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Flashcards in study designs Deck (19)
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1
Q

What are two main types of study design? (broadly)

A
Descriptive studies (studies which describe things)
Analytic studies (studies which test hypotheses)
2
Q

What are two types of analytic studies?

A

Experimental studies -eg; randomised controlled trials

Observational studies eg; cohort studies, case control studies

3
Q

What is the definition of a population?

A

Complete set of entities of elements or units or people that we wish to describe or make inference about

4
Q

What is a sample?

A

A subset of a population

5
Q

What is a census?

A

A complete enumeration of a population. Rare.

6
Q

What is a sampling frame?

A

List of items in a population from which a sample is drawn

Rarely coincides with the entire population of interest:

  • Telephone numbers
  • Electoral role

Often don’t exist

  • All people with depression
  • All potential users of a new drug

Even without a list, we can ensure an ‘unbiased’ sample if every individual has the same chance of being drawn.

7
Q

What is random sampling?

A
  • Choose the sample in such a way that every individual in the population has a known chance of being selected
  • In a simple random sample, everyone has an equal chance of being chosen
  • This method is the best way of obtaining a sample which is representative of the population.
8
Q

What are two types of error?

A
  • Systematic error (bias)

- Random error (chance)

9
Q

Describe random error (chance)

A
  • Due to natural biological variability
  • Increasing the sample size will reduce the random fluctuations in the sample mean
  • Statistical methods allow us to quantify the influence of random error on our estimate
10
Q

Describe systematic error in a descriptive study (bias)

A
  • Due to aspects of the design or conduct of the study which systematically distorts the results
  • Cannot be reduced by increasing the sample size
11
Q

What are some types of sampling? (4)

A

Probability sampling (those below are examples)

  • Simple random sampling
  • Stratified random sampling
  • Cluster random sampling
12
Q

Describe simple random sampling

A
  • The simplest form of probability sampling
  • For a finite population of size N draw a sample size n such that each possible sample has the same probability of being selected
13
Q

Describe stratified random sampling

A
  • Much of statistical design theory is about controlling variation
  • Statified sampling is useful when the population comprises several groups of similar individuals
    (A stratum is a population sub-division of similar units)
    -Take s ample random sample from each stratum
14
Q

Describe some benefits of stratified sampling

A
  • More precise estimate for the same sample size
  • Can take different sized samples from different strata (A device for reducing overall variability)
  • Useful if you are interested in the results for each stratum and some of the strata are small
15
Q

What are two types/variants of stratified sampling

A

Probability proportional to size
- Everyone has the same chance of being selected

Equal numbers from each strata
- Those in smaller strata are more likely to be selected

16
Q

Describe cluster sampling

A

The population may be composed of similar and naturally occurring groups

  • Cluster samples take a random sample of groups
  • In a single stage cluster sample, everyone is included in the study
  • In a two stage cluster sample, there is a simple random sample of one person from each cluster. Probability of someone being in the study depends on the number in their cluster

(similar to stratified)

17
Q

What are two important characteristics of a RCT

A
  1. Randomisation/random allocating. Means every study unit has the same chance of being in each group. This allows some of the underlying model fitting assumptions to be met for a more straightforward analysis
  2. Control group
18
Q

What is confounding?

A

Confounding is a distortion of the association between exposure and outcome caused by the presence of a third factor.

19
Q

What are traits for a confounder?

A

A confounder is a variable which causes the distortion (confounding). To be a confounder, a variable must be both:

  • Associated with the exposure (independent of outcome); and
  • Associated with the outcome (independent of the exposure)