Biomaterial Database

Sample size and power calculations for correlations between bivariate longitudinal data. 2010

W Scott Comulada, and Robert E Weiss

UCLA Center for Community Health, 10920 Wilshire Blvd Suite 350, Los Angeles, CA 90024-6543, USA. scomulad@ucla.edu

The analysis of a baseline predictor with a longitudinally measured outcome is well established and sample size calculations are reasonably well understood. Analysis of bivariate longitudinally measured outcomes is gaining in popularity and methods to address design issues are required. The focus in a random effects model for bivariate longitudinal outcomes is on the correlations that arise between the random effects and between the bivariate residuals. In the bivariate random effects model, we estimate the asymptotic variances of the correlations and we propose power calculations for testing and estimating the correlations. We compare asymptotic variance estimates to variance estimates obtained from simulation studies and compare our proposed power calculations for correlations on bivariate longitudinal data to power calculations for correlations on cross-sectional data.

UI	MeSH Term	Description	Entries
D008137	Longitudinal Studies	Studies in which variables relating to an individual or group of individuals are assessed over a period of time.	Bogalusa Heart Study,California Teachers Study,Framingham Heart Study,Jackson Heart Study,Longitudinal Survey,Tuskegee Syphilis Study,Bogalusa Heart Studies,California Teachers Studies,Framingham Heart Studies,Heart Studies, Bogalusa,Heart Studies, Framingham,Heart Studies, Jackson,Heart Study, Bogalusa,Heart Study, Framingham,Heart Study, Jackson,Jackson Heart Studies,Longitudinal Study,Longitudinal Surveys,Studies, Bogalusa Heart,Studies, California Teachers,Studies, Jackson Heart,Studies, Longitudinal,Study, Bogalusa Heart,Study, California Teachers,Study, Longitudinal,Survey, Longitudinal,Surveys, Longitudinal,Syphilis Studies, Tuskegee,Syphilis Study, Tuskegee,Teachers Studies, California,Teachers Study, California,Tuskegee Syphilis Studies
D003198	Computer Simulation	Computer-based representation of physical systems and phenomena such as chemical processes.	Computational Modeling,Computational Modelling,Computer Models,In silico Modeling,In silico Models,In silico Simulation,Models, Computer,Computerized Models,Computer Model,Computer Simulations,Computerized Model,In silico Model,Model, Computer,Model, Computerized,Model, In silico,Modeling, Computational,Modeling, In silico,Modelling, Computational,Simulation, Computer,Simulation, In silico,Simulations, Computer
D006801	Humans	Members of the species Homo sapiens.	Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D000465	Algorithms	A procedure consisting of a sequence of algebraic formulas and/or logical steps to calculate or determine a given task.	Algorithm
D012725	Sexual Behavior	Sexual activities of humans.	Anal Sex,Oral Sex,Sexual Activity,Sexual Orientation,Premarital Sex Behavior,Sex Behavior,Sex Orientation,Sexual Activities,Activities, Sexual,Activity, Sexual,Behavior, Premarital Sex,Behavior, Sex,Behavior, Sexual,Orientation, Sexual,Sex, Anal,Sex, Oral
D015233	Models, Statistical	Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc.	Probabilistic Models,Statistical Models,Two-Parameter Models,Model, Statistical,Models, Binomial,Models, Polynomial,Statistical Model,Binomial Model,Binomial Models,Model, Binomial,Model, Polynomial,Model, Probabilistic,Model, Two-Parameter,Models, Probabilistic,Models, Two-Parameter,Polynomial Model,Polynomial Models,Probabilistic Model,Two Parameter Models,Two-Parameter Model
D015658	HIV Infections	Includes the spectrum of human immunodeficiency virus infections that range from asymptomatic seropositivity, thru AIDS-related complex (ARC), to acquired immunodeficiency syndrome (AIDS).	HTLV-III Infections,HTLV-III-LAV Infections,T-Lymphotropic Virus Type III Infections, Human,HIV Coinfection,Coinfection, HIV,Coinfections, HIV,HIV Coinfections,HIV Infection,HTLV III Infections,HTLV III LAV Infections,HTLV-III Infection,HTLV-III-LAV Infection,Infection, HIV,Infection, HTLV-III,Infection, HTLV-III-LAV,Infections, HIV,Infections, HTLV-III,Infections, HTLV-III-LAV,T Lymphotropic Virus Type III Infections, Human
D015928	Cognitive Behavioral Therapy	A directive form of psychotherapy based on the interpretation of situations (cognitive structure of experiences) that determine how an individual feels and behaves. It is based on the premise that cognition, the process of acquiring knowledge and forming beliefs, is a primary determinant of mood and behavior. The therapy uses behavioral and verbal techniques to identify and correct negative thinking that is at the root of the aberrant behavior.	Behavior Therapy, Cognitive,Cognitive Behaviour Therapy,Cognitive Therapy,Psychotherapy, Cognitive,Cognition Therapy,Cognitive Behavior Therapy,Cognitive Psychotherapy,Therapy, Cognition,Therapy, Cognitive,Therapy, Cognitive Behavior,Behavior Therapies, Cognitive,Behavioral Therapies, Cognitive,Behavioral Therapy, Cognitive,Behaviour Therapies, Cognitive,Behaviour Therapy, Cognitive,Cognition Therapies,Cognitive Behavior Therapies,Cognitive Behavioral Therapies,Cognitive Behaviour Therapies,Cognitive Psychotherapies,Cognitive Therapies,Psychotherapies, Cognitive,Therapies, Cognition,Therapies, Cognitive,Therapies, Cognitive Behavior,Therapies, Cognitive Behavioral,Therapies, Cognitive Behaviour,Therapy, Cognitive Behavioral,Therapy, Cognitive Behaviour
D015982	Bias	Any deviation of results or inferences from the truth, or processes leading to such deviation. Bias can result from several sources: one-sided or systematic variations in measurement from the true value (systematic error); flaws in study design; deviation of inferences, interpretations, or analyses based on flawed data or data collection; etc. There is no sense of prejudice or subjectivity implied in the assessment of bias under these conditions.	Aggregation Bias,Bias, Aggregation,Bias, Ecological,Bias, Statistical,Bias, Systematic,Ecological Bias,Outcome Measurement Errors,Statistical Bias,Systematic Bias,Bias, Epidemiologic,Biases,Biases, Ecological,Biases, Statistical,Ecological Biases,Ecological Fallacies,Ecological Fallacy,Epidemiologic Biases,Experimental Bias,Fallacies, Ecological,Fallacy, Ecological,Scientific Bias,Statistical Biases,Truncation Bias,Truncation Biases,Bias, Experimental,Bias, Scientific,Bias, Truncation,Biase, Epidemiologic,Biases, Epidemiologic,Biases, Truncation,Epidemiologic Biase,Error, Outcome Measurement,Errors, Outcome Measurement,Outcome Measurement Error
D016013	Likelihood Functions	Functions constructed from a statistical model and a set of observed data which give the probability of that data for various values of the unknown model parameters. Those parameter values that maximize the probability are the maximum likelihood estimates of the parameters.	Likelihood Ratio Test,Maximum Likelihood Estimates,Estimate, Maximum Likelihood,Estimates, Maximum Likelihood,Function, Likelihood,Functions, Likelihood,Likelihood Function,Maximum Likelihood Estimate,Test, Likelihood Ratio