Minimizing Bias  History: external events that occur during the course of a study that could explain why people changed. E.g. death in the family. Negative life events, such as these, may offer an alternative explanation for study outcomes. Consequently, it is very useful that random assignment equalizes these occurrences across the treatment and control conditions.  Maturation: processes that occur within individuals over the course of study participation that provide an alternate explanation of why they changed. E.g. depression increases after the onset of puberty. Thus, if more children in the control than the treatment group reached puberty during the course of the study, that might explain why the control group finished the study with more depression than the treated group.  Temporal Precedence: in order to establish a causal relation between the intervention and outcome, the intervention must occur before the outcome.
  63. 63. Blinding/ Masking Blinding is an attempt to reduce bias arising out of errors of assessment a) Single blind trial: Participant not aware b) Double blind trial: Neither doctor/ investigator nor the participant is aware c) Triple blind trial: Doctor/investigator , participant and the person analyzing the data are all not aware of the assigned t/t
  64. 64. Minimizing Threats to External Validity  To what extent can the results be extended to people, settings, interventionists different than those used in this particular study?  Sample Characteristics: External validity can be enhanced by having a broadly representative sample. It enables the findings to be generalized to a diverse population.  Setting Characteristics: (e.g., clinic, therapists, study personnel).  Effects Due to Testing: refers to the potential for participants to respond differently because they know they are being assessed as part of research
  65. 65. Testing Efficacy vs. Effectiveness An efficacy trial (also k/a explanatory trials) answers the question: "Does this intervention work under optimal conditions?” An effectiveness trial(also k/a pragmatic trials) answers the question: "Does this intervention work under usual conditions?" Very few trials actually fall wholly into one of these categories, but rather fall along a continuum of pragmatic-explanatory.
  66. 66. Significance Testing and Beyond Estimation vs. Statistical Significance Testing Assessing the Effect Size of an Intervention Assessing Clinical Significance
  67. 67. Significance Testing and Beyond Estimation vs. Statistical Significance Testing  Whether the effect of a treatment reaches a conventional significance level (p < 0.05) depends heavily on factors such as sample size. Assessing the Effect Size of an Intervention  An effect size describes the magnitude of an intervention's effect on the study outcome.  In the case of RCTs, the effect size represents the magnitude of the difference between the control and intervention conditions on a key outcome variable adjusted for the standard deviation of either group.
  68. 68. Significance Testing and Beyond Assessing Clinical Significance When testing interventions that address health problems The Number Needed to Treat (NNT) expresses the number of patients who need to receive the intervention to produce one good outcome compared to control. NNT is a widely used index of clinical significance NNT= 1 _ (Rate in untreated gp) - (Rate in treated gp)
  69. 69. Example The number of deaths and patients treated were 3671/50496 (7.26%) in the test group 3903/50467(7.73%) in the control group.  Pt – Pc =0.47%= 0.0047  NNT = 1/0.0047 = 213  Hence, such treatment saves about 5 lives /1000 treated
  70. 70. Reporting Results The Consolidated Standards of Reporting Trials (CONSORT) has become the gold standard for reporting the results of RCTs. A checklist and flow diagram. The most up-to-date revision of the CONSORT Statement is CONSORT 2010. Extensions of the CONSORT Statement have been developed for other types of study designs, interventions and data.
  71. 71. Ethical Issues Investigators are responsible to uphold ethical standards and guidelines Declaration of Helsinki- (developed by the World Medical Association)- This set of ethical principles guides medical researchers in conducting research on human subjects.
  72. 72.
  73. 73. Advantages of RCT The use of randomization provides a basis for an assumption-free statistical test of the equality of treatments Random assignment ensures that known and unknown person and environment characteristics that could affect the outcome of interest are evenly distributed across conditions. Random assignment equalizes the influence of nonspecific processes not integral to the intervention whose impact is being tested. Nonspecific processes might include effects of participating in a study, being assessed, receiving attention, self-monitoring, positive expectations, etc.
  74. 74. Pros Random assignment and the use of a control condition ensure that any extraneous variation not due to the intervention is either controlled experimentally or randomized. That allows the study's results to be causally attributed to differences between the intervention and control conditions. In sum, the use of an RCT design gives the investigator confidence that differences in outcome between treatment and control were actually caused by the treatment, since random assignment (theoretically) equalizes the groups on all other variables.
  75. 75. Cons Drawbacks of conducting an RCT are: Time- and energy- intensive Expensive May not be feasible for all interventions or settings (e.g., Some institutions have policies that prohibit random assignment)
  76. 76. THANK YOU
  78. 78. Four types of comparisons in RCT design Parallel RCT design is most commonly used, which means all participants are randomized to two (the most common) or more arms of different interventions treated concurrently
  79. 79. Superiority trials A new treatment is more effective than a standard treatment from a statistical point of view or from a clinical point of view,  Its corresponding null hypothesis is that: The new treatment is not more efficacious than the control treatment by a statistically/clinically relevant amount.
  80. 80. Equivalence trials The objective of this design is to ascertain that the new treatment and standard treatment are equally effective. The null hypothesis of that is: Both two treatments differ by a clinically relevant amount.
  81. 81. Non-inferiority trials Non-inferiority trials are conducted to show that the new treatment is as effective but need not superior when compared to the standard treatment.  The corresponding null hypothesis is: The new treatment is inferior to the control treatment by a clinically relevant amount. One-sided test is performed in both superiority and non-inferiority trials, and two-sided test is used in equivalence trials.
  82. 82. Assuming RCT has two comparison groups and both groups have the same size of subjects
  83. 83. Parameter definitions N=size per group; p=the response rate of standard treatment group;  p0= the response rate of new drug treatment group;  zx= the standard normal deviate for a one or two sided x;  d= the real difference between two treatment effect; δ0= a clinically acceptable margin;  S2= Polled standard deviation of both comparison groups
  84. 84. Dichotomous variable
  85. 85. Example 1: Calculating sample size when outcome measure is dichotomous variable.
  86. 86. Problem: The research question is whether there is a difference in the efficacy of mirtazapine (new drug) and sertraline (standard drug) for the treatment of resistant depression in 6-week treatment duration. A ll parameters were assumed as follows: p =0.40; p0=0.58;α=0.05;β=0.20; δ=0.18; δ0=0.10. Parameter definitions N=size per group; p=the response rate of standard treatment group; p0= the response rate of new drug treatment group; zx= the standard normal deviate for a one or two sided x; d= the real difference between two treatment effect;δ0= a clinically acceptable margin; S2= Polled standard deviation of both comparison groups.
  87. 87. Example 2: Calculating sample size when outcome measure is continuous variable
  88. 88. Problem: The research question is whether there is a difference in the efficacy of A CE II antagonist (new drug) and A CE inhibitor (standard drug) for the treatment of primary hypertension. Change of sitting diastolic blood pressure (SDBP, mmHg) is the primary measurement, compared to baseline. All parameters were assumed as follows: mean change of SDBP in new drug treatment group=18 mm Hg; mean change of SDBP in standard treatment group =14 mm Hg;α=0.05;β=0.20; δ=4 mmHg; δ0=3 mm Hg; s=6mm Hg.
  89. 89. DISCUSSION  Firstly, the researcher should specify the null and alternative hypotheses, along with the type I error rate and the power (1- type II error rate).  Secondly, the researcher can gather the data of relevant parameters of interest but sometimes a pilot study may be required.