Dr. Abhaya Indrayan, PhD - University College of Medical Sciences and Max Healthcare - Professor and Head (Retd.) and Biostatistics Consultant

Dr. Abhaya Indrayan

PhD

University College of Medical Sciences and Max Healthcare

Professor and Head (Retd.) and Biostatistics Consultant

Delhi, Delhi | India

Main Specialties: Epidemiology, Public Health, Statistics

Additional Specialties: Medical Biostatistics

ORCID logohttps://orcid.org/0000-0002-5940-9666


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Dr. Abhaya Indrayan, PhD - University College of Medical Sciences and Max Healthcare - Professor and Head (Retd.) and Biostatistics Consultant

Dr. Abhaya Indrayan

PhD

Introduction

Abhaya Indrayan completed Master’s and PhD in Biostatistics from the Ohio State University. He has been working in medical institutions since the beginning of his career and has extensively interacted with his medical colleagues for their research endeavors. He is the founder Chair of the Department of Biostatistics and Medical Informatics at Delhi University College of Medical Sciences, and has endeavored to redefine the subject of biostatistics to medical issues so that it is perceived as a medical and not as a mathematical subject. His popular book Medical Biostatistics (CRC Press), now into the Fourth Edition, serves this purpose well and has been extensively hailed as ‘perhaps the most comprehensive book on biostatistics’ and ‘encyclopedic in breadth’. His Concise Encyclopedia of Biostatistics for Medical Professionals (CRC Press 2016) is making extensive appeal all over the world. Dr Indrayan has more than 230 publications to his credit and has completed a large number of biostatistics-related projects for the World Health Organization, the World Bank, and the UNAIDS.

Primary Affiliation: University College of Medical Sciences and Max Healthcare - Delhi, Delhi , India

Specialties:

Additional Specialties:


View Dr. Abhaya Indrayan’s Resume / CV

Education

Aug 1977
The Ohio State University, Columbus, Ohio, USA
PhD
Biostatistics
May 1976
The Ohio State University, Columbus, Ohio, USA
MS
Statistics

Experience

Sep 2014
Biostatistics Consultant
Max Healthcare
From 2014 to date
Jun 2005
Professor and Head
Department of Biostatistics and Medical Informatics
From 2005 to 2010
Feb 1995
Part time Temporary Adviser and Consultant
World Health Organization
From 1995 to 2015
Oct 1987
Professor Incharge
Computer Unit and Division of Biostatistics and Medical Informatics
From 1987 to 2005
Oct 1986
Professor
Department of Preventive and Social Medicine
From 1986 to 1995

Publications

49Publications

449Reads

279Profile Views

98PubMed Central Citations

Attack on statistical significance: A balanced approach for medical research.

Authors:
Abhaya Indrayan

Indian J Med Res 2020 04;151(4):275-278

Biostatistics Consultant, Max Healthcare, Saket, New Delhi 110 017, India.

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http://dx.doi.org/10.4103/ijmr.IJMR_980_19DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7371070PMC
April 2020
1.661 Impact Factor

Reporting of Basic Statistical Methods in Biomedical Journals: Improved SAMPL Guidelines.

Authors:
Abhaya Indrayan

Indian Pediatr 2020 01;57(1):43-48

Department of Clinical Research, Max Healthcare, Saket, New Delhi, India. Correspondence to: Dr Abhaya Indrayan, A-037 Telecom City, B-9/6 Sector 62, NOIDA 201 309, India.

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January 2020
1.014 Impact Factor

Statistical medicine: An emerging medical specialty.

Authors:
A Indrayan

J Postgrad Med 2017 Oct-Dec;63(4):252-256

Former Professor & Head, Department of Biostatistics and Medical Informatics, University College of Medical Sciences, Delhi, India.

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http://dx.doi.org/10.4103/jpgm.JPGM_189_17DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5664870PMC
September 2019
2 Reads
0.972 Impact Factor

Simple Biostatistics for MBBS, PG Entrance, USMLE

Authors:
Abhaya Indrayan

Brillion Publishing, Delhi, 2019

Book

CONTENTSTo Our Readers1       Introduction to Biostatistics1.1  Meaning of Biostatistics1.2  Sources of Medical Uncertainties1.3  Role of Biostatistics in the Management of Medical Uncertainties1.4  Biostatistics in Health Planning and Medical ResearchExercises2       Sources of Existing Medical Data2.1  Health Informatics System2.2  Patients’ Existing Records2.3  Web-Based Resources2.4  Government Records2.5  Reports of Various AgenciesExercises3       Design of Medical Studies3.1  Fundamentals of Design3.2  Sampling for Descriptive Studies3.3  Design for Analytical Studies; Prospective, Case-control, and Clinical TrialsExercises4       Data collection Types and Their Quality4.1  Tools of Data collection4.2  Types of Data: Qualitative/Quantitative, Nominal/Ordinal/Metric, Continuous/Discrete4.3  Quality of Data: Validity/Reliability, Sensitivity/Specificity, and PredictivitiesExercises5       Measure of Morbidity and Mortality5.1  Percentage, Rate and Ratio5.2  Morbidity Indicators5.3  Mortality Indicators5.4  Crude and Standardized Death Rates5.5  Expectation of LifeExercises6       Measure of Fertility, Demography and Social Health6.1  Fertility Indicator6.2  Demography6.3  Indicator of Social and Mental HealthExercises7       Summarization of Medical Data7.1  Tabular Representation7.2  Graphical Representation7.3  Summarizing Uncertainties: Probability  7.4  Numerical Summarization7.5  Reference RangeExercises8       Strength and Type of Relationships: Regression, Correlation and Association8.1  Types of Relationship in Quantitative Measurements: Regression8.2  Strength of Relationship Between Quantitative Measurements: Correlation8.3  Association Between Qualitative Attribute: Relative Risk and Odds Ratio8.4  Cause-Effect RelationshipsExercises9       Confidence Intervals (CIs):9.1  Point Estimation and Standard Error9.2  CI for Mean and Difference in Mean9.3  CI for Proportion and Difference in Proportions9.4  Sample Size in Estimation SetupExercises10    Test of Statistical Significance – I10.1         Court Judgement, Type I and Type II Errors, P-values, and Power10.2         General Principles of Statistical Tests10.3         Student’s t-test10.4         Outline of ANOVA F-test for More than Two groupsExercises11    Test of Statistical Significance – II11.1         Nonparametric Tests11.2         Chi-square Test11.3         Z-test for Proportion11.4         Medical significance and Statistical Significance11.5         Sample Size for Test of Hypothesis SetupExercisesHealth Statistics SheetFormula SheetSolution of ExercisesPractice Questions at Advance Level for USMLEAppendix Index

