Evolution of (bio)statistics in medical research Khurshid. Department of Mathematical and Physical...

DataCrítica: International Journal of Critical Statistics, 2013, Vol. 4, No. 1: 5-17 5

Evolution of (bio)statistics in medical research: Fifty eight years of “Numbering Off”

Anwer Khurshid. Department of Mathematical and Physical Sciences College of Arts and Sciences, University of Nizwa, Sultante of Oman. E-mail: [email protected], [email protected]

Mohammed I. Ageel. Department of Mathematics College of Science, Jazan University, Saudi Arabia. E-mail: [email protected]

Sumayya Anwer. Department of Physics Simon Fraser University, Burnaby Campus, Vancouver, Canada. E-mail: [email protected]

Mathematical advances reached their zenith in the seventeenth century. Pierre-Simon

Laplace and Pierre-Charles-Alexandre Louis, among others, advocated that probability

theory and numerical procedures could be useful in all scientific disciplines, including

medicine and clinical tests. However, they were opposed by most of the clinicians of the

day. Prominent among the opposition was Claude Bernard, arguably the father of modern

medicine, who urged doctors to reject statistics as a foundation for experimental, therapeutic

and pathological science. For over a century, his disciples neglected his words almost

entirely. In 1954, the British Medical Journal published “Numbering Off”, the proceedings of a

debate sponsored by the Royal Statistical Society on the growing application and influence

of statistics in medicine. In this article, we discuss the changes in the field since the

publication of the paper and the increase in mathematical sophistication and use of

computers. A brief history of biostatistics is also presented. Currently, researchers depend

on statistical software, which makes calculations extremely simplistic; but the increased use

of computer software has resulted in the misuse of biostatistics, when data is entered into

computers without understanding it and a result is generated, instead of the result. Briefly,

the future is also discussed.

Keywords: biostatistics, history, misuse of statistical methods

tatistical designs for producing reliable data

are perhaps the single most leading

contribution of statistics to the advancement of

knowledge (Moore and McCabe, 1993). Statistics

is indeed a 21st century discipline and the

impact of statistical sciences on a variety of

scientific disciplines has increased rapidly

during the last few decades. Medical and

biological sciences are no exception as statistical

principles and techniques are also being

increasingly employed with great success in

these fields. Physicians practice on the basis of

clinical knowledge, which is framed after a

series of tests, treatments and statistical

analyses. A physician may not have a sound

knowledge of statistical principles or techniques,

but the information he uses in the clinical

decision-making process is undoubtedly always

based on statistical evidence. However,

conclusions drawn from the statistical evidence

may be inaccurate or misleading and therefore,

without a sound understanding of statistics, a

physician may not be able to reach the most

appropriate decision.

The aim of statistics is to make data more

meaningful, useful, reproducible, and objective,

regardless of the scientific discipline to which

that set of data belongs to. Today, statistics is

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6 DataCrítica: International Journal of Critical Statistics, 2013, Vol. 4, No. 1: 5-17

applied to anything that contains data. Statistics

is not an end in itself, but helps other disciplines

and cannot be viewed in isolation. Neyman

(1955) appropriately called statistics the “servant

of all sciences”. As a scientific discipline, the

importance seems insignificant and is easily

overlooked due to the behind-the-scene nature.

Statistics is a discipline that has changed

science, medicine, and public policy. For

instance, statistical techniques indicated that

contaminated water was the source of cholera

long before the cholera bacillus was identified.

These results saved thousands of lives by

improving water supplies and controlling the

cholera epidemic. Today, like most aspects of

science, biostatistics is in rapid flux.

Biostatistical principles are necessary in all

branches of biology and medicine, and have

become a mandatory part of medical research.

