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Presentation
at the Annual Research Day of the University of Qasim, Buraydah on April 14,
2013 by Professor Omar Hasan Kasule Sr. MB ChB (MUK), MPH (Harvard), DrPH
(Harvard) Faculty of Medicine King Fahad Medical City Riyadh Saudi Arabia. EM:
omarkasule@yahoo.com
TRUTH, haqq, and OBJECTIVITY, istiqamat
·
Truth, haqq, and objectivity, istiqamat,
are the default position in a normal person and are a bed-rock of valid un-biased
research.
·
Truth is at 3 levels: ‘ain al yaqiin, haqq
al yaqiin, ‘ilm al yaqiin. Truth at the human level is relative, nisbiyat
al haqiiqat, because of limited knowledge.
·
The unknown is 2 levels: ghaib mutlaq
(e.g five known only by Allah) and ghaib nisbi (accessed through
empirical research)
CAUSES OF BIAS
·
Diseases of the heart, amradh al qalb, that can color and distort objective observation
and interpretation resulting in bias manifesting as (a) personal whims, hiwa al naf (b) conjecture not based on evidence, dhann, (c) laziness
and negligence, ghaflat (d) human error, khata’u (e)
forgetfulness, nisyaan (prevented by recording, kitaabat,
and witnessing, ishhaad)
·
Distorted world view can lead to bias. King
Nimrod’s concept of life and death based on his thinking he was a god. The
objective world view is the ruuyat kawniyat quraniyat.
·
False knowledge that manifests as: usturat, khurafat, kadhb, lahw, and wahm, blind following, taqlid.
·
Failure to base knowledge on evidence, hujjiyat
al burhaan: burhan, daliil, bayyinat,
tathabbut, and sidq, and hujjat.
BIASES IN HYPOTHESIS
FORMULATION
·
The scientific method consists of formulating
and testing hypotheses. Bias can start at the hypothesis formulation stage. The
researcher must acknowledge these biases and develop systematic approaches to
identifying and eliminating or curing them.
·
Using the refutationist null hypothesis
alongside the alternative hypothesis protects the researcher from the effect of
dogmatic assumptions.
·
The Bayesian approach in statistics enables a
researcher to acknowledge and incorporate prior beliefs to reach posterior
conclusions.
PROBLEMS OF THE EMPIRICAL METHODOLOGY 1
·
The empirical methodology is Qur’anic. The
Qur’an calls upon humans to observe the universe, al nadhar fi al anfaaq wa
al anfus afaaq & anfus. Ibrahim used empirical evidence to calm his
heart, liyatumainnat qalbi
·
The empirical methodology is innately good but
the manner and context of its use lead to the following 4 problems: biases due
to a priori assumptions, limitations of observation by human senses,
limitations of human intellect, and lack of an integrating paradigm
·
A priori
assertions or non-assertions, (assertions by default) bias the selection of
fields/issues of investigation, formulation of hypotheses, selection of
hypotheses for testing, reporting of data, interpretation of data, and use of
information. These assumptions arise from the western world view that is
basically materialistic and lacks an integrating paradigm like tauhid.
There is no morally/ethically neutral, hiyaad
akhlaqi because of these
assumptions.
·
Limitations of empirical observations: Empirical
knowledge is relativistic and probabilistic. It depends on human senses that
are limited and can be deceived. There are 3 sources of knowledge: wahy,
kawn, & ‘aql. Kawn is superior in quantity buy wahy is superior
in quality. ‘Aql is a tool used by both sources.
PROBLEMS OF THE EMPIRICAL METHODOLOGY 2
·
Lack of balance, tawazun: The way science
and technology are used today shows lack of balance which leads to
transgression. The Qur’an told the story of the people of thamud who were
technologically advanced but lacked spiritual and social balance and the
end-result was evil-doing.
·
Lack of purpose, abath: Technology seems
to have become an automaton with its own dynamism that is sometimes not related
to any understandable human purpose. This is what the Qur’an described as
building structures for amusement only with no underlying purpose.
·
Lack of an integrating paradigm, tauhid:
Knowing the parts and not the whole. Tauhid is needed as an integrating
paradigm to understand the whole.