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August 2019
5 Reads

Response to the Editor (Biostatistics vs Biometrics). 2019;36(2);3-4

Authors:
Abhaya Indrayan

Biometric Bulletin 2019;36(2);3-4

Biometric Bulletin

Response to the EditorThe Editor has raised a very pertinent question regarding prevailing confusion over the domain of Biometrics and Biostatistics (Biometric Bulletin, 36(1), 2019). The overlap in the use of these terms is indeed substantial and sometimes they are interchangeably used. My views on this issue are as follows. I add Medical Biostatistics to this conundrum for more clarity.To provide a broader perspective, perhaps it would help to start with a clear understanding of what statistics is about. Although the term is used as plural for various kinds of data, I restrict to its use in singular form. From the epitome of data crunching (Statistics for Big Data for Dummies by Anderson  and Semmelroth, Wiley, 2015 ), the subject of statistics has travelled a long way to now rightly understood as the one that helps in managing uncertainties (A Beginners Guide to Uncertainty of Measurement by Bell, NPL, 1999). To this I must qualify that statistical methods are equipped to handle only the data-based uncertainties, which are commonly seen in all empirical sciences, and other kinds of uncertainties, if any, are excluded. Management is in terms of their measurement of uncertainties by probability and their control by following a proper study design. Statistical methods are a definite help in reaching to a decision in the face of such uncertainties. Although the methods are primarily for uncertainties generated by sampling fluctuations due to intrinsic and man-made variations but other uncertainties in the data, particularly those in epistemic (unknown) domain, remain confounded in most situations. Failure to adequately handle epistemic uncertainties is the primary cause for deprecable comments about Statistics’ credibility as a decision science (Editorial, Basic and Applied Social Psychology, 2015). Errors and biases further complicate the decision process, but I keep those aside for the present communication.In my opinion, the subject Biometrics is primarily statistical part of biology. It is akin to the other ‘metrics’ sciences such as Econometrics, Psychometrics and Technometrics. Though ‘metrics’ signifies the science of measurement, Biometrics in particular conventionally extends to statistical aspects of all living organisms and does not restrict to measurement. As described by the Editor, it incorporates subjects such as ecology, animal science, and forestry besides the human beings. Some papers recently appearing in the journal Biometrics pertain to these subjects (see, e.g., Scharf et al., Animal measurements, 2019, and Stevenson et al., Aerial survey of animal populations, 2019). Most papers in this journal are on development of new statistical methods for studying biology rather than on application of the existing methods for new results. That, I think, delineates the subject of Biometrics very well. Management of uncertainties remains an integral component in development of these methods.Biostatistics, on the other hand, has come to establish itself as a statistical science concerned predominantly with the issues of human health and conventionally excludes issues pertaining to the other biological creatures despite the ‘Bio’ suffix. It does include animal experimentation though because such experiments are sometimes considered a precursor to human experimentation. Boston University on its website defines Biostatistics as “application of statistical principles to questions and problems in medicine, public health or biology”, and Washington University School of Public Health website says “Using the tools of statistics, biostatisticians help answering pressing research questions in medicine, biology, and public health”. Both emphasize applications although both include biology within its domain. But I consider Biostatistics as a science exclusive to human beings. It is clear that Biostatistics is primarily application of the existing methods rather than development of new methods. There is hardly any place for theorems and lemmas in Biostatistics – other experts may differ. I consider Medical Statistics a British name for Biostatistics. Whereas American universities mostly have departments of Biostatistics, British universities mostly have departments of Medical Statistics.Much of the confusion is removed by the term Medical Biostatistics that has come up in the past couple of decades. There are now departments of Medical Biostatistics (e.g., The University of Vermont) and books (e.g., Medical Biostatistics by Indrayan and Sarmukaddam, CRC Press, 2002). This subject can be defined as that part of Biostatistics that is predominantly concerned with promotion, prevention, diagnosis, treatment and prognosis of health conditions in individual human beings and communities. Referring to “Medical + Bio” > “Statistics”, this book explains that Medical Biostatistics is more medical than statistical and seeks to integrate the subject with medicine, away from mathematics. This subject is more focussed to medicine than Medical Statistics has been, and the primary audience is medical and health professionals. The dominant feature of Medical Biostatistics is the management of data-based medical uncertainties in terms of their measurement by probability and control by design as already mentioned for Statistics, but the emphasis now is on medical uncertainties – both aleatoric and epistemic.In summary, Biometrics is for development of new statistical methods for studying biology, Biostatistics is application of the existing methods to the health issues of individual humans and their population, and Medical Biostatistics is oriented to the issues faced by medical and health professionals. The core of all is the management of data-based uncertainties.~ Abhaya Indrayan Biostatistics Consultant, Max Healthcare, New Delhi Former Professor and Head, Department of Biostatistics and Medical informatics, Delhi University College of Medical Sciences, Delhi Response to the Editor’s Proposition 2

