Medical Biostatistics, Fourth Edition
Contents
Preface to Fourth
Edition 

Summary Tables 

Frequently Used
Notations 

1 Medical Uncertainties 

1.1 Uncertainties in Health
and Disease 

1.1.1 Uncertainties due to Intrinsic Variation –
Biologic, Genetic, Behavioral and Other Host Factors, Environmental, Chance,
Sampling Fluctuations 

1.1.2 Natural Variation in Assessment – Observer,
Treatment Strategies, Instrument and Laboratory, Imperfect Tools, Incomplete
Information on the Patient, Poor Compliance with the Regimen 

1.1.3 Inadequate Knowledge – Epistemic
Uncertainties; Diagnostic, Therapeutic, and Prognostic Uncertainties;
Predictive and Other Uncertainties 

1.2 Uncertainties in
Medical Research 

1.2.1 Empiricism in Medical Research – Laboratory
Experiments, Clinical Trials, Surgical Procedures, Epidemiological Research 

1.2.2 Elements of Minimizing the Impact of
Uncertainties on Research – Proper Design,
Improved Medical Methods, Analysis and Synthesis 

1.2.3 Critique of a Report of a Medical Study –
Introduction, Methodology, Results, Discussion and Conclusions 

1.3 Uncertainties in Health
Planning and Evaluation 

1.3.1 Health Situation Analysis – Identification of
the Specifics of the Problem, Size of the Target Population, Magnitude of the
Problem, Health Infrastructure, Feasibility of Remedial Steps 

1.3.2 Evaluation of Health Programs 

1.4 Management of
Uncertainties: About This Book 

1.4.1
Contents of the Book – Limitations and Strengths, New in Third Edition 

1.4.2 Salient Features of the Text – System of
Notations, Guide Chart of the Biostatistical Methods 

References 

2 Basics of Medical
Studies 

2.1 Study Protocol 

2.1.1 The Problem, Objectives, and Hypotheses 

2.1.2 Protocol Content 

2.2 Types of Medical
Studies 

2.2.1
Elements of Design 

2.2.2
Basic Types of Study Design – Descriptive, Analytical, Basic Types of
Analytical Studies 

2.2.3
Choosing a Design – Recommended Design for Particular Setups, Choice of
Design by Level of Evidence 

2.3 Data Collection 

2.3.1 Nature of Data – Factual, KnowledgeBased, and
OpinionBased Data; Method of Obtaining the Data 

2.3.2 Tools of Data Collection – Existing Records,
Questionnaires and Schedules, Likert Scale 

2.3.3 Pretesting and Pilot Study 

2.4 Nonsampling Errors and
Other Biases 

2.4.1
Nonresponse 

2.4.2
Variety of Biases to Guard Against – List of Biases, Steps for Minimizing
Bias 

References 

Exercises 3 Sampling Methods 

3.1 Sampling Concepts 

3.1.1 Advantages and Limitations of Sampling –
Sampling Fluctuations, Advantages and
Limitations 

3.1.2 Some Special Terms Used in Sampling – Unit of
Enquiry and Sampling Unit, Sampling Frame, Parameters and Statistics, Sample
Size, Nonrandom and Random Sampling 

3.2 Common Methods of Random
Sampling 

3.2.1 Simple Random Sampling 

3.2.2 Stratified Random Sampling 

3.2.3 Multistage Random Sampling 

3.2.4
Cluster Random Sampling 

3.2.5
Systematic Random Sampling 

3.2.6
Choice of Method of Random Sampling 

3.3 Some Other Methods of Sampling 

3.3.1 Other Random Methods of Sampling – Probability
Proportional to Size, Area Sampling, Inverse Sampling, Consecutive Subjects
Attending a Clinic, Sequential Sampling 

3.3.2 Nonrandom Methods of Sampling – Convenience
Samples, Other Types of Purposive Samples 

References 

Exercises 4 Designs for
Observational Studies 

4.1
Some Basic Concepts 

4.1.1 Antecedent and Outcome 

4.1.2 Confounders 

4.1.3 Effect Size 

4.2 Prospective Studies 

4.2.1 Variations of Prospective Studies – Cohort
Study, Longitudinal Study, Repeated Measures Study 

4.2.2 Selection of Subjects for a Prospective Study
– Comparison Group in a Prospective Study 

4.2.3 Potential Biases in Prospective Studies –
Selection Bias, Bias due to Loss in FollowUp, Assessment Bias and Errors,
Bias due to Change in the Status, Confounding Bias, Post Hoc Bias, Validity
Bias 