·
Misclassification is inaccurate assignment of
exposure or disease status. Random or non-differential misclassification of
disease biases the effect measure towards the null and underestimates the
effect measure but does not introduce bias. Non-random or differential
misclassification is a systematic error that biases the effect measures away
from the null exaggerating or underestimating the effect measure. Positive
association may become negative and negative associations association may
become positive.
·
Misclassification bias is classified as
information bias, detection bias, and proto-pathic bias. Information bias is
systematic incorrect measurement on response due to questionnaire defects,
observer errors, respondent errors, instrument errors, diagnostic errors, and
exposure mis-specification. Detection bias arises when disease or exposure are
sought more vigorously in one comparison more than the other group. Protopathic
bias arises when early signs of disease cause a change in behaviour with regard
to the risk factor.
·
Misclassification bias can be prevented by using
double-blind techniques to decrease observer and respondent bias. Treatment of
misclassification bias is by the probabilistic approach or measurement of
inter-rater variation.
·
Selection bias arises when subjects included in
the study differ in a systematic way from those not included. It is due to
biological factors, disease ascertainment procedures, or data collection
procedures.
·
Selection bias due to biological factors
includes the Neyman fallacy and susceptibility bias. The Neyman fallacy arises
when the risk factor is related to prognosis (survival) thus biasing prevalence
studies. Susceptibility bias arises when susceptibility to disease is
indirectly related to the risk factor.
·
Selection bias due to disease ascertainment
procedures includes publicity, exposure, diagnostic, detection, referral,
self-selection, and Berkson biases.
·
The Hawthorne self selection bias is also called
the healthy worker effect since sick people are not employed or are dismissed.
·
The Berkson fallacy arises due to differential
admission of some cases to hospital in proportions such that studies based on
the hospital give a wrong picture of disease-exposure relations in the
community.
·
Selection bias during data collection is
represented by non-response bias and follow-up bias.
·
Prevention of selection bias is by avoiding its
causes that were mentioned above. There
is no treatment for selection bias once it has occurred. There are no easy
methods for adjustment for the effect of selection bias once it has occurred.
·
Confounding is mixing up of effects. Confounding
bias arises when the disease-exposure relationship is disturbed by an
extraneous factor called the confounding variable. The confounding variable is
not actually involved in the exposure-disease relationship. It is however
predictive of disease but is unequally distributed between exposure groups.
·
Being related both to the disease and the risk
factor, the confounding variable could lead to a spurious apparent relation
between disease and exposure if it is a factor in the selection of subjects
into the study.
·
A confounder must fulfil the following criteria:
relation to both disease and exposure, not being part of the causal pathway,
being a true risk factor for the disease, being associated to the exposure in
the source population, and being not affected by either disease or exposure.
·
Prevention of confounding at the design stage by
eliminating the effect of the confounding factor can be achieved using 4
strategies: pair-matching, stratification, randomisation, and restriction.
·
Confounding can be treated at the analysis stage
by various adjustment methods (both non-multivariate and multi-variate).
Non-multivariate treatment of confounding employs standardization and
stratified Mantel-Haenszel analysis. Multivariate treatment of confounding employs
multivariate adjustment procedures: multiple linear regression, linear
discriminant function, and multiple logistic regression.
·
Care must be taken to deal only with true
confounders. Adjusting for non-confounders reduces the precision of the study.
·
Mis-specification bias arises when a wrong
statistical model is used.
·
Use of parametric methods for non-parametric
data biases the findings.
·
Total survey error is the sum of the sampling
error and three non-sampling errors (measurement error, non-response error, and
coverage error). Sampling errors are easier to estimate than non-sampling
errors. Sampling error decreases with increasing sample size. Non-sampling
errors may be systematic like non-coverage of the whole sample or they may be
non-systematic. Non-systematic errors cause severe bias.
·
Sampling bias, positive or negative, arises when results from the sample are consistently wrong
(biased) away from the true population parameter.
·
The sources of bias are: incomplete or
inappropriate sampling frame, use of a wrong sampling unit, non-response bias,
measurement bias, coverage bias, and sampling bias.