the anatomy and physiology of clinical research part 4

Question 1

Learning objectives

Focus on the variables and statistical issues

variables - predictor and outcome, confounding, hypothesis

statistical errors - hypothesis, sample size and analytical approach

Answer 1

Understand the different types of variables in a clinical study / trial.
Understand the concept of hypothesis testing and sample size effects and how they are applied in a clinical study / trial.
Distinguish between accuracy and precision, and ways to improve accuracy and precision respectively.
Understand how statistical issues are encountered in a clinical study / trial.
Distinguish statistical significance from biological significance.
Appreciate and understand the applications of the abovementioned points to real-world scenarios.

Question 2

2 types of variables

Answer 2

Continuous variables and Categorical variables

Question 3

What are continuous variables

Answer 3

quantitative variables

Question 4

2 Examples of quantitative variables

Answer 4

interval and ratio

Question 5

What are discontinuous variable

Answer 5

qualitative variables

Question 6

3 Examples of qualitative variables

Answer 6

nominal, dichotomous, ordinal

Question 7

Interval (continuous variable)

Answer 7

Quantified on an infinite scale and which have a numerical value e.g. temperature, Fahrenheit

The scale can take on both positive and negative values
It is assumed that the intervals keep the same importance throughout the scale
This allows us not only to rank order the items that are measured but also to quantify and compare the magnitudes of differences between them

Question 8

Ratio (continuous variable)

Answer 8

These are interval variables, but with the added condition that zero of the measurement indicates that there is none of the variable.
Hence temperature is not a ratio variable as 0 does not indicate that there is no temperature.
The name ratio reflects that you can use the ratio of measurements e.g. distance of 10 m is twice the distance of 5 m
Examples of ratio variables include height, weight, distance, number of cigarettes

Question 9

Nominal (categorical variable)

Answer 9

These are variables that have 2 or more categories, but which do not have an intrinsic order
E.g. Blood type for human, there are 4 distinct groups, A, B, AB and O. So blood type is a nominal variable with 4 categories
They tend to have a qualitative and absolute character that makes them very straight forward to measure e.g. 35 people have blood type O

Question 10

Dichotomous (categorical variable)

Answer 10

These are nominal variables which have only TWO categories or levels
E.g. gender, we would most likely categorize someone as “male” or “female”
E.g. “Dead” or “Alive”
E.g. do you own a mobile phone? Or do you have blood type O? the answer would be either a “Yes” or “No”

Question 11

Ordinal (categorical variable)

Answer 11

Variables have 2 or more categories like nominal variables only that the categories can be ordered or ranked
E.g. Ranking the frequency of consumption of mala from not frequent to very frequent

Question 12

What kind of variable is temperature

Answer 12

continuous variable (interval)

Question 13

What kind of variable is weight

Answer 13

continuous variable (ratio)

Question 14

What kind of variable is gender

Answer 14

category variable (dichotomous)

Question 15

What kind of variable is stage of cancer

Answer 15

continuous variable (ordinal)

Question 16

What kind of variable is eye color

Answer 16

continuous variable (nominal)

Question 17

what kind of variable is blood type

Answer 17

continuous variable (nominal)

Question 18

what kind of variable is education level

Answer 18

continuous variable (ordinal(

Question 19

what kind of variable is satisfaction level

Answer 19

continuous variable (ordinal)

Question 20

what kind of variable is birth weight

Answer 20

Continuous Variable (Dichotomous)

Question 21

what kind of variable is bubble tea consumption

Answer 21

Continuous Variable (Ordinal

Question 22

Continuous variables give more data than categorical variables

Answer 22

Continuous variables provide additional information, which helps to improve statistical efficiency compared to categorial variables.

Since continuous variables provide more information, a smaller sample size can be established and provides more meaningful results.

Even when categorical data is more meaningful, it is still a better option to collect data as continuous variables as it will leave the analytical option open for discussion.

Question 23

BP in mmHG

Answer 23

continuous

Question 24

normotensive vs hypertensive

Answer 24

categorical - dichotomous

Question 25

low, normotensive, moderately hypertensive, severely hypertensive

Answer 25

ordinal

Question 26

what is precision

Answer 26

The precision of a variable is the degree to which it is reproducible, with nearly the same value each time it is measured.

Precision has a very important influence on the power of a study (the ability of a study to detect a difference that is real), the more precise the measurement, the greater the statistical power at a given sample size to estimate mean values and to test hypothesis (proposed explanation for a phenomenon)

This is a function of random error, the greater the error, the less precise the measurement

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Precision represents how close different sample measurements you take are to one another, and accuracy represents how close those sample measurements are to the true measurement

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Precision has a very important influence on the power of a study (the ability of a study to detect a difference that is real)

The more precise the measurement, the greater the statistical power at a given sample size to estimate mean values and to test hypothesis (proposed explanation for a phenomenon)

This is a function of random error, the greater the error, the less precise the measurement

Question 27

what is accuracy

Answer 27

The accuracy of a variable is the degree to which it actually represents what it is intended to represent.

