The document discusses personalized statistical medicine as a sub-discipline focused on applying statistical methods to individual health assessments, contrasting it with conventional group-based statistics. It emphasizes the importance of individual health metrics, homeostasis, and the use of statistical tools for diagnosis, treatment, and prognosis tailored to the unique characteristics of each patient. Various scoring systems and statistical models are introduced to improve clinical decision-making and patient outcomes, while advocating for enhanced validation and relevance of statistical tools in clinical practice.

1. PERSONALISED STATISTICAL MEDICINE
(A specialty of medicine and
not of statistics)
Dr A. Indrayan, MSc,MS,PhD (OhioState)
FAMS,FSMS,FRSS,FASc
Biostatistics Consultant, Max Healthcare
Former Professor and Head of Biostat & MedInfo, UCMS

2. MEDICAL BIOSTATISTICS
More biomedical than statistics
(Medical + Bio > Statistics)
A sub-discipline of Statistics with applications of
statistical methods to medicine
Similar to:
- Psychometrics
- Econometrics
Statistics is mostly for groups and not individuals

3. HEALTH and MEDICINE
My version - to establish baseline for personalized
statistical medicine
Health: Dynamic balance of body, mind, and soul when
homeostasis is going on perfectly well
(Homeostasis – Our mechanisms to continually adjust
to internal and external environment – perspiration,
shivering, palpitation, dilation)
Disease: Aberrations – Difficulty in carrying out the usual life
functions – walk, talk, laugh, breath, recall, enjoy
Medicine: Treatment (Interventions to try to bring the system
back on track)
Cure: Restore health to its normal level

4. CONVENTIONAL STATISTICAL MEDICINE
Using group-based results to individuals
Low and high levels (averages)
Probability of disease, of death
Efficacy of a regimen (estimation and testing)
Prevalence
Incidence and risk (RR, OR, rate of occurrence)
Relationships (correlation, logistic, ordinary, and Cox regressions)
Cause-effect
All based on averages or probabilities (frequency of occurrence)
Lot of uncertainty at individual level because of individual variation –
nobody is ‘average’ patient

5. PERSONAL MEDICINE
Concerned with individuals, opposed to Community Medicine
Community medicine: Mostly for prevention, protection, and
promotion of health through
- studying agent, host, environment, interventions
- epidemiological reasoning
- advocating diet, exercise, behaviour, immunization,
control of pollution (focus is community, not individual)
Personal medicine: Focus on individuals
- Age, sex, BMI, history
- Signs and symptoms
- Lab. and radiological investigations
- Now, genetic profile
Reduced variation and better assessment (of conditional) probability
Objective: Restore homeostasis

6. PERSONALISED STATISTICAL MEDICINE
Use of statistical tools for diagnosis, treatment, and prognosis at individual level
A specialty of medicine such as
 Laboratory medicine (use lab. results for ……...)
 Nuclear medicine (use radioactive substances for ……...)
 Transfusion medicine (use blood transfusions for ……...)
 Academic medicine (use academics for ……...)
 Computer medicine (use computer for ……...)
 Why not statistical medicine (use statistical tools for ……...)
All serve as adjunct or support to clinical evaluation
Wider scope of biostatistics
Can we have MD (Statistical Medicine)?

7. STATISTICAL TOOLS FOR PERSONALISED MEDICINE
Clinical decisions
- Establishing diagnosis
- Deciding treatment strategies targeted on diagnosis
- Assessing prognosis
Statistical tools (already widely used for clinical decisions)
Statistical medicine can develop better and more widely applicable tools
- Scoring systems
- Scales
- Indicators and indexes
- Statistical models
- Decision trees
- AI & ML
- Probabilities
- Normal values (reference values)

8. SCORING SYSTEM
•Combines qualitative characteristics and quantitative
measurements to get one comprehensive score. Each of
these items is given weight (points) according to their
importance (mostly based on logistic coefficients)
•Guides diagnosis, treatment, and prognosis
•Tends to remove personal bias and allow uniformity across
physicians and hospitals
•Repeatedly found to perform better than the clinical
assessment – yet needs to be used as an adjunct, just as
laboratory investigations are used

9. SCORING SYSTEMS FOR DIAGNOSIS
• Alvarado scoring system for diagnosis of acute appendicitis
‘Best’ cut-off ≥ 7 out of 10
(https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9142662/)
Sensitivity = 94.9%, Specificity = 72.7%
Positive predictivity = 98.4%, Negative predictivity = 44.4%
Can be improved
• Scoring system for diagnosis of a atypical haemolytic uremic syndrome
(https://www.sciencedirect.com/science/article/pii/S2666572720300122)
Based on 15 parameters such as Hb level, renal failure, history, bloody stool,
etc.
Range −15 to +20
Best cut off ≤5
Sensitivity = 0.990, Specificity = 0.220
Positive predictivity = 0.961, Negative predictive value = 0.667
at cut-off 5 (Is that OK?)
Abdominal pain that migrates
to the right iliac fossa
1
Anorexia (loss of appetite) or
ketones in the urine
1
Nausea or vomiting 1
Tenderness in the right iliac
fossa
2
Rebound tenderness 1
Fever of 37.3 °C or more 1
Leukocytosis > 10,000 2
Neutrophilia > 70% 1
TOTAL 10
Alvarado scoring system
Symptoms
Signs
Laboratory

