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Online Adaptive Clinical Trial Design (Commercial Version: ExpDesign Studio)

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)

Online Classical Clinical Trial Design (Commercial Version: ExpDesign Studio)

Two-Sample Hypothesis Test for Mean Difference

Two-sample t-Test 

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 

Two-Sample Hypothesis Test for Survival Difference (Unequal sample size)

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 

Exponential survival distribution with uniform patient enrollment rate over time T0 and a follow-up period - with censoring 

LogRank test for survival analysis 

Exponential survival distribution with a Bernoulli confounding variable (uniform patient enrollment rate over time T0 and a follow-up period)

Exponential survival distribution with uniform patient enrollment rate over time T0 and a follow-up period with lost to follow-up  

 

 One-Sample or Paired-sample Hypothesis Test for Mean

One-sample t-Test 

Paired-sample t-Test 

Sign Test for Median Difference - Paired sample 

Wilcoxon Sum Rank Test - One Sample

One-Sample or Paired-sample Hypothesis Test for Proportion

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 

Hypothesis Test based on Regression

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. 

Dose-Response Trial

One Way Contrast between means 

William's Test For Minimum Effective Dose 

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

Biostatistics and Adaptive Design Book References

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

2.       Chang, M. (2007). Adaptive Design Theory and Implementation Using SAS and R. Chapman & Hall/CRC, Taylor & Francis.

3.       Chang, M. (2008). Classical and Adaptive Designs using ExpDesign Studio, John-Wiley and Sons, Inc, New York.

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.

6.       Chow, S.C., Chang, M. (2011). Adaptive Design Methods in Clinical Trials, Second Ed, Chapman & Hall/CRC.

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

9.       Carini, C. Menon, S., and Chang, M. (2014). Clinical and Statistical Considerations in Personalized Medicine. Taylor and Francis, CRC.

10.   Chang, M. (2014). Adaptive Design Theory and Implementation Using SAS and R. 2nd Ed. Chapman & Hall/CRC, Taylor & Francis.

11.    Chang M. (2015). Introductory Adaptive Design – A practical guide with R. Taylor and Francis, CRC, Chapman & Hall.

SAS and R Code for Adaptive Design Theory and Implementation using SAS and R

SAS Programs

R Program

R Source Code for Introductory Adaptive Design with R

R Code

JavaScript Code for Monte Carlo Simulation for the Pharmaceutical Industry

JavaScript Code