The Anatomy and Physiology of Clinical Research (Part 3) Flashcards
Learning objectives part 3
Focus on subjects which are selection criteria and sampling design
At the end of this lecture, you should be able to:
Understand how a study / trial work.
Know how subjects are recruited in a study / trial in terms of the inclusion and exclusion criteria.
Understand the types of control groups in a study / trial.
Understand what is randomization and its importance in a study / trial.
Understand the types of errors encountered in a study / trial.
Know and understand the different types of sampling methods used in a study / trial.
Appreciate and understand how these are applied in real-world situations.
the 3 things we need to consider when designing a clinical trial or study
Target population - focus on the target population first and formulate a general concern/question
Research question - after we start from the target population, we will narrow down to formulate the research question
Study plan - a study plan is formulated based on the research question
When designing a clinical trial, we start from the population first, we then narrow down the question to formulate the research question
From the research question, we formulate a study plan
When we carry out the study plan, we have to ensure it has high internal validity. It allows us to draw conclusions about the predictor and outcome variable.
This way, it allow us to apply these conclusions to people and events outside the study or generalize the results back to the population
External validity and internal validity of a trial/study
from research question to target population (external validity) - Having high internal validity allows us to apply these conclusions back to the target population.
from study plan to research questions(internal validity) - When conducting a study plan, we need to ensure a high internal validity (i.e. draw correct conclusions on the predictor & outcome variables)., draw correct conclusions on predictor and outcome variables
External validity
We need external validity and internal validity for research to work
Choosing of subjects, inclusion and exclusion criteria, to ensure that we can generalize the results back to the target population.
formulate a question based on the population - What is the prevalence of soya milk consumption among Chinese men from 50-60 years old with hypertensions?
narrow down the question to research question - what is the prevalence of soya milk consumption among Chinese men from 50-60 years old with hypertension seen at TTSH in 2014?
formulate a study plan from the research question - consuming 1 serving of soya milk per day is able to reduce blood pressure in Chinese men from 50-60 years old suffering from hypertension?
Internal validity
We need internal validity and external validity for research to work
Design of the study such that a correct conclusion is drawn between the study predictor and outcome variable
Design of clinical trial
Study of the accessible population (study population) – via internal validity–> accessible population (define geographic and temporal, relating to the location of the study and duration of the study) -> target population (define demographic and clinical, relating to human population, information collected about them, relating to the condition/disease)
Inclusion criteria
Demographic, clinical, geographic and temporal
demographic - information collected about the human population
clinical - condition/disease
geographic - the location of the study
temporal - the duration of the study
Exclusion criteria
Indicates subsets of individuals who would be suitable for the research question were it not for certain characteristics such as
- success of certain follow up efforts
- data quality
- acceptability of the randomized treatment
the 3 Characteristics of exclusion criteria
- success of follow-up efforts
- quality of the data
- acceptability of the randomized treatment
These characteristics will interfere with the research question by affecting the quality of data and randomization of the treatments and success to follow-up
What does characteristics of exclusion criteria affects ?
quality of the data
randomization of treatments and success to follow up
e. g quality of data - children might lite about the number of hours they work or cannot remember how many hours they cannot work
e. g follow-up efforts - if the parents of the children migrate, it can be difficult to do follow-ups with the children
Vulnerable subjects and exclusion of vulnerable subjects
Children, people with cognitive disabilities etc.
Those who are significantly less able to protect their own interest
Do not recruit vulnerable subjects even if they satisfy all the inclusion criteria listed
Safety of the participant is the most important
note about exclusion criteria
exclusion without a good reason may be unfair or discriminatory
How to reduce biases and clinical errors?
randomization, ensuring that control and treatment group have similar baselines minimizing confounding variables & blinding
Control group
A control group in a scientific experiment is a group separated from the rest of the experiment where the independent variable being tested cannot influence the results. This isolates the independent variable’s effects on the experiment and can help rule out alternate explanations of the experimental results. (To ensure that the results seen is due to the independent variable and not other variables)
This group of scientific control enables the experimental study of one variable at a time and is an essential part of the scientific method
study plan
What type of control groups do we want? How do we perform randomization?
subject
Who do we include into the study? How many should we include into our study
variables measured
Precision and accuracy
statistical issues
Hypothesis testing and how do we use statistics to determine sample size?
How to ensure high internal validity
we would need to reduce both systemic and random error
Systematic error
are errors in the study due to bias
is a consistent, repeatable error associated with faulty equipment or a flawed experiment design.
these errors are usually caused by measuring
instruments that are incorrectly calibrated or are used incorrectly. However, they can creep into your experiment from many sources
produceconsistent errors, either a fixed amount (like 1 lb) or a proportion (like 105% of the true value).If you repeat the experiment, you’ll get the same error.
Random error
errors in the study that occur due to chance
has no pattern. One minute your readings might be too small. The next they might be too large. You can’t predict random error and these errors are usually unavoidable.
