Medical Statistics

1. Statistics “Five”
Mohamed Ahmed Hefny, MD.

3. Designing a study

4. TARGET
POPULATION
All low-birth weight
babies born in the ---
in 2007.
STUDY
POPULATION
All low-birth weight
babies born in
three maternity
units in
---- in 2007.
SAMPLE
The last 300 babies
born in these three
maternity units.
The target population, the study population and the sample

5. Samples and populations
• In clinical research, we usually study a sample of
individuals who are assumed to be representative of a
wider group, to whom the research might apply. This wider
group is known as the target population.
• It would be impossible to study every single baby in such a
large target population (or every member of any
population). So instead, we might choose to take a sample
from a more accessible group. This more restricted group is
the study population.
• Suppose we take as our sample the last 300 babies born
in these three maternity units. What we find out from this
sample we hope will also be true of the study population,
and ultimately of the target population.

6. Population
Target Population
Study Sample

7. Sampling error
• Samples are never perfect replicas of their populations, so when
we draw a conclusion about a population based on a sample, there
will always be what is known as sampling error.
• For example, if the percentage of women in the with recurrent
abortion is 3.50 % and a sample produces a sample percentage of
2.90 %, then the difference between these two values, 0.60 per
cent, is the sampling error.
• We can never completely eliminate sampling error, since this is an
inherent feature of any sample.

8. Collecting the data – types of sample
Simple random sample and its offspring
• Any sample should be representative of the population from which it is
taken. i.e. if your population has equal numbers of male and female
babies, but your sample consists of twice as many male babies as
female, then any conclusions you draw are likely to be, at least,
misleading.
• Generally, the most representative sample is a simple random sample.
The only way that a simple random sample will differ from the
population will be due to chance alone.
• For a sample to be truly random, every member of the population must
have an equal chance of being included in the sample. Unfortunately,
this is rarely possible in practice, since this would require a complete
and up-to-date list (name and contact details). Such a list is called a
sampling frame.

9. The two close relatives of simple random sampling are:
Systematic random sampling: Fixed fraction of the
sampling frame is selected, say every 10th or every 50th
member, until a sample of the required size is obtained.
Provided there are no hidden patterns in the sampling
frame, this method will produce samples as representative
as a random sample.

10. Stratified random sampling, the sampling frame is first
broken down into strata relevant to the study, for example
men and women; or nonsmokers, ex-smokers and smokers.
Then each separate stratum is sampled using a systematic
sampling approach, and finally these strata samples are
combined. But both methods require a sampling frame.

11. Contact or consecutive samples
The need for an accurate sampling frame makes random
sampling impractical in any realistic clinical setting. One
common alternative is to take as a sample, individuals in
current or recent contact with the clinical services, such as
consecutive attendees at a clinic.
Alternatively, researchers may study a group of subjects in situ,
for example on a ward, or in some other setting.

12. Types of study

13. All studies
Analytic (PICO – PECO)Descriptive (PO)
ExperimentalObservational
Cross SectionalCohort Case Control
QualitativeSurvey
(Cross
Sectional)
Randomized
Parallel group
Randomized
Crossover
Population (P) Outcomes (O)
Interventions (I) Exposures (E)

14. • A non-analytic or descriptive study does not try to
quantify the relationship but tries to give us a picture of
what is happening in a population Example, the
prevalence, incidence, or experience of a group.
• Descriptive studies include case reports, case-series,
qualitative studies and surveys (cross-sectional) studies,
which measure the frequency of several factors, and
hence the size of the problem.

15. • An analytic study attempts to quantify the relationship
between two factors, that is, the effect of an intervention
(I) or exposure (E) on an outcome (O).
• To quantify the effect we will need to know the rate of
outcomes in a comparison (C) group as well as the
intervention or exposed group.
• Whether the researcher actively changes a factor or
imposes uses an intervention determines whether the
study is considered to be observational (passive
involvement of researcher), or experimental (active
involvement of researcher).

