statistical methods and determination of sample size
These guidelines focus on the validation of the bioanalytical methods generating quantitative concentration data used for pharmacokinetic and toxicokinetic parameter determinations.
Multiple Linear Regression II and ANOVA IJames Neill
Explains advanced use of multiple linear regression, including residuals, interactions and analysis of change, then introduces the principles of ANOVA starting with explanation of t-tests.
Multiple Linear Regression II and ANOVA IJames Neill
Explains advanced use of multiple linear regression, including residuals, interactions and analysis of change, then introduces the principles of ANOVA starting with explanation of t-tests.
Sample size and how to calculate it
- Why sample size is important
- Alpha and beta errors
- Main outcome and Effect size
- Practical examples using Means-Proportions-Correlation- Confidence Interval
Researchers, as a whole, tend to underestimate the need for power. I'm just now starting to get it.
I recently gave a brief, easy-to-follow presentation on statistical power, it's importance, and how to go about getting it.
Hope you find it useful.
Disease screening and screening test validityTampiwaChebani
Full lecture covering screening tests and validity testing. Covers topics such as calculation and interpretation of sensitivity, specificity, positive predictive value and negative predictive value of a screening test.
This presentation will address the issue of sample size determination for social sciences. A simple example is provided for every to understand and explain the sample size determination.
The number that divides the normal distribution into region where we will reject the null hypothesis and the region where we fail to reject the null hypothesis. For normal distribution Z at 5% level of significance (z= plus-minus 1.96) is often referred to as the critical value (or critical region).
Sample size and how to calculate it
- Why sample size is important
- Alpha and beta errors
- Main outcome and Effect size
- Practical examples using Means-Proportions-Correlation- Confidence Interval
Researchers, as a whole, tend to underestimate the need for power. I'm just now starting to get it.
I recently gave a brief, easy-to-follow presentation on statistical power, it's importance, and how to go about getting it.
Hope you find it useful.
Disease screening and screening test validityTampiwaChebani
Full lecture covering screening tests and validity testing. Covers topics such as calculation and interpretation of sensitivity, specificity, positive predictive value and negative predictive value of a screening test.
This presentation will address the issue of sample size determination for social sciences. A simple example is provided for every to understand and explain the sample size determination.
The number that divides the normal distribution into region where we will reject the null hypothesis and the region where we fail to reject the null hypothesis. For normal distribution Z at 5% level of significance (z= plus-minus 1.96) is often referred to as the critical value (or critical region).
In general, a factorial experiment involves several variables.
One variable is the response variable, which is sometimes called the outcome variable or the dependent variable.
The other variables are called factors.
New Directions in Targeted Therapeutic Approaches for Older Adults With Mantl...i3 Health
i3 Health is pleased to make the speaker slides from this activity available for use as a non-accredited self-study or teaching resource.
This slide deck presented by Dr. Kami Maddocks, Professor-Clinical in the Division of Hematology and
Associate Division Director for Ambulatory Operations
The Ohio State University Comprehensive Cancer Center, will provide insight into new directions in targeted therapeutic approaches for older adults with mantle cell lymphoma.
STATEMENT OF NEED
Mantle cell lymphoma (MCL) is a rare, aggressive B-cell non-Hodgkin lymphoma (NHL) accounting for 5% to 7% of all lymphomas. Its prognosis ranges from indolent disease that does not require treatment for years to very aggressive disease, which is associated with poor survival (Silkenstedt et al, 2021). Typically, MCL is diagnosed at advanced stage and in older patients who cannot tolerate intensive therapy (NCCN, 2022). Although recent advances have slightly increased remission rates, recurrence and relapse remain very common, leading to a median overall survival between 3 and 6 years (LLS, 2021). Though there are several effective options, progress is still needed towards establishing an accepted frontline approach for MCL (Castellino et al, 2022). Treatment selection and management of MCL are complicated by the heterogeneity of prognosis, advanced age and comorbidities of patients, and lack of an established standard approach for treatment, making it vital that clinicians be familiar with the latest research and advances in this area. In this activity chaired by Michael Wang, MD, Professor in the Department of Lymphoma & Myeloma at MD Anderson Cancer Center, expert faculty will discuss prognostic factors informing treatment, the promising results of recent trials in new therapeutic approaches, and the implications of treatment resistance in therapeutic selection for MCL.
Target Audience
Hematology/oncology fellows, attending faculty, and other health care professionals involved in the treatment of patients with mantle cell lymphoma (MCL).