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August 2019
4 Reads

Evaluation of quality of multivariable logistic regression in Indian medical journals using multilevel modeling approach.

Indian J Public Health 2016 Apr-Jun;60(2):99-106

Director Professor, Department of Community Medicine, University College of Medical Sciences, New Delhi, India.

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http://dx.doi.org/10.4103/0019-557X.184538DOI Listing
March 2019
21 Reads

Statistical fallacies & errors can also jeopardize life & health of many.

Authors:
Abhaya Indrayan

Indian J Med Res 2018 Dec;148(6):677-679

Biostatistics Consultant, Max Healthcare, Saket, New Delhi 110 017 & Former Professor, Department of Biostatistics & Medical Informatics, University College of Medical Sciences, Delhi 110 095, India.

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http://dx.doi.org/10.4103/ijmr.IJMR_853_18DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6396556PMC
December 2018
1.661 Impact Factor

Diagnostic questionnaire and its validation. Biostatistician's perspective.

Authors:
Abhaya Indrayan

Indian Pediatr 2014 Jul;51(7):536-8

Former Professor and Head, Department of Biostatistics and Medical Informatics, University College of Medical Sciences, Delhi, India.

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July 2014
8 Reads
1.014 Impact Factor

Demystifying LMS and BCPE methods of centile estimation for growth and other health parameters.

Authors:
Abhaya Indrayan

Indian Pediatr 2014 Jan;51(1):37-43

Former Professor and Head, Department of Biostatistics and Medical Information, University college of Medical Sciences, Delhi, India. Correspondence to: Dr A Indrayan, A-037 Telecom City, B-9/6 Sector 62, NOIDA 201 309, India.

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http://dx.doi.org/10.1007/s13312-014-0310-6DOI Listing
January 2014
32 Reads
5 Citations
1.014 Impact Factor

Receiver operating characteristic (ROC) curve for medical researchers.

Indian Pediatr 2011 Apr;48(4):277-87

Department of Biostatistics and Medical Informatics, University College of Medical Sciences, Delhi, India.

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http://dx.doi.org/10.1007/s13312-011-0055-4DOI Listing
April 2011
18 Reads
56 Citations
1.014 Impact Factor

Statistical fallacies in orthopedic research.

Authors:
Abhaya Indrayan

Indian J Orthop 2007 Jan;41(1):37-46

Department of Biostatistics and Medical Informatics, University College of Medical Sciences, Delhi - 110 095, India.

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http://dx.doi.org/10.4103/0019-5413.30524DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2981893PMC
January 2007
16 Reads
0.624 Impact Factor

Effect of vitamin A supplementation on childhood morbidity and mortality: critical review of Indian studies.

Indian Pediatr 2002 Dec;39(12):1099-118

Department of Pediatrics, University College of Medical Sciences and GTB Hospital, Delhi 110 095, India.

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December 2002
10 Reads
4 Citations
1.014 Impact Factor

A nomogram for single-stage cluster-sample surveys in a community for estimation of a prevalence rate.

Int J Epidemiol 2002 Apr;31(2):463-7

Division of Biostatistics and Medical Informatics, University College of Medical Sciences, Dilshad Garden, Delhi, India.

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April 2002
11 Reads
1 Citation
9.180 Impact Factor

Estimates of the years-of-life-lost due to the top nine causes of death in rural areas of major states in India in 1995.

Natl Med J India 2002 Jan-Feb;15(1):7-13

University College of Medical Sciences, Dilshad Garden, Delhi, India.

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March 2002
15 Reads
2 Citations

12. Multiple measurements and their simultaneous consideration.

Indian Pediatr 2001 Jul;38(7):741-56

Division of Biostatistics and Medical Informatics, University College of Medical Sciences, Dilshad Garden, Delhi 110 095, India.

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July 2001
10 Reads
1.014 Impact Factor

Statistical inference from qualitative data: proportions, relative risks and odds ratios.

Indian Pediatr 2000 Sep;37(9):967-81

Division of Biostatistics and Medical Informatics, University College of Medical Sciences, Dilshad Garden, Delhi 110 095, India.

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September 2000
9 Reads
1.014 Impact Factor

Basic philosophy of statistical tests, confidence intervals and sample size determination.

Indian Pediatr 2000 Jul;37(7):739-51

Division of Biostatistics and Medical Informatics, University College of Medical Sciences, Dilshad Garden, Delhi 110 095, India.

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July 2000
14 Reads
1.014 Impact Factor

Hypothesis testing (Part I).

