About Randomized Controlled trial all are trueexcept:
Dropouts are excluded from the study [Ref: Epidemiology and health services By Haroutune K. Annenian, Sum Shapiro p63; http://www3. interscience. wiley. corn/ journa1/8901 1300/abstract ?CRETRY =1 &SRETRY =0 Park 20/e p1 Repeat from Nov 08 Randomised controlled trials (RCT), are experimental studies where the effect of an intervention is assessed by collecting data before and after an intervention has taken place. RCT are used to compare an intervention with one or more other interventions or with no intervention. In RCT, an intervention is investigated by comparing one group of people who receive the intervention with a control group or control arm who do not. The control group receives the usual or no treatment and their outcome measure, or the change in measure from the staing point or baseline, is compared with that of the intervention group. RCT are designed to minimize bias: Subject variation or Performance bias: there may be bias on the pa of the paicipants, who may subjectively feel better or repo improvement if they may subjectively feel better or repo improvement if they knew they were receiving a new form of treatment. Observer bias: the investigator measuring the outcome of a therapeutic trial may be influenced if he knows beforehand the paicular procedure of therapy to which the patient has been subjected. Bias in evaluation: the investigator may subconsciously give a orable repo of the outcome of the trial, if he has beforehand knowledge of the group getting treatment. "Randomization cannot guard against these sos of bias, nor the size of the sample. In order to reduce these problems, 'blinding' is adopted."- Park Blinding can be done in three ways: Single blinding- here the paicipants are not aware whether they belong to the study group or the control group. b. Double blinding- here neither the doctor nor the paicipant are aware of the group allocation and the treatment received. Triple blinding- here the paicipants, the investigator or person analyzing the data are all "blind". Ideally triple blinding should be used, but double blinding is the most common method used. Allocation bias Allocation bias occurs when the measured treatment effect differs from the true treatment effect because of how paicipants were selected into the intervention or control groups. In RCT, once the paicipants are entered into the study, they are randomised to either an intervention group or the control group. Randomisation ensures that characteristics that might affect the relationship between intervention and outcome measures will be roughly equal across all arms of the study, minimising potential bias. Random allocation of patients is preferable to other methods of allocation because only randomization has the ability to create truly comparable groups. All factors related to prognosis, whether or not they are known before the study takes place or have been measured, tend to be equally distributed in the comparison groups. Patients in one group are, on the average, as likely to possess a given characteristic as patients in another. In the long run, with a large number of patients in the trial, randomization usually works as described above. However, random allocation does not guarantee that the groups will be similar. Dissimilarities between groups can arise by chance alone, paicularly when the number of patients randomized is small. To assess whether this kind of 'bad luck' has occurred, authors of randomized controlled trials often present a table comparing the frequency of a variety of characteristics in the treated and control groups, especially those known to be related to outcome. These are called baseline characteristics because they are present before randomization and so should be equally distributed in the treatment groups. Attrition bias Attrition bias (also call loss-to-follow-up bias) occurs when patients drop out of the study from one or other of the study groups preferentially. For example, if halfway through a study the treatment has been successful paicipants may drop out, and information about the success of the treatment is then lost. Conversely, paicipants in the control group may be unhappy with their lack of progress and may drop out of the study in order to seek alternative help. Sample size The size of the sample required when carrying out RCT is dependent upon the power of the test and what size of intervention impact is considered meaningful. It also depends on the type of hypothesis the RCT is testing. The smaller the magnitude of difference between groups that is to be detected and the greater the variability in outcomes, the larger the sample size that will be required. Randomised controlled trials are the most rigorous way of determining whether a cause-effect relation exists between treatment and outcome, however they are generally more costly and time consuming than other studies. Now lets come to the last option that's our answer- "Dropouts are excluded from the study." Though it sounds absurd but the truth is that the Dropouts are not excluded from the study. This is known as Intention to treat. The dropouts are included in the study. The aphorism is "Once randomized, always analyzed" Intention to treat (ITT) analysis (sometimes also called Intent to Treat) is an analysis based on the initial treatment intent, not on the treatment eventually administered. For example, if people who have a more refractory or serious problem tend to drop out at a higher rate, even a completely ineffective treatment may appear to be providing benefits if one merely compares those who finish the treatment with those who were enrolled in it. For the purposes of ITT analysis, everyone who begins the treatment is considered to be pa of the trial, whether they finish it or not. Rationale: Intention to treat analyses are done to avoid the effects of crossover and drop-out, which may break the randomization to the treatment groups in a study. Intention to treat analysis provides information about the potential effects of treatment policy rather than on the potential effects of specific treatment. If dropouts and noonadherent subjects are ignored, there is the possibility that bias will be introduced. For example consider two weight loss diets. one of which is effective while the other isn't. People on the effective diet lose weight and stay in the study. On the ineffective diet - Some will lose weight regardless and will stay in the study. - Those who fail to lose weight are more likely to drop out, if only to try something else. This will make the ineffective diet look better than it really is--and, by comparison, the effective diet looks worse than it really is--because the only subjects who remain in the study following the ineffective diet are those losing weight! A popular phrase used to describe ITT analyses is 'Analyze as randomized!" Once subjects are randomized, their data must be used for the ITT analysis!
>Baseline characteristics are comparable
>Bias can be eliminated by double blinding
>Sample size depends on the type of study
>Dropouts are excluded from the study