This has an important influence on the validity of the study (the degree to which the observed findings lead to the correct inferences about phenomena taking place in the study sample and in the universe)

Accuracy is a function of systematic error (bias), the greater the error, the less accurate the variable. The three main classes of measurement error noted for precision each have their counterparts here.

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Accuracy is a function of systematic error (bias), the greater the error, the less accurate the variable. The three main classes of measurement error noted for precision each have their counterparts here.
Observer bias – is a distortion, conscious or unconscious, in the perception or reporting of the measurement by the observer. It may represent systematic errors in the way an instrument is operated e.g. the tendency to round down measurements, or in the way an interview is carried out e.g. the use of leading questions etc.
Instrument bias – this can result from a faulty function of a mechanical instrument. A scale that is not calibrated recently may have drifted downward, producing consistently low BW readings
Subject bias – is a distortion of the measurement by the study subject, e.g. reporting an event. Patients with lung cancer who believe that the number of cigarettes smoked per day is the cause of the disease would exaggerate the number of cigarettes they smoke per day
The accuracy of a measurement is best assessed by comparing it, when possible to a “gold standard”. This is a reference technique that is considered to be accurate.

Question 28

Illustration of accuracy

Answer 28

Accuracy can be illustrated by throwing the darts onto the centre of the target (the true value).

Question 29

illustration of precision

Answer 29

Precision can be illustrated by throwing the darts closer to each other.

Question 30

the 3 sources of random errors

Answer 30

Observer variability – variability in the measurement that is due to the observer and includes aspects such as skill in using a mechanical instrument, methods of asking questions and conducting interviews etc.

Instrument variability – variability in the measurement due to changing environmental factors e.g. temperature, aging mechanical components, different reagent, different brand etc.

Subject variability – intrinsic biologic variability in the study subjects due things such as mood, time of last medication, food intake etc.

Question 31

what are random errors

Answer 31

Random errors are errors that affect the precision of a measurement. Random errors are two-sided errors, because, in the absence of other types of errors, repeated measurements yield results that fluctuate above and below the true or accepted value.

Measurements subject to random errors differ from each other due to random, unpredictable variations in the measurement process.

Question 32

5 ways to improve precision

Answer 32

1. standardizing measurement methods - written directions and guidelines, creating SOP,
2. training and certifying observers - improve the consistency of measurements technique
3. refining instruments - mechanical and electronical instruments
4. automating instrument - the influence of random error is reduced by repeating the measurements and using the mean of 2 or more readings

Question 33

Standardizing the measurement methods

Answer 33

All the study protocols should include operational definitions (specific instructions for making the measurements)

This includes written directions on how to prepare the environment and the subject, how to carry out and record the interview etc.

The operational manual is essential for large and complex studies and recommended for smaller ones.
Specific written guidelines for making each measurement will help the observer’s performance and ensure uniformity over the duration of the study.
This will also serve as the basis for describing the methods when the results are published

Question 34

Training and Certifying the Observers

Answer 34

Training will improve the consistency of measurement techniques especially when several observers are involved.

It is often desirable to design a formal test of the mastery of techniques specified in the operations manual and to certify that observers have achieved the prescribed level of performance

Question 35

Refining the instruments

Answer 35

Mechanical and electronic instruments can be engineered to reduce variability

Questionnaires and interviews can also be written in a certain style/manner to avoid potential ambiguities and to increase clarity

Question 36

Automating the instruments

Answer 36

The use of automated mechanical devices and self-response questionnaires can reduce observer variability by eliminating variations in the way human observers make measurements

Question 37

Repetition

Answer 37

The influence of random error from any source is reduced by repeating the measurement and using the mean of the two or more readings.

Precision will be substantially increased by this strategy

The limitation being the added cost and practical difficulties of repeating the measurements

Question 38

Making Unobtrusive Measurements

Answer 38

It is sometimes possible to design measurements that the subjects are not aware of, thereby eliminating the possibility that they will consciously bias the variable.

Example: A study on healthy eating patterns for school children, could measure the number of candy bar wrappers in the trash rather than questioning each child on the number of candy he or she consumes

Question 39

Calibrating the instruments

Answer 39

The accuracy of many instruments, especially those that are mechanical or electrical can be increased by periodic calibration using a gold standard

Question 40

Blinding

Answer 40

This is intended to ensure that subjective assessments and decisions are not affected by knowledge of treatment assigned.