Learning Objectives
1.) Identify clinical and biological prognostic factors that can guide treatment decision making for older adults with MCL
2.) Evaluate emerging data on targeted therapeutic approaches for treatment-naive and relapsed/refractory MCL and their applicability to older adults
3.) Assess mechanisms of resistance to targeted therapies for MCL and their implications for treatment selection
Couples presenting to the infertility clinic- Do they really have infertility...Sujoy Dasgupta
Dr Sujoy Dasgupta presented the study on "Couples presenting to the infertility clinic- Do they really have infertility? – The unexplored stories of non-consummation" in the 13th Congress of the Asia Pacific Initiative on Reproduction (ASPIRE 2024) at Manila on 24 May, 2024.
Flu Vaccine Alert in Bangalore Karnatakaaddon Scans
As flu season approaches, health officials in Bangalore, Karnataka, are urging residents to get their flu vaccinations. The seasonal flu, while common, can lead to severe health complications, particularly for vulnerable populations such as young children, the elderly, and those with underlying health conditions.
Dr. Vidisha Kumari, a leading epidemiologist in Bangalore, emphasizes the importance of getting vaccinated. "The flu vaccine is our best defense against the influenza virus. It not only protects individuals but also helps prevent the spread of the virus in our communities," he says.
This year, the flu season is expected to coincide with a potential increase in other respiratory illnesses. The Karnataka Health Department has launched an awareness campaign highlighting the significance of flu vaccinations. They have set up multiple vaccination centers across Bangalore, making it convenient for residents to receive their shots.
To encourage widespread vaccination, the government is also collaborating with local schools, workplaces, and community centers to facilitate vaccination drives. Special attention is being given to ensuring that the vaccine is accessible to all, including marginalized communities who may have limited access to healthcare.
Residents are reminded that the flu vaccine is safe and effective. Common side effects are mild and may include soreness at the injection site, mild fever, or muscle aches. These side effects are generally short-lived and far less severe than the flu itself.
Healthcare providers are also stressing the importance of continuing COVID-19 precautions. Wearing masks, practicing good hand hygiene, and maintaining social distancing are still crucial, especially in crowded places.
Protect yourself and your loved ones by getting vaccinated. Together, we can help keep Bangalore healthy and safe this flu season. For more information on vaccination centers and schedules, residents can visit the Karnataka Health Department’s official website or follow their social media pages.
Stay informed, stay safe, and get your flu shot today!
MANAGEMENT OF ATRIOVENTRICULAR CONDUCTION BLOCK.pdfJim Jacob Roy
Cardiac conduction defects can occur due to various causes.
Atrioventricular conduction blocks ( AV blocks ) are classified into 3 types.
This document describes the acute management of AV block.
Anti ulcer drugs and their Advance pharmacology ||
Anti-ulcer drugs are medications used to prevent and treat ulcers in the stomach and upper part of the small intestine (duodenal ulcers). These ulcers are often caused by an imbalance between stomach acid and the mucosal lining, which protects the stomach lining.
||Scope: Overview of various classes of anti-ulcer drugs, their mechanisms of action, indications, side effects, and clinical considerations.
- Video recording of this lecture in English language: https://youtu.be/lK81BzxMqdo
- Video recording of this lecture in Arabic language: https://youtu.be/Ve4P0COk9OI
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
- Link to NephroTube website: www.NephroTube.com
- Link to NephroTube social media accounts: https://nephrotube.blogspot.com/p/join-nephrotube-on-social-media.html
These simplified slides by Dr. Sidra Arshad present an overview of the non-respiratory functions of the respiratory tract.
Learning objectives:
1. Enlist the non-respiratory functions of the respiratory tract
2. Briefly explain how these functions are carried out
3. Discuss the significance of dead space
4. Differentiate between minute ventilation and alveolar ventilation
5. Describe the cough and sneeze reflexes
Study Resources:
1. Chapter 39, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 34, Ganong’s Review of Medical Physiology, 26th edition
3. Chapter 17, Human Physiology by Lauralee Sherwood, 9th edition
4. Non-respiratory functions of the lungs https://academic.oup.com/bjaed/article/13/3/98/278874
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Ve...kevinkariuki227
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
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The prostate is an exocrine gland of the male mammalian reproductive system
It is a walnut-sized gland that forms part of the male reproductive system and is located in front of the rectum and just below the urinary bladder
Function is to store and secrete a clear, slightly alkaline fluid that constitutes 10-30% of the volume of the seminal fluid that along with the spermatozoa, constitutes semen
A healthy human prostate measures (4cm-vertical, by 3cm-horizontal, 2cm ant-post ).