Authors:
A Indrayan P Gupta

Natl Med J India 2000 Mar-Apr;13(2):86-93

Department of Biostatistics and Medical Informatics, University College of Medical Sciences, Dilshad Garden, Delhi.

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June 2000
9 Reads

Sampling techniques, confidence intervals, and sample size.

Authors:
A Indrayan P Gupta

Natl Med J India 2000 Jan-Feb;13(1):29-36

University College of Medical Sciences, Dilshad Garden, Delhi, India.

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April 2000
11 Reads
1 Citation

Reference values in medicine and validity of diagnostic test.

Indian Pediatr 2000 Mar;37(3):285-91

Division of Biostatistics and Medical Informatics, University College of Medical Sciences, Dilshad Garden, Delhi 110 095, India.

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March 2000
4 Reads
1.014 Impact Factor

Indicators of social and mental health.

Authors:
A Indrayan

World Health Forum 1998 ;19(4):430-1

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March 1999
13 Reads

Is the heredity of reticulate acro pigmentation of Kitamura always autosomal dominant?

J Dermatol 1998 Jan;25(1):57-9

Department of Dermatology & STD, University College of Medical Sciences, Delhi, India.

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http://dx.doi.org/10.1111/j.1346-8138.1998.tb02348.xDOI Listing
January 1998
4 Reads
1 Citation
2.354 Impact Factor

Meta-analysis of prevalence of hypertension in India.

Authors:
A Indrayan

Indian Heart J 1997 May-Jun;49(3):337-8

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September 1997
11 Reads
2 Citations

Informatics technology in health care in India.

Natl Med J India 1997 Jan-Feb;10(1):31-5

University College of Medical Sciences, New Delhi, India.

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May 1997
9 Reads
1 Citation

Computer-assisted learning package for frequency distribution of physiological variables.

Authors:
R Bajpai A Indrayan

Indian J Physiol Pharmacol 1996 Oct;40(4):330-4

Division of Biostatistics & Medical Informatics, University College of Medical Sciences, Delhi.

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October 1996
9 Reads

Clinical agreement in quantitative measurements.

Authors:
A Indrayan R Chawla

Natl Med J India 1994 Sep-Oct;7(5):229-34

Department of Biostatistics, University College of Medical Sciences, New Delhi, India.

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February 1995
11 Reads

Epidemiology of hypertension.

Authors:
A Indrayan

J Assoc Physicians India 1994 Feb;42(2):175-6

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February 1994
9 Reads

Measurement of systolic blood pressure with a pulse oximeter.

J Clin Monit 1992 Apr;8(2):147

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http://dx.doi.org/10.1007/BF01617436DOI Listing
April 1992
9 Reads

Computer based application software for histopathological reporting system.

Indian J Pathol Microbiol 1991 Oct;34(4):241-6

University College of Medical Sciences, Shahdara, Delhi.

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October 1991
5 Reads
0.642 Impact Factor

Childbirth spacing in a rural community of Delhi: profile in various marriage cohorts by decades.

Indian Pediatr 1989 Sep;26(9):894-9

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September 1989
9 Reads
1.014 Impact Factor

Weight pattern of pre-school children in a rural area of Delhi.

Indian J Pediatr 1983 Jul-Aug;50(405):367-70

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http://dx.doi.org/10.1007/BF02753373DOI Listing
May 1984
10 Reads
0.920 Impact Factor

Effect of garlic on normal blood cholesterol level.

Indian J Physiol Pharmacol 1979 Jul-Sep;23(3):211-4

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March 1980
11 Reads
2 Citations

A study of urinary creatinine and creatinine coefficient in healthy young adults.

Indian J Physiol Pharmacol 1978 Jul-Sep;22(3):270-8

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February 1979
9 Reads

A study of fly density & meteorological factors in occurrence of diarrhoea in a rural area.

Indian J Public Health 1975 Jul-Sep;19(3):115-21

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October 1976
10 Reads

A study of the comparative values of coliform and faecal streptococci as presumptive pollution indicator in rural drinking water.

Indian J Med Res 1976 Jul;64(7):1035-40

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July 1976
10 Reads
1.661 Impact Factor

An epidemiological study of domestic accidents in the families of army personnel at a peace station.

Indian J Med Res 1976 Jun;64(6):858-65

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June 1976
8 Reads
1 Citation
1.661 Impact Factor

Multivariate analysis of blood pressure correlates in an Indian urban community.

Indian J Public Health 1974 Apr-Jun;18(2):93-104

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September 1975
10 Reads
2 Citations

Epidemiological triad in domestic accidents.

Indian J Med Res 1975 Sep;63(9):1344-52

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September 1975
11 Reads
1 Citation
1.661 Impact Factor

Multifactorial analysis of blood pressure level in Allahabad urban community.

Indian J Public Health 1974 Jan-Mar;18(1):3-7

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June 1975
9 Reads
2 Citations

Clinical patterns of abdominal tuberculosis.

Am J Proctol 1975 Apr;26(2):75-86

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April 1975
10 Reads
1 Citation

Impact of lepromatous leprosy on fecundity.

Fertil Steril 1973 Apr;24(4):324-5

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http://dx.doi.org/10.1016/s0015-0282(16)39620-0DOI Listing
April 1973
7 Reads
4.590 Impact Factor

A sociomedical study of deafmutes.

Indian J Med Res 1973 Feb;61(2):285-91

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February 1973
9 Reads
1.661 Impact Factor

Age regression of blood pressure in an urban population of age 15-59 years.

