InfluentialPoints.com Biology, images, analysis, design... 

"It has long been an axiom of mine that the little things are infinitely the most important" 

Measures of mortality and natality Deaths, births, and rates of changeOn this page: Simple measures of mortality Standard cohort life table Cohort life table with censoring KaplanMeier life table Fertility tables Static life tableSimple measures of mortalityBefore we look at examples of cohort and static life tables, we will first give a worked example showing how the different measures of mortality are calculated. Worked exampleWe will use a similar example to that used when looking at measures of disease frequency, except that now we have a number of deaths occurring as a result of the infections. Eight pigs are observed over twelve weeks.
Standard cohort life tableWorked exampleThe table below is just the first part of a standard life table of a cohort of cattle after first
So what exactly are these three 'functions'?
Note especially the interval to which the particular rates apply. This affects how we plot the cumulative survival function (S) against time, known as a survival plot. The value of S is plotted against the starting time of the next interval, shown here typically as a step function. The hazard function (shown in the second figure) is also plotted as a step function but against the starting time of the same interval. The arrows show that the times where there is a largerthanaverage drop in the proportion surviving follow the peaks in the hazard function. So why do we work out cumulative survival by multiplying together all the interval survival probabilities, rather than just by dividing the number surviving by the original Cohort life table with censoringWorked exampleWe correct our estimates by subtracting half the number of censored individuals from the number of individuals at the start of each interval. This then gives an 'effective number' during the interval (n'_{i}) which is used for subsequent calculations:
We are making a number of assumptions here which we need to clarify:
There is one obvious way you can make the interval shorter, and also make more efficient use of the data. That is to deal with each event/death individually and use a variable interval length set by the time between individual deaths. This is known as the KaplanMeier (or productlimit) approach. In this approach the event times themselves define the length of the intervals at which the cumulative survival probability (S) is calculated. This ensures that the interval times used are the shortest possible and we use all the available data (usually a good idea!). KaplanMeier life tableWorked exampleWe will use the KaplanMeier approach to look data on the development of encephalopathy in patients treated for sleeping sickness with melarsoprol (data are based on but not identical to study of Burri et al.
The survival plot derived from this data is known as a KaplanMeier plot. It is similar to our previous survival plot except that changes no longer occur at regular intervals (= sampling period) but whenever there is an event.
Fertility tablesWorked example of a fertility tableFor a fertility table we start the table with a column giving the proportion of females surviving to the midpoint of the interval. The next column gives the number of female offspring new column added, the m_{i} column, which details the agespecific fertility. We then multiply these two columns together to give the total number of females born in each age category. The sum of this column is the net reproductive 'rate' (R_{o}):
The net reproductive rate is not a true rate, but a multiplication factor  in fact the multiplication factor per generation. It will equal the number of females in generation (n+1) divided by the number in generation n. The cohort generation time (T_{c}) is defined as the mean length of time elapsing between the birth of the parents, and the birth of the offspring. It is estimated by dividing the sum of the V_{i}x column (81.45) by the net reproductive rate (3.139) to give 26.1 weeks. In summary, the population should increase by a factor of 3.139 times every 26.1 weeks. We look at how the net reproductive rate is related to the population rate of increase, per unit time, in the Related Topic on population Static life tableA static life table is derived from the age structure of a single sample of a population, at a particular time. The age structure is taken to reflect the fate of a cohort of animals born at time 0. As we have pointed out above, this is only true if there is a stable age distribution and the population is stationary. Providing these conditions are met, mortality rates can be calculated in the same way as with cohort life tables. Worked exampleThe data represent a random sample taken from an insect population. The age distribution of the sample is therefore assumed to be representative of that of the population.
The mortality rate per day appears to be rather high initially (0.081/indiv/day), then varies between 0.0404 and 0.0667/indiv/day, before increasing in later life to 0.15/indiv/day. Note that, because we do not to know the exact ages, the estimates of cumulative mortality and mortality rate are a little biased in this type of life table. Instead we only know the number within rather wide age ranges. However it seems this error of age classification does not affect the estimate too In the example above, survival appears to vary between age groups. But this may just be a result of sampling error. In some insect populations it may be valid to assume that survival (or at least adult survival) is constant over the different age categories. In this case there are various ways to obtain a pooled estimate of the mortality rate. A weighted mean is one possibility  in this case giving an average mortality rate of 0.070. But another method is more widely used, especially in medical entomology. Providing there is a stable age distribution, and the population is stationary, the age structure will reflect the fate of a cohort of animals born at time 0. Hence we can represent the total sample in the following way:
It is then straightforward to show that S is given by the proportion that age groups older than N_{o} make up of the Worked exampleUsing the above example, there are 80 individuals in the youngest age class out of a total of 165 individuals. Hence S is given by 80/165 = 0.485 The daily cumulative survival is the bth root of S, where b is the length of the age interval. This equals 0.930 in this example. The daily instantaneous mortality rate = − ln (daily cumulative survival) = 0.072 The big problem with this approach is that a stable age distribution is very rarely achieved in natural populations, and a stationary population is even rarer! When a population is increasing, survivorship will be underestimated, and mortality rates will be overestimated. The opposite bias is produced when the population is decreasing. It is sometimes argued that, despite the biases, a static life table is better than no life table, especially since they are relatively easy to construct. But this is not good advice. The methods given here should only be used when:
In fact it is now possible to correct an estimate, depending on whether the population is increasing or decreasing. There are also other methods which require neither a stable age distribution nor a stationary population. References for these methods are given below.
