Statisticians.org

Copyright by Mark Chang Since Feb-06-2000

Group Sequential Design for Two Means

Group Sequential Design for Proportions

Group Sequential Design for Survival Medians

Sample-Size Re-estimation Design for Two Means

Sample-Size Re-estimation Design for Proportions

Pick-the-Winner Design for Means (Phase II/III Seamless Design)

Pick-the-Winner Design for Proportions (Phase II/III Seamless Design)

Two-sample z-Test (large sample or
population variance known)

Test for treatment mean difference
with 2x2 crossover design

One way Repeated measures ANOVA for Two groups

Test
two correlation coefficients -- Fisher's arctan transformation

**Two-Sample
Hypothesis Test for Proportion Difference**

Arcsine Method
(with continuity Correction) (n1 P1>10, n2 P2>10)

Poisson Method
for two unequal sample proportions(np1<10, nP2<10)

Asymptotic
z-method considering variance difference

Person's
Chi-square Test (Improved, Fleiss)- Large sample

Snedecor's Method
for Normal but Heterogeneous Large Samples

Whitehead
proportional odds ratio model with k categories and two treatments (logistic
regression)

Repeated
measures for two group proportions

Chi-square test(normal approximation) for one sample
proportion with k categories

Mantel-Haenszel test for odds ratio with k strata
-Nam-method (with continuity correction))

Kendall's Test of Independence

Pasternack-Gilbert
Method: Exponential survival distribution - No censoring

Test for mean
survival time with exponential distribution (with censoring)

Exponential
survival distribution with uniform patient enrollment rate over time T - with
censoring

LogRank test
for survival analysis

** One-Sample
or Paired-sample Hypothesis Test for Mean**

Sign Test for
Median Difference - Paired sample

Wilcoxon Sum Rank
Test - One Sample

Chi-square
test(normal approximation) for one sample proportion

Chi-square
test(normal approximation) for one sample proportion - adjusted for finite
population

Chi-square
test(normal approximation) for one sample proportion with k categories

Test for Bloch-Kraemer intraclass Kappa coefficient
(binary outcome)

McNemar's Test for a Paired Sample

Ho: Test
Correlation (regression coefficient) from zero - Fisher's arctan
transformation

Test Ho:
regression coefficient = zero -- Fisher's arctan transformation

Logistic
Regression on x for binary outcome

Logistic
Regression on x for binary outcome with covariates

Linear regression
y=a+bx, test Ho: b=b0, vs. Ha: b<>b0.

Linear regression
y1=a1+b1x, y2=a2+b2x. Test Ho: b1=b2, vs. Ha: b1<>b2.

One Way Contrast
between means

William's Test
For Minimum Effective Dose

Cochran-Armitage
Test for linear/Monotonic Trend (dose response)

1.
Chow,
S.C., Chang, M. (2006). Adaptive Design Methods in Clinical Trials. Chapman
& Hall/CRC.

4.
Chang,
M. (2010). Monte Carlo Simulation for the pharmaceutical industry. Chapman
& Hall/CRC

5.
Chang,
M. (2011). Modern Issues and Methods in Biostatistics, Springer, NY.

7.
Chang,
M. (2012). Paradoxes in Scientific Inference. Taylor & Francis Group, LLC.

8.
Mark
Chang (2014). Principles of Scientific Methods. Mark Chang, Taylor &
Francis Group

FDA-Industry Workshop 2020 Short Course Slides Part 1

FDA-Industry Workshop 2020 Short Course Slides Part 2