Sample size and power calculations for medical studies by simulation when closed form expressions are not available

Statistical Methods in Medical Research

Volumn 22, Issue 3, 2013, Pages 324-345

  Landau, Sabine ^a   Stahl, Daniel ^a  

^a KING'S COLLEGE LONDON   (United Kingdom)

Author keywords

causal effect estimation; hybrid approach; measurement error; missing data; Monte Carlo simulation; power; precision; sensitivity analysis

Indexed keywords

ACCURACY; ANALYTIC METHOD; ARTICLE; CALCULATION; LONGITUDINAL STUDY; MEASUREMENT ERROR; MEDICAL RESEARCH; METHODOLOGY; MONTE CARLO METHOD; OBSERVATIONAL STUDY; POPULATION MODEL; POWER ANALYSIS; RANDOMIZED CONTROLLED TRIAL (TOPIC); SAMPLE SIZE; SAMPLING; SIMULATION;

CAUSAL EFFECT ESTIMATION; HYBRID APPROACH; MEASUREMENT ERROR; MISSING DATA; MONTE CARLO SIMULATION; POWER; PRECISION; SENSITIVITY ANALYSIS;

DATA INTERPRETATION, STATISTICAL; LONGITUDINAL STUDIES; MONTE CARLO METHOD; SAMPLE SIZE;

EID: 84878507709     PISSN: 09622802     EISSN: 14770334     Source Type: Journal    
DOI: 10.1177/0962280212439578     Document Type: Article

References (64)

