### Hypnotherapy - Swedish Medical Center, Seattle, Washington

Hypnotherapy - Swedish Medical Center, Seattle, Washington

Please wander around this site and see how and what hypnotherapy helps.

Also -read THIS:

Please wander around this site and see how and what hypnotherapy helps.

Also -read THIS:

**Problems in Performing Double-blind Trials**

However, conducting a proper double-blind, placebo-controlled study isn't easy.

One problem is that participants may be able to discern whether they are getting a real treatment or placebo. For example, the smell and taste of a liquid preparation of some herbs is distinctive. Creating a substance that looks and tastes similar but lacks any active ingredients is difficult. This means that it's possible for those in the treatment group to know they are taking the real thing and for those in the control group to know they are taking placebo. Technically, this is described as "breaking the blind," and it can invalidate the results of a study. Similar difficulties occur in studies of conventional medications. If a treatment causes side effects, participants and physicians may be able to tell whether they are part of the treated group rather than the untreated (placebo) group. A top quality study will report on the success researchers had in efforts to keep the participants "blind." Surprisingly, many studies of medications reported in prestigious medical journals fail to do so.

In addition, some treatments are difficult or impossible to fit into the double-blind format, and others may be impossible. Studies on therapies such as acupuncture, physical therapy, diet, surgery, chiropractic, and massage are quite challenging to design in a double-blind manner. How do you keep the acupuncturist or surgeon in the dark as to whether he or she is performing real or fake treatment? How do you make study participants unaware of what they are eating?

Even properly designed double-blind studies aren't perfect. 4 For example, individuals willing to participate in studies may not be representative of the general population. This could skew the results. It's not clear what can be done to eliminate this issue.

Statistical Significance

Another important issue regards a subject called statistical significance. Sometimes you will read that people in the treatment group did better than those in the placebo group but that the results were not statistically meaningful. This means you cannot assume that the results proved the treatment was effective.

Evaluation of statistical significance is a mathematical analysis used to ensure that the apparent improvement seen in the treated group represents a genuine difference, rather than just chance. Consider the following analogy: suppose you flip one coin 20 times and end up with 9 heads. Then, you flip a second coin 20 times and count 12 heads. Does this mean that the first coin is less likely to fall with the head side up than the second coin? Or was the difference just due to chance? A special mathematical technique can help answer this question. The bottom line is that when study results look good but aren't statistically significant, they can't be taken any more seriously than the apparent "bias" of the coin that happens to fall heads more often when you flip it a few times.

A related issue is called statistical power. If a study enrolls too few people, the chance of discovering a true treatment effect diminishes. The number of enrollees necessary to identify a benefit depends on the strength of the treatment—a powerful treatment can be identified as effective in a relatively small study, but a modestly effective treatment may require hundreds of study participants to identify an effect. This effect is compounded when it is tricky to measure the benefits of a treatment.

Antidepressant drugs and herbs are a good example of a form of treatment requiring very large studies to demonstrate benefit. There are two reasons for this. First, in antidepressant studies, people given placebo typically show about 75% as much improvement as those in the treated group. 5 Additionally, the method of rating depression severity—a questionnaire—is relatively coarse and subject to wide variations in interpretation. The net result is a great deal of statistical "noise." In consequence, numerous studies of antidepressants have failed to identify any difference between treatment and placebo. This doesn't mean that the drugs don't work—only that very large studies are necessary to show that they work. Similarly, when small trials fail to find an herb effective, one shouldn't think they have proven it ineffective. They simply have failed to find it effective. Only relatively large negative trials truly prove that a treatment doesn't work. Small trials may simply lack sufficient statistical power to show benefit.

Data Dredging

Another statistical problem involves what is called data dredging. Before performing an experiment, researchers are supposed to pick one or two hypotheses that their study will test. This is called the primary outcome measure(s). For example, in a study of a treatment for Alzheimer's disease, the primary outcome measure may be the score on a given memory test. The researchers hypothesize that scores on this test will improve, and then conduct the study to determine whether their hypothesis is correct.

Once a study has begun, however, there's a temptation to gather more information by applying numerous tests to the participants. These are called secondary outcome measures. In the Alzheimer's example, these may involve such ratings as questionnaire assessments of ability to perform a daily task, physician opinion of overall progress, caregiver assessment of overall progress, and other perfectly reasonable ways of evaluating the success of therapy. There is a problem, however, with using a multitude of secondary outcomes: by the laws of statistics, if you measure enough things, some will indicate improvement, just by chance. Researchers who look at dozens of factors in hopes of finding evidence of improvement in a few of them are said to be engaged in data dredging. Only the results on the primary outcome measure are trustworthy. There is simply too much leeway to find favorable data by digging deep in the mass of other data recorded.