Indian J Med Res 1972 Jun;60(6):966-72

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June 1972
9 Reads
2 Citations
1.661 Impact Factor

Influence of some corelates of blood pressure on its distribution in an adult urban population of Allahabad.

Indian J Med Res 1972 Apr;60(4):651-60

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April 1972
9 Reads
2 Citations
1.661 Impact Factor

Effects of oral contraceptives on carbohydrate and lipid metabolism.

Indian J Med Res 1971 Nov;59(11):1712-9

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November 1971
8 Reads
1.661 Impact Factor

A study of serum copper and P.P.D. oxidase in different types of leprosy.

Indian J Pathol Bacteriol 1971 Oct;14(4):174-9

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October 1971
8 Reads

Mathematical models in the assessment of infective force in filariasi.

Indian J Med Res 1970 Aug;58(8):1100-3

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August 1970
9 Reads
1.661 Impact Factor

Statistical medicine: An emerging medical specialty. J Post Grad Med 2017; 63:252-6.

Authors:
Abhaya Inrayan

J Post Grad Med 2017; 63:252-6

Journal of Post Graduate Medicine

:: Introduction Statistical methods now widely pervade medical thought and practice on the strength of their ability to manage most empirical uncertainties [1] and such management improves outcome in substantial cases. Yet, surprisingly, statistical medicine has not been proposed as a medical specialty, not even as an idea whose time has come. If academic medicine,[2] which deals with medical education and not directly with health and disease in individuals or communities, can be accepted and flourish as a discipline, there is no reason that much more direct statistical medicine will not be able to make a respectable place for itself in the course of time, and establish itself as a self-sustained medical subject. Computer medicine arose in the nineties as a specialty although could not sustain the momentum due to unmanageable intricacies, but that is unlikely to happen with statistical medicine. This communication proposes and tries to justify that statistical medicine can be positioned as a specialty by itself that is capable of taking medical decisions in many cases primarily based on statistical arguments. Statistical medicine is not restricted to the usual rigmarole of using group observations for decisions on individual patients in the hope that the future cases will follow nearly the same pattern but comprises a large number of statistical tools that can determine many medical decisions.Realize at the outset that this article is not talking about medical statistics that is either understood as medical data as plural, nor about the intricacies of statistical methods applied to medicine as singular whereby inferences are drawn by confidence intervals and tests of significance. This article is also not discussing the details of various statistical methods such as ANOVA, quantitative regression, logistic regression, survival analysis, and meta-analysis that have found extremely useful applications in medical research. Many books and articles are available on these methods, and it would be a wastage of efforts to talk about such methods. This article is also not about statistical fallacies that so frequently occur in medical literature due to misuse and abuse of statistical methods as these also have been talked about at length by many workers and a book [1] devotes full 32-page chapter on these fallacies. Instead, the focus of this article is the widespread use of statistical arguments that play a significant role in diagnosis and prognosis, in prevention and control of diseases, in promotion of health, and in medical research, This article proposes to establish that statistical medicine is not just semantically reverse of medical statistics but a leap forward in managing health and disease, both at individual level and at community level. The attempt here is to show that many medical decisions are best taken with the help of statistical tools, and statistical medicine can provide relief to the patients in particular and to the humanity in general. This aspect is seldom realized or appreciated.Statistical medicine is not a virgin term. It has been loosely used in different contexts, primarily for medical data. According to Braud,[3] Pierre Louis is remembered as an early ardent proponent of statistical medicine as a key element of his medical observations. This was cited to contrast statistical medicine with intuition, and the term seems to be used primarily for data based inferences. Cinteza and Jinga [4] acknowledged in a recent article that today's medicine is statistical; although, they think that this would get obituary with personalized medicine. Their usage was to underscore that medical decisions are based on empiricism, but does not propose that statistical medicine can be a science by itself.Earliest traceable reference to the term “statistical medicine” seems to be in 1822 in the First Report of the Royal Metropolitan Infirmary for Sick Children [5] that has this title although it presented statistics of medical care. Cartwright [6] in 1840 described that “statistical medicine furnishes the key which opens to public view in a manner the most convincing, simple and summary the actual results of the regular and empirical practice.” This use too apparently is for conventional medical data. Datta et al.[7] recently emphasized statisticians' expertise in extracting information from data and converting it to medical knowledge and used the term statistical medicine for this process, and Shalabh [8] used this term for statistical tests such as Chi-square. Mellman [9] cited statistical fallacies to call statistically based medicine bogus and used the term statistical medicine for such anomalies. This article is not about such uses of the term but is about a subject as explained next.Medical science is generally understood as a tool to prevent, diagnose, and manage a disease,[10] where the disease is any aberration that restricts normal functioning, and management includes activities of treatment, disability limitation, and rehabilitation. At the individual level, medicine comprises efforts to put the physical and mental systems back on track when derailment occurs due to injury, pathogens, stresses, degenerations, etc., that our homeostatic system fails to manage. All these efforts produce varying results, and uncertainties remain a prominent constituent. These uncertainties arise from the fact that medicine for the most part evolves from the study of groups rather