The Human Genome Project, for example, while

relying on advanced biological techniques, also

depends heavily on statistical techniques for

extracting the right data out of a large pool of

gene sequences. The evolution of (bio)statistics,

is parallel to development in other scientific

fields, particularly medicine. Florence

Nightingale, a founding member of the Royal

Statistical Society, said that statistics could be

viewed as the most important science in the

whole world (Ridgway, Nicholson and

McCusker, 2007). Her wish of improving the

practice of medicine through the collection and

use of data has still not been fulfilled.

Statistics in biomedical research

tatistics in biomedical research, a scientific

discipline which affects everyone in one way

or another, began more than a century ago

(Sprent, 2003). Medicine brings many interesting

and often difficult problems, which employ

sophisticated statistical methods and models.

Statisticians try to analyze and interpret

observations from clinical experiments and

epidemiological studies and work with

physicians to contribute partially to the

development of medicine, not only as a science

but also as an improvement to health care itself.

The main aim of statistical methods in medical

research is to secure accuracy and proficiency of

statistical data evaluation and the interpretation

of the acquired results. Emphasis is mostly

placed on the statistical design of medical

studies, which aim to study the prevention of

civilizational disease incidence which include

cardiovascular diseases, metabolic disorders,

allergy manifestations and the incidence of

cancer diseases. Statistical methodologies are

used in designing systems for decision making,

such as the evaluation of risk factors in a

monitored patient for disease incidence and the

evaluation of genetic tendencies of monitored

disorders or anomalies.

Terminology

ne often faces a dilemma when asked to

define biostatistics. The problem begins

with the word itself. To quote Morgan (1986),

“The union of bio and statistics was a shotgun

wedding at the best”. Its roots are Greek where

the component bios involves biology; the study

of living things and the component statistics

involves the amassing, tracking, analysis, and

application of data. A large variety of terms in

scientific literature are interchangeably used:

biostatistics (Chiang, 1985), biometry (Armitage,

1985); biometrics, biological statistics, medical

statistics (Armitage, 1985; Cox, 2005), clinical

statistics (Armitage, 1983); biostatistical science

(Zelen, 1983; Zelen, 2006), sometimes even

biomedical statistics, medical biostatistics,

environmental statistics, pharmaceutical statistics

(Day, 2002), biopharmaceutical statistics (Gould,

2000), and public health statistics. The

terminology is inconsistent and is confusing at

best. Looking at these terms without an

understanding, it seems that they all deal with

different topics. Despite these differences, the

terms are used to mean the same thing.

Molenberghs (2005) remarked that “we view all

of these names as directed at the same general

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enterprise of the use and development of

statistical theory and methods to address design,

analysis and interpretation of information in the

biological science.”

Cummings and Rivara (2003) acknowl-

eged that “Biostatistics, like the rest of medicine,

is a changing field” and involves the

development and application of statistical

techniques of aa to scientific research in health-

related fields, including medicine,

epidemiology, and public health. A rapidly

expanding field, biostatistics is a sub-discipline

that is essential in clinical and non-clinical

medical research, and has become "a pillar of

medicine" (Editorial, 1966). From the beginning

of this century, the field of biostatistics has

become an essential tool in improving health

and reducing illness.

Generally speaking, biostatistics is the

application and development of statistical

techniques for biological sciences. Literally,

biostatistics is defined as “statistical method(s)

in medicine and the health sciences”

(Greenberg, 1982). Biostatistical activity spans a

broad range of medical and biological sciences

including clinical medicine, laboratory studies,

epidemiology, genetics, public health,

pharmacology, health care administration,

health policy, animal studies, health economics

and insurance, environmental health, nursing,

and dentistry, to name some. Feinstein (1983)

argued that “Some of these biostatistical

problems are more ‘bio’ than ‘statistical’.”

The preface to the Encyclopedia of

Biostatistics (Armitage and Colton, 1998, p. ix)

states: ...the term ‘Biostatistics’ [is used] to

denote statistical methods in

medicine and the health sciences.

