Measuring treatment effects

In some diseases a simple ordinal scale or a VAS scale cannot describe the full spectrum of the disease. There are many examples of this including depression and erectile dysfunction. Measurement in such circumstances involves the use of multiple ordinal rating scales, often termed items. A patient is scored on each item and the summation of the scores on the individual items represents an overall assessment of the severity of the patient s disease status at the time of measurement. Considerable amoimts of work have to be done to ensure the vahdity of these complex scales, including investigations of their reprodu-cibihty and sensitivity to measuring treatment effects. It may also be important in international trials to assess to what extent there is cross-cultural imiformity in the use and imderstand-ing of the scales. Complex statistical techniques such as principal components analysis and factor analysis are used as part of this process and one of the issues that need to be addressed is whether the individual items should be given equal weighting. 

Note that we have included a column H —T this is the number of heads minus the number of tails. This is done in order to link with what we do when we are comparing treatments where we use differences to measure treatment effects. 

Chapter 3 together with testing hypotheses and the (dreaded ) p-value. Common statistical tests for various data types are developed in Chapter 4 which also covers different ways of measuring treatment effect for binary data, such as the odds ratio and relative risk. 

Randomization means that the sequence of preparing experimental units, assigning treatments, miming tests, taking measurements, and so forth, is randomly deterrnined, based, for example, on numbers selected from a random number table. The total effect of the uncontrolled variables is thus lumped together into experimental error as unaccounted variabiUty. The more influential the effect of such uncontrolled variables, the larger the resulting experimental error, and the more imprecise the evaluations of the effects of the primary variables. Sometimes, when the uncontrolled variables can be measured, their effect can be removed from experimental error statistically. 

The next level of presentation is a technical summary that gives details of the risks including the system s importance measures systems, effects of data changes, and assumptions that are critical to the conclusions. It details the conduct of the analysis - especially the treatment of controversial points. The last level of presentation includes all of the details including a roadmap to the analysis so a peer can trace the calculations and repeat them for verification. 

Recently, there has been a growth of interest in the development of in vitro methods for measuring toxic effects of chemicals on the central nervous system. One approach has been to conduct electrophysiological measurements on slices of the hippocampus and other brain tissues (Noraberg 2004, Kohling et al. 2005). An example of this approach is the extracellular recording of evoked potentials from neocortical slices of rodents and humans (Kohling et al. 2005). This method, which employs a three-dimensional microelectrode array, can demonstrate a loss of evoked potential after treatment of brain tissue with the neurotoxin trimethyltin. Apart from the potential of in vitro methods such as this as biomarkers, there is considerable interest in the use of them as alternative methods in the risk assessment of chemicals, a point that will be returned to in Section 16.8. 

An effective HE or cost-effectiveness analysis is designed to answer certain questions, such as Is the treatment effective What will it cost and How do the gains compare with the costs By combining answers to all of these questions, the technique helps decision makers weigh the factors, compare alternative treatments, and decide which treatments are most appropriate for specific situations. Typically, one chooses the option with the least cost per unit of measure gained the results are represented by the ratio of cost to effectiveness (C E). With this type of analysis, called a cost-effectiveness analysis (CEA), various disease end points that are affected by therapy (risk markers, disease severity, death) can be assessed by corresponding indexes of therapeutic outcome (mmHg blood pressure reduction, hospitalizations averted, life years saved, respectively). It is beyond the scope of this chapter to elaborate further on principles of cost-effectiveness analyses. A number of references are available for this purpose [11-13]. 

As the affordability of new medical technologies continues to be the subject of heated debate, so attention increasingly focuses on cost-effectiveness (the balance between costs and outcomes). Drug therapy, which is perhaps the most readily measured treatment cost, has attracted particular scrutiny. 

Authors Soy protein/isoflavone treatments Effect on bone measurements... 

Although increases in bone mineral density have been reported at other sites, most of the clinically significant fractures occur in the hip or spine, and these sites have become clinically important measures in the trials. These increases in bone mineral density are an important marker of treatment effects and are related to the benefits found in larger trials of decreased fracture risk. 

