Longitudinal data monitoring for Child Health Indicators

Transcription

1 Longitudinal data monitoring for Child Health Indicators Vincent Were Statistician, Senior Data Manager and Health Economist Kenya Medical Research institute [KEMRI] Presentation at Kenya Paediatric Association 25 th April,

2 Outline Definitions Designing Longitudinal Studies Advantages of Longitudinal Studies Data Management Process for monitoring longitudinal Studies Statistical Considerations Way forward 2

3 Definitions Longitudinal studies are defined as studies in which the outcome variable is repeatedly measured; i.e. the outcome variable is measured in the same individual on several different occasions. In longitudinal studies, the observations of one individual over time are not independent of each other It is necessary to apply special statistical techniques, which take into account the fact that the repeated observations of each individual are correlated Independent variables and outcome variables are measured repeatedly over time 3

4 Classification of study designs Did investigator assign exposure? Yes No Experimental study Observational study Random allocation Yes No Yes Comparison group No Randomised trial Quasirandomised trial Analytical study Descriptive study Direction? There are Both Observation and Experimental Longitudinal Studies Cohort study Forward Backward Casecontrol At same time Cross sectional study 4

5 Four advantages of modern longitudinal methods 1. You have much more flexibility in research design Not everyone needs the same rigid data collection schedule cadence can be person specific Not everyone needs the same number of waves can use all cases, even those with just one wave! 2. You can identify temporal patterns in the data Does the outcome increase, decrease, or remain stable over time? Is the general pattern linear or non-linear? Are there abrupt shifts at substantively interesting moments? 3. You can include time varying predictors (those whose values vary over time) Participation in an intervention Family composition, employment Stress, self-esteem 4. You can include interactions with time to test hether a predi tor s effe t aries o er ti e Some effects dissipate they wear off Some effects increase they become more important Some effects are especially pronounced at particular times. 5

6 EXAMPLE: Integrated Data System provides longitudinal perspective Moms Cle First Home Dept. Visiting of Public (Prenatal) Health Cuyahoga Out-of-Home Dept. Employment Placement & Family Services Cuyahoga Child Dept. Care of Employment Voucher & Family Services Head Access Start, to Early Starting Learning Point Out-of-school Starting Point time Cuyahoga Juvenile Juvenile Court Filing Court Ohio Birth Dept. of Certificates Health Cuyahoga Child Dept. Abuse of Employment & Neglect & Family Services Cuyahoga Dept. of Medicaid Employment & Family Services Universal Starting Point Pre-K Cleveland Metropolitan CMHA Housing Tenant Authority Birth - Early Childhood - School Age - Youth - Young Adult Help Help Me Me Grow Cuyahoga Grow Home County Visiting Cuyahoga Early Childhood Board of Mental Health Cuyahoga Dept. Public of Employment Assistance & Family Services Kindergar- Cleveland ten Metropolitan Readiness School (KRA-L) District Cleveland Ohio Metropolitan Graduation School Test District Cuyahoga Newborn Board of Home Visiting Health Special Starting Needs Point Child Care Cuyahoga Dept. of SNAP Employment & Family Services Cleveland Public Metropolitan School School District National Postsecondary Student Clearing house Longitudinal Data Source Integration Data Pipeline 6

7 Observational longitudinal studies In observational longitudinal studies investigating individual development Each measurement taken on a subject at a particular time-point is influenced by three factors [ Confounders]: (1) age (time from date of birth to date of measurement), (2) period (time moment at which the measurements taken),and (3) birth cohort (group of subjects born in the same year). 7

8 Data Structure : Long vs Broad 8

9 Broad Data Structure 9

10 Type of Effects / Confounders of effects Period or Time of measurement effect Cohort Effect Missing data or drop-outs during follow-up 10

11 Experimental longitudinal studies Experimental (longitudinal) studies are by definition prospective cohort studies. Type of designs 11