This usage is standard in many, if

not most, parts of the world but of

course it is etymologically curious:

we make no attempt to cover

systematically the more general use

of statistics in biology, for which

the term ‘Biometry’ is perhaps now

more widely used. Our scope

might have been defined as

‘Medical Statistics’, a term which

we avoided as it is sometimes

taken to imply a more restricted

field.

In the early days, biometry was more often used

for biological or agricultural applications of the

science. Snedecor and Cochran (1937) declared

that “Biometrics is a delineation of living thing”.

Federer (1984) provided the definition,

“Biometry is the study, development and

application of procedures and techniques in

computer science, mathematics, operations

research, probability, statistical systems analysis

for biological investigations and phenomena.”

Solomon (1998) emphasized that the term

biostatistics was primarily used in North

America and medical statistics was used in

Europe and other parts of the world.

Biometry encompasses a wide variety of

applications of statistics to the biological

sciences. This includes the design and analysis

of biological experiments and surveys, the

quantification of biological phenomena, the use

of statistical principles in managing biological

processes, etc. Biometry originated from

statistical applications for agriculture, but its

scope now includes diverse areas such as

environmental sciences, food and water quality

assurance, pharmaceutical development and

risk assessment, international development, and

others. Exciting new areas are opening up to

developments in areas such as biotechnology

and computing.

A little bit of history

lthough the boundary between statistics

and biostatistics, both theoretically and

practically, is quite blurred, the history of

biostatistics is a huge part of the history of

statistics. The history of biostatistics is too

complex to be adequately summarized in this

paper. For an excellent and current review, refer

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to Chen (2003). Pearl (1923) traced the origin of

medical statistics to John Graunt’s 1662

observations on the London bills of mortality

and to Halley’s more systematic construction of

the life table for the city of Beslav in 1693.

English physician Francis B. Hawkins foresaw

the need for statistics in medicine, “Statistics has

become the key to several sciences… there is

reason to believe, that a careful cultivation of it,

would materially assist the completion of a

philosophy of medicine… Medical statistics

affords the most convincing proofs of the

efficacy of medicine” (Hawkins, 1829, pp. 2-3).

Later, French physiologist Claude Bernard,

argued that for medicine to be truly scientific, it

must be “based only on certainty, on absolute

determinism, not on probability” (Matthews,

1995). Bernard (1957) was skeptical about

statistics and believed that it was not a science,

“Statistics can never yield scientific truth." He

went on to urge doctors “to reject statistics as a

foundation for experimental therapeutic and

pathological science”. In contrast, the French

mathematician Pierre-Simon de Laplace (1749-

1827) claimed that our knowledge was full of

uncertainties, and believed that the probability

theory could be applied to the entire system of

human knowledge. Based on the probabilistic

argument, Laplace and other researchers,

particularly Pierre-Charles-Alexandre Louis

(1787-1872) and Louis Denis Jules Gavarret

(1809-1890), introduced statistics in medicine

(Matthews, 1995). Adolphe Quetelet (1796-1874)

originally applied statistical methods to

problems in medicine and biology. The English

scientist Francis Galton (1822-1911) strongly

believed that virtually everything could be

proven mathematically, that everything was

quantifiable. Kilgore (1920) noted that statistics

was of great practical significance and should be

required in the premedical curriculum.

Dunn (1929) published an extensive

review (consisting of approximately 694

references) on the fundamental principles of

analysis and interpretation of statistical data in

one of the major physiology journals. Statistical

probability was first employed in medical

literature in 1934 (Mainland, 1934). In 1937, the

journal Lancet published an article entitled

"Mathematics and Medicine", which surveyed

the role of mathematical methods in medicine

(Anonymous, 1937). During the same year,

Lancet published a series of 17 articles on the

principles of medical statistics, authored by Sir

A. B. Hill, which proved so popular that they

were immediately reprinted in book form under

the title A Short Textbook of Medical Statistics

(Hill, 1937). The twelfth edition of this

remarkable book was published in 1991 (Hill

and Hill, 1991). Recently, Farewell and Johnson

(2010) have traced the origins of the early

treatments on vital and medical statistics from

the 17th to early 20th centuries. Furthermore, they

made detailed comparisons between Hill's

Principles of Medical Statistics and a little known

book called An Introduction to Medical Statistics

(Woods and Russel, 1931). In 1948, Fisher (1948)

called biometry “the active pursuit of biological

knowledge by quantitative methods”. Luykx

(1949) saw the need to apply statistics to

medicine in a more rigorous way and wrote, “It

is now almost inconceivable that a study of any

dimension, in medical science, can be planned

without the advice of a statistician.” Mainland

(1952) commented that the growing use of

statistics in medicine was mixed blessings.

Subsequently, in 1954, the British Medical Journal

published the proceedings of a debate,

sponsored by the Royal Statistical Society, on

growing application and influence of statistics in

medicine, “Numbering Off” (Anonymous,

1954).

Numbering Off: The debate that started it all

he "Numbering Off" debate was organized

by the Study Circle on Medical Statistics of

the Royal Statistical Society (Anonymous, 1954).

The proceedings of the debate started with an

interesting notion: the doctors who had

qualified prior to the Second World War might

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have passed in medicine but had failed

mathematically.

The debate proposed the motion that

the house should welcome the increasing

influence of statistics in all medical fields.

Opening speaker in favor of the motion, Dr. R.

A. J. Asher, linked the field to magic because

statistics tended to wreak havoc with "cherished

conclusions". Dr. Asher suggested that statistics

should be welcomed as they influenced all

branches of medical sciences and life itself. He

listed the conventionally accepted applications

of the field as well as the moral field that he

found more significant. In his opinion, common

sense was not sufficient. He considered drawing

obvious conclusions "fallacious".

Mr. R. S. Murley provided the opening

comments for the opposition. While he was

unable to disagree with Dr. Asher entirely, his

argument hinged on the basis that medicine was

an art, statistics were scientific, and thus, the

two could not be effectively combined. Another

speaker for the opposition also raised the issue

of the misuse of statistics by statisticians

themselves. This speaker found that there

should not be any tables in a scientific paper.

In his final summation, Dr. Asher said

that everyone should be their own statistician,

whereas Mr. Murley ended on a note the

reviewer deemed “undemocratic” by saying that

statistics were fine for the "elite" but were just

more problematic for the average people.

The motion, which is the central idea for

this paper, was passed with a narrow margin on

a show of hands. Even though the author in

1954 questioned the significance of the results,

there is no doubt that the initial debate lead to

more discussions over the years, with much

more favorable results.

Wade (2000) recounted the debate: ... (In 1954) British Medical Journal

published excerpts from a debate

held by the Study Circle on

Medical Statistics as to whether the

then growing influence of statistics

in medicine was, in fact, welcome.

One speaker declared that,

“medicine was an art, statistics a

science; he conceded that latter had

its uses, but when it came to

mixing science and art, statistics

was as out of place as a skillet in a

Crown Derby tea-service”. He

concluded “statistics might be well

for the elite but were a menace to

the mob”. Someone else “referred

darkly to the deliberate misuse of

statistics, fostered – for what

purpose? – by statisticians

themselves. “Statistical publica-

tions”, he said, “could be

recognized by the prolixity of their

tables. In his view no papers

should contain any tables at all.”

The debate concluded with the

motion that the influence of

statistics should be welcomed in all

branches of medicine and this was

carried by a narrow majority on a

show of hands.

