Event rates

About 1,000 miscellaneous failure rates, event rates, and probabilities. There is some treatment of human error probability... 

The book contains, in alphabetical order, failure rates, event rates and probabilities, and descriptive information which has been collected since 1970 in the course of doing risk and reliability assessments. Twenty appendices contain results of surveys on bursting discs, pipes, valves, relief valves, pump failures and information on human error, international fire losses, and blast effects. 

Recommendations in this section may change based on the results from the recent EPO-3 trial (epoetin alfa versus placebo). A difference in red blood cell transfusion rates was not observed between groups. Epoetin alpha therapy improved survival in trauma patients. Epoetin alfa did not have a measurable clinical benefit in medical/surgical non-trauma patients. Epoetin alpha therapy was associated with an increased thrombotic event rate, particularly in patients not receiving pharmacological deep vein thrombosis prophylaxis. 

The treatment of elderly patients with hypertension, as well as those with isolated systolic hypertension, should follow the same approach as with other populations with the exception that lower starting doses may be warranted to avoid symptoms and with special attention paid to postural hypotension. This should include a careful assessment of orthostatic symptoms, measurement of blood pressure in the upright position, and caution to avoid volume depletion and rapid titration of antihypertensive therapy.2 In individuals with isolated systolic hypertension, the optimal level of diastolic pressure is not known, and although treated patients who achieve diastolic pressures less than 60 to 70 mm Hg had poorer outcomes in a landmark trial, their cardiovascular event rate was still lower than those receiving placebo.69... 

The Log-Rank Test provides a method for comparing risk-adjusted event rates, useful when test subjects in a study are subject to varying degrees of opportunity to experience the event. Such situations arise frequently in toxicology studies due to the finite duration of the study, early termination of the animal or interruption of treatment before the event occurs. 

Both the Log-Rank and the Generalized Wilcoxon Tests are nonparametric tests, and require no assumptions regarding the distribution of event times. When the event rate is greater early in the trial than toward the end, the Generalized Wilcoxon Test is the more appropriate test since it gives greater weight to the earlier differences. 

In some events rates of oxidation of glycogen are limited by the supply of oxygen from the blood to the muscle, so that conversion of glycogen to lactic acid can generate additional ATP, which will allow a faster pace. How much additional ATP can be generated from this process is difficult to calculate. 

Initiate therapy with 20 mg orally once daily, then increase by 10 mg/week, or longer intervals, to attain adequate control of blood pressure. The usual maintenance dosage is 20 to 40 mg once daily. BP response increases over the 10 to 60 mg/day dose range, but adverse event rates also increase. Doses more than 60 mg once daily are not recommended. 

We saw in the previous section methods for calculating confidence intervals for the difference in the SAE rates, or the event rates themselves. We will now look at methods for calculating a confidence interval for the odds ratio. 

As mentioned previously, all of the measures difference in event rates, OR, RR, RRR and NNT, expressed in isolation, have limitations. What we are trying to do with such quantities is to use a single measure to summarise the data. All of the information is actually contained in the two event proportions/rates and V2 and attempting to summarise two numbers by a single number is inevitably going to lead to problems in particular cases. Beware of those limitations and revert back to and T2, if need be, to tell the full story. 

Measures such as the difference in event rates, OR, RR, RRR and NNT do not easily translate into the categorical data context. If we want to construct such measures in these cases we would collapse the outcome categories to two, the binary case, and proceed as before. In the categorical example covered earlier this could involve collapsing categories A, B and C to produce a binary outcome death/survival. 

For the test we need to know the success/event rate in the control group and as usual some measure of the treatment difference we are looking to detect. 

The standard deviations referred to above often provide the biggest challenge. The information for this will come from previous data for that same endpoint, from a similar population/sample of patients, treated for the same period of time etc., similarly for the success/event rate in the control group for binary data. We should try and match as closely as possible the conditions of the historical data with those pertaining to the trial being planned. 

It will sometimes be the case that there are gaps in our knowledge and it will not be possible to give values for the standard deviation or for the event rate in the... 

In tong term trials there will usually be an opportunity to check the assumptions which underlay the original design and sample size calculations. This may be particularly important if the trial specifications have been made on preliminary and/or uncertain information. An interim check conducted on the blinded data may reveal that overall response variances, event rates or survival experience are not as anticipated. A revised sample size may then be calculated using suitably modified assumptions... ... 

Estimates of the basic quantities needed for the calculation such as the standard deviation or the event rate in the control group and the sources of those estimates. 

It is usual to estimate and plot the probability of being event-free, but there will be occasions when interpretation is clearer when the opposite of this, cumulative incidence (or cumulative probability of experiencing the event by that time), is plotted. This is simply obtained as 1 - probability of being event-free. Pocock et al. (2002) discuss issues associated with the interpretation of these plots. These authors point out that interpretation in the conventional type of plot, when the event rates are low, can be exaggerated visually by a break in the y-axis, so take care ... 

In order to be able to understand what a hazard ratio is, you first need to know what a hazard rate is. The hazard rate (function) is formally defined as the conditional death (or event) rate calculated through time. What we mean by this is as follows. Suppose in a group of 1000 patients in month 1, 7 die the hazard rate for month 1 is 7/1000. Now suppose that 12 die in month 2 the hazard rate for month 2 is 12/993. If now 15 die in month 3 then the hazard rate for month 3 is 15/981 and so on. So the hazard rate is the death (event) rate for that time period amongst those patients still alive at the start of the period. 

Pooling the data gives an overall adverse event rate on treatment A of 5.7 per cent, compared to 6.0 per cent on treatment, a pooled difference (A — B) of —0.3 per cent. This is clearly misleading and a more appropriate measure of the treatment difference is given by the average absolute difference of 1.75 per cent. 

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