This is not a complete list of the challenges involved in designing a proper double-blind trial. There are numerous other tricky considerations, including study dropouts, ethical issues that interfere with an accurate determination of outcome, and many more. Nonetheless, when properly designed, the double-blind, placebo-controlled trial is the best method we have of objectively determining the effectiveness of a treatment.However, conducting a proper double-blind, placebo-controlled study isn't easy.

One problem is that participants may be able to discern whether they are getting a real treatment or placebo. For example, the smell and taste of a liquid preparation of some herbs is distinctive. Creating a substance that looks and tastes similar but lacks any active ingredients is difficult. This means that it's possible for those in the treatment group to know they are taking the real thing and for those in the control group to know they are taking placebo. Technically, this is described as "breaking the blind," and it can invalidate the results of a study. Similar difficulties occur in studies of conventional medications. If a treatment causes side effects, participants and physicians may be able to tell whether they are part of the treated group rather than the untreated (placebo) group. A top quality study will report on the success researchers had in efforts to keep the participants "blind." Surprisingly, many studies of medications reported in prestigious medical journals fail to do so.

In addition, some treatments are difficult or impossible to fit into the double-blind format, and others may be impossible. Studies on therapies such as acupuncture, physical therapy, diet, surgery, chiropractic, and massage are quite challenging to design in a double-blind manner. How do you keep the acupuncturist or surgeon in the dark as to whether he or she is performing real or fake treatment? How do you make study participants unaware of what they are eating?

Even properly designed double-blind studies aren't perfect. 4 For example, individuals willing to participate in studies may not be representative of the general population. This could skew the results. It's not clear what can be done to eliminate this issue.

Statistical Significance

Another important issue regards a subject called statistical significance. Sometimes you will read that people in the treatment group did better than those in the placebo group but that the results were not statistically meaningful. This means you cannot assume that the results proved the treatment was effective.

Evaluation of statistical significance is a mathematical analysis used to ensure that the apparent improvement seen in the treated group represents a genuine difference, rather than just chance. Consider the following analogy: suppose you flip one coin 20 times and end up with 9 heads. Then, you flip a second coin 20 times and count 12 heads. Does this mean that the first coin is less likely to fall with the head side up than the second coin? Or was the difference just due to chance? A special mathematical technique can help answer this question. The bottom line is that when study results look good but aren't statistically significant, they can't be taken any more seriously than the apparent "bias" of the coin that happens to fall heads more often when you flip it a few times.

A related issue is called statistical power. If a study enrolls too few people, the chance of discovering a true treatment effect diminishes. The number of enrollees necessary to identify a benefit depends on the strength of the treatment—a powerful treatment can be identified as effective in a relatively small study, but a modestly effective treatment may require hundreds of study participants to identify an effect. This effect is compounded when it is tricky to measure the benefits of a treatment.

Antidepressant drugs and herbs are a good example of a form of treatment requiring very large studies to demonstrate benefit. There are two reasons for this. First, in antidepressant studies, people given placebo typically show about 75% as much improvement as those in the treated group. 5 Additionally, the method of rating depression severity—a questionnaire—is relatively coarse and subject to wide variations in interpretation. The net result is a great deal of statistical "noise." In consequence, numerous studies of antidepressants have failed to identify any difference between treatment and placebo. This doesn't mean that the drugs don't work—only that very large studies are necessary to show that they work. Similarly, when small trials fail to find an herb effective, one shouldn't think they have proven it ineffective. They simply have failed to find it effective. Only relatively large negative trials truly prove that a treatment doesn't work. Small trials may simply lack sufficient statistical power to show benefit.

Data Dredging

Another statistical problem involves what is called data dredging. Before performing an experiment, researchers are supposed to pick one or two hypotheses that their study will test. This is called the primary outcome measure(s). For example, in a study of a treatment for Alzheimer's disease, the primary outcome measure may be the score on a given memory test. The researchers hypothesize that scores on this test will improve, and then conduct the study to determine whether their hypothesis is correct.

Once a study has begun, however, there's a temptation to gather more information by applying numerous tests to the participants. These are called secondary outcome measures. In the Alzheimer's example, these may involve such ratings as questionnaire assessments of ability to perform a daily task, physician opinion of overall progress, caregiver assessment of overall progress, and other perfectly reasonable ways of evaluating the success of therapy. There is a problem, however, with using a multitude of secondary outcomes: by the laws of statistics, if you measure enough things, some will indicate improvement, just by chance. Researchers who look at dozens of factors in hopes of finding evidence of improvement in a few of them are said to be engaged in data dredging. Only the results on the primary outcome measure are trustworthy. There is simply too much leeway to find favorable data by digging deep in the mass of other data recorded.

This is not a complete list of the challenges involved in designing a proper double-blind trial. There are numerous other tricky considerations, including study dropouts, ethical issues that interfere with an accurate determination of outcome, and many more. Nonetheless, when properly designed, the double-blind, placebo-controlled trial is the best method we have of objectively determining the effectiveness of a treatment.

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