10. SCORING SYSTEMS FOR PROGNOSIS – 1
•APACHE-II score for survival of ICU patients at
admission
(https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10060092/)
Based on age, Glasgow coma scale, vitals, biochemical
parameters, etc.
C-index = 0.863
3 – 10 100% survived Sensitivity = 92.8%
11 – 20 71.5% survived Specificity = 80.2%
21+ 0% survived
Arbitrary
categories
PPV = 23.1%
Best cut-off 17 out of a
maximum of 71
NPV = 87.8%

11. SCORING SYSTEMS FOR PROGNOSIS – 2
•Prostate cancer survival at admission
(https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9939188/)
Based on stage, surgery method, PSA value, therapy received, etc.
C-index = 0.776 Cancer-specific survival = 0.875
Higher the score, worse is the prognosis
Arbitrary categories – Should be related to cluster analysis of survival
rates with different categories of scores
10-year survival (cancer-specific)
Grade 1 0 – 25 98.7%
Thus, can help select
individualized
treatment
Grade 2 26 – 35 95.0%
Grade 3 36 – 50 84.7%
Grade 4 51 – 100 40.9%

12. STATISTICAL MODELS FOR DIAGNOSIS
•Diagnosis of sarcopenia in overweight patients with head and neck cancer
(https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9813227/)
Based on measures at third cervical level (C3), age, sex, and weight
Used multivariable regression
L3 – CSA (cm2) = 124.838 +[1.881 C3 – CSA (cm2)] – 24.687×Sex – Age (yrs) + [0.472 Wt (kg)]
(Sex = 1 for M, Sex = 2 for F – 0,1 may be better); CSA = cross-sectional area
R2 = 0.79 (Not great)
Best cut-off not given but
Sensitivity = 80.0% Specificity = 85.0%
•Diagnosis of spinal TB
(https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10080113/)
Based on conventional lab. Indices (TB antibody, RBC, Mono%, BUN, etc. – 40 variables)
Variables screened by Lasso method
Logistic regression used and probability of disease estimated
C-index = 0.919 (Good)
Also provided nomogram for calculating probability of TB for each score

13. STATISTICAL MODELS FOR PROGNOSIS
•Nonlinear model for severity of COPD
Based on lung functions (breathing data) – FEV1, vital capacity
Values of inspiratory and expiratory resistance based on the non-
linear model measure severity
•Mortality risk prediction in children with acute myocarditis
(https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9928813/)
Based on sex, fever, LVEF, etc.
C-index = 0.827

14. MACHINE LEARNING FOR DIAGNOSIS
•Diagnosis of drug addiction
(https://onlinelibrary.wiley.com/doi/10.1111/adb.13267)
Based on neuroimaging
Machine learning used for classification and classification used to
diagnose
•Diagnosis of CoViD-19
(https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9817417/)
Used machine learning on genomics data
C-index = 0.99 GREAT!

15. MACHINE LEARNING FOR PROGNOSIS
•Remission and survival in acute myeloid leukemia
(https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9973482/)
Based on clinical, lab., cytogenic and molecular
genetic data
C-index for complete remission = 0.80
C-index for 2-year overall survival = 0.75
(Not good)

16. NORMAL OR REFERENCE RANGES
We all know Mean − 2 SD to Mean + 2 SD (for healthy persons)
gives normal (or reference) range
Must be at least 120 healthy persons of each group for
establishing norms
Values far off from mean have ‘disease’
- Used for diagnosis (and treatment)
- Used for assessing prognosis
The other (more appropriate but difficult)
method is to find the point of intersection
of the distributions of the measurements
in healthy and nonhealthy people.

17. INCREASING CLINICAL RELEVANCE
OF STATISTICAL TOOLS - 1
Most of the statistical tools not adequate and need improvement
(statistical medicine can do that)
Representative training and validation cohorts and should be independent
•Must stop copying the West
•In place of two categories now mostly studied, the scores should be divided
into at least three categories for diagnosis
<a negative (with at least 90% probability)
a–b doubtful
≥b positive (with at least 90% probability)
None, mild, moderate, serious, critical for assessing prognosis (5 categories)
C-statistic (AUROC Curve) must be at least 90%

18. INCREASING CLINICAL RELEVANCE OF
STATISTICAL TOOLS - 2
•In place of sensitivity-specificity, the cut-offs should be
based on positive and negative predictivities (Se-Sp are
retrospective – disease status already known. They are not
good for prediction)
•Predictivities will depend on prevalence – thus, the cut-off
will be population specific, thus more relevant

19. INCREASING THE VALIDITY OF
STATISTICAL TOOLS
•Since these tools are to be used on future cases, try to anticipate the
changes in addition to the past trends (such as in nutrition of patients,
increasing age, new machines, new understandings, and new methods)
besides the existing status and past occurrences
•Validate the tools by agreement – not percent predictivity – for one-to-
one matching
•Investigate more and more relevant variables to increase predictivity
(R2) – not restrict to the known correlates
•Minimize errors and uncertainties in measurement
•Select representative sample
•Check that they help to restore homeostasis