4.2.4 Merits and Demerits of Prospective Studies 

4.3 Retrospective Studies 

4.3.1 CaseControl Design – Nested CaseControl
Design 

4.3.2 Selection of Cases and Controls – Sampling
Methods in Retrospective Studies, Confounders and Matching 

4.3.3 Merits and Demerits of CaseControl Studies 

4.4 CrossSectional Studies 

4.4.1
Selection of Subjects for a CrossSectional Study 

4.4.2
Merits and Demerits of CrossSectional Studies 

4.5 Comparative Performance of Prospective, Retrospective,
and CrossSectional Studies 

4.5.1
Performance of Prospective Studies 

4.5.2
Performance of Retrospective Studies 

4.5.3
Performance of CrossSectional Studies 

References 

Exercises 5 Medical Experiments 

5.1 Basic Features of
Medical Experiments 

5.1.1 Statistical Principles of Experimentation –
Control Group, Randomization, Replication 

5.1.2
Advantages and Limitations of Experiments 

5.2 Design of Experiments 

5.2.1 Classical Designs: OneWay Design, TwoWay
Design, Interaction, KWay and Factorial Experiments 

5.2.2 Some Unconventional Designs – Repeated
Measures Design, Crossover Design, Other Complex Designs 

5.3 Choice and Sampling of
Units for Laboratory Experiments 

5.3.1
Choice of Experimental Unit 

5.3.2
Sampling Methods in Laboratory Experiments 

5.3.3
Choosing a Design of Experiment 

5.3.4
Pharmacokinetic Studies 

References 

Exercises 6 Clinical Trials 

6.1 Therapeutic Trials 

6.1.1
Phases of a Clinical Trial – Phases I to IV 

6.1.2 Selection of Subjects – Selection of
Participants for RCT, Control Group in a Clinical Trial 

6.1.3 Randomization and Matching 

6.1.4 Methods of Random Allocation – Allocation out
of a Large Number of Available Subjects; Random Allocation of Consecutive
Patients Coming to a Clinic; Block, Cluster and Stratified Randomization 

6.1.5 Blinding and Masking 

6.2 Issues in Clinical
Trials 

6.2.1 Outcome Assessment – Specification of
Endpoint or Outcome, Causal Inference, Side Effects, Efficacy versus
Effectiveness, Pragmatic Trials 



6.2.2
Various Equivalences in Clinical Trials – Superiority, Equivalence, and
Noninferiority Trials; Therapeutic Equivalence and Bioequivalence 

6.2.3
Designs for Clinical Trials – OneWay, TwoWay, and Factorial Designs;
Crossover and Repeated Measures Designs; Nof1,
UpandDown, and Sequential Designs; Choosing a Design for a Clinical Trial 

6.2.4 Designs
with Interim Appraisals – Design with Provision to Stop Early, Adaptive Designs 

6.2.5
Biostatistical Ethics for Clinical Trials – Equipoise, Ethical Cautions,
Statistical Considerations in a Multicentric Trial, Multiple Treatments with
Different Outcomes in the Same Trial, Size of the Trial, Compliance 

6.2.6
Reporting Results of a Clinical Trial – CONSORT, Open Access 

6.3 Trials Other than for
Therapeutics 

6.3.1 Clinical Trials for Diagnostic and
Prophylactic Modalities 

6.3.2 Field Trials for Screening,
Prophylaxis, and Vaccines 

6.3.3
Issues in Field Trials – Randomization and Blinding in Field Trials, Designs
for Field Trials 

References 

Exercises 7 Numerical Methods for
Representing Variation 

7.1 Types of Measurement 

7.1.1
Nominal, Metric, and Ordinal Scales 

7.1.2
Other Classifications of the Types of Measurement – Discrete and Continuous
Variables, Qualitative and Quantitative Data, Stochastic and Deterministic
Variables 

7.2 Tabular Presentation 

7.2.1 Contingency Tables and Frequency Distribution
– Empty Cells, Problems in Preparing a Contingency Table on Metric Data 

