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A term usually used for unobserved individual heterogeneity. Such variation is of main concern in the medical statistics particularly in the analysis of the survival times where hazard functions can be strongly influenced by the selection effects operating in the population. There are several possible sources of this heterogeneity, the most apparent of which is that it reflects the biological differences, so that, for instance, some individuals are born with the weaker heart, or a genetic disposition for cancer. A further prospect is that the heterogeneity happens from the occured weaknesses which result from the stresses of life. Failure to take account of this kind of variation might often obscure comparisons between groups, for instance, by measures of relative risk. A simple model which attempts to permit for the variation between individuals is given as follows where Z is the quantity specific to an individual, considered as the random variable over the population of individuals, and the base rate is denoted by λ(t) . What is observed in a population for which this type of model holds is not the individual hazard rate but the net result for several individuals with different values of Z.
This process of estimating from a data set those values lying beyond range of the data. In the regression analysis, for instance, a value of the response variable might be estimate
Infant mortality rate is the ratio of the number of deaths during the calendar year among the infants under one year of age to the total number of live births during that particul
Cellular proliferation models : Models are used to describe the growth of the cell populations. One of the example is the deterministic model where N(t) is the number of cel
Reciprocal transformation is a transformation of the form y =1/x, which is specifically useful for certain types of variables. Resistances, for instance, become conductances, and
importance of time series on the number of babies given birth
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Multicentre study : The clinical trial conducted simultaneously in the number of participating hospitals, with all centres following an agreed-upon study of the protocol and with
The graph for Partial Autocorrelation Function for RES1 shows that there is no autocorrelation even though there are alternating spikes because they fall inside the 5% significance
Data which occur when failure period is recorded which are dependent. Such type of data can arise in number contexts, for instance, in epidemiological cohort studies in which th
Models which make use of the smoothing techniques such as locally weighted regression to identify and represent the possible non-linear relationships between the explanatory and th
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