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To Research in Statistics, Mathematics,  and SAS programming

We have technical expertise in Statistical Analysis Plan, Reporting and interpreting analysis results, SAS programming, and Validation of TFLs (Safety and Efficacy tables, figures, listings using SAS or S-Plus/R.) for clinical trials in phases I-IV. We have expertise in most of therapeutic areas and our specialties are Oncology, Osteoporosis, Diabetes, Alzheimer's disease and Cardiovascular, Chronic Obstructive Pulmonary Disease (COPD), devices such Elite SPY, surgical Mesh analysis. We have over 20 years of pharmaceutical experiences and clinical data manipulation. We had played great role in number of successful NDA submissions within pharmaceutical industries. We are familiar with FDA requirements and knowledgeable of drug approval process, ICH guidelines, GCP, and EMEA. We can take your clinical trial studies to a successful FDA submission using CRT (Case Report Tabulation) for electronic submissions, implementing SOPs and Working Guidelines.

We have expertise working with the following statistical and mathematical software packages:

SAS, R, WinBugs, BUGS,  East, Adaptive, Bayesian, JMP, nQuery, MATHLAB, IBM-SPSS,  NCSS/PASS. 

 To discuss more in detail how we can help you to design clinical trials and to reach to your successful FDA submissions, please get in touch with us.

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Articles to consider for Clinical Trial Design

About 10000 clinical trials are undertaken annually in all areas of medicine, from the treatment of acne to the prevention of cancer. Correct interpretation of the data from such trials depends largely on adequate design and on performing the appropriate statistical analyses. The statistical aspects of both the design and analysis of trials are important, with particular emphasis on recently developed methods of analysis.

1. Guidance for determining the number of centers in clinical trials

2. Guidelines on Data Safety and Monitoring

This guideline is to provide the oversight and necessity of interim monitoring to ensure the safety of the study participants and the validity and integrity of the trial data. Phase III clinical trials usually have a Data and Safety monitoring Board (DSMB), with broad responsibility for monitoring the conduct of the trial.

3. Guidelines for the Creation of Analysis Data Files and Documentation of Statistical Analyses for Submission

 Optimal Two-Stage Designs For Phase II Clinical TrialsThe purpose of this document is to provide guidelines for the creation of analysis data sets and associated documentation that are submitted to the FDA statistical reviewer in support of the primary and important secondary study objectives.

4. Guidance for Contents of the Clinical Study Report 

The purpose of this document is to provide guidelines on what information need to be included in statistical section of the Clinical Study Report (CSR).

5. Guidance for Non-Inferiority Clinical Trials

6. Fundamentals of Biostatistics ( 1. Clinical Trials (RCT), 2. Validity/Reliability, 3. Assessing Evidence)

 

Oncology: Optimal Two-Stage Designs For Phase II Clinical Trials

Richard Simon: Optimal Two-Stage Designs


Enter probability of accepting poor drug (alpha) [between 0 and 0.3 only]
Enter probability of rejecting good drug (beta) [between 0 and 0.3 only]
Enter response probability of poor drug (P0) [between 0 and 1]
Enter response probability of good drug (P1) [P1 should be greater than P0]

Example of this Calculation

Your input

Alpha --------------------------------------------> 0.10

Beta ---------------------------------------------> 0.10

Response Probability of Poor Drug (P0) ---> 0.40

Response Probability of Good Drug (P1) ---> 0.60

===================================================================

Your Output

Optimal Two Stage Design Optimum Design MinMax Design
First Stage Sample Size (n1)
18
28
Upper Limit For 1st Stage Rejection of Drug (r1)
7
11
Maximum Sample Size (n)
46
41
Upper Limit for 2nd Stage Rejection of Drug (r)
22
20
Expected Sample Size If Response Probability = P0
30.22
33.84
Probability of Early Termination at P0
0.56
0.55
 

 

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Publisher:  Davar (Dave) Hamadani, MS, PHD resident/ Director/ Biostatistician