16. • In experimental studies, the researcher manipulates the
exposure, that is he or she allocates subjects to the
intervention or exposure group.
• Experimental studies, or randomized controlled trials
(RCTs), subjects are allocated to two or more groups to
receive an intervention or exposure and then followed up
under carefully controlled conditions.
• Such studies controlled trials, particularly if randomized
and blinded, have the potential to control for most of the
biases that can occur in scientific studies but whether this
actually occurs depends on the quality of the study design
and implementation.

17. • In analytic observational studies, the researcher simply
measures the exposure or treatments of the groups.
• Analytical observational studies include case-control
studies, cohort studies and some population (cross-
sectional) studies.
• These studies all include matched groups of subjects and
assess of associations between exposures and outcomes.

18. • Observational studies investigate and record exposures
(such as interventions or risk factors) and observe outcomes
(such as disease) as they occur.
• Such studies may be purely descriptive or more analytical.

19. Spotting the Study Design

20. Q1. What was the aim of the study?
• Describe a population (PO questions) ……. Descriptive.
• Quantify the relationship between factors (PICO
questions) ……. Analytic.

21. Q2. If analytic, was the intervention randomly
allocated?
1. Yes? ……. RCT
2. No? ……. Observational study

22. Q3. When were the outcomes determined?
• Some time after the exposure or intervention? cohort
study (Prospective study)
• At the same time as the exposure or intervention? Cross
sectional study or survey
• Before the exposure was determined? case-control study
(Retrospective study)

23. Advantages and Disadvantages of
the Designs

25. Randomized Controlled Trial
Experimental comparison study in which participants are
allocated to treatment/intervention or control/placebo groups
using randomization. Best for study the effect of an
intervention.
Advantages:
• Unbiased distribution of confounders;
• Blinding more likely;
• Randomization facilitates statistical analysis.
Disadvantages:
• Expensive: time and money;
• Volunteer bias;
• Ethically problematic at times.

28. Crossover Design
A controlled trial where each study participant has both therapies, e.g.,
is randomized to treatment A first, at the crossover point they then
start treatment B. Only relevant if the outcome is reversible with time,
e.g., symptoms.
Advantages:
• All subjects serve as own controls and error variance is reduced thus
reducing sample size needed;
• All subjects receive treatment (at least some of the time);
• Statistical tests assuming randomization can be used;
• Blinding can be maintained.
Disadvantages:
• All subjects receive placebo or alternative treatment at some point;
• Washout period lengthy or unknown;
• Cannot be used for treatments with permanent effects

30. Cohort Study
Data are obtained from groups who have been exposed, or not exposed,
to the new technology or factor of interest (e.g. from databases). No
allocation of exposure is made by the researcher. Best for study the
effect of predictive risk factors on an outcome.
Advantages:
• Ethically safe;
• Subjects can be matched;
• Can establish timing and directionality of events;
• Eligibility criteria and outcome assessments can be standardized;
• Administratively easier and cheaper than RCT.
Disadvantages:
• Controls may be difficult to identify;
• Exposure may be linked to a hidden confounder;
• Blinding is difficult;
• Randomization not present;
• For rare disease, large sample sizes or long follow-up necessary.

32. Case-Control Studies
Patients with a certain outcome or disease and an appropriate group of
controls without the outcome or disease are selected (usually with careful
consideration of appropriate choice of controls, matching, etc.) and then
information is obtained on whether the subjects have been exposed to the
factor under investigation.
Advantages:
• Quick and cheap;
• Only feasible method for very rare disorders or those with long lag
between exposure and outcome;
• Fewer subjects needed than cross-sectional studies.
Disadvantages:
• Reliance on recall or records to determine exposure status;
• Confounders;
• Selection of control groups is difficult;
• Potential bias: recall, selection.

33. Cross-Sectional Survey
A study that examines the relationship between diseases (or other health-
related characteristics) and other variables of interest as they exist in a
defined population at one particular time (i.e. exposure and outcomes are
both measured at the same time). Best for quantifying the prevalence of a
disease or risk factor, and for quantifying the accuracy of a diagnostic
test.
Advantages:
• Cheap and simple;
• Ethically safe.
Disadvantages:
• Establishes association at most, not causality;
• Recall bias susceptibility;
• Confounders may be unequally distributed;
• Neyman bias;
• Group sizes may be unequal.

34. Cross-Sectional Survey

35. Thank You