Sampling strategies - Revision history

Gauche at 07:37, 5 May 2011

2011-05-05T07:37:02Z

Gauche at 12:42, 4 May 2011

2011-05-04T12:42:55Z

Bara at 14:06, 6 January 2011

2011-01-06T14:06:45Z

Gauche at 09:15, 15 December 2010

2010-12-15T09:15:52Z

Gauche at 16:36, 13 December 2010

2010-12-13T16:36:03Z

Gauche at 16:35, 13 December 2010

2010-12-13T16:35:53Z

Gauche at 12:16, 10 December 2010

2010-12-10T12:16:34Z

Gauche at 12:06, 10 December 2010

2010-12-10T12:06:19Z

Gauche at 11:46, 10 December 2010

2010-12-10T11:46:56Z

Gauche at 11:40, 10 December 2010

2010-12-10T11:40:23Z

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 ===Clustering===
 When cluster or multistage sampling techniques are used, the effect of clustering of the outcome of interest within clusters will have an effect on the required sample size, since animals within the same cluster would be expected to be more similar to each other than to those from other clusters. Therefore, formulas are available in order to calculate the 'design effect' (or DEFF), which indicates the factor by which the calculated sample size needs to be increased by in order to account for this.
 ===Multivariable studies===

← Older revision		Revision as of 12:42, 4 May 2011
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−	[[Category:Veterinary Epidemiology - General Concepts\|F]]	+	[[Category:Veterinary Epidemiology - General Concepts\|G]]

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 ==Sample size calculation==
-As mentioned earlier, it is important in any study not only that bias is minimised, but that the sample has sufficient [[Random variation#Precision|precision]] and [[Random variation#Hypothesis testing and study power|power]] (in the case of analytic studies) to answer the question(s) for which the study is intended. Both of these are closely related to the random variability in any sample taken from a population. Although this can be reduced by increasing the sample size, a number of other considerations (usually logistical and economic considerations) will also be acting in order to reduce the number of samples which can realistically be taken. Statistical techniques are therefore available in order to calculate the required sample size. Counterintuitively, these require assumptions to be made regarding the final results of the study, as well as information regarding the required [[Random variation#Confidence intervals|level of confidence]], precision or power of the study. Sample size formulae are not given here, but can be found in most statistical textbooks.
+As mentioned earlier, it is important in any study not only that bias is minimised, but that the sample has sufficient [[Random error#Precision|precision]] and [[Random error#Hypothesis testing and study power|power]] (in the case of analytic studies) to answer the question(s) for which the study is intended. Both of these are closely related to the random variability in any sample taken from a population. Although this can be reduced by increasing the sample size, a number of other considerations (usually logistical and economic considerations) will also be acting in order to reduce the number of samples which can realistically be taken. Statistical techniques are therefore available in order to calculate the required sample size. Counterintuitively, these require assumptions to be made regarding the final results of the study, as well as information regarding the required [[Random error#Confidence intervals|level of confidence]], precision or power of the study. Sample size formulae are not given here, but can be found in most statistical textbooks.
 ===Expected variation in the data===
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 ===Level of confidence===
-This is used in descriptive studies in order to indicate the level of confidence that the confidence interval of the estimate produced will contain the true population value. Usually, a confidence level of 95% is used. The level of confidence is also The concept of confidence intervals is explained further in the section on [[Random variation]].
+This is used in descriptive studies in order to indicate the level of confidence that the confidence interval of the estimate produced will contain the true population value. Usually, a confidence level of 95% is used. The level of confidence is also The concept of confidence intervals is explained further in the section on [[Random error|random variation]].
 ===Power===

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 ===Study sample===
-The sample population includes those animals which are included in the final study. It is important to remember that in most epidemiological studies, we are not interested in this population ''per se'' - rather, we are interested in using this sample in order to make statements regarding the source population (and possibly the target population). Because not all members of the source population have been sampled, statistical techniques need to be applied to the results from the study group in order to estimate what the characteristics of the source population are expected to be. Due to this extrapolation, there is always a possibility that any estimates from a sample are incorrect due to [[Random variation|'''random variation''']] in the sample. Although this random variation cannot be controlled without increasing the sample size (or redefining the source population), the accuracy of the estimate can be maximised by ensuring that sources of [[Bias|'''bias''']] are minimised.
+The sample population includes those animals which are included in the final study. It is important to remember that in most epidemiological studies, we are not interested in this population ''per se'' - rather, we are interested in using this sample in order to make statements regarding the source population (and possibly the target population). Because not all members of the source population have been sampled, statistical techniques need to be applied to the results from the study group in order to estimate what the characteristics of the source population are expected to be. Due to this extrapolation, there is always a possibility that any estimates from a sample are incorrect due to [[Random error|'''random variation''']] in the sample. Although this random variation cannot be controlled without increasing the sample size (or redefining the source population), the accuracy of the estimate can be maximised by ensuring that sources of [[Bias|'''bias''']] are minimised.
 ==Approaches to sampling==

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 When cluster or multistage sampling techniques are used, the effect of clustering of the outcome of interest within clusters will have an effect on the required sample size, since animals within the same cluster would be expected to be more similar to each other than to those from other clusters. Therefore, formulas are available in order to calculate the 'design effect' (or DEFF), which indicates the factor by which the calculated sample size needs to be increased by in order to account for this.
-===Multivariate studies===
+===Multivariable studies===
 When the effect of confounding or interaction is to be accounted for in the study, the sample size needs to be increased accordingly.
 [[Category:Veterinary Epidemiology - General Concepts|F]]

← Older revision		Revision as of 16:35, 13 December 2010
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 ===Power===
-This relates to the ability to detect a difference in a parameter of interest between two groups, and so relates to analytic studies using hypothesis testing. The power indicates the probability that a study will detect a difference between groups, assuming that a difference of a specified size does exist, at a specified level of confidence. - for example, if
+This relates to the ability to detect a difference in a parameter of interest between two groups, and so relates to analytic studies. The power indicates the probability that a study will detect a 'significant' difference between groups (using a specified p-value [usually 0.05] to indicate significance), assuming that a difference of a specified size does exist. For example, if there is a true difference in mean annual milk yield of 500 litres between two groups of cows, a study with a power of 80% will detect a statistically significant difference 80% of the time. That is, if the same study was repeated again and again, selecting the calculated required number of cows from each herd, 80% of these studies would detect a difference between groups and 20% would not.
 ===Clustering===
 [[Category:Veterinary Epidemiology - Introduction|F]]