Avoiding systematic errors:
o Trying to use sample mean to estimate the population mean
o Statistic = parameter + bias + confounding bias + chance

**Biostatistics**

·
Choosing study subjects:

o Populations may be people, institutions, records or events

o Sampling frame is a complete list of individuals in the accessible
population

o A sampling procedure is used to select a representative intended sample
from the sample frame

·
Avoiding systematic errors:

o Trying to use sample mean to estimate the population mean

o Statistic = parameter + bias + confounding bias + chance

o Statistic: summary measure in a sample

o Parameter: underlying value in the target population

o Bias or Confounding ® ¯internal validity

o External validity is whether the study can be applied to the population
I‟m interested in – is it similar enough to the study population?

· Bias:

o Bias = systematic deviation between the statistic and the parameter, due
to defect in the design, conduct or interpretation

o Occurs predominantly in design and data collection

o Selection bias: systematic error due to those who were selected and those who were not, so sample not representative of the defined population

o Information bias: a flaw in measurement exposures or outcomes that results in a differential quality of information between sub-groups/individuals.

§ Misclassification bias: Subjects erroneously categorised. If a random bias then ¯ association in results and odds ratio moves towards 1

§ Interviewer bias: systematic difference in soliciting, recording and interpreting of responses (¯ by training the interviewers – always check this has been done)

§ Recall bias: should be < 2 weeks for health events. Diet recall ~ 24 hours. If not random (eg case-control studies) then biased (eg if cases taken from records then there is variability in what was asked and recorded, verses uniform questionnaire for controls)

o Response bias: systematic error due to differences between those who
volunteer and those who do not (eg bias from drop-outs and non-responders)

o See Topic: EBM Glossary, for further examples of bias

o Confounding bias:

§ A measure of the effect of an exposure on the risk of an outcome is
distorted by an association of the exposure with other factors that influence
the outcome

§ Standard ones: age, gender, ethnicity, socio-economic status, obesity,
smoking, alcohol

§ As long as you collect data about the confounding factor, you can do
something about it

§ Can control for confounding using matching, logistic regression or
stratifying data

·
Chance effect:

§ Quantifies the precision with which the sample mean estimates the
population mean

§ Says NOTHING about variability in the data

o Confidence interval:

§ Turns standard error into something we can interpret: sample mean +/-
1.96 * standard error

§ 95% sure the underlying value lies in the range

§ Width is dependent on:

· Variation in observed data

·
The sample size (larger sample ® narrower
confidence interval ® more precise estimate)

·
Degree of confidence we want

§ Accuracy depends on presence or absence of bias

o Tests of significance:

§ Tests of significance are a tool for statistical inference

§ Test compatibility of a set of data with the null-hypothesis: assume there is no difference between the means – what is the probability we would observe a difference as big by chance

§ P value: the probability of getting a value at least as extreme as the observed statistic. Threshold usually 0.05

§ Most common test statistics are chi-squared and t-statistic (compares two means). Both depend on degrees of freedom

o Power: = probability that the study will find a statistically significant
difference if a true difference of a given size exists

·
Data:

o Qualitative: not numeric (eg hair colour)

o Quantitative: can be continuous or discrete

o Measurement scales can be nominal (categorical and unordered), ordinal
(categorical and ordered) or interval (continuous)

o Data description:

§ Categorical and discrete date: bar graphs, frequency distributions

§ Continuous data: histograms, frequency polygons

§ Central tendency: Mean or median (best measure of central tendency if
skewed distribution)

§ Spread/variability: Standard deviation, percentiles or inter-quartile
range

§ Correlation co-efficiency – degree of clustering around a straight line

§ If two variables are categorical and unordered then use relative risks
and odds ratios

Study Material, Lecturing Notes, Assignment, Reference, Wiki description explanation, brief detail

Medicine Study Notes : Public Health : Biostatistics |

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