7.2.2
Multiple Response Tables and Other Features 

7.2.3
Other Types of Statistical Tables – What is a Good Statistical Table? 

7.3 Rates and Ratios 

7.3.1
Proportion, Rate, and Ratio 

7.4 Central and Other
Locations 

7.4.1 Central Values: Mean, Median, and Mode –
Understanding Mean, Median, and Mode, Calculation in Case of Grouped Data,
Which Central Value to Use?, Geometric Mean, Harmonic Mean 

7.4.2 Other Locations: Quantiles – Ungrouped and
Grouped Data, and Interpretation 

7.5 Measuring Variability 

7.5.1 Variance and Standard Deviation – Ungrouped
and Grouped Data, Variance of Sum or Difference of Two Measurements 

7.5.2 Coefficient of Variation 

References 

Exercises 8 Presentation of Variation by Figures 

8.1 Graphs for Frequency Distribution 

8.1.1 Histogram and Its Variants – Histogram,
StemandLeaf Plot, Line Histogram 

8.1.2 Polygon and Its Variants – Frequency Polygon,
Area Diagram 

8.1.3 Frequency Curve 

8.2 Pie, Bar, and Line Diagrams 

8.2.1 Pie Diagram – Useful Features, Donut Diagram 

8.2.2 Bar Diagram 

8.2.3 Scatter and Line Diagrams 

8.2.4 Choice and Cautions in Visual Display of Data 

8.2.5 Mixed and ThreeDimensional Diagrams – Mixed
Diagram, BoxandWhiskers Plot, ThreeDimensional Diagram, Biplot, Nomogram 

8.3 Special Diagrams in Health and Medicine 

8.3.1 Diagrams Used in Public Health – Epidemic
Curve, Lexis Diagram 

8.3.2 Diagrams Used in Individual Care and Research –
Growth Charts, Partogram, Dendrogram,
Area Under the Concentration Curve, Radar Graph 

8.4 Charts and Maps 

8.4.1 Charts – Schematic Chart, Pedigree Chart 

8.4.2 Maps – Spot Map, Thematic Choroplethic Map,
Cartogram 

References 

Exercises 9 Some Quantitative Aspects of Medicine 

9.1 Some Epidemiological Measures of Health and
Disease 

9.1.1 Epidemiological Indicators of Neonatal Health
– Birth Weight, Apgar Score 

9.1.2 Epidemiological Indicators of Growth in
Children – WeightforAge, WeightforHeight and HeightforAge, ZScores and Percent of Median, Growth
Velocity, Skinfold Thickness 

9.1.3 Epidemiological Indicators of Adolescent
Health – Growth in Height and Weight in Adolescence, Sexual Maturity Rating 

9.1.4 Epidemiological Indicators of Adult Health –
Obesity, Smoking, Physiological Functions, Quality of Life 

9.1.5 Epidemiological Indicators of Geriatric Health
– Activities of Daily Living, Mental Health of the Elderly 

9.2 Reference Values 

9.2.1 Gaussian and Other Distributions – Checking
Gaussianity 

9.2.2 Reference or Normal Values – Implications 

9.2.3 Normal Range – Disease Threshold, Clinical
Threshold, Statistical Threshold 

9.3 Measurement of Uncertainty: Probability 

9.3.1 Elementary Laws of Probability – Law of
Multiplication, Law of Addition 

9.3.2 Probability in Clinical Assessments –
Probabilities in Diagnosis, Assessment of Prognosis, Choice of Treatment, 

9.3.3 Further on Diagnosis: Bayes Rule 

9.4 Validity of Medical Tests 

9.4.1 Sensitivity and Specificity – Features of
Sensitivity and Specificity, Likelihood Ratio 

9.4.2 Predictivities – Positive and Negative
Predictivity, Predictivity and Prevalence, The Meaning of Prevalence for
Predictivity, Features of Positive and Negative Predictivities 

9.4.3 Combination of Tests – Tests in Series, Tests
in Parallel, Gains from a Test, When Can a Test Be Avoided? 

9.4.4 Gains from a Test – When can a Test be Avoided 

9.5 Search for the Best Threshold of Continuous Test:
ROC Curve 

9.5.1 Sensitivity–Specificity Based ROC Curve,
Methods to Find the ‘Optimal’ Threshold Point, Area Under the ROC Curve 

9.5.2 Predictivities Based ROC Curve 

References 

Exercises 10 Clinimetrics and EvidenceBased Medicine 

10.1 Indicators, Indexes, and Scores 

10.1.1 Indicators – Merits and Demerits of
Indicators, Choice of Indicators 

10.1.2 Indexes – Some Commonly Used Indexes,
Advantages and Limitations of Indexes 

10.1.3 Scores – Scoring System for Diagnosis,
Scoring for Gradation of Severity 

10.2 Clinimetrics 

10.2.1 Method of Scoring – Method of Scoring for
Graded Characteristics, Method of Scoring for Diagnosis, Regression Method of
Scoring 

10.2.2 Validity and Reliability of a Scoring System 

10.3 EvidenceBased Medicine 

10.3.1 Decision Analysis – Decision Tree 

10.3.2 Other Statistical Tools for EvidenceBased
Medicine – Etiology Diagram, Expert System 

References 

Exercises 11 Measurement of Community Health 

11.1 Indicators of Mortality 

11.1.1 Crude and Standardized Death Rates – Crude
Death Rate, AgeSpecific Death Rate, Standardized Death Rate, Comparative
Mortality Ratio 

11.1.2 Specific Mortality Rates – Fetal Deaths and
Mortality in Children, Maternal Mortality, Adult Mortality, Other Measures of Mortality 

11.1.3 Death Spectrum 

11.2 Measures of Morbidity 

11.2.1 Prevalence and Incidence – Point Prevalence,
Period Prevalence, Incidence, The Concept of PersonTime, Capture–Recapture
Methodology 

11.2.2 Duration of Morbidity – Prevalence in
Relation to Duration of Morbidity, Incidence from Prevalence,
Epidemiologically Consistent Estimates 

11.2.3 Morbidity Measures for Acute Conditions –
Attack Rates, Disease Spectrum 

11.3 Indicators of Social and Mental Health 

11.3.1 Indicators of Social Health – Education,
Income, Occupation, Socioeconomic Status, Dependency Ratio, Health Inequality 

11.3.2 Indicators of Health Resources – Health
Infrastructure, Health Expenditure 

11.3.3 Indicators of Lack of Mental Health – Smoking
and Other Addictions, Divorces, Vehicular Accidents and Crimes, Others
Measures of Lack of Mental Health 

11.4 Composite Indexes of Health 

11.4.1 Indexes of Status of Comprehensive Health –
Human Development Index, Physical Quality of Life Index 

11.4.2 Indexes of Health Gap – DALYs Lost, Human
Poverty Index, Index of Need for Health Resources 

References 

Exercises 12 Confidence Intervals, Principles of Tests of
Significance, and Sample Size 

12.1 Sampling Distributions 

12.1.1 Basic Concepts – Sampling Error, Point
Estimate, Standard Error of p and 

12.1.2 Sampling Distribution of p and – Gaussian
Conditions 

12.1.3 Obtaining Probabilities from a Gaussian
Distribution – Gaussian Probability, Continuity Correction, Probabilities
Relating to the Mean and the Proportion 

12.1.4 The Case of σ Not Known (tDistribution) 

12.2 Confidence Intervals 

12.2.1 Confidence Interval for π, μ
and Median (Gaussian Conditions) – Confidence Interval for Proportion π
(Large n), Lower and Upper Bounds for π (Large n),
Confidence Interval for Mean μ (Large n), Confidence
Bounds for Mean μ (Large
n), CI for Median (Gaussian Distribution) 

12.2.2 Confidence Interval for Differences (Large n)
– Two Independent Samples, Paired Samples 

12.2.3 Confidence Interval for π, μ
and Median: NonGaussian Conditions – Confidence Interval for π
(Small n), Confidence Bound for π When the Success or the
Failure Rate in the Sample is Zero Percent, Confidence Interval for Median
(Small n): NonGaussian Conditions 

12.3 PValues and Statistical Significance 

12.3.1 What Is Statistical Significance? – Court Judgment, Errors in Diagnosis, Null
Hypothesis, Philosophical Basis of Statistical Tests, Alternative Hypothesis,
OneSided Alternatives: Which Tail is Wagging? 

12.3.2 Errors, PValues, and Power – TypeI
Error, TypeII Error, Power 

12.3.3 General Procedure to Obtain Pvalue –
Subtleties of Statistical Significance 

12.4 Assessing Gaussian Pattern 

12.4.1 Significance Tests for Assessing Gaussianity 

12.5 Initial Debate on Statistical Significance 

12.5.1 Confidence Interval versus Test of H_{0} 

12.5.2 Medical Significance versus Statistical
Significance 

12.6 Sample Size Determination in Some Cases 

12.6.1 Sample Size Required in Estimation Setup –
General Considerations in the Estimation Setup, General Procedure for
Determining Size of Sample for Estimation, Formulas for Sample Size
Calculation for Estimation in Simple Situations 

12.6.2 Sample Size for Testing a Hypothesis with
Specified Power – General Considerations in a TestingofHypothesis Setup,
Sample Size Formulas for Test of Hypothesis in Simple Situations, Nomograms
and Tables of Sample Size, Thumb Rules, Power Analysis 

12.6.3 Sample Size Calculation in Clinical Trials –
Stopping Rules in Case of Early Evidence of Success or of Failure: Lan–deMets
Procedure, Sample Size Reestimation 

References 

Exercises 13 Inference from Proportions 

13.1 One Qualitative Variable 

13.1.1 Dichotomous Categories: Binomial Distribution
– Large n: Gaussian Approximation to Binomial 

13.1.2 Poisson Distribution 

13.1.3 Polytomous Categories (Large n):
GoodnessofFit Test – ChiSquare and Its Explanation, Degrees of Freedom,
Cautions in Using ChiSquare, Further Analysis: Partitioning of Table 

13.1.4 Goodness of Fit to Assess Gaussianity 

13.1.5 Polytomous Categories (Small n): Exact
Multinomial Test – GoodnessofFit in Small Samples 

13.2 Proportions in 2×2 Tables 

13.2.1 Structure of 2×2 Table in Different Types of
Study – Structure in Prospective Study, Structure in Retrospective
Study, Structure in CrossSectional
Study 

13.2.2 Two Independent Samples (Large n):
ChiSquare Test and Proportion Test – Chisquare Test, Yates Correction for
Continuity, ZTest for Proportions, Detecting a Medically Important
Difference in Proportions, Crossover Design with Binary Response (Large n) 

13.2.3 Equivalence Tests – Superiority, Equivalence
and Noninferiority; Equivalence; Determining Inferiority Margin 

13.2.4 Two Independent Samples (Small n):
Fisher Exact Test – Crossover Design (Small n) 

13.2.5 Proportions in Matched Pairs: McNemar Test
(Large n) and Exact Test (Small n) – Large n: McNemar
Test, Small n: Exact Test (Matched Pairs), Comparison of Two Tests for
Sensitivity and Specificity: Paired Setup 

13.3 Analysis of R × C Tables (Large n) 

13.3.1 One Dichotomous and the Other Polytomous Variable
(2×C Table) – The Test Criterion, Trend in Proportions in Ordinal
Categories, Dichotomy in Repeated Measures: Cochran Q Test (Large n) 

13.3.2 Two Polytomous Variables – Chisquare Test
for Large n, Matched Pairs: I×I
Tables 

13.4 ThreeWay Tables 

13.4.1 Assessment of Association in ThreeWay Tables 

13.4.2 Log–Linear Models – TwoWay Tables, ThreeWay
Tables 

References 

Exercises 14 Relative Risk and Odds Ratio 

14.1 Relative and Attributable Risks (Large n) 

14.1.1 Risk, Hazard, and Odds – Ratios of Risks and
Odds 

14.1.2 Relative Risk – RR in Independent Samples,
Confidence Interval for RR (Independent Samples), Test of Hypothesis on RR
(Independent Samples), RR in the Case of Matched Pairs 

14.1.3 Attributable Risk – AR in Independent
Samples, AR in Matched Pairs, Number Needed to Treat, Relative Risk
Reduction, Population Attributable Risk 

14.2 Odds Ratio 

14.2.1 OR in Two Independent Samples – CI for OR
(Independent Samples), Test of Hypothesis on OR (Independent Samples) 

14.2.2 OR in Matched Pairs – Confidence Interval for
OR (Matched Pairs), Test of Hypothesis on OR (Matched Pairs), Multiple
Controls 

14.3 Stratified Analysis, Sample Size and MetaAnalysis 

14.3.1 Mantel–Haenszel Procedure – Pooled Odds Ratio
and Chisquare 

14.3.2 Sample Size Requirement for Statistical Inference
on RR and OR 

14.3.3 MetaAnalysis 

References Exercises 

15 Inference from Means 

15.1 Comparison of Means in One and Two Groups (Gaussian
Conditions): Student tTest 

15.1.1 Comparison with a Prespecified Mean – Student
tTest for One Sample, 

15.1.2 Difference in Means in Two Samples – Paired
Samples Setup, Unpaired (Independent) Samples Setup, Some Features of Student
t, Effect of Unequal n,
DifferenceinDifferences Approach 

15.1.3 Analysis of Crossover Designs – Test for
Group Effect, Test for CarryOver Effect, Test for Treatment Effect 

15.1.4 Analysis of Data of UpandDown Trials 

15.2 Comparison of Means in Three or More Groups
(Gaussian Conditions): ANOVA FTest 

15.2.1 OneWay ANOVA – The Procedure to Test H_{0,
}Checking the Validity of the Assumptions of ANOVA 

15.2.2 TwoWay ANOVA – TwoFactor Design, The
Hypotheses and Their Test, Main Effect and Interaction (Effect), Repeated
Measures 

15.2.3 Repeated Measures – Random Effects versus
Fixed Effects, Sphericity and Hynh–Feldt Correction, Repeated Measures versus
Twoway ANOVA, Area Under the Concentration Curve 

15.2.4 Multiple Comparisons: Bonferroni, Tukey and
Dunnett Tests – Intricacies of Multiple Comparisons 

15.3 NonGaussian Conditions: Nonparametric Tests
for Location 

15.3.1 Comparison of Two Groups: Wilcoxon Tests – Paired
Data, Independent Samples 

15.3.2 Comparison of Three or More Groups:
Kruskal–Wallis Test 

15.3.3 TwoWay Layout: Friedman Test 

15.4 When Significant is Not Significant 

15.4.1 The Nature of Statistical Significance 

15.4.2 Testing for Presence of Medically Important
Difference in Means – Detecting Specified Difference in Mean, Equivalence
Tests for Means 

15.4.3 Power and Level of Significance – Balancing
TypeI and TypeII Error 

References Exercises 

16 Relationships: Quantitative Data 

16.1 Some General Features of a Regression Setup 

16.1.1 Dependent and Independent Variables – Simple,
Multiple, and Multivariate Regression 

16.1.2 Linear, Curvilinear, and Nonlinear
Regressions 

16.1.3 The Concept of Residuals 

16.1.4 General Method of Fitting a Regression 

16.2 Linear Regression Models 

16.2.1 Adequacy of a Regression Fit – 1 – Goodness
of Fit and η^{2}, Multiple Correlation in Linear
Regression, Stepwise Procedure, Statistical Significance of Individual
Regression Coefficients 

16.2.2 Adequacy of Regression – 2 – Validity of
Assumptions, Choice of Form of Regression, Outliers and Missing Values 

16.2.3 Interpretation of the Regression Coefficients
– Standardized Coefficients, Other Implications of Regression Models 

16.3 Some Issues in Linear Regression 

16.3.1 Confidence Interval, Confidence Band, and
Tests – SEs and CIs for the Regression, Confidence Band for Simple Linear
Regression, Equality of Two Regression
Lines, DifferenceinDifferences Approach with Regression 

16.3.2 Some Variations of Regression – Ridge
Regression, Multilevel Regression, Regression Splines, Analysis of Covariance,
Some Generalizations 

16.4 Measuring the Strength of Quantitative
Relationship 

16.4.1 Product–Moment and Related Correlations –
Multiple Correlation, Product–Moment Correlation, Covariance, Statistical
Significance of r, Intraclass
Correlation, Serial Correlation 

16.4.2 Rank Correlation – Spearman Rho, Kendall Tau 

16.5 Assessment of Quantitative Agreement 

16.5.1 Agreement in Quantitative Measurements 

16.5.2 Approaches for Measuring Quantitative
Agreement – Limits of Disagreement Approach, Intraclass Correlation as a
Measure of Agreement, Relative Merits of the Two Methods, An Alternative
Simple Approach 

References Exercises 

17 Relationships: Qualitative Dependent 

17.1 Binary Dependent: Logistic Regression (Large n) 

17.1.1 Meaning of a Logistic Model 

17.1.2 Assessing Overall Adequacy of a Logistic
Regression – Log Likelihood, Classification Accuracy, Hosmer–Lemeshow Test, 

17.2 Inference from Logistic Coefficients 

17.2.1 Interpretation of the Logistic Coefficients –
Dichotomous Predictors, Polytomous and Continuous Predictors 

17.2.2 Confidence Interval and Test of Hypothesis on
Logistic Coefficients 

17.3 Issues in Logistic Regression 

17.3.1 Conditional Logistic for Matched Data 

17.3.2 Polytomous Dependent – Nominal Categories:
Multinomial Logistic, Ordinal Categories 

17.4 Some Models for Qualitative Data and
Generalizations 

17.4.1 Cox Regression for Hazards 

17.4.2 Classification and Regression Trees 

17.4.3 Further Generalizations 

17.5 Strength of Relationship in Qualitative
Variables 

17.5.1 Both Variables Qualitative – Dichotomous
Categories, Polytomous Categories: Nominal, Proportional Reduction in Error,
Polytomous Categories: Ordinal Association 

17.5.2 One Qualitative and the Other Quantitative
Variable 

17.5.3 Agreement in Qualitative Measurements (Matched
Pairs) – The Meaning of Qualitative Agreement, Cohen Kappa 

References Exercises 

18 Survival Analysis 

18.1 Life Expectancy 

18.1.1 Life Table 

18.1.2 Other Forms of Life Expectancy – Potential
Years of Life Lost, Healthy Life Expectancy, Application to Other Setups 

18.2 Analysis of Survival Data 

18.2.1 Nature of Survival Data – Types of Censoring,
Collection of Survival Time Data, Statistical Measures of Survival 

18.2.2 Survival Observed in Time Intervals: Life
Table Method 

18.2.3 Continuous Observation of Survival Time:
Kaplan–Meier Method – Using the Survival Curve, Standard Error of Survival
Rate, Hazard Function 

18.3 Issues in Survival Analysis 

18.3.1 Comparison of Survival in Two Groups –
Comparing Survival Rates, Comparing Survival Experience: LogRank Test 

18.3.2 Factors Affecting Survival: Cox Model –
Parametric Models, Cox Model for Survival, Proportional Hazards 

18.3.3 Sample Size for Survival Studies 

References Exercises 

19 Simultaneous Consideration of Several Variables 

19.1 Scope of Multivariate Methods 

19.1.1 The Essentials of a Multivariate Setup 

19.1.2 Statistical Limitation on the Number of
Variables 

19.2 Dependent and Independent Sets of Variables 

19.2.1 Dependents and Independents Both
Quantitative: Multivariate Multiple Regression 

19.2.2 Quantitative Dependents and Qualitative
Independents: Multivariate Analysis of Variance (MANOVA) – MANOVA for
Repeated Measures 

19.2.3 Classification of Subjects into Known Groups:
Discriminant Analysis – Discriminant Function, Classification Rule,
Classification Accuracy 

19.3 Identification of Structure in the Observations 

19.3.1 Identification of Clusters of Subjects:
Cluster Analysis – Measures of Similarity, Hierarchical Agglomerative
Algorithm, Deciding on the Number of Natural Clusters 

19.3.2 Identification of Unobservable Underlying
Factors: Factor Analysis – Steps for Factor Analysis, Features of a
Successful Factor Analysis, Factor Scores 

References Exercises 

20 Quality Considerations 

20.1 Statistical Quality Control in Medical Care 

20.1.1 Statistical Control of Medical Care Errors –
Adverse Patient Outcomes, Monitoring Fatality, Limits of Tolerance 

20.1.2 Quality of Lots – The Lot Quality Method,
LQAS in Health Assessment 

20.1.3 Quality Control in a Medical Laboratory –
Control Chart, Cusum Chart, Other Errors in Medical Laboratory, Six Sigma
Methodology, Nonstatistical Issues 

20.2 Quality of Measurements 

20.2.1 Validity of Instruments – Types of Validity 

20.2.2 Reliability of Instruments – Internal
Consistency, Cronbach Alpha, Test–Retest Reliability 

20.3 Quality of Statistical Models: Robustness 

20.3.1 External Validation – SplitSample Method,
Another Sample Method 

20.3.2 Sensitivity Analysis and Uncertainty Analysis 

20.3.3 Resampling – Bootstrapping, Jackknife
Resampling 

20.4 Quality of Data 

20.4.1 Errors in Measurement – Lack of
Standardization in Definitions, Lack of Care in Obtaining or Recording
Information, Inability of the Observer to Get Confidence of the Respondent,
Bias of the Observer, Variable Competence of the Observers 

20.4.2 Missing Values – Approaches for Missing
Values, Handling Nonresponse, Imputations, IntentiontoTreat Analysis 

20.4.3 Lack of Standardization in Values –
Standardization Methods Already Described, Standardization for Calculating
Adjusted Rates, Standardized Mortality Ratio 

References Exercises 

21 Statistical Fallacies 

21.1 Problems with the Sample 

21.1.1 Biased Sample – Survivors, Volunteers,
Clinical Subjects, Publication Bias, Inadequate Specification of Sampling
Method, Abrupt Series 

21.1.2 Inadequate Size of the Sample – Problems with
Calculation of Sample Size 

21.1.3 Incomparable Groups – Differential in Group
Composition, Differential Definitions, Differential Compliance, Variable Periods of Exposure, Improper
Denominator 

21.1.4 Mixing of Distinct Groups – Effect on
Regression, Effect on Shape of the Distribution, Lack of Intragroup
Homogeneity 

21.2 Inadequate Analysis 

21.2.1 Ignoring Reality – Looking for Linearity, Overlooking
Assumptions, Selection of Inappropriate Variables, Area Under the Concentration
Curve, Further Problems with Statistical Analysis, Anomalous PersonYears,
Problems with IntentiontoTreat Analysis and Equivalence 

21.2.2 Choice of Analysis – Mean or Proportion?
Forgetting Baseline Values 

21.2.3 Misuse of Statistical Packages –
OverAnalysis, Data Dredging, Quantitative Analysis of Codes, Soft Data
versus Hard Data 

21.3 Errors in Presentation of Findings 

21.3.1 Misuse of Percentages and Means – Unnecessary
Decimals 

21.3.2 Problems in Reporting – Incomplete Reporting,
OverReporting, Selective Reporting, SelfReporting versus Objective
Measurement, Misuse of Graphs 

21.4 Misinterpretation 

21.4.1 Misuse of PValues – Magic Threshold
0.05, OneTail or TwoTail PValues, Multiple Comparisons, Dramatic PValues,
PValues for Nonrandom Sample, “Normal” with Respect to Several
Parameters, Absence of Evidence is not Evidence of Absence 

21.4.2 Correlation versus Cause–Effect Relationship
– Criteria for Cause–Effect, Other Considerations 

21.4.3 Sundry Issues – Diagnostic Test is Only an
Additional Adjunct, Medical Significance versus Statistical Significance,
Interpretation of Standard Error of p, Univariate Analysis but
Multivariate Conclusions, Limitation of Relative Risk, Misinterpretation of Improvements 

21.4.4 Final Comments 

References Exercises Brief Solutions and Answers to the Selected
Exercises 

Appendix A: Statistical Software 

A.1 General Purpose Statistical Software 

A.2 Special Purpose Statistical Software 

Appendix B: Some Statistical Tables 

Appendix C: Software Illustrations 

C.1 ROC Curves 

C.2 Repeated Measures
ANOVA 

C.3 Oneway ANOVA and
Tukey Test 

C.4 Stepwise Multiple
Linear Regression 

C.5 Curvilinear Regression 

C.6 Analysis of Covariance
(ANCOVA) 

C.7 Logistic Regression 

C.8 Survival Analysis
(Life Table Method) 

C.9 Cox Proportional
Hazards Model 

Index 

Data sets in the Examples in this text
are available in Excel for ready download at
http://MedicalBiostatistics.synthasite.com. Use these data sets to rework some
of the examples of your interest and to do further analysis where needed.