1. Halpern SD, Karlawish JHT, Berlin JA. The continuing unethical conduct of underpowered clinical trials. J Am Med Assoc. 2002 ; 288: 358-362
2. Shoukri MM, Asyali MH, Donner A. Sample size requirement for the design of reliability study: review and new results. Stat Methods Med Res. 2004 ; 13: 251-271
3. Venter A, Maxwell SE, Bolig E. Power in randomized group comparisons: the value of adding a single intermediate time point to a traditional pretest-posttest design. Psychol Methods. 2002 ; 7: 194-209
4. Faul F, Erdfelder E, Lang A-G, et al. G*Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav Res Methods. 2007 ; 39: 175-191
5. Raudenbush SW. Statistical analysis and optimal design for cluster randomized trials. Psychol Methods. 1997 ; 2: 173-185
6. Raudenbush SW, Liu X. Effects of study duration, frequency of observation, and sample size on power in studies of group differences in polynomial change. Psychol Methods. 2001 ; 6: 387-401
7. Christopher M Monte Carlo simulation. Thousand Oaks, CA: Sage Publications ; 1997 :
8. Rubinstein RY, Kroese DP Simulation and the Monte Carlo method. Hoboken, NJ: John Wiley & Sons ; 2008 :
9. Shonkwiler RW, Mendivil F Explorations in Monte Carlo methods. New York: Springer ; 2009 :
10. Gastaňaga VM, McLaren CE, Delfino RJ. Power calculations for generalized linear models in observational longitudinal studies: a simulation approach in SAS. Comput Methods Program Biomed. 2006 ; 84: 27-33
11. Tu XM, Zhang J, Kowalski J, et al. Power analyses for longitudinal study designs with missing data. Stat Med. 2007 ; 26: 2958-2981
12. Williams MS, Ebel ED, Wagner BA. Monte Carlo approaches for determining power and sample size in low-prevalence applications. Preventive Veterinary Med. 2007 ; 82: 151-158
13. Richardson DB. Power calculations for survival analyses via Monte Carlo estimation. Am J Ind Med. 2003 ; 44: 532-539
14. Muthén LK, Muthén BO. How to use a Monte Carlo study to decide on sample size and determine power. Struct Equ Modell. 2002 ; 9: 599-620
15. Browne WJ, Lahi MG, Parker RMA A guide to sample size calculation for random effects models via simulation and the MLPowSim software package. Manual, University of Bristol ; 2009 :
16. Fieveson AH. Power by simulation. Stata J. 2002 ; 2: 107-114
17. Rizzo ML Statistical computing with R. Boca Raton, FL: Chapman & Hall/CRC ; 2008 :
18. Harris AHS, Cronkite R, Moos R. Physical activity, exercise coping, and depression in a 10-year cohort study of depressed patients. J Affective Disorders. 2006 ; 93: 79-85
19. Hedeker D, Gibbons RD, Waternaux C. Sample size estimation for longitudinal designs with attrition: comparing time-related contrasts between two groups. J Edu Behav Stat. 1999 ; 24: 70-93
20. Roy A, Bhaumik DK, Aryal S, et al. Sample size determination for hierarchical longitudinal designs with differential attrition rates. Biometrics. 2007 ; 63: 699-707
21. Basagaña X, Spiegelman D. Power and sample size calculations for longitudinal studies estimating a main effect of a time-varying exposure. Stat Methods Med Res. 2010 ; 19: 1-17
22. Rubin DB Multiple imputation for nonresponse in surveys. New York: Wiley ; 1987 :
23. Schafer JL, Graham JW. Missing data: our view of the state of the art. Psychol Methods. 2002 ; 7: 147-177
24. Cohen J Statistical power analysis for the behavioral sciences. 2 nd edn. Hillsdale, NJ: Lawrence Erlbaum Associates, 1988. Hillsdale, NJ: Lawrence Erlbaum Associates ; 1988 :
25. White PD, Goldsmith KA, Johnson AL, et al. Comparison of adaptive pacing therapy, cognitive behaviour therapy, graded exercise therapy, and specialist medical care for chronic fatigue syndrome (PACE): a randomised trial. Lancet. 2011 ; 377: 823-836
26. Cohen J. The cost of dichotomization. Appl Psychol Meas. 1983 ; 7: 249-254
27. Greenland S. Avoiding power loss associated with categorization and ordinal scores in dose-response and trend analysis. Epidemiology. 1995 ; 6: 450-454
28. MacCallum RC, Zhang S, Preacher KJ, et al. On the practice of dichotomization of quantitative variables. Psychol Methods. 2002 ; 7: 19-40
29. Streiner DL. Breaking up is hard to do: the heartbreak of dichotomizing continuous data. Can J Psychiatry. 2002 ; 47: 262-266
30. Taylor AB, West SG, Aiken LS. Loss of power in logistic, ordinal logistic, and probit regression when an outcome variable is coarsely categorized. Edu Psychol Meas. 2006 ; 66: 228-239
31. Altman DG, Royston P. The cost of dichotomizing continuous variables. Br MedJ. 2006 ; 332: 1080-1080
32. Carroll RJ, Ruppert D, Stefanski LA, et al Measurement error in nonlinear models. Boca Raton, FL: Chapman and Hall/CRC ; 2006 :
