Outcome variable defined

Endpoint. An indicator measured in a patient or biological sample to assess safety, efficacy, or another trial objective. Some endpoints are derived from primary endpoints (e.g., cardiac output is derived from stroke volume and heart rate). Synonyms include outcome, variable, parameter, marker, and measure. See surrogate endpoint in the text. Also defined as the final trial objective by some authors. 

If the outcome variables were binary, categorical or ordinal then the CMH test would be used with age and sex as above defining the four strata. The emphasis here, is still of course, to compare the treatments, but adjustment for these baseline factors has improved the efficiency of the analysis by comparing the treatments within each stratum (like with like) and then averaging those effects over the strata, as in the multi-centre setting. Again we will come out of this analysis with, for example in the binary case, an adjusted OR and a corresponding confidence interval. 

Multiple regression as presented so far is for continuous outcome variables y. For binary, categorical and ordinal outcomes the corresponding technique is called logistic regression. Suppose that in our earlier example we defined success to be disease-free for five years then we might be interested identifying those variables/factors at baseline that were predictive of the probability of success. 

Define y now to take the value one for a success and zero for a failure. For mathematical reasons, rather than modelling y as we did for continuous outcome variables, we now model the probability that y=l, written pr y= 1). 

The sample size calculation should be detailed in the trial publication, indicating the estimated outcomes in each of the treatment groups (and this will define, in particular, the clinically relevant difference to be detected), the type I error, the type II error or power and, for a continuous primary outcome variable in a parallel group trial, the within-group standard deviation of that measure. For time-to-event data details on clinically relevant difference would usually be specified in terms of the either the median event times or the proportions event-free at a certain time point. 

Performance parameters reflect the outcome of a given step and indicate that the process gave the desired result [14] or quality attribute. They are uncontrolled performance variables [15] without a control action [35]. Their natural variation is defined by operating history specifically, their variability is characterized from known historical data or estimated based on similar process performance [35]. Similarly, output variables reflect the step outcome and indicate performance was acceptable in terms of performance attributes for the step (e.g., titer and yield) or properties of the product stream (e.g., product homogeneity, purity, contaminant levels, and chromatography peak shape) [3,14]. Still another term used is critical Ys (analogous to dependent variables), defined as product and process output variables that relate to critical quality attributes (CQAs), which are measurable outputs of each process step that are used to provide evidence that the step performed correctly [37]. 

Simplex optimization was used for conductivity optimization but, of course, will be useful for the optimization of all battery parameters, e.g., aging of the battery due to unstable composition of the additives and electrolyte. The method can also be used for improving the power or energy density of the cell, if it is applied to the electrode composition. Because the simplex method is not only suitable for optimizing problems of one single outcome variable, this method can be used for the optimization of the total battery. By defining the desired parameters and their importance for the performance... 

In this review we concentrate on individual-based studies. In case-control studies, subjects with already existing disease are retrospectively compared with control persons with respect to previous exposure. In cohort studies, individual exposure is measured at baseline and the cohort is followed over time for newly diagnosed outcome variables. Both case-control and cohort studies estimate the (relative) risk associated with exposure. Intervention studies utilize an experimental design and are mostly double blind trials with a defined treatment or placebo assigned randomly. Results from intervention studies, followed by cohort studies, make the greatest contributions to obtaining evidence of a causal relationship. Case-control studies are liable to particular bias, which give their results less credibility. 

Let the vector X denote the set of aU basic random variables pertaining to a structure and assume the joint PDF /x(x) is known. Included in this vector are variables defining loads, material properties, member sizes, etc. For spatially varying quantities, discretization in space is employed, as described later in this section. In deference to S, we denote the outcome space of X as the load space. These two vectors are related through the mechanical tranrformation... 

A random variable is a real-valued function defined over the sample space S of a random experiment (Note that tliis application of probability tlieorem to plant and equipment failures, i.e., accidents, requires tliat the failure occurs randomly, i.e., by chance). The domain of tlie function is S, and tlie real numbers associated witli tlie various possible outcomes of the... 

Most traditional models focus on looking for equilibrium solutions among some set of (pre-defined) aggregate variables. The LEs are effectively mean-field equations, in which certain variables (i.e. attrition rate) are assumed to represent an entire force, the equilibrium state is explicitly solved for and declared the battle outcome. In contrast, ABMs focus on understanding the kinds of emergent patterns that might arise while the overall system is out of (or far from) equilibrium. 

Figure 22.4 Monte Carlo techniques were used to simulate different hypothetical individuals for different instances of the trial design, using variability and uncertainty distributions from the model analysis. The result is a collection of predicted outcomes, shown as a binned histogram (top figure). Success was defined as a difference in end point measurement of X or smaller between drug and comparator. Likelihood of success (shown in the bottom figure as a cumulative probability) for this example (low/medium drug dose and high comparator dose) is seen to be low, about 33%.

Each product system consists of a variable number of processes involved in the product life cycle. However, the product under consideration is often related to other processes that may no longer be important for the LCA study. The system boundary serves to the separation of essential and non-essential processes of the product life cycle. Since the choice of system boundaries significantly affects LCA study outcomes and in addition, its intensity and complexity, system boundaries should always be well considered and clearly defined. The choice of system boundaries is carried out with regard to the studied processes, studied environmental impacts and selected complexity of the study. Not-including any life cycle stages, processes or data must be logically reasoned and clearly explained [32]. 

The selection of variables is of central importance for the outcome of a system comparison on environmental and resource use impacts. The ideal variable or set of variables respectively provides information and describes the state of environmental phenomena with certain significance. Thus, applying a set of variables should make it possible to monitor and assess the state of the environment, to identify changes and trends, to transmit scientific data to become relevant for policy, and to evaluate already implemented policy measures. The concept of environmental indicators is broadly accepted as an adequate tool. Accordingly, an indicator is defined as a parameter or a value derived from parameters, which indicates the state of the environment with significance extending beyond that which is directly associated with a parameter value. A parameter s definition in this context is a property that is measured or observed (OECD 1994). Fieri et al. (1996) states that the purposes of indicators are as follows ... 

A discrete distribution function assigns probabilities to several separate outcomes of an experiment. By this law, the total probability equal to number one is distributed to individual random variable values. A random variable is fully defined when its probability distribution is given. The probability distribution of a discrete random variable shows probabilities of obtaining discrete-interrupted random variable values. It is a step function where the probability changes only at discrete values of the random variable. The Bernoulli distribution assigns probability to two discrete outcomes (heads or tails on or off 1 or 0, etc.). Hence it is a discrete distribution. 

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