Sample size and power calculations in Mendelian randomization with a single instrumental variable and a binary outcome

被引:398
|
作者
Burgess, Stephen [1 ]
机构
[1] Univ Cambridge, Dept Publ Hlth & Primary Care, Cambridge CB1 8RN, England
基金
英国医学研究理事会;
关键词
Mendelian randomization; sample size; power; binary outcome; allele score; CAUSAL ODDS RATIO; WEAK INSTRUMENTS; BIAS;
D O I
10.1093/ije/dyu005
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
Background: Sample size calculations are an important tool for planning epidemiological studies. Large sample sizes are often required in Mendelian randomization investigations. Methods and results: Resources are provided for investigators to perform sample size and power calculations for Mendelian randomization with a binary outcome. We initially provide formulae for the continuous outcome case, and then analogous formulae for the binary outcome case. The formulae are valid for a single instrumental variable, which may be a single genetic variant or an allele score comprising multiple variants. Graphs are provided to give the required sample size for 80% power for given values of the causal effect of the risk factor on the outcome and of the squared correlation between the risk factor and instrumental variable. R code and an online calculator tool are made available for calculating the sample size needed for a chosen power level given these parameters, as well as the power given the chosen sample size and these parameters. Conclusions: The sample size required for a given power of Mendelian randomization investigation depends greatly on the proportion of variance in the risk factor explained by the instrumental variable. The inclusion of multiple variants into an allele score to explain more of the variance in the risk factor will improve power, however care must be taken not to introduce bias by the inclusion of invalid variants.
引用
收藏
页码:922 / 929
页数:8
相关论文
共 50 条
  • [1] POWER AND SAMPLE SIZE CALCULATIONS FOR MENDELIAN RANDOMIZATION STUDIES.
    Freeman, Guy
    Cowling, Benjamin
    Schooling, Mary
    [J]. AMERICAN JOURNAL OF EPIDEMIOLOGY, 2013, 177 : S117 - S117
  • [2] Power and sample size calculations for Mendelian randomization studies using one genetic instrument
    Freeman, Guy
    Cowling, Benjamin J.
    Schooling, C. Mary
    [J]. INTERNATIONAL JOURNAL OF EPIDEMIOLOGY, 2013, 42 (04) : 1157 - 1163
  • [3] A review of instrumental variable estimators for Mendelian randomization
    Burgess, Stephen
    Small, Dylan S.
    Thompson, Simon G.
    [J]. STATISTICAL METHODS IN MEDICAL RESEARCH, 2017, 26 (05) : 2333 - 2355
  • [4] Consistency and Collapsibility Are They Crucial for Instrumental Variable Analysis with a Survival Outcome in Mendelian Randomization?
    Burgess, Stephen
    [J]. EPIDEMIOLOGY, 2015, 26 (03) : 411 - 413
  • [5] Mendelian randomization as an instrumental variable approach to causal inference
    Didelez, Vanessa
    Sheehan, Nuala
    [J]. STATISTICAL METHODS IN MEDICAL RESEARCH, 2007, 16 (04) : 309 - 330
  • [6] Instrumental variable model average with applications in Mendelian randomization
    Seng, Loraine Liping
    Liu, Ching-Ti
    Wang, Jingli
    Li, Jialiang
    [J]. STATISTICS IN MEDICINE, 2023, 42 (19) : 3547 - 3567
  • [7] An adjusted instrumental-variable model for Mendelian randomization
    Palmer, T. M.
    Burton, P. R.
    Thompson, J. R.
    Tobin, M. D.
    [J]. GENETIC EPIDEMIOLOGY, 2007, 31 (06) : 641 - 641
  • [8] MENDELIAN RANDOMIZATION: THE USE OF GENES IN INSTRUMENTAL VARIABLE ANALYSES
    Scholder, Stephanie von Hinke Kessler
    Smith, George Davey
    Lawlor, Debbie A.
    Propper, Carol
    Windmeijer, Frank
    [J]. HEALTH ECONOMICS, 2011, 20 (08) : 893 - 896
  • [9] Efficient Design for Mendelian Randomization Studies: Subsample and 2-Sample Instrumental Variable Estimators
    Pierce, Brandon L.
    Burgess, Stephen
    [J]. AMERICAN JOURNAL OF EPIDEMIOLOGY, 2013, 178 (07) : 1177 - 1184
  • [10] 'Mendelian randomization' equals instrumental variable analysis with genetic instruments
    Wehby, George L.
    Ohsfeldt, Robert L.
    Murray, Jeffrey C.
    [J]. STATISTICS IN MEDICINE, 2008, 27 (15) : 2745 - 2749