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Statistical Literature

  • Altman DG (1980) Statistics and ethics in medical research. VI - Presentation of results. British Medical Journal 281:1542-1544.
  • Altman DG (1991) Practical statistics for medical research. London: Chapman and Hall.
  • Altman DG (1998) Confidence intervals for the number needed to treat. British Medical Journal 317: 1309-1312.
  • Altman DG, Gardner MJ (1988) Calculating confidence intervals for regression and correlation. British Medical Journal 296:1238-1242.
  • Altman DG, Gore SM, Gardner MJ, Pocock SJ (1983) Statistical guidelines for contributors to medical journals. British Medical Journal 286:1489-1493.
  • Armitage P (1955) Tests for linear trends in proportions and frequencies. Biometrics 11:375–386.
  • Armitage P, Berry G, Matthews JNS (2002) Statistical methods in medical research. 4th ed. Blackwell Science.
  • Bland M (2000) An introduction to medical statistics, 3rd ed. Oxford: Oxford University Press.
  • Bland JM, Altman DG (1986) Statistical method for assessing agreement between two methods of clinical measurement. The Lancet i:307-310.
  • Bland JM, Altman DG (1995) Comparing methods of measurement: why plotting difference against standard method is misleading. The Lancet 346:1085-1087.
  • Bland JM, Altman DG (1997) Statistics notes: Cronbach's alpha. British Medical Journal 314:572.
  • Bland JM, Altman DG (1999) Measuring agreement in method comparison studies. Statistical Methods in Medical Research, 8:135-160.
  • Bland JM, Altman DG (2007) Agreement between methods of measurement with multiple observations per individual. Journal of Biopharmaceutical Statistics. 17:571-582.
  • Bulpitt CJ (1987) Confidence intervals. The Lancet i:494-497.
  • Campbell MJ, Gardner MJ (1988) Calculating confidence intervals for some non-parametric analyses. British Medical Journal, 296:1454-1456.
  • CLSI (2008) Defining, establishing, and verifying reference intervals in the clinical laboratory: approved guideline - third edition. CLSI Document C28-A3. Wayne, PA: Clinical and Laboratory Standards Institute.
  • Cohen J (1960) A coefficient of agreement for nominal scales. Educational and Psychological Measurement 20:37-46.
  • Conover WJ (1999) Practical nonparametric statistics, 3rd edition. New York: John Wiley & Sons.
  • Cornbleet PJ, Gochman N (1979) Incorrect least-squares regression coefficients in method-comparison analysis. Clinical Chemistry 25:432-438.
  • Cronbach LJ (1951) Coefficient alpha and the internal structure of tests. Psychometrika 16:297-334.
  • Daly LE (1998) Confidence limits made easy: interval estimation using a substitution method. American Journal of Epidemiology 147: 783-1998.
  • DeLong ER, DeLong DM, Clarke-Pearson DL (1988): Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44:837-845.
  • Dewitte K, Fierens C, Stöckl D, LM Thienpont (2002) Application of the Bland-Altman plot for interpretation of method-comparison studies: a critical investigation of its practice. Clinical Chemistry 48:799-801.
  • Feldt LS (1965) The approximate sampling distribution of Kuder-Richardson reliability coefficient twenty. Psychometrika 30:357-371.
  • Fleiss JL (1981) Statistical methods for rates and proportions, 2nd edn. New York: John Wiley & Sons.
  • Fleiss JL, Levin B, Paik MC (2003) Statistical methods for rates and proportions, 3rd ed. Hoboken: John Wiley & Sons.
  • Gardner MJ, Altman DG (1986) Confidence intervals rather than P values: estimation rather than hypothesis testing. British Medical Journal 292:746-750.
  • Girden ER (1992) ANOVA: repeated measures. Sage University Papers Series on Quantitative Applications in the Social Sciences, 84. Thousand Oaks, CA: Sage.
  • Glantz SA, Slinker BK (2001) Primer of applied regression & analysis of variance. 2nd ed. McGraw-Hill.
  • Greenhouse SW, Geisser S (1959) On methods in the analysis of profile data. Psychometrika 24:95-112.
  • Griner PF, Mayewski RJ, Mushlin AI, Greenland P (1981) Selection and interpretation of diagnostic tests and procedures. Annals of Internal Medicine, 94:555-600.
  • Grubbs FE (1969) Procedures for detecting outlying observations in samples. Technometrics 11:1-21.
  • Hanley JA, Hajian-Tilaki KO (1997) Sampling variability of nonparametric estimates of the areas under receiver operating characteristic curves: an update. Academic Rediology 4:49-58.
  • Hanley JA, McNeil BJ (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143:29-36.
  • Hanley JA, McNeil BJ (1983) A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology 148:839-843.
  • Hilgers RA (1991) Distribution-free confidence bounds for ROC curves. Methods of Information in Medicine, 30:96-101.
