Statistical Reasoning for Surgeons
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Statistical Reasoning for Surgeons
Restrepo, Camilo; Maltenfort, Mitchell G.; Chen, Antonia F.
Taylor & Francis Ltd
12/2020
14
Dura
Inglês
9781138091795
15 a 20 dias
453
Descrição não disponível.
1 Introduction - Why Does a Surgeon Need Statistics?
2 Interpreting Probability: Medical School Axioms of Probability
3 Statistics, the Law of Large Numbers, and the Confidence Interval
4 The Basics of Statistical Tests
5 How Much Data Is Enough?
6 Showing the Data to Yourself First - Graphs and Tables, Part 1
7 How Normal Is a Gaussian Distribution? What to Do with Extreme Values?
8 All Probabilities Are Conditional
9 Quality versus Quantity in Data
10 Practical Examples
11 All Things Being Equal - But How? (Designing the Study)
12 Binary and Count Outcomes
13 Repeated Measurements and Accounting for Change
14 What If the Data Is Not All There?
15 Showing the Data to Others - Graphs and Tables, Part 2
16 Further Reading
GLOSSARY
REFERENCES
INDEX
2 Interpreting Probability: Medical School Axioms of Probability
3 Statistics, the Law of Large Numbers, and the Confidence Interval
4 The Basics of Statistical Tests
5 How Much Data Is Enough?
6 Showing the Data to Yourself First - Graphs and Tables, Part 1
7 How Normal Is a Gaussian Distribution? What to Do with Extreme Values?
8 All Probabilities Are Conditional
9 Quality versus Quantity in Data
10 Practical Examples
11 All Things Being Equal - But How? (Designing the Study)
12 Binary and Count Outcomes
13 Repeated Measurements and Accounting for Change
14 What If the Data Is Not All There?
15 Showing the Data to Others - Graphs and Tables, Part 2
16 Further Reading
GLOSSARY
REFERENCES
INDEX
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
MCID Estimate;surgery;Generate Propensity Scores;recovery;Null Hypothesis Significance Testing;post-operative outcome;Breast Cancer Cohort;Solid statistical analysis plan;Data Set;Research manuscript;Observed Test Statistic;Surgeons;Straight Line Regression;Null Hypothesis;Generalized Linear Model;Negative Binomial;Count Data Regression;MCID;Missing Data;Propensity Scores;Multiple Imputation;BMI Range;Roc;Cumulative Distribution Function;Holm Bonferroni Adjustment;Test Parameter Values;Opaque Model;Math Destruction;Lord's Paradox;BMI Category;Random Assignment
1 Introduction - Why Does a Surgeon Need Statistics?
2 Interpreting Probability: Medical School Axioms of Probability
3 Statistics, the Law of Large Numbers, and the Confidence Interval
4 The Basics of Statistical Tests
5 How Much Data Is Enough?
6 Showing the Data to Yourself First - Graphs and Tables, Part 1
7 How Normal Is a Gaussian Distribution? What to Do with Extreme Values?
8 All Probabilities Are Conditional
9 Quality versus Quantity in Data
10 Practical Examples
11 All Things Being Equal - But How? (Designing the Study)
12 Binary and Count Outcomes
13 Repeated Measurements and Accounting for Change
14 What If the Data Is Not All There?
15 Showing the Data to Others - Graphs and Tables, Part 2
16 Further Reading
GLOSSARY
REFERENCES
INDEX
2 Interpreting Probability: Medical School Axioms of Probability
3 Statistics, the Law of Large Numbers, and the Confidence Interval
4 The Basics of Statistical Tests
5 How Much Data Is Enough?
6 Showing the Data to Yourself First - Graphs and Tables, Part 1
7 How Normal Is a Gaussian Distribution? What to Do with Extreme Values?
8 All Probabilities Are Conditional
9 Quality versus Quantity in Data
10 Practical Examples
11 All Things Being Equal - But How? (Designing the Study)
12 Binary and Count Outcomes
13 Repeated Measurements and Accounting for Change
14 What If the Data Is Not All There?
15 Showing the Data to Others - Graphs and Tables, Part 2
16 Further Reading
GLOSSARY
REFERENCES
INDEX
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
MCID Estimate;surgery;Generate Propensity Scores;recovery;Null Hypothesis Significance Testing;post-operative outcome;Breast Cancer Cohort;Solid statistical analysis plan;Data Set;Research manuscript;Observed Test Statistic;Surgeons;Straight Line Regression;Null Hypothesis;Generalized Linear Model;Negative Binomial;Count Data Regression;MCID;Missing Data;Propensity Scores;Multiple Imputation;BMI Range;Roc;Cumulative Distribution Function;Holm Bonferroni Adjustment;Test Parameter Values;Opaque Model;Math Destruction;Lord's Paradox;BMI Category;Random Assignment