33. Chen H, Cohen P, Chen S. Biased odds ratios for dichotomization of age. Stat Med. 2007 ; 26: 3487-3497
34. Dunn G, Maracy M, Dowrick C, et al. Estimating psychological treatment effects from a randomized controlled trial with both non-compliance and loss to follow-up. Br J Psychiatry. 2003 ; 183: 323-331
35. Little RJA, Yau LHY. Statistical techniques for analyzing data from prevention trials: treatment of no-shows using Rubin's causal model. Psychol Methods. 1998 ; 3: 147-159
36. Bellamy SL, Liu JY, Ten Have TR. An introduction to causal modeling in clinical trials. Clin Trials. 2007 ; 4: 58-73
37. White IR. Uses and limitations of randomization-based efficacy estimators. Stat Methods Med Res. 2005 ; 14: 327-347
38. Angrist JD, Imbens GW, Rubin DB. Identification of causal effects using instrumental variables. J Am Stat Assoc. 1996 ; 91: 444-455
39. Frangakis CE, Rubin DB. Principal stratification in causal inference. Biometrics. 2002 ; 58: 21-29
40. Little RJA, Long Q, Lin X. A comparison of methods for estimating the causal effect of a treatment in randomized clinical trials subject to noncompliance. Biometrics. 2009 ; 65: 640-649
41. Hui J, Rubin DB. Principal stratification for causal inference with extended partial compliance. J Am Stat Assoc. 2008 ; 103: 101-111
42. Jo B. Statistical power in randomized intervention studies with non-compliance. Psychol Methods. 2002 ; 7: 178-193
43. Rubin DB. Causal inference using potential outcomes: design, modeling, decisions. J Am Stat Assoc. 2005 ; 100: 322-331
44. Bloom HS. Accounting for no-shows in experimental evaluation designs. Eval Rev. 1984 ; 8: 225-246
45. Rabe-Hesketh S, Skrondal A, Pickles A. Generalized multilevel structural equation modelling. Psychometrika. 2004 ; 69: 167-190
46. Jo B, Muthén B New developments and techniques in structural equation modeling. Marcoulides GA Schumacker RE, ed. Hillsdale, NJ: Lawrence Erlbaum Associates ; 2001: 57-87.
47. Jo B. Estimation of intervention effects with noncompliance: alternative model specifications. J Educ Behav Stat. 2002 ; 27: 385-409
48. Skrondal A, Rabe-Hesketh S Generalized latent variable modeling: multilevel, longitudinal, and structural equation models. Boca Raton, FL: Chapman & Hall/CRC ; 2004 :
49. Hardin JW, Hilbe JM Generalized linear models and extensions. 2 nd edn. College Station, TX: Stata Press, 2007. College Station, TX: Stata Press ; 2007 :
50. Serlin R. Testing for robustness in Monte Carlo studies. Psychol Methods. 2000 ; 5: 230-240
51. Turner EL, Dobson JL, Pocock SJ. Categorisation of continuous risk factors in epidemiological publications: a survey of current practice. Epidemiol Perspect Innov. 2010 ; 7: 9-9
52. Yin P, Fan X. Assessing the reliability of Beck Depression Inventory scores: reliability generalization across studies. Educ Psychol Meas. 2000 ; 60: 201-223
53. Wang F, Gelfand AE. A simulation based approach to Bayesian sample size determination for performance under a given model and for separating models. Stat Sci. 2002 ; 17: 193-208
54. Inoue LYT, Berry DA, Parmigiani G. Relationship between Bayesian and frequentist sample size determination. Am Stat. 2005 ; 59: 79-87
55. Adcock CJ. Sample size determination: a review. The Statistician. 1997 ; 46: 261-283
56. Pezeshk H. Bayesian techniques for sample size determination in clinical trials: a short review. Stat Methods Med Res. 2003 ; 12: 489-504
57. Brown WB, Herson J, Atkinson EN, et al. Projection from previous studies: a Bayesian and frequentist compromise. Control Clin Trials. 1987 ; 8: 29-44
58. Feng Z, Grizzle JE. Correlated binomial variates: properties of estimator of intraclass correlation and its effect on sample size calculation. Stat Med. 1997 ; 11: 1607-1614
59. Joseph L, Du Berger R, Belisle P. Bayesian and mixed Bayesian/likelihood criteria for sample size determination. Stat Med. 1997 ; 16: 769-781
60. Lee SJ, Zelen M. Clinical trials and sample size considerations: another perspective. Stat Sci. 2000 ; 15: 95-110
61. Spiegelhalter DJ, Abrams KR, Myles JP Bayesian approaches to clinical trials and health-care evaluation. Chichester: Wiley ; 2004 :
62. Turner R, Prevost T, Thompson SG. Allowing for imprecision of the intracluster correlation coefficient in the design of cluster randomized trials. Stat Med. 2004 ; 23: 1195-1214
63. Campbell MJ, Donner A, Klar N. Developments in cluster randomized trials and in statistics in medicine. Stat Med. 2007 ; 26: 2-19
64. Hade EM, Murray DM, Pen Pennell ML, et al. Intraclass correlation estimates for cancer screening outcomes: estimates and applications in the design of group-randomized cancer screening studies. J Natl Cancer Inst Monogr. 2010 ; 40: 97-103