Listening to an MP3 player for just an hour can lead to temporary hearing loss, according to a small study in the Archives of Otolaryngology—Head & Neck Surgery.
Researchers in Belgium had 21 young adults with normal hearing listen to pop rock on an MP3 player at comfortable volumes for an hour, on six different occasions at least 2 days apart. The researchers found that, after listening, subjects experienced significant deterioration in hearing at high and low frequencies. Analyses revealed that hearing loss was temporary — participants recovered their normal hearing in between listening sessions.
The authors call for more research but say their findings "indicate the potential harmful effects" of listening to MP3 players.
Saturday, August 28, 2010
Thursday, August 19, 2010
experimental data
Intention to Treat Analysis
P-value
Confidence Interval
Absolute Risk Reduction or Relative Risk Reduction
Number Need to Treat an Number Need to Harm
Odds Ratio
When looking at the results it is important to understand if an intention to treat analysis was applied. An intention to treat analysis is a statistical analysis of all the patients that started the trial, regardless of whether they were in the treatment or placebo group, or completed the trial. For instance, consideration should be given when there are 150 patients in the treatment group and 150 patients in the placebo group, but 50 patients in the treatment group drop-out half-way through the trial because of side effects. In this situation, it would be biased to look at just the 100 patients in the treatment group left at the end of trial and then artificially report a favourable benefit/risk profile for the treatment. However, when equal numbers of patients drop out in both groups, for known and unknown reasons, then the results are likely to be more valid. An intention to treat analysis, therefore maintains the integrity of the randomization process, reveals the similarities/differences between groups and attempts to provide a likely scenario of what would happen if this intervention was implemented in clinical practice.86
A p-value of ≤ 0.05 (or 1 divided by 20) means that if the trial was repeated 20 times that the results of 1 of those trials would probably be due to chance. A p-value of ≤ 0.05 is often acceptable and gives credence to the strength of the results but not the magnitude of treatment effect.
To understand the true treatment effect the confidence interval (CI) should be reported. The CI is expressed as a percentage of the likelihood that the treatment effect lays within a specified range. For instance, if 50% of the patients were likely to benefit from the treatment, and a 95% CI (40% to 60%) was reported, this means you can be confident 95% of the time that the treatment effect would lie within this range. A reporting of 50% is therefore a point estimate where it could be as low as 40% or as high 60% reasonable to expect a treatment effect of 50%. It is generally satisfactory to have a 95% CI with a tight range. However, any time a zero is reported in the interval range, you cannot assume the treatment effect to be true.85
The treatment effect can be reported as an absolute risk reduction (ARR) or relative risk reduction (RRR). For example, in a scenario where stroke occurred in 2% of patients (2 out of 100) given active treatment to lower blood pressure versus 4% of patients (4 out of 100) given placebo over a 5 year-period: a relative risk reduction of 50% (2% divided by 4%) or absolute risk reduction of 2% (4%-2%) could be reported. The magnitude of the RRR obviously looks much more significant than the ARR. However, what you want to know in this case is the number need to treat (NNT), which is 1 divided by the ARR. In this case you would get a NNT of 50 (1/.02) , which means you would have to give the blood pressure treatment to 50 patients for 5 years before you prevented 1 patient from having a stroke. The number need to harm (NNH) is calculated in the same way and tells you what the harm would be and how many patients you would have to give the treatment to before you saw 1 patient being harmed in that specific way. Obviously, you could then compare the NNT to NNH to understand the benefits versus the risks but the key is to have confidence in the results or a 95% CI or higher.86
It should be noted that in some instances the results are reported as an odds ratio (OR), which is comparable but not identical to the relative risk reduction. The OR is typically used in case control studies to assess risk of an adverse event or in cohort studies to assess disease outcome. The OR is calculated by dividing the number of occurrences in the intervention/treatment group by the number of occurrences in the control group. For instance, if an adverse effect of a drug is reported as an OR of 1.0 this means that the number of events in treatment group where equal to number of events in the placebo group. Similarly, if an adverse effect of drug is reported as an OR of 1.0, this means that the number of events in the treatment group were equal to number of events in the placebo group. Similarly, if an adverse effect of a drug is reported as an OR of greater than 1, this means that more individuals experienced an event in the treatment group compared to the placebo group. Therefore, an OR of less than 1 in this case means that the number of individuals in the treatment group that experienced an adverse effect was less than the placebo group.86
Taking this one step further, let’s say a particular treatment effect on reducing colds was reported as an OR of 0.42 with 95% CI of 0.25-0.71 and p<0.001. It is therefore accurate to say that the intervention group experienced a 58% (calculated as 1-0.42 X 100) reduction in catching a cold compared to the placebo group. However, regardless of whether the OR or RRR is used the CI and p-value should be reported in order for the trial outcome to be viewed as reliable. The NNT and NNH is not as easily calculated for OR because it requires a much more involved mathematical formula or can be estimated using tables.