It surrounds the urethra just below the urinary bladder. It has anterior, median, posterior and two lateral lobes
It’s work is regulated by androgens which are responsible for male sex characteristics
Generalised disease of the prostate due to hormonal derangement which leads to non malignant enlargement of the gland (increase in the number of epithelial cells and stromal tissue)to cause compression of the urethra leading to symptoms (LUTS
3. • Statistical analyses of PK measures (e.g.,
AUC ad Cmax) based on two one-sided
tests procedure to determine whether the
mean PK values for T & R are comparable
• BE concluded if the 90% CI for the ratio of
geometric means for T & R is within limits
of 80 – 125% (exceptions)
STATISTICAL ANALYSIS
4. TO SOLVE THIS NIGHTMARE
LET STARTS WITH THIS………
HYPOTHESIS TESTING AND CONFIDENCE INTERVAL
5. HYPOTHESIS TEST
Convectional hypothesis test (frequentist statistics)
- Ho: θ= θ1 H1: θ≠ θ1 (in this case it is two-sided)
Usually expressed as a difference: Ho: d= 0, H1: d≠ 0
-If P<0.05 we can conclude that statistical significant difference exists
-If P>0.05 we cannot conclude
• With the available potency we cannot detect a difference
• But it does not mean that the difference does not exist
• And it does not mean that they are equivalent or equal
We only have certainty when we reject the null hypothesis
-In superiority trials: H1 is for existence of differences
This conventional test is inadequate to conclude about “equalities”
-In fact, it is impossible to conclude “equality”
6. NULL VS. ALTERNATIVE HYPOTHESIS
Fisher, R.A. The Design of Experiments, Oliver
and Boyd, London, 1935
“The null hypothesis is never proved or
established, but is possibly disproved in the
course of experimentation. Every experiment
may be said to exist only in order to give the
facts a chance of disproving the null
hypothesis”
Frequent mistake: the absence of statistical
significance has been interpreted incorrectly as
absence of clinically relevant differences
7. (BIO) EQUIVALENCE
We are interested in verifying (instead of rejecting)
the null hypothesis of a conventional hypothesis
test
We have to redefine the alternative hypothesis as a
range of values with an equivalent effect
The differences within this range are considered clinically irrelevant
Problem: It's very difficult to define the maximum
difference without clinical relevance for the Cmax
and AUC of each drug
Solution: 20% difference considered clinically
irrelevant based on a survey among physicians in
1970s.
8. INTERVAL HYPOTHESIS OR TWO ONE-
SIDED TESTS
Redefine the null hypothesis: How?
Solution: It is like changing the null to the alternative
hypothesis and vice versa.
Alternative hypothesis test: Schuirmann, 1981
This is equivalent to:
H 0 : T - R < D1 or T - R > D2
H A : D1 T - R D2
It is called as an interval hypothesis because the equivalence hypothesis is in
the alternative hypothesis and it is expressed as an interval
bioequivalence
bioinequivalence
T and R population mean for test and reference
formulation respectively
[D1 ; D2] Absolute equivalence
interval
H 0 : T - R < D1
H 0 : T - R > D2
H A : D1 T - R
H A : T - R D2
9. INTERVAL HYPOTHESIS OR TWO ONE-
SIDED TESTS
The new alternative hypothesis is decided with a
statistic that follows a distribution that can be
approximated to a t-distribution
To conclude bioequivalence a P value <0.05 has
to be obtained in both one-sided tests
The hypothesis tests do not give an idea of
magnitude of equivalence (P<0.001 vs. 90% CI:
0.95 – 1.05).
That is why confidence intervals are preferred
Source: Slides from Dr. Alfredo Garcia – Addis Ababa, Ethiopia 2010
10. THE TWO ONE-SIDED TESTS
(SCHUIRMAN)
10
bioequivalence
H 0 : T - R < D1 or T - R > D2
H A : D1 T - R D2
H 0 : T - R < D1
H A : D1 T - R
H 0 : T - R > D2
H A : T - R D2
First one-sided test second one-sided test
Bioequivalence when the 2 tests reject H0
11. EQUIVALENCE STUDY
d < 0
Negative effect
d = 0
No difference
d > 0
Positive effect
-d +d
Region of
clinical
equivalence
Slides from Dr. Alfredo Garcia – Addis Ababa, Ethiopia 2010
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26. ANOVA MODEL
•Non replicate designs
–General linear model procedure (PROC GLM)
–Linear mixed effects model procedure (PROC
MIXED)
•Replicate crossover designs
–Linear mixed effects procedure (PROC MIXED)
NB: For parallel and replicate designs – do not
assume equal variances.