than isolated individuals, and knowledge gained from the past groups is applied to the future cases in the hope that they would behave in the same manner. Note that this argument is doubly probabilistic – first because group results are applied to individuals, and second because past experience is used on future cases. However, this is just part of the story: the other part is the increasing use of statistical tools such as scoring systems that by themselves can drive medical decisions.The foregoing discussion may have given an indication of how statistical methods form the basis of some aspects of medicine because of omnipresent uncertainties. A universally accepted measure of uncertainties is the probability, which is the sheet anchor of the science of statistics. Management of empirical uncertainties in clinical activities and in medical research requires a science that understands randomness and uncertainties – which statistics is known for.With this background, statistical medicine can be defined as that part of medical science that uses statistical tools and methods to take decisions regarding health and disease in individuals and communities. Thus, it comprises those statistical tools and methods that help to improve the medical outcomes. This is different from clinical epidemiology that uses epidemiological principles for clinical decisions. Clinical epidemiology does require some statistical methods but is not a direct application of the statistical tools to medical care as proposed in this article. The specifics and examples of statistical medicine are as follows, which show how it is practiced.:: Statistical Medicine in Clinics A large number of clinical issues can be cited that are aptly handled with statistical tools. These can be divided into four broad groups – reference values, medical indicators, scoring systems, and probabilities. All these help in diagnosis, treatment, and prognosis – some even define the health conditions and determine the consequences.Reference rangesReference ranges for various medical parameters are used every day in clinics for determining whether a new patient has values well within the normal range, at borderline, or outside permissible limits for healthy persons, and how much – thus whether the patient needs immediate intervention and how much, or can wait and watch.How do we know that the normal range of, say, total bilirubin levels is 0.20–1.10 mg/dL? Barring few exceptions such as blood pressure (BP) levels and blood sugar levels that have clinical thresholds based on prognostic implications, normal range for quantitative medical measurements is generally established as 2.5th and 97.5th percentiles (mean ±2 standard deviation in the case of Gaussian distribution) of the values seen in the healthy segment of the target population.[11] For example, Prsa et al.[12] recently used this method to obtain reference ranges of parameters that measure blood flow in the major vessels of the normal human fetal circulation at term. Since these limits promise to include 95% values in healthy subjects, anybody crossing these limits is suspected to be not normal and is a candidate to start therapy. Thus, the clinical decision is almost exclusively based on statistical consideration in these cases: complaints, if any, provide supplementary information. This process seems to be doing well as there is hardly any complaint regarding the validity of these limits despite the limitation that they exclude 5% healthy values equally divided at both the extremes and despite that some non-healthy people may also have values within these limits. Such a possibility of missed diagnosis and misdiagnosis exists with the clinical thresholds as well, since some people with, say, BP >140/90 mmHg may be absolutely healthy and some requiring intervention despite BP ≤140/90 mmHg.When the ranges of levels in healthy and disease people are available, they will most likely overlap, and the best cutoff can be obtained at the intersection of the two distributions [Figure 1] to minimize the misclassifications. This too is a statistical exercise and does not rule out misdiagnosis and missed diagnosis. The method of receiver operating characteristics (ROC) curves that locates the threshold of a quantitative tests where the sum of sensitivity and specificity is the highest (Youden index) is also statistical, and determines values to be directly used for medical decisions when the area under the ROC curve is high and the sensitivity–specificity at the threshold is satisfactory. Karasahin et al.[13] used Youden index to find a cutoff of 78.31 mg/L of C-reactive protein beyond which 30-day mortality in patients undergoing percutaneous endoscopic gastrotomy increased by about 9 times. Similar is the usage of Z- score and T-score for, say growth assessment of children and for bone mineral density. They assess measurement of a patient in relation to healthy subjects in the population and allow us to take a graded medical decision regarding the kind of intervention required for treatment, including none at all.Figure 1: The pattern and overlap of measurement in healthy and nonhealthy subjects (the best cutoff is at the point a)Click here to viewMedical indicators and indexesAlthough practical usages of the terms indicator and index substantially overlap, an indicator is a univariate quantitative measure of a specific aspect of health, and an index is a meaningful combination of two or more indicators for enhanced context. In this sense, all directly obtained measurements are indicators and those calculated are indexes. Thus, BP level is an indicator and shock index is an index, birth weight is an indicator and birth weight ratio (ratio actual weight to expected weight for gestational age) is an index. Besides an obvious body mass index, measures such as waist-hip ratio and waist-height ratio are indexes for obesity. My review of the medical literature suggests that there might be more than 1000 kind of indexes in use. In many situations, these indexes can be used directly for medical decisions regarding starting or stopping treatment, to discharge or not from the hospital, to warn about grave prognosis, etc. For example, the bispectral index can be used for distinguishing levels of consciousness in severely damaged brain patients [14] and ankle-brachial index for the detection of peripheral arterial disease of lower extremities.