ATP is an ideal indicator of cell viability. Blood or blood cell concentrates prepared for transfusion are stored for periods of a few days to several weeks in the blood bank. Viability checking of the blood cells is necessary to avoid posttransfusional reactions [94], This quality control of the conserved red blood cells and platelets can easily be performed by measuring the ATP concentration as an expression of their integrity. By the same measurement it was possible to confirm the diagnosis and monitor the treatment effects in various cases of platelet disease [97], The possibility of determining cells viability can be exploited to examine more free cells or tissue, as in the spermatozoa viability test, based on the correlation between ATP content and mobility. 

Clonazepam is the most extensively studied BZ for treatment of generalized SAD. It improved fear and phobic avoidance, interpersonal sensitivity, fears of negative evaluation, and disability measures. Adverse effects include sexual dysfunction, unsteadiness, dizziness, and poor concentration. Clonazepam should be tapered at a rate not to exceed 0.25 mg every 2 weeks. 

Another test paradigm for detecting treatment effects on brain functioning in Fj offspring measures auditory startle habituation. In this test, the animal is placed in a chamber with a floor that detects movement. The animal is exposed to a sequence of 50 to 60 auditory stimuli, each at 110 to 120 decibels for 20 to50 sec and separated by 5 to 20 sec. The gradual diminution of the animal s movement response is indicative of normal habituation. 

It is important when choosing a particular measurement scale to answer a number of questions. Is the choice that is made of clinical relevance How is the endpoint to be measured Can we measure the clinical endpoint directly, or must we choose an indirect approach Is the choice that is made sensitive enough to measure real treatment effects Having collected the information how are we to analyse it Some of these issues are illustrated in the following sections. 

Any inferences about the difference between the effects of the two treatments that may be made upon such data are the observed rates, or proportions of deteriorations by the intrathecal route. In this example, amongst those treated by the intrathecal route 22/58 = 0.379 of patients deteriorated, and the corresponding control rate is 37/60 = 0.617. The observed rates are estimates of the population incidence rates, jtt for the test treatment and Jtc for the controls. Any representation of differences between the treatments will be based upon these population rates and the estimated measure of the treatment effect will be reported with an associated 95% confidence interval and/or p-value. 

In Table 8.5, we compare the response rates for the two primary endpoints - disease deterioration and mortality for the Flindle et al. study. What is interesting is that for the mortality endpoint ARR shows less deviation from the null than in the case of disease deterioration, while the converse holds for the RR. This is often regarded as a major defect of the RR as a measure of treatment effect, in that it does... 

Laupacis et al. introduced the number needed to treat (NNT) into the medical literature" as an easily imderstood and useful measure of treatment effect for clinical trials in which the main outcome variable is binary. It has been argued that the NNT is more easily... 

The coefficient c measures the impact that treatment has on pr(y= 1). If c = 0 then pr(y = I) is unaffected by which treatment group the patients are in there is no treatment effect. Having fitted this model to the data and in particular obtained an estimate of c and its standard error then we can test the hypothesis Hq c = 0 in the usual way through the signal-to-noise ratio. 

Covariates affected by treatment allocation. Variables measured after randomisation (e.g. compliance, duration of treatment) should not be used as covariates in a model for evaluation of the treatment effect as these may be influenced by the treatment received. A similar issue concerns late baselines , that is covariate measures that are based on data captured after randomisation. The term time-dependent covariate is sometimes used in relation to each of the examples above. 

One very simplistic way of handling missing data is to remove those patients with missing data from the analysis in a complete cases analysis or completers analysis. By definition this will be a per-protocol analysis which will omit all patients who do not provide a measure on the primary endpoint and will of course be subject to bias. Such an analysis may well be acceptable in an exploratory setting where we may be looking to get some idea of the treatment effect if every subject were to follow the protocol perfectly, but it would not be acceptable in a confirmatory setting as a primary analysis. 

As before, the coefficient c measures the effect of treatment on the hazard rate. If c < 0 then the log hazard rate, and therefore the hazard rate itself, in the active group is lower than the hazard rate in the control group. If c> 0 then the reverse... 

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