Years after the publication of “Numbering Off”,

significant changes have occurred in statistics,

biostatistics and the interface of these

disciplines. These 58 years have seen a great

deal of activity and an explosive growth in the

development of biostatistics that show no sign

of abatement as Hopkins (1958) stated that

“biostatistics is here to stay as an essential part

of the medical school curriculum”. Some

commentators believe that the development of

statistics in the 19th century might have had a

bigger influence on the practice of medicine than

the development of antibiotics. During the 20th

century, particularly in the latter half, a marked

progress had been made; clinical research

methods had improved significantly and new

methods were developed as the use of statistical

techniques continued to increase. Clinicians and

health policy leaders were asking for statistical


evidence that a certain intervention was

effective (Oxman and Guyatt, 1993). In 1984, the American Association for the

Advancement of Science polled leading U. S.

scientists and asked which were the most

important scientific, technological and medical

discoveries since 1900. The top 23 contributions

to our lives are listed, according to importance,

in Table 1.

In 2000, the New England Journal of

Medicine (NEJM) chose the application of

statistics to medicine as one of the eleven most

important medical developments during the last

millennium, along with milestone discoveries

such as the discovery of anesthesia and

antibiotics (Editorial, 2000). In the last two

centuries there were many problems (Feinstein,

1996) and disagreements in accepting statistics

as a necessary tool in medicine (Breslow, 2003).

Despite the advances made in medicine that

statistics has contributed to, physicians have

recently attacked the field and its importance,

once again. Penston (2010), for example,

published a book with the provocative title of

Stats.con: How we Have Been Fooled by Statistics-

based Research in Medicine. In a similar spirit,

Shuster (2011) called statistics the “weed in

biomedical research”. However, it seems that

these concerns result from misconceptions about

statistical tests and probability.

Statistical fallacies in medical research

tatistics is probably the most misused,

misunderstood and misinterpreted

discipline. The dilemma in the understanding

and application of statistical concepts has

frustrated scientists, leading to errors in

research. Sometimes these errors pass

undetected in medical research (Ludwig and

Collette, 2006). Lang and Secic (2006) added that

“since the 1930s, researchers in several fields of

medicine have found high rates of statistical

errors in large numbers of scientific articles,

even in the best journals. The problem of poor

statistical reporting is, in fact, long-standing,

widespread, potentially serious, and almost

unknown”. Farewell, Johnson, and Armitage

(2006) summed it well, “Misuse of statistics is

tendentious no matter what the position being

defended.”

TABLE 1

The 23 most significant scientific contributions to our life in the 20th Century

Order of importance

Discovery

1 antibiotics

2 double helix (DNA and RNA)

3 computers

4 oral contraceptives

5 nuclear (atomic) fission

6 power controlled flight

7 Einstein's theory of relativity

8 solid state electronics (transistors)

9 television

10 Hubble's "big bang" theory

11 quantum mechanics

12 drugs for mental illness

13 plastic

14 networks such as the internet

15 blood types

16 plant breeding

17 lasers

18 plate tectonics

19 the vacuum tube

20 pesticides

21 the Taung skull

22 statistics (chi-square test)

23 the IQ test

Source: Adapted from: Hacking, 1984; Barnard, 1992

It is repeatedly emphasized that the misuse of

statistics in medical research is unethical and

can have serious clinical consequences (see, for

example, Altman, 1981; Gardenier and Resnik,

2002). During the last half of the 20th century

both, the theory and practice of biostatistics,

have become increasingly more controversial.

For example, Yates and Healy (1964) wrote, “It

is depressing to find how much good biological

work is in danger of being wasted through

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incompetent and misleading analysis.” Twenty

years later, Hoogstraten (1984) echoed their

opinions when he said, “It is nearly impossible

to read an issue of leading cancer journals

without giving rise to serious questions about

study design, data collection, definitions of

response, determination of results, and the

reporting of results.” Altman and Bland (1991)

were alarmed that “… errors in statistical

analysis are common; many believe that as

many as 50% of the articles in the medical

literature have statistical flaws”. Later, Zolman

(1993) observed that “... about 25% of biological

research is flawed because of incorrect

conclusions drawn from confounded

experimental designs and the misuse of

statistical methods”. The questions and issues

raised by these individuals are still relevant

today.

In an editorial in the British Medical

Journal (BMJ), Altman (1994) makes a very

strong case against the scandal in medical

research. He contends that many flaws and

errors in the design, analysis and statistical

reporting research are because researchers “use

the wrong technique, use the right techniques

wrongly, misinterpret their results, report their

results selectively, cite the literature selectively,

and draw unjustified conclusions”. Finney

(1995) presents numerous examples of what he

calls “anarchies and horrors” in medical

research.

The British Medical Association, in its

November 1997 meeting, realized that 12% of

papers contained falsified data (Roberts, 1999).

The true level is much higher if statistical

malpractice (either intentionally or in ignorance)

is included. Rushton (2000) reported rates of

statistical errors in medical literature ranging

from 30% to 90%.

Recently, Olsen (2003) has shown deep

concern about the use of statistics:

The use of statistics in scientific and

medical journals has been subjected

to considerable review in recent

years. Many journals have

published systematic reviews of

statistical methods. These reviews

indicate room for improvement.

Typically, at least half of the

published scientific articles that use

statistical methods contain

statistical errors.

In a paper that strongly advocated statistical

integrity, Lang (2004) highlighted the most

common and easily identified statistical errors.

In his eyes, the most important issue was

confusing statistical significance with clinical

importance. Small changes in a large medical

trial group while statistically significant could

be meaningless clinically. Similarly, large

differences between small groups, while

unimportant statistically, could be of clinical

importance. Even if one person in a study of

terminally ill patients survives, the survival has

a clinical importance, even though the statistical

significance of the group may be the same as the

control.

Similar concerns were shown by Garcia-

Berthou and Alcaraz (2004) who examined

statistical errors in two renowned scientific

journals, Nature and BMJ. The authors found

that, despite statistical guidelines for medical

research, approximately 11% of the computation

were incongruent and at least one statistical

error appeared in 38% of Nature and 25% of

BMJ. They emphasized the need for better

quality control for the papers submitted.

Seeking the appropriate use of statistical

methods, Neville, Lang, and Fleischer (2006)

found that 38.1% contained errors or omissions

related to the statistical analyses in two

dermatology journals and they came to the

conclusion that the “misuse of statistical

methods is prevalent in the dermatology

literature …”.

In their book entitled The Cult of Statistical

Significance, Ziliak and McCloskey (2008) talk

about the misuse of statistics and its effects on

public health. A salmonella outbreak in South

Carolina was largely ignored because the


reliance on significance tests limited any action.

More recently Strasak, Zaman, Marinell, Pfeiffer,

and Ulmer (2007) pointed out that the

occurrence of statistical errors is high even in

renowned medical publications, namely NEJM

and Nature Medicine. Their findings are

disturbing. Of the papers reviewed, 16.1% from

NEJM and 27.3% from Nature Medicine were

guilty of statistical errors. They concluded that

“as statistical errors seem to remain common in

medical literature, closer attention to statistical

methodology should be seriously considered to

raise standards”.

The harsh comments above by noted

researchers reflect their frustration over the

misconceptions held by scientists. The findings

of any scientific investigation should be based

on the appropriate use of research methods. Not

all research data requires statistical analysis for

its validity, but proper use of statistical

techniques should be employed when required.

This fact is addressed in a recent article by Hirji

(2008) where he makes a distinction between the

twin pathologies of "numerosis" and

"numeritis".

It is equally important that the quality of

medical research should continue to improve.

The readers of medical literature should become

more cautious, diligent, and should apply their

knowledge appropriately when interpreting

statistical issues and developing a critical eye

when considering evidence from published

reports. The use of badly designed,

underpowered and inappropriately analyzed

studies is not only an indefensible waste of

precious resources but is also highly unethical

behavior. Unfortunately, such research is all too

common. In order to maximize the use of

available resources, researchers should make

every effort to design their experiments

properly, apply statistical techniques correctly,

and every effort should be made to prevent

these situations from arising. Statisticians have always given due

emphasis to the use of correct and appropriate

selection of statistical techniques by medical and

biological researchers. However, there is

considerable room for improvement and the

enlightened use of statistical techniques in

medical and biological sciences will add new

dimensions to the existing concepts and findings

in these disciplines.