They areunpredictableandunavoidable even if the experiments are repeated
example: the temperature reading of a solution varies within each user or having small sample size increases the uncertainties of conclusions being drawn
How to reduce random errors
Using an average measurement from a set of measurements, or Increasing sample size.
3 types of systematic errors
Observer bias - incorrect reporting or perception of the measurement of the observer
Instrument bias - results from a faulty instrument or improperly calibrated instrument
Subject bias - distortion of the measurement by the study subject
Observer bias
incorrect reporting or perception of the measurement by the observer e.g habit of rounding up/down measurements
e.g habit of rounding up or down measurements
Instrument bias
results from a faulty instrument or improperly calibrated instrument
e.g using an improperly calibrated weighing scale
Subject bias
distortion of the measurement by the study subject
e.g a lung cancer patient exaggerates the number of cigarettes he smokes each day that causes his conditions
Importance of precision
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
3 types of random errors
Observer validity - variability in the measurement caused by the observer
Instrument validity - variability in the measurement due to changing environmental factors
Subject validity - intrinsic biology variability in the subjects caused by various factors
Observer validity
Variability in the measurement caused by the observer.
E.g. the observer’s skill in operating the instrument or data collection methods such as asking questions
Instrument validity
Variability in the measurement due to changing environmental factors.
E.g. temperature, aging mechanical components
Subject validity
Intrinsic biology variability in the subjects caused by various factors.
E.g. the subjects’ mood, daily food intake or time of last medication taken
Accuracy
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.
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.
Observer bias (accuracy)
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 (accuracy)
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 (accuracy)
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
Ideal control group
would comprise patients with the same characteristics of those in the experimental group, including age, race, maturation stage, same tendency for the disease
2 types of external control group
Defined external control and undefined external control
an external control group is a group of patients that is external to the study, of similar disease severity (at equipoise) but who received a different treatment.
Defined external control group
Group of patients treated at an earlier time i.e. historical control
Group of patients treated during the same time period but in another setting
Non-defined external control group
A comparator group based on general medical knowledge of outcome without reference to specific control population
Why it is not desirable to have an external control group?
- This group is not part of the same randomized study, and also not derived from the same population as the concurrently controlled group
- Since it is not from the same population, blinding and randomization are not possible. Thus, the risk of bias of the study is high.
- Furthermore, the groups may not be similar with respect to other factors than the study treatment, which greatly affects the study outcome
Not concurrently controlled
external
Concurrently controlled
different active treatment, different dose/religion, no treatment and placebo
Dose related concurrent control
Subjects are randomized to one of several fixed dose groups with or without a placebo group
Advantages to include a placebo group
It avoids studies that are uninterpretable because all doses produces similar effects so that one cannot assess whether all doses are equally ineffective or effective
It permits an estimate of the total pharmacologically mediated effect of treatment although the estimate may not be precise if the dosing groups are small
As the drug placebo difference is generally larger than inter dose difference, use of a placebo may permit smaller sample size
Placebo concurrent control
The name of the control suggests that its purpose is to control for “placebo” effect i.e. improvement in a subject resulting from thinking that he or she is taking a drug
Subjects are RANDOMLY assigned to a test treatment or to an identical – appearing treatment that DOES NOT contain the test drug
They seek to show a difference between
Treatments when they are studying effectiveness OR
Lack of difference in evaluating safety measurement
Active concurrent control
Subjects are randomly assigned to the test treatment or to an active control treatment
Such trials are usually double blinded, but this is not always possible
Objectives
To show efficacy of the test treatment by showing it is as good as a known effective treatment
To show efficacy by showing superiority of the test treatment to the active control
Used to compare safety of two treatment
When comparing the test drug with an active control, it is important to choose an appropriate dose and dose regimen of control and test drugs
Randomization
Randomization is a process where people are assigned to groups by chance (i.e. randomly assigned).
This helps prevent bias.
Ensures that each patient has an equal chance of receiving any of the treatments under study, generate comparable intervention groups, which are alike in all the important aspects except for the intervention each groups receives
Bias occurs when a trial’s results are affected by human choices or other factors not related to the treatment being tested. For example, if doctors could choose which patients to assign to which groups, some might assign healthier patients to the treatment group and sicker patients to the control group, without meaning to. This might affect trial results. Randomization helps ensure that this does not happen.
What is a external control group
An externally controlled trial is one in which the control group consists of patients who are not part of the same randomized study as the group receiving the investigational agent; i.e., there is no concurrently randomized control group. The control group is thus not derived from exactly the same population as the treated population. Usually, the control group is a well documented population of patients observed at an earlier time (historical control), but it could be a group at another institution observed contemporaneously, or even a group at the same institution but outside the study.
Limited by its ability to minimize bias (Patient, observer and analyst) and in many cases make the design unsuitable
Groups may be dissimilar with respect to a wide range of factors other than the study treatment that could affect outcome (many confounding variables)
Blinding & Randomization are NOT possible
Benefits of randomization
1) eliminates the selection bias
2) balances the groups with respect to many known and unknown confounding or prognostic variables
Bias
Biasis a prejudice in favor of or against one thing, person, or group compared with another usually in a way that’s considered to be unfair. Biases may be held by an individual, group, or institution and can have negative or positive consequences.