12 Data management Process 12

13 Missing data in longitudinal studies One of the main methodological problems in longitudinal studies is missing data or attrition, i.e. the (unpleasant) situation when not all N subjects have data on all T measurements When subjects have missing data at the end of a longitudinal study they are often referred to as drop-outs It is, however, also possible that subjects miss one particular measurement, and then return to the study at the next follow-up. This type of missing data is often referred to as intermittent missing data 13

14 Example of missing data dataset Subject HRSD 1 HRSD 2 HRSD 3 HRSD 4 Subject Subject Subject Subject

15 Types of Missing data (1) missing completely at random (MCAR: missing, independent of both unobserved and observed data) (2) missing at random (MAR: missing, dependent on observed data, but not on unobserved data, or, in other words, given the observed data, the unobserved data are random), and (3) missing not at random (MNAR: missing, dependent on unobserved data) (Little and Rubin, 1987). 15

16 Ignorable or informative missing data? Conduct analysis of the missing data for outcome or predictor variables Use test tests, chi-square tests or logistic regression 16

17 Handling missing data : continuous data Imputation : calculation of the average value (mean or median) of the available data for a particular variable at a particular time-point. This average value is imputed for the missing values The si plest lo gitudi al i putatio ethod is alled the last value carried forward LVCF method. value of a variable at t = 1 for a particular subject is imputed for a missing value for that same subject at t = 2 linear interpolation With this method, for a missing value at t = 2 the average of the values at t = 1 and t = 3 is imputed, assuming a linear development over time of the variables with missing data. 17

18 Dichotomous and categorical outcome variables imputation of the category with the highest frequency for the subject(s) with missing data The most frequently used longitudinal imputation method available for di hoto ous a d ategori al issi g data is the LVCF ethod. 18

19 Statistical Considerations: Continuous outcome variables Research Question Does the outcome variable Y ha ge o er ti e? Or, i other ords: Is there a difference in the outcome variable Y between time periods t1 and t2? The paired t-test is used to test the hypothesis that the mean difference between Yt1 and Yt2 equals zero. non-parametric equivalent of the paired t-test, the (Wilcoxon) signed rank sum test 19

20 Hypothetical dataset for a longitudinal study with two measurements The test statistic of the paired t-test is the average of the differences divided by the standard deviation of the differences divided by the square root of the number of subjects 20

21 More than two continuous measurements Parametric test: MANOVA ; non-parametric Friedman test 21

22 Generalized Estimating Equations (GEE) where Yi t are observations for subject i at time t, β0 is the intercept, Xijt is the independent variable j for subject i at time t, β1j is the regression coefficient for independent variable j, J is the number of independent variables t is time, β2 is the regression coefficient for time, Zikt is the time-dependent covariate k for subject i at time t, β3k is the regression coefficient for time dependent covariate k, K is the number of time-dependent covariates, Gim is the time-independent covariate m for subject i β4m is the regression coefficient for time-independent covariate m, M is the number of time independent covariates, and εit is the error for su je t i at time t. 22

23 Dichotomous and categorical outcome variables More complex analysis than continuous variable In general, for categorical outcome variables with C categories, the change between subsequent measurements is another categorical variable with C2 categories. 23

24 Analysis of experimental studies Experimental (longitudinal) studies differ from observational longitudinal studies in that experimental studies (in epidemiology often described as trials) include one or more interventions. Manova analysis can be used correcting for baseline differences Example of GEE used analyse the effects of the therapy on systolic blood pressure, the following statistical model can used used where Yit are observations for subject i at time t, β0 is the intercept, β1 is the regression coefficient for therapy versus placebo, β2 is the regression coefficient for time, and εit is the error for su je t i at time t. 24

25 Considerations for Data Management System Key matters to consider in designing a system to manage data are What is required by regulation? How would a person find relevant data? What makes the data fit for purpose? How will the data be shared and with whom? How will it be secured for future use? 25

26 Thank you: Any Questions? Contact: