Allocation bias

Different kinds of evaluations have been conducted to determine the effect of repellents on disease incidence. Randomized controlled trials (RCTs) are currently considered to be the gold standard for testing the effectiveness of interventions for disease reduction in a population, provided that they are well conducted. The most important feature of an RCT is that the individuals recruited into the trial are randomly assigned to the intervention or a control, thereby minimizing selection and allocation bias to control as much as possible for both known and unknown confounders that... 

As far as possible, the study should be conducted under double-blind conditions. Sometimes, pharmacological effects, desired or undesired, tend to unblind the study but even in these circumstances the identity of treatment will be unknown to subjects and observers at the time of dosing and before onset of effects, thereby minimising bias. Specified persoimel, such as the pharmacist, bioanalyst and pharmacokineticist, may need to know the treatment allocation code... 

Bias in subject selection may not be avoided simply by randomisation. Randomisation will avoid weighted allocation to one treatment regimen rather than another, but it will not avoid selection of the wrong kind of subject in the first place, which will subsequently affect the degree to which the data can be extrapolated. Thus, an investigator may have a preconceived idea about the safety of a drug or about its effectiveness in a particular subset of subjects who nonetheless meet the entry criteria. This prejudice may be avoided by stratification of subjects... 

Minimisation then favours B since this has the smallest total. There are two ways of favouring B. One is to bias the allocation probability in favour of B, for example, B has an 80% chance of being chosen, and A a 20% chance. The other approach - deterministic minimisation, allocates B with 100% chance. 

There are two major types of weaknesses associated with meta-analysis. Bias and heterogeneity. When planning a clinical trial every effort is made to minimise the impact of bias on the results. For example, clinicians and patients are kept unaware of the treatment being used for each individual patient, so-called double-blind studies and patients are randomly allocated to a treatment. However, in meta-analysis there... 

To avoid any bias in the allocation of the patients to the treatment groups... 

Randomisation to avoid bias in allocating subjects to treatments... 

If the objective of free allocation is to compensate existing assets for the impact of new regulation, it should not be required for new entrants. In practice, most governments set aside free new entrant reserves , which economically amount to an investment subsidy. If the volume were unlimited, such subsidies might reduce the product price - which may be part of the aim, but is not actually achieved." Governments use NERs to help support new construction, but giving free allowances in proportion to the carbon intensity of new plants, can bias the incentive towards more carbon-intensive investments (Neuhoff et al., this issue). When projected forwards, such distortions are amplified by the multi-period nature of the EU ETS, to which we now turn. 

It is important that the method of randomization is actually random. Treatment allocation according to day of the week, date of birth, date of admission or by alternate cases, is not random. The investigator will often know what treatment the patient will get if they enter the trial and so these methods are open to bias. Randomization must be based on tables of random numbers or computer-generated random allocation. It is also important that randomization is secure. Central telephone randomization is preferable to other methods, such as sealed envelopes containing the treatment allocation. 

We recorded the water content of individual vials in the order of their moving out from the freeze-dryer and constructed the water content distribution over the vials in the freeze-drayer. Based on those data we obtained information about the operation state, i.e., the bias of heat flux in the shelf or the residence of water vapor in the chamber. The information was very helpful in the maintenance of the machine to achieve uniform conditions in the chamber. Furthermore, we determined the uniformity of the vials in each batch or the consistency among batches based on the information of the water content distribution by statistical analysis. An example of allocation of trays in a freeze-dryer and three-dimensional distribution of moisture levels in individual vials is shown in Figure 24. Frequency distributions of vial moisture and their normal distribution curves in different batches made with different freeze-dryers are shown in Figure 25. 

Methods of numerical sensitivity and uncertainty analysis can be used to examine uncertainty and identify the key sources of bias and imprecision in quantitative estimates of risk. Once identified, limited resources (e.g., time, funding) can be efficiently allocated to obtain new information and data for those major sources of uncertainty and reduce it. These analyses can be repeated until uncertainties associated with the risk estimates are of an acceptable degree or until uncertainties cannot be further reduced. 

In summary, the design of a clinical trial incorporates methods of minimizing noise and the prevention of bias. This is done through the use of appropriate subject allocation procedures, such as randomization and blinding or through the use of stratification and blocking. 

When we have two (or more) treatments , we will usually wish to carry out a hypothesis test. This is making a decision about whether it is plausible that the difference (if any) between the treatments is real . In Table 7.2, we might wish to consider whether treatment B really does give a higher rate of side-effects than treatment A. To address this question, the alternative non-causal explanations for the apparent difference must be considered. They are (a) bias in allocation or group membership, (b) assessment or measurement bias, and (c) chance. 

Allocation or group membership bias means that the patients in the two treatment groups differ systematically from each other. Studies need to be designed, conducted and analysed to prevent this possibility - this will be considered further later. 

A difiSculty frequently arises in clinical trials because not all patients allocated to a treatment will complete the designated period of treatment or follow up. Non-completion may be a function of the particular treatment allocated, in that the reason for discontinuation of therapy may be inadequate efficacy or an adverse effect. If non-completing patients are omitted from fhe treatment comparison, bias may be introduced. Simply, if a treatment s failures are omitted from further consideration, the treatment will appear to be better than it really is. Thus, the primary efficacy analysis for the comparison of treatments must include all patients allocated to each treatment, whatever their ultimate fate. This is known as intention-to-treat (ITT) analysis. The implementation of an ITT analysis is not always easy, but usually either an endpoint analysis (in which the final assessment, whenever it was made, is used for fhe treatment comparison) or one of the strategies based on ranking the patients (from the best to the worst in the study) proposed by Gould. ... 

Thus, the description of a study as being a prospective, randomised double-blind clinical trial implies that both allocation and assessment bias have been eliminated in the design of the trial. 

The calculation of a statistical test and the obtaining of a value for P addresses only chance as the possible explanation for the difference. Being statistically significant is therefore not a sufficient basis for a conclusion that the difference is real - allocation and assessment bias must also be considered. 

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