One’s age, gender, gender identity physical abilities, religion, sexual orientation, weight, and many other characteristics are subject to bias.
Conscious bias
Explicit bias
Unconscious bias
implicit bias
Social stereotypes about certain groups of people that individuals form outside their own conscious awareness. Everyone holds unconscious beliefs about various social and identity groups, and these biases stem from one’s tendency to organize social worlds by categorizing.
Unconscious bias is far more prevalent than conscious prejudice and often incompatible with one’s conscious values. Certain scenarios can activate unconscious attitudes and beliefs. For example, biases may be more prevalent when multi-tasking or working under time pressure.
4 ways of randomization
- Systematic sampling
- Simple random sampling
- Stratified sampling
- Cluster sampling
Simple random sampling
Select a subset of individuals (sample) from a larger set (population).
Each individual is chosen randomly and entirely by chance, such that each individual has the same probability of being chosen at any stage during a sampling process
It is a fair way to select a sample, it will be reasonable to generalize the results from the sample back to the population
Procedure – Use a table of random numbers e.g. a computer random number generator or mechanical device to select the sample
Example of random sampling
Establish our sampling frame
- Sampling frame – source material or device from which a sample is drawn, list of all those within a population who can be sampled.
- Go through the clinic’s records to identify every hypertensive patient that visited in the last 6 months
From these patients who had visited the clinic in the last 6 months to seek help for hypertension, we decide to sample 100 patients
To draw the samples, we may use computers to generate a series of random numbers
Systematic sampling
Systematic sampling involves selecting members from a larger population based on a random starting point but with a fixed and periodic interval.
System sampling is relatively more straightforward than random sampling but is also MORE likely to lead to bias and errors.
It should only be applied if the given population is homogenous.
Equation for systematic sampling
k = N/n k = 8000 / 1000 k - systematic sampling interval N = population size n = sample size
Meaning of sampling interval is 8
Hence every 8th, 17th, 25th etc. patient will be chosen.
The starting point is chosen at random (not necessarily need to start from the first patient!!)
Appropriate use of systematic sampling
This sampling is more likely to lead to bias as compared to simple random sampling
Errors can be caused by natural periodicities in the population and may allow the PI to predict and perhaps manipulate those who will be in the sample
It should only be applied if the given population is homogenous
Stratified sampling
Stratified sampling involves dividing a population into homogenous subgroups also known as strata.
- Each strata is formed based on the shared attributes or characteristics in every individual.
A random sample from each stratum is taken in a number that is proportional to the stratum’s size when compared to the population.
These subsets of the strata are then pooled to form a random sample.
Strata
Division of population into homogenous subgroups
- Group them first – strata
- Grouping them according to some characteristics e.g. colour, look etc.
Then use simple random sampling or systemic sampling
Subsets of strata
Each strata is formed based on subject’s shared attributes or characteristics.
A random sample from each stratum is taken in a number that is proportional to the stratum’s size when compared to the population.
These subsets of the strata are then pooled to form a random sample
Advantage of stratified sampling
An advantage of using stratified sampling is that the not only the strata represents the overall population, but also the key subgroups of the population.
Hence it is the (MOST representative of a population).
The size of the sample in each stratum is taken in proportion to the size of the stratum is called proportion allocation.
Example of stratified sampling
The size of each stratum is taken in proportion to the size of the stratum.
5 out of 10 blue stickmen were picked from sub-group 1.
3 out of 6 orange stickmen were picked from sub-group 2.
2 out of 4 green stickmen were picked from sub-group 3.
Therefore, the percentage of:
Blue stickmen = 50%
Orange stickmen = 30%
Green stickmen = 20%
Cluster sampling
Random sampling of natural groupings of individuals in the population.
Useful when the population is widely distributed and dispersed, and impractical to list and sample from all its elements
4 steps in cluster sampling
- Divide the population into clusters – using geographical boundaries
- Clusters are randomly selected to be sampled
- Clusters to be sampled are visited by field team who will make a list of all the addresses in each cluster
- From these addresses, the team randomly selects a subsample for the study
Appropriate use of cluster sampling
Useful when the population is widely distributed and dispersed and it is impractical to list and sample from all its elements
Process of cluster sampling
Divide the population into clusters – using geographical boundaries
Clusters are randomly selected to be sampled
Clusters to be sampled are visited by field team who will make a list of all the addresses in each cluster
From these addresses, the team randomly selects a subsample for the study
Blinding
Procedure in which one or more parties in a trial are kept unaware of which treatment arms participants have been assigned to.
Important aspect of any clinical trial to avoid conscious or unconscious bias in the design and execution of the clinical trial.
How to overcome random error
averaging
When is systematic sampling appropiate
Appropriate here as you only need a fraction of the total population in XYZ Primary School for the study.