[15] Besides their statistical content, all these indexes need to be checked for their reliability and validity for providing usable results – both of which are statistical measures.Scoring systems for diagnosis and for assessing severityThere is an increasing tendency around the world to depend more on quantitative measurements than on qualitative assessment since this minimizes subjective element in the sense that a score of, say, 7.2 is more than 7.1; although, both may look qualitatively same. Scoring in health and disease requires that qualities are somehow turned into quantities, and unmeasurable continua such as severity of disease are measured on some objective basis. When properly validated, such scores help in reducing some of the epistemic uncertainties [1] that can arise from the inadequate realization of how much weight is to be given to various pieces of information for correct medical decisions.Among simplest medical scores is Apgar that assigns quantities to the presence of specific signs, but the most popular possibly is the APACHE score. Glasgow coma scale, Yale observation scale, and peritonitis severity score are the other examples. These scores measure the severity of the condition and are widely accepted guide for appropriate clinical action.There are a large number of scores for diagnosis also. For example, these have been developed for the diagnosis of benign paroxysmal positional vertigo,[16] for necrotizing soft-tissue infections,[17] for chronic lymphocytic leukemia,[18] and for acute appendicitis.[19] All such scoring systems are statistical tools, and now increasingly used for diagnosis of diseases and for prognosis assessment, lending credence to our plea that statistical medicine is an appropriate candidate to be considered a medical specialty.Probabilities in diagnosis, treatment, and prognosisBe it univariate disease such as anemia, hypertension and diabetes, or multifactorial conditions such as cancer and coronary artery disease, the diagnosis is always a statistical entity, as this is a name to given to statistically more extreme values in one case, and to a cluster of signs-symptoms-measurements that occur more frequently together and follow the same course, in the other case. Variations occur, and the chance remains an integral part in either situation. When a diagnosis is reached on the basis of complaints and physical examination, this is generally only the most likely diagnosis. As the investigation reports become available or the response to the therapy is known, the probability changes under Bayes rule – sometimes even the most likely diagnosis also changes. Bayes rule is indispensable in sensitizing clinicians that probability of complaints in disease, P(C|D), can be very different from the clinically suitable probability of disease in a case with given complaints, P(D|C), depending on the prevalence of the disease. In any case, probability serves as a crucial tool in converting unpredictable uncertainties to predictable uncertainties. We cannot predict individual toss of a coin but can predict that out of 1000 tosses, nearly half will be the head.Sensitivity–specificity, and predictivities for local adoption are entirely statistical considerations that independently validate the medical tests – not only just laboratory and radiological investigations but also signs–symptoms syndromes that form the backbone of diagnosis. However, a clinician has to realize that they can be misleading too. Indrayan [20] has cited a telling example where the sensitivity and specificity of pap smear is nearly 95% each, but the positive predictivity is only 48% because of the low prevalence of cervical cancer even among those who are screened. This is an extremely useful information for a clinician for application to individual cases since it tells that positive pap smear does not tell so much about the presence of disease in a case as is generally believed, and further investigations are required to confirm or exclude the disease. Sometimes, likelihood ratios are calculated for positive and negative results that measure the utility of a diagnostic test in increasing or decreasing our confidence one way or the other in a suspected case.When several possible modalities are available for treating a disease, the choice mostly is based on probabilities, since the one that is most likely to provide the best relief to the patient is chosen. This is more so when the treatment is started on the basis of signs–symptoms in situ ations where the time-elapsed in waiting for confirmation of diagnosis can be hazardous. These uncertainties are not only just due to probability attached to the diagnosis but also due to individual's uncertain response to therapy. All this affects the efficacy of treatment for which we need a measure that can guide. Relative risk and odds ratio have become invaluable tools to measure efficacy of various treatment modalities on one hand and to assess the relative importance of the risk factors on the other. For example, Oresanya et al.[21] used odds ratios and relative risks to find which geriatric pre-operative conditions are more intimately associated with adverse surgical outcomes – a useful result for application in surgeries of older patients.Being an exercise in predicting the future, the prognosis can never be free of uncertainties. While correlating the spectrum of possible outcomes with the existing state, statistical chances unwittingly play a significant role. Conventional scoring systems such as APACHE ignores process variables such as correct diagnosis, promptness of treatment, type of patient, the attention of medical personnel, and their competency, yet seem to predict prognosis very well. Next generation statistical medicine may incorporate all these and come up with a comprehensive prognostic score for various health conditions.:: Most Medical Research Results Are Statistical The objective of the medical research is to devise new medical methods that can be used on individual patients and communities for improved outcomes. Most such efforts require empirical investigations where data do the talking. Overriding role of statistical considerations in research also stems from uncertainties that are an integral part of such medical research framework. This, coupled with the limitation of our knowledge about biological processes, throws indomitable challenge in reaching to a definitive conclusion. Luckily, statistical methods help discern signals from noise, waves from turbulence, and trends from chaos, despite limitations. Thus, results are obtained that can be confidently used in clinics and communities when the study is carried out with accepted scientific principles.An essential ingredient in almost all primary medical research is an observation of what goes on naturally, or after a deliberate intervention, but