Computers and abuse of biostatistics

dvances in computing and statistical

software have had a tremendous impact on

health science research in general, and

biostatistical analysis in particular. Zelen (2003)

stressed that “Probably the single biggest area

that has impacted biostatistics is the

development and widespread availability of

computing”. Without proper understanding of the

limitations of the analysis and required

assumptions, the potential for serious data

misuse is high (Jolliffe, 2001; Shimada, 2001). As

Hofacker (1983) eloquently writes, “The good

news is that statistical analysis is becoming

easier and cheaper. The bad news is that the

statistical analysis is becoming easier and

cheaper." Becker, Viljoen, Wolmarans, and

IJsselmuiden (1995) stated that “The user-

friendly nature of current statistical software has

brought statistical data analysis within easy

reach of biomedical researcher, resulting the

frequent use and knowingly or unknowingly,

abuse of biostatistics." Hacking (2001) observed

that

Today many investigators use a

statistical software package

without really understanding what

it does. You can just enter data, and

press a button to select a program.

As a result, some research seems

quite mindless. It looks for

associations, without having any

theoretical model in mind at all ....

The situation is sometimes even

worse with more sophisticated

statistical techniques contained in

easy-to-use software packages.

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Often people who use them have

no idea what the packages are for.

(p. 217).

Despite this concern, the blame for statistics

abuse rests mainly with the user of a software

rather than the software itself.

Future challenges for biostatistics

ince Altman (1982) remarked that “the

general standard of statistics in medical

journals is poor”, it seems that the level of

understanding has improved (Altman, 2000).

Nevertheless, Olsen (2003), Garcia-Bertgou and

Alcaraz (2004), Cooper, Schriger and Close

(2002) and Marshall (2004) feel that there is little

evidence that standards have improved over

time. Irrespective of its success or failure,

biostatistics cannot remain static but must

evolve to meet the changing needs of medical

researchers and scientists. The continuing

debate on the future of biostatistics is apt and

must be based on a historical appraisal of

biostatistics’ past (Louis, 2007). The future of

biostatistics is bright and it holds tremendous

potential as medical researchers develop new

and effective diagnostic tools especially in

societies that place importance on knowledge

and information (Rao and Szekely, 2000). A large part of the future of statistics lies

in interdisciplinary and collaborative research

and applications. It is therefore important for

statisticians to be adept at working with

scientists in other fields (Murray, 1990). While it

might require some effort to do so, it is

important to do this so that statistics can

continue to thrive in the future. It is vital that the

quality of medical research continues to

improve and readers develop a critical eye when

considering evidence from published reports. By

closing the gap between researchers in fields, we

can successfully put together collectively solid,

viable and useful research teams using the

partnership between biostatisticians,

epidemiologists, and clinicians.

Armitage (2001) stated that there are two

current sources of concerns. The first is the over-

mathematization of biostatistics. This trend is

reflected in journals such as Biometrika and

Biometrics, that initially sent out to be

comprehensible to the less academic

practitioners. Newer journals such as Statistics in

Medicine, Biostatistics and Statistical Methods in

Medical Research are more application oriented.

The second concern is that the evaluation of

biostatistics, which relies increasingly on

important contributions from computing, can

lead to the over-emphasis of the role of theory at

the expense of practice in the teaching of

epidemiological methods for researchers.”

Although theory may be the best guide in

practice, the stress in the application of

biostatistics should be on the prefix bio.

Friedman (2001) concurred by saying that we

should “moderate our romance with

mathematics”. However, Gehan (2001)

disagreed with Armitage’s notion, “Biostatistics

will progress more towards a branch of

information science than mathematics or

mathematical statistics."

Concluding remarks

espite lengthy discussions and arguments

by both statisticians, a lot of issues still

need to be addressed for this debate to come to a

conclusion, What have we achieved in the last

58 years of statistical research? Is there any

improvement in the statistical content of medical

papers during the last 58 years? What is the

extent of the contributions of statistics in

medical and biological research? How can

statistics be applied more effectively to more

practical fields where numerical results may not

be everything? And, finally, what does the

future hold for both statistics and disciplines

that employ it? These questions need to be

answered to provide a solution that can be

beneficial.

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Acknowledgements

The authors are greatly indebted to the

anonymous referee and the staff of DataCrítica

for their comments and editorial assistance. The

valuable comments and suggestions by Dr.

Rehan Qayyum are also appreciated.

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Anwer Khurshid is a former professor of

s9tatistics at University of Karachi, Pakistan.

Currently he has a faculty position at the

Department of Mathematical and Physical

Sciences at University of Nizwa, Oman. Prior to

that, he worked as a faculty member at the

Sultan Qaboos University, Oman. In addition to

theoretical contributions in statistics his current

research interests are in the area of statistical

epidemiology, statistical quality control and

biostatistics. He has coauthored two books with

Professor Hardeo Sahai with titles Statistics in

Epidemiology: Methods, Techniques and

Applications (1996), CRC Press, Florida, USA

and Pocket Dictionary of Statistics (2002),

McGraw-Hill/Irwin Illinois, USA. He is the

author or coauthor of over 75 papers.

Mohammed I. Ageel is the chairperson of the

Mathematics Department at Jazan University,

Saudi Arabia. Previously he was professor at

King Saud University, King Khalid University

and Najran University, Saudi Arabia. He is

founder and president of the Saudi Association

of Statistical Sciences. His current research


focuses on stochastic processes applications in

animal populations, biostatistics and medical

statistics. He has published more than 50

research articles in both, theoretical and applied

areas. He is coauthor with Hardeo Sahai of the

book The Analysis of Variance: Fixed, Random and

Mixed Models (2000), published by Birkhauser,

Boston.

Sumayya Anwer based in Vancouver, Canada, is

a freelance science writer with a background in

biology and physics. The relation between

nature, medicine and quantitative methods is of

particular interest to her, as well as the study of

the misuse of data in scientific applications.

RESUMEN

Los avances matemáticos llegaron a su apogeo en el siglo XVII. Pierre-Simon Laplace y

Louis Pierre-Charles-Alexandre, entre otros, abogaron por el uso de la teoría de la

probabilidad y los procedimientos numéricos en todas las disciplinas científicas, incluyendo

la medicina y las investigaciones clínicas. Ellos enfrentaron, sin embargo, la oposición de la

mayoría de los médicos de la época. Claude Bernard, a quien algunos consideran ser el

padre de la medicina moderna, se destacó entre la oposición e instó a los médicos a rechazar

las estadísticas como base para la ciencia experimental, terapéutica y patológica. Durante

más de un siglo sus discípulos ignoraron sus palabras casi en su totalidad. En 1954, el British

Medical Journal publicó "Numbering Off", las actas de un debate patrocinado por la Real

Sociedad de Estadística, sobre la aplicación e influencia creciente de las estadísticas en la

medicina. En este artículo se analizan los cambios en el campo desde aquella publicación, y

el aumento en la sofisticación matemática y el uso de las computadoras. Se presenta además

una breve historia de la bioestadística. Actualmente, los investigadores dependen de software

estadísticos que hacen extremadamente simples los cálculos. Pero el aumento en el uso de

éstos resulta en el uso indebido de la bioestadística cuando los datos se introducen en las

computadoras sin que los mismos se entiendan y éstas generan un resultado, en lugar de el

resultado. El artículo termina con una breve exposición sobre el futuro de la disciplina.

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