27. ANOVA MODEL – MULTIPLE GROUPS
• Multiple groups
– Model should be modified to reflect the group nature
– e.g. reflect that the periods of 1st group are different
from those of the second group.
– 2 groups from different sites or same site but separated
by longer period e.g. months: results may not be
combined in single analysis
• Sequential design: where decision for 2nd group is based on
results of the 1st group- different statistical methods are
required
28. WHAT DOES ANOVA DO?
At its simplest (there are extensions)
ANOVA tests the following hypotheses:
H0: The means of all the groups are equal.
Ha: Not all the means are equal
• doesn’t say how or which ones differ.
• Can follow up with “multiple
comparisons”
Note: we usually refer to the sub-populations
as “groups” when doing ANOVA.
29. ANOVA ASSUMPTIONS
• Random and independent: subjects chosen for the BE
study should be randomly assigned to the sequences of
the study
• Data must be normally distributed: check this by looking
at histograms and/or normal quantile plots, or use
assumptions
· can handle some nonnormality, but not severe outliers
• Homogeneity of variance: The variability of scores in all
groups is similar; rule of thumb: ratio of largest to smallest
sample standard. dev. must be less than 2:1
30. NOTATION FOR ANOVA
• n = number of individuals all together
• I = number of groups
• = mean for entire data set is
Group i has
• ni = # of individuals in group i
• xij = value for individual j in group i
• = mean for group i
• si = standard deviation for group i
31. HOW ANOVA WORKS (OUTLINE)
ANOVA measures two sources of variation in the data and
compares their relative sizes
• variation BETWEEN groups
• for each data value look at the difference between
its group mean and the overall mean
• variation WITHIN groups
• for each data value we look at the difference
between that value and the mean of its group
32. Sum of Squared Deviations
Total Sum of Squares = Sum of Squared between-group
deviations + Sum of Squared within-group deviations
SSTotal = SSBetween + SSWithinb
33. The ANOVA F-statistic is a ratio of the
Between Group Variation divided by the
Within Group Variation:
A large F is evidence against H0, since it
indicates that there is more difference
between groups than within groups.
34. AN EXAMPLE ANOVA
SITUATION
Subjects: 25 patients with blisters
Treatments: Treatment A, Treatment B, Placebo
Measurement: # of days until blisters heal
Data [and means]:
• A: 5,6,6,7,7,8,9,10 [7.25]
• B: 7,7,8,9,9,10,10,11 [8.875]
• P: 7,9,9,10,10,10,11,12,13 [10.11]
Are these differences significant?
35. MINITAB ANOVA OUTPUT
Analysis of Variance for days
Source DF SS MS F P
treatment 2 34.74 17.37 6.45 0.006
Error 22 59.26 2.69
Total 24 94.00
Df Sum Sq Mean Sq F value Pr(>F)
treatment 2 34.7 17.4 6.45 0.0063 **
Residuals 22 59.3 2.7
R ANOVA Output
36. MINITAB ANOVA OUTPUT
Analysis of Variance for days
Source DF SS MS F P
treatment 2 34.74 17.37 6.45 0.006
Error 22 59.26 2.69
Total 24 94.00
SS stands for sum of squares
• ANOVA splits this into 3 parts
37. MINITAB ANOVA OUTPUT
MSG = SSG / DFG
MSE = SSE / DFE
Analysis of Variance for days
Source DF SS MS F P
treatment 2 34.74 17.37 6.45 0.006
Error 22 59.26 2.69
Total 24 94.00
F = MSG / MSE
P-value
comes from
F(DFG,DFE)
(P-values for the F statistic are in Table E)
38. F = Differences Among Treatment Means
Differences Among Subjects Treated Alike
F = Treatment Effect + (Experimental Error)
Experimental Error
F = Between-group Differences
Within-group Differences
Logic of F Ratio
39. SO HOW BIG IS F?
Since F is
Mean Square Between / Mean Square Within
= MSG / MSE
A large value of F indicates relatively more
difference between groups than within groups
(evidence against H0)
To get the P-value, we compare to F(I-1,n-I)-distribution
• I-1 degrees of freedom in numerator (# groups -1)
• n - I degrees of freedom in denominator (rest of df)
40. WHERE’S THE DIFFERENCE?
Analysis of Variance for days
Source DF SS MS F P
treatment 2 34.74 17.37 6.45 0.006
Error 22 59.26 2.69
Total 24 94.00
Individual 95% CIs For Mean
Based on Pooled StDev
Level N Mean StDev ----------+---------+---------+------
A 8 7.250 1.669 (-------*-------)
B 8 8.875 1.458 (-------*-------)
P 9 10.111 1.764 (------*-------)
----------+---------+---------+------
Pooled StDev = 1.641 7.5 9.0 10.5
Once ANOVA indicates that the groups do not all
appear to have the same means, what do we do?