P-value
Confidence Interval
Absolute Risk Reduction or Relative Risk Reduction
Number Need to Treat an Number Need to Harm
Odds Ratio
When looking at the results it is important to understand if an intention to treat analysis was applied. An intention to treat analysis is a statistical analysis of all the patients that started the trial, regardless of whether they were in the treatment or placebo group, or completed the trial. For instance, consideration should be given when there are 150 patients in the treatment group and 150 patients in the placebo group, but 50 patients in the treatment group drop-out half-way through the trial because of side effects. In this situation, it would be biased to look at just the 100 patients in the treatment group left at the end of trial and then artificially report a favourable benefit/risk profile for the treatment. However, when equal numbers of patients drop out in both groups, for known and unknown reasons, then the results are likely to be more valid. An intention to treat analysis, therefore maintains the integrity of the randomization process, reveals the similarities/differences between groups and attempts to provide a likely scenario of what would happen if this intervention was implemented in clinical practice.86
A p-value of ≤ 0.05 (or 1 divided by 20) means that if the trial was repeated 20 times that the results of 1 of those trials would probably be due to chance. A p-value of ≤ 0.05 is often acceptable and gives credence to the strength of the results but not the magnitude of treatment effect.
To understand the true treatment effect the confidence interval (CI) should be reported. The CI is expressed as a percentage of the likelihood that the treatment effect lays within a specified range. For instance, if 50% of the patients were likely to benefit from the treatment, and a 95% CI (40% to 60%) was reported, this means you can be confident 95% of the time that the treatment effect would lie within this range. A reporting of 50% is therefore a point estimate where it could be as low as 40% or as high 60% reasonable to expect a treatment effect of 50%. It is generally satisfactory to have a 95% CI with a tight range. However, any time a zero is reported in the interval range, you cannot assume the treatment effect to be true.85
The treatment effect can be reported as an absolute risk reduction (ARR) or relative risk reduction (RRR). For example, in a scenario where stroke occurred in 2% of patients (2 out of 100) given active treatment to lower blood pressure versus 4% of patients (4 out of 100) given placebo over a 5 year-period: a relative risk reduction of 50% (2% divided by 4%) or absolute risk reduction of 2% (4%-2%) could be reported. The magnitude of the RRR obviously looks much more significant than the ARR. However, what you want to know in this case is the number need to treat (NNT), which is 1 divided by the ARR. In this case you would get a NNT of 50 (1/.02) , which means you would have to give the blood pressure treatment to 50 patients for 5 years before you prevented 1 patient from having a stroke. The number need to harm (NNH) is calculated in the same way and tells you what the harm would be and how many patients you would have to give the treatment to before you saw 1 patient being harmed in that specific way. Obviously, you could then compare the NNT to NNH to understand the benefits versus the risks but the key is to have confidence in the results or a 95% CI or higher.86
It should be noted that in some instances the results are reported as an odds ratio (OR), which is comparable but not identical to the relative risk reduction. The OR is typically used in case control studies to assess risk of an adverse event or in cohort studies to assess disease outcome. The OR is calculated by dividing the number of occurrences in the intervention/treatment group by the number of occurrences in the control group. For instance, if an adverse effect of a drug is reported as an OR of 1.0 this means that the number of events in treatment group where equal to number of events in the placebo group. Similarly, if an adverse effect of drug is reported as an OR of 1.0, this means that the number of events in the treatment group were equal to number of events in the placebo group. Similarly, if an adverse effect of a drug is reported as an OR of greater than 1, this means that more individuals experienced an event in the treatment group compared to the placebo group. Therefore, an OR of less than 1 in this case means that the number of individuals in the treatment group that experienced an adverse effect was less than the placebo group.86
Taking this one step further, let’s say a particular treatment effect on reducing colds was reported as an OR of 0.42 with 95% CI of 0.25-0.71 and p<0.001. It is therefore accurate to say that the intervention group experienced a 58% (calculated as 1-0.42 X 100) reduction in catching a cold compared to the placebo group. However, regardless of whether the OR or RRR is used the CI and p-value should be reported in order for the trial outcome to be viewed as reliable. The NNT and NNH is not as easily calculated for OR because it requires a much more involved mathematical formula or can be estimated using tables.
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