such observations seldom provide infallible evidence. Laboratory experiments outscore over observational studies and clinical trials in providing more valid evidence of cause-effect relationship because of controlled conditions; although, the result is never 100% perfect in this setup also. A clinical trial in any case has a large number of interfering factors that can hardly be taken care of together. Epistemic bottlenecks confound the problem further – thus the results have to be necessarily presented in terms of probability. These factors are even more prominent in epidemiological research in communities and clinics.Proper study designs, including for clinical and prophylactic trials, improved medical tools, adequate statistical analysis and correct interpretation of results are advised [1] to control these uncertainties and to come up with a reliable and valid conclusion. All these steps belong to the domain of statistics, and make empirical medical research results primarily statistical in content.Whereas counterfactuals can be used to disprove a hypothesis, data-based medical research has to pass through the rigors of statistical confidence intervals or tests of hypotheses to be confident that the results are not due to sampling fluctuations, and that they are prima facie repeatable. In addition to the Type-I and Type-II errors, fallacies do occur as in any setup, but that does not deter us from moving forward. Furthermore, variation in results propel research synthesis through meta-analysis and systematic reviews, but conclusions here too remain probabilistic than definitive in most cases.An important area of statistical applications to medical care now emerging is the cost-benefit and cost-effectiveness analysis of various interventions. These analyses help clinicians to determine which intervention could be more acceptable for a given patient. For example, in a small study, Henson et al.[22] reported a potential savings of $142,822 per month to a hospital in the U.S. by a reduction of 2.9 meticillin-resistant Staphylococcus aureus hospital-acquired infection/month associated with polymerase chain reaction screening. :: Conclusion Positive developments are occurring with personalized medicine that could kill statistical medicine at the individual level [4] but that may take decades to get a firm foothold. Till then, statistical medicine will continue to have a say and deserves to be recognized as a medical specialty. We still have to see whether tools such as scores would find any place in personalized medicine. If yes, statistical medicine would continue to have a definite role in foreseeable future.Financial support and sponsorshipNil.Conflicts of interestThere are no conflicts of interest.:: References  1.Indrayan A. Medical Biostatistics. 3rd ed. New York: CRC Press; 2012.       2.Association of American Medical Colleges. Planning in Academic Medicine. Available from: https://www.aamc.org/members/gip/strategicplanning/353164/planninginacademicmedicine.html. [Last accessed on 2016 Nov 17].       3.Braud HD. Intuition in Medicine: A Philosophical Defense of Clinical Reasoning. Chicago: University of Chicago Press; 2012. p. 107.       4.Cinteza M, Jinga DC. The tomorrow's personalized medicine - The killer of today's statistical medicine. Maedica (Buchar) 2014;9:119-20.       5.Anonymous. Statistical Medicine:First report of the royal metropolitan infirmary for sick children. Lond Med Phys J 1822;48:120-4. Available from: https://books.google.co.in/books?id=2tiXgqil4QC and dq=Statistical+Medicine:+First+report+of+the+Royal+Metropolitan+Infirmary+for+Sick+Children&source=gbs_navlinks_s. [Last accessed on 2016 Nov 18].       6.Cartwright SA. Remarks on statistical medicine, contrasting the result of the empirical with the regular practice of Physic, in Natchez. West J Med Surg 1840;2:1-21. Available from: https://www.books.google.co.in/books?id=2wQDAAAAYAAJ& pg=PA1&dq=Statistical+ Medicine++Drake+Yandell & source=gbs_toc_r&cad=3#v= onepage&q=Statistical%20Medicine%20%20Drake%20Yandell& f=false. [Last accessed on 2016 Nov 18].       7.Datta S, Xia XQ, Bhattacharjee S, Jia Z. Advances in statistical medicine. Comput Math Methods Med 2014;2014:316153.       8.Shalabh HT. Statistical Analysis of Designed Experiments. Springer; 2006. p. 7.       9.Mellman M. Bogus Statistical Medicine. Available from: http://www.thehill.com/opinion/mark-mellman/237558-mark-mellman-bogus-statistical-medicine. [Last accessed on 2016 Nov 17].      English Oxford Living Dictionaries. Available from: https://www.en.oxforddictionaries.com/definition/medicine. [Last accessed on 2016 Nov 16].       11.Tembe N, Joaquim O, Alfai E, Sitoe N, Viegas E, Macovela E, et al. Reference values for clinical laboratory parameters in young adults in Maputo, Mozambique. PLoS One 2014;9:e97391.       12.Prsa M, Sun L, van Amerom J, Yoo SJ, Grosse-Wortmann L, Jaeggi E, et al. Reference ranges of blood flow in the major vessels of the normal human fetal circulation at term by phase-contrast magnetic resonance imaging. Circ Cardiovasc Imaging 2014;7:663-70.       13.Karasahin O, Tasar PT, Timur O, Binici DN, Yilmaz TK, Aslan A, et al. High C-reactive protein and low albumin levels predict high 30-day mortality in patients undergoing percutaneous endoscopic gastrotomy. Gastroenterology Res 2017;10:172-6.       14.Schnakers C, Ledoux D, Majerus S, Damas P, Damas F, Lambermont B, et al. Diagnostic and prognostic use of bispectral index in coma, vegetative state and related disorders. Brain Inj 2008;22:926-31.       15.Niazi K, Khan TH, Easley KA. Diagnostic utility of the two methods of ankle brachial index in the detection of peripheral arterial disease of lower extremities. Catheter Cardiovasc Interv 2006;68:788-92.       16.Imai T, Higashi-Shingai K, Takimoto Y, Masumura C, Hattori K, Inohara H, et al. New scoring system of an interview for the diagnosis of benign paroxysmal positional vertigo. Acta Otolaryngol 2016;136:283-8.           17.McGillicuddy EA, Lischuk AW, Schuster KM, Kaplan LJ, Maung A, Lui FY, et al. Development of a computed tomography-based scoring system for necrotizing soft-tissue infections. J Trauma 2011;70:894-9.       18.Promsuwicha O, Sontgmuang W, Auewarakul CU. Utilization of a scoring system for diagnosis of chronic lymphocytic leukemia in Thai patients. J Med Assoc Thai 2011;94 Suppl 1:5232-8.       19.Di Saverio S, Birindelli A, Kelly MD, Catena F, Weber DG, Sartelli M, et al. WSES jerusalem guidelines for diagnosis and treatment of acute appendicitis. World J Emerg Surg 2016;11:34.      

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