  • Huitema BE (1980) The analysis of covariance and alternatives. Wiley-Interscience.
  • Husted JA, Cook RJ, Farewell VT, Gladman DD (2000) Methods for assessing responsiveness: a critical review and recommendations. Journal of Clinical Epidemiology 53:459-168.
  • Huynh H, Feldt LS (1976) Estimation of the Box correction for degrees of freedom from sample data in randomised block and split-plot designs. Journal of Educational Statistics 1:69-82.
  • Jones R, Payne B (1997) Clinical investigation and statistics in laboratory medicine. London: ACB Venture Publications.
  • Krouwer JS (2008) Why Bland-Altman plots should use X, not (Y+X)/2 when X is a reference method. Statistics in Medicine 27:778-780.
  • Krouwer JS, Monti KL (1995) A simple, graphical method to evaluate laboratory assays. Eur J Clin Chem Clin Biochem 33:525-527.
  • Lecoutre B (1991) A correction for the e approximate test in repeated measures designs with two or more independent groups. Journal of Educational Statistics 16:371-372.
  • Lin L.I-K (1989) A concordance correlation coefficient to evaluate reproducibility. Biometrics 45:255-268.
  • Lin L.I-K (2000) A note on the concordance correlation coefficient. Biometrics 56:324-325.
  • Lentner C (Ed) (1982) Geigy Scientific Tables, 8th edition, Volume 2. Basle: Ciba-Geigy Limited.
  • McBride GB (2005) A proposal for strength-of-agreement criteria for Lin's Concordance Correlation Coefficient. NIWA Client Report: HAM2005-062.
  • McGill R, Tukey JW, Larsen WA (1978) Variations of box plots. The American Statistician, 32:12-16.
  • McGraw KO, Wong SP (1996) Forming inferences about some intraclass correlation coefficients. Psychological Methods 1:30-46. (Correction: 1:390).
  • Metz CE (1978) Basic principles of ROC analysis. Seminars in Nuclear Medicine 8:283-298.
  • Moses LE (1987) Graphical methods in statistical analysis. Annual Review of Public Health 8:309-353.
  • NCCLS (2000) How to define and determine reference intervals in the clinical laboratory: approved guideline - second edition. NCCLS document C28-A2. Wayne, PA: NCCLS.
  • Neter J, Kutner MH, Nachtsheim CJ, Wasserman W (1996) Applied linear statistical models. 4th ed. McGraw-Hill.
  • Norman GR, Wyrwich KW, Patrick DL (2007) The mathematical relationship among different forms of responsiveness coefficients. Quality of Life Research 16:815-822.
  • Pampel FC (2000) Logistic regression: A primer. Sage University Papers Series on Quantitative Applications in the Social Sciences, 07-132. Thousand Oaks, CA: Sage.
  • Passing H, Bablok W (1983) A new biometrical procedure for testing the equality of measurements from two different analytical methods. Application of linear regression procedures for method comparison studies in Clinical Chemistry, Part I. J Clin Chem Clin Biochem 21:709-720.
  • Petrie A, Bulman JS, Osborn JF (2003) Further statistics in dentistry. Part 8: systematic reviews and meta-analyses. British Dental Journal 194:73-78.
  • Pocock SJ (1984) Clinical trials. A practical approach. Chichester: John Wiley & Sons.
  • Reed AH, Henry RJ, Mason WB (1971) Influence of statistical method used on the resulting estimate of normal range. Clinical Chemistry 17:275-284.
  • Rosner B (1983) Percentage points for a generalized ESD many-outlier procedure. Technometrics 25:165-172.
  • Schoonjans F, De Bacquer D, Schmid P (2011) Estimation of population percentiles. Epidemiology 22: 750-751.
  • Schwartz D, Mayaux MJ (1980) Mode of evaluation of results in artificial insemination. In: Human Artificial Insemination and Semen Preservation (eds David G and Price WS). New York: Plenum Press, pp 197-210.
  • Sheskin DJ (2004) Handbook of parametric and nonparametric statistical procedures. 3rd ed. Boca Raton: Chapman & Hall /CRC.
  • Sheskin DJ (2011) Handbook of parametric and nonparametric statistical procedures. 5th ed. Boca Raton: Chapman & Hall /CRC.
  • Shrout PE, Fleiss JL (1979) Intraclass correlations: uses in assessing rater reliability. Psychological Bulletin 86:420-428.
  • Snedecor GW, Cochran WG (1989) Statistical methods, 8th edition. Ames, Iowa: Iowa State University Press.
  • Spiegel MR (1961) Theory and problems of statistics. New York: McGraw-Hill Book Company.
  • Tukey JW (1977) Exploratory data analysis. Reading, Mass: Addison-Wesley Publishing Company.
  • Wildt AR, Ahtola OT (1978) Analysis of covariance. Sage Publications.
  • Zhou XH, NA Obuchowski, DK McClish (2002) Statistical methods in diagnostic medicine. New York: Wiley.
  • Zweig MH, Campbell G (1993) Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clinical Chemistry 39:561-577.