Clearest difference: P is worse than A (CI’s don’t overlap)
41. Logic of F Test and Hypothesis Testing
Form of F Test: Between Group Differences
Within Group Differences
Purpose: Test null hypothesis: Between Group = Within Group =
Random Error
Interpretation: If null hypothesis is not supported (F > 1) then
Between Group diffs are not simply random error, but
instead reflect effect of the independent variable.
Result: Null hypothesis is rejected, alt. hypothesis is
supported
(BUT NOT PROVED!)
44. AT THE END OF THE
SESSION…
You should be able to:
• Recognise the key factors in
calculation of sample size for BE
studies;
• Integrate the concepts for sample size
determination in the overall design of
the study.
46. HOW TO CALCULATE THE SAMPLE
SIZE OF A 2X2 CROSS-OVER
BIOEQUIVALENCE STUDY
47. FACTORS AFFECTING THE SAMPLE SIZE
• The error variance (CV%) of the primary PK
parameters
– Published data
– Pilot study
• The significance level desired (5%): consumer’s risk
• The statistical power desired (>80%): producer’s risk
• The expected mean deviation from comparator
• The acceptance criteria: (usually 80-125% or ±20%)
48. REASONS FOR A CORRECT CALCULATION OF
THE SAMPLE SIZE
• Too many subjects
– It is unethical to expose more subjects than necessary
– Unnecessary risk for some subjects
– It is an unnecessary waste of some resources ($)
• Too few subjects
– A study unable to reach its objective is unethical
– All subjects at risk for nothing
– All resources ($) is wasted when the study is
inconclusive
• Minimum number of subjects: 12
49. FREQUENT MISTAKES
• To calculate the sample size required to detect a 20%
difference assuming that treatments are e.g. equal
– Pocock, Clinical Trials, 1983
• To use calculation based on data without log-
transformation
– Design and Analysis of Bioavailability and Bioequivalence
Studies, Chow & Liu, 1992 (1st edition) and 2000 (2nd edition)
• Too many extra subjects. Usually no need of more than
10%. Depends on tolerability
– 10% proposed by Patterson et al, Eur J Clin Pharmacol 57: 663-
670 (2001)
50. • Exact value has to be obtained with power curves
• Approximate values are obtained based on
formulae
–Best approximation: iterative process (t-test)
–Acceptable approximation: based on Normal
distribution
• Calculations are different when we assume
products are really equal and when we assume
products are slightly different
• Any minor deviation is masked by extra subjects to
be included to compensate drop-outs and
withdrawals (10%)
METHODS TO CALCULATE THE
SAMPLE SIZE
51. 51
• Both treatments are equal
SAMPLE SIZE CALCULATION
2
2
1
2
1
2
25
.
1
2
Ln
Z
Z
s
N
w
2
2
1
1
2
25
.
1
2
Ln
Ln
Z
Z
s
N
R
T
w
• Assumptions on difference between treatments
• Treatments are different
1
R
T
1
R
T
2
2
1 CV
Ln
sw
CV expressed as 0.3 for 30%
52. • Calculation assuming that
treatments are equal
ASSUMPTIONS ON DIFFERENCE BETWEEN
TREATMENTS
2
2
1
2
1
2
25
.
1
2
Ln
Z
Z
s
N
w
2
2
1
1
2
25
.
1
2
Ln
Ln
Z
Z
s
N
R
T
w
• Z(1-(/2)) = DISTR.NORM.ESTAND.INV(0.05) for
90% 1-
• Z(1-(/2)) = DISTR.NORM.ESTAND.INV(0.1) for 80%
1-
• Z(1- ) = DISTR.NORM.ESTAND.INV(0.05) for 5%
• Calculation assuming that
treatments are not equal
Z(1-) = DISTR.NORM.ESTAND.INV(0.1) for 90% 1-
Z(1-) = DISTR.NORM.ESTAND.INV(0.2) for 80% 1-
Z(1-) = DISTR.NORM.ESTAND.INV(0.05) for 5%