Different groups should be treated and observed in the same way during a trial

Clinical trials are often “double blinded” to ensure that subject and investigators (as well as investigator staff) involved in the treatment or clinical evaluation of subjects are unaware of the subject’s assigned treatment.

Blinding is intended to minimize the potential biases resulting from differences in management, treatment or assessment of patients or interpretation of results that could arise as a result of subject or investigator knowledge of assigned treatment

Examples include
Subjects on active drug might report more favorable outcomes, because they expect a benefit or might be more likely to stay in a study if they knew they were on active drug

Observers might be less likely to identify and report treatment responses in a non-treatment group or might be more sensitive to a favorable outcome or adverse event in patients receiving active drug

Question 41

Hypothesis testing

Answer 41

Research hypothesis is a specific version of the research question that summarizes the main elements of the study

Hypothesis testing is needed for studies that will use test of statistical significance to compare findings among groups
Most observational studies
ALL experimental research questions involved in making comparisons

Stating the hypothesis in advance would help the researcher and research efforts focused on the primary objective

Question 42

Null hypothesis

Answer 42

States that there is NO association between the predictor and outcome variables in the population

Question 43

Alternative hypothesis

Answer 43

The proposition that there is an association between predictor and outcome variable

Question 44

Statistical significance

Answer 44

Statistical significance means that there is a good chance that we are right in finding that a relationship exists between two variables.

But statistical significance is not the same as practical significance. We can have a statistically significant finding, but the implications of that finding may have no practical application. The researcher must always examine both the statistical and the practical significance of any research finding.

Question 45

A one-sided hypothesis

Answer 45

Specifies the direction of the association between the predictor and outcome variable.

A one sided hypothesis may be appropriate when there is a very strong evidence from prior studies that an association is unlikely to occur in one of the two directions

Question 46

A two-sided hypothesis

Answer 46

States only that an association exists and does not specify the direction.

This is usually the case as both sides of the alternate hypothesis (i.e. greater or lesser) are interesting and investigators would want to publish results no matter which direction is observed

Question 47

if they ask study design

Answer 47

they ask for a case control study

Question 48

type 1 error

Answer 48

Reject Null Hypothesis when the Null Hypothesis is TRUE
False Positive
Probability of committing a Type 1 Error is assigned α
α is often set at 5% or 0.05 with a conventional range of 0.01 – 0.10.
Type 1 Errors are generally more serious than Type 2 Errors.

Question 49

type 2 error

Answer 49

Fail to reject the null hypothesis when the null hypothesis is false i.e. there is actually an association between predictor and outcome variable
False negative
Probability of committing a Type 2 Error is assigned β
β is often set at 0.2

Question 50

Power

Answer 50

defined as the ability to prove that there is a difference where there is one.

power is defined as the probability of correctly rejecting the null hypothesis when it is actually false

Question 51

Power formula

Answer 51

Power can be determined by subtracting the Type II error (β) from the total probability of 1.

Question 52

The minimum value for power

Answer 52

80%

Question 53

If power is not sufficiently large

Answer 53

then you will not be able to detect a difference even though it is significant

Question 54

example of power

Answer 54

If the investigator sets β to 0.10, it means that the investigator is willing to accept a 10% chance of missing an association of given effect size if it exists.

This represents a power of 0.90 i.e. 90% chance of finding an association of that effect size or greater.
If the investigator sets a higher β value (e.g. 0.50), then there is only a 50% chance of finding an association.

Question 55

sample size affects power

Answer 55

Even if your effect size is large but your sample size small, the power of the study may be poor and you might not be able to detect a treatment effect when there is actually one

Question 56

definition of effect size

Answer 56

The size of association that is expected to be present in the sample = Effect size

The likelihood that a study will be able to detect an association between a predictor and an outcome variable in a sample depends on the magnitude of that association in the population.

A larger effect size will make it easier to detect an association between the predictor and outcome variable, and vice versa.

Question 57

choosing an effect size

Answer 57

Choosing an Effect Size
Often investigators do not know the exact size of the association and would have to make an estimate
Make an informed guess about a reasonable effect size
In addition to determining the effect size, pilot studies may also investigate how to design recruitment approaches, measurements and interventions

Larger the effect size  increase ease in detecting an association between predictor and outcome variable
Smaller the effect size  increase difficulty in detecting an association between predictor and outcome variable
Effect size would affect the sample size i.e. smaller the effect size, the larger the sample size required to obtain statistically significant results

Question 58

effect size and sample size

Answer 58

Effect size has an inverse relationship with the sample size

A smaller effect size will require a larger sample size to obtain statistically significant results, and vice versa.

Question 59

Statistical vs Biological Significance

Answer 59

SMALL treatment differences which are biologically UNIMPORTANT can be statistically significant if sample size is large.

LARGE treatment differences which are biologically IMPORTANT may not be statistically significant if sample size is small.