Testing the performance of the two-fold FCS algorithm for multiple imputation of longitudinal clinical records Catherine Welch 1, Irene Petersen 1, Jonathan Bartlett 2, Ian White 3, Richard Morris 1, Louise Marston 1, Kate Walters 1, Irwin Nazareth 1 and James Carpenter 2 1 Department of Primary Care and Population Health, UCL 2 Department of Medical Statistics, LSHTM 3 MRC Biostatistics, Cambridge Funding: MRC

The Health Improvement Network (THIN) primary care database GP records 9 million patients over 15 years in 450 practices Powerful data source for research into coronary heart disease (CHD) Studies complicated by missing data Up to 38% of health indicator measurements are missing in newly registered patients 1 1 Marston et al, 2010 Pharmacoepidemiology and Drug Safety

Partially observed data in THIN Missing data never intended to be recorded Data recorded at irregular intervals Non-monotone missingness p pattern

Multiple Imputation (MI) and THIN Most MI designed for cross-sectional data Impute both continuous and discrete variables at many time points –Standard ICE using Stata struggles with this New method developed by Nevalainen et al –Two-fold fully conditional specification (FCS) algorithm –Imputes each time point separately –Uses information recorded before and after time point Nevalainen et al, 2009 Statistics in Medicine

A graphical illustration of the two-fold FCS algorithm Within-time iteration Among-time iteration Nevalainen et al, 2009 Statistics in Medicine

Algorithm validation Nevalainen et al –Proposed the two-fold FCS approach –Validated algorithm using data sampled from case-control –3 time points included with a linear substantive model Our previous work Imputed data had accurate coefficients and acceptable level of variation in these settings

Simulation Before we apply the algorithm to THIN we want to test it in a complex setting similar to THIN Test algorithm in simulation study: –Create 1000 full datasets –Remove values –Apply two-fold FCS algorithm –Fit regression model for risk of CHD Full data Complete case data Imputed data –Compare results

Advantages of using simulated data We know the original distributions so we can compare with distribution of imputed data and test for bias Create different scenarios to test the algorithm Design data so it is close to THIN data

Simple dataset 5000 men, 10 years of data CHD diagnosis from 2000 – yes/no Age – 5 year age bands Smoking status recorded in 2000 –smokers, ex- and non-smokers Anti-hypertensive drug prescription – yes/no Systolic blood pressure (mmHg) Weight (kg) Townsend score quintile – 1 (least) to 5 (most) Registration – indicate if patient registered in 1999

Results from exponential regression model Outcome : Time to CHD Exposures in year 2000: age, Townsend score quintile, weight, blood pressure, smoking status, anti-hypertensive drug treatment, registration in 1999 Analysis of 1000 datasets

Generated data results VariablesTHIN data log risk ratio Full simulated data Log risk ratioSE Anti-hypertensive drug treatment Systolic blood pressure (mmHg) Weight (kg) Smoking status Non- smoker Reference Ex- smoker Current smoker Adjusted for age, registration in 1999 and Townsend score quintile Results of fitting exponential regression model

70% missing completely at random (MCAR) missingness mechanisms Missing data on blood pressure, weight, smoking In THIN: – % missing in any given year, E.g. 70% missing equivalent to a health indicator recorded approximately every 3 years –If one variable is missing other variables also more likely to be missing

70% MCAR results VariablesTHIN data Simulated data Log risk ratio Full dataComplete case Log risk ratioSE Log risk ratioSE Anti-hypertensive drug treatment Systolic blood pressure (mmHg) Weight (kg) Smoking status Non- smoker Reference Ex- smoker Current smoker Adjusted for age, registration in 1999 and Townsend score quintile

Two-fold FCS algorithm Stata ICE – series of chained equations 3 among-time iterations, 10 within-time iterations Produce 3 imputed datasets 1 year time window ii+1i+2i+3i-3i-2i-1

Imputing time-independent variables Algorithm designed to impute time-dependent variables and does not account for imputing time- independent variables Smoking status in 2000 is a time-independent variable Need to extend algorithm for this

Imputing time-independent variables For each among-time iteration, time-independent variables imputed first Algorithm will be cycle through time points with smoking status included as an auxiliary variable. Impute time-independent variables

Results following imputation We would expect to see similar log risk ratios to the THIN data The standard errors for variables with no missing data will be close to those from the full data The standard errors for variables with missing data will be smaller to the complete case analysis but not recover to the size of the full data

Results following imputation VariablesTHIN data Simulated data Log risk ratio Full dataComplete caseImputed data Log risk ratioSE Log risk ratioSE Log risk ratioSE Anti-hypertensive drug treatment Systolic blood pressure (mmHg) Weight (kg) Smoking status Non- smoker Reference Ex- smoker Current smoker Adjusted for age, registration in 1999 and Townsend score quintile

Results following imputation VariablesTHIN data Simulated data Log risk ratio Full dataComplete caseImputed data Log risk ratioSE Log risk ratioSE Log risk ratioSE Anti-hypertensive drug treatment Systolic blood pressure (mmHg) Weight (kg) Smoking status Non- smoker Reference Ex- smoker Current smoker Adjusted for age, registration in 1999 and Townsend score quintile

Results following imputation VariablesTHIN data Simulated data Log risk ratio Full dataComplete caseImputed data Log risk ratioSE Log risk ratioSE Log risk ratioSE Anti-hypertensive drug treatment Systolic blood pressure (mmHg) Weight (kg) Smoking status Non- smoker Reference Ex- smoker Current smoker Adjusted for age, registration in 1999 and Townsend score quintile

Results following imputation VariablesTHIN data Simulated data Log risk ratio Full dataComplete caseImputed data Log risk ratioSE Log risk ratioSE Log risk ratioSE Anti-hypertensive drug treatment Systolic blood pressure (mmHg) Weight (kg) Smoking status Non- smoker Reference Ex- smoker Current smoker Adjusted for age, registration in 1999 and Townsend score quintile

Correlations Previous results imply accurate imputations for missing data in 2000 Alternative method required: –Assess correlations between measurements recorded at different times We would like to maintain the correlations structure in the generated and imputed data at all time points

Correlations

Increase time window Increased the time window to 2 and 3 years This slightly improves the estimates of coefficients and SE ii+1i+2i+3i-3i-2i-1 2 year time window 3 year time window

Increase time window

In summary The two-fold FCS algorithm gives unbiased imputations with: –70% missing data –Exponential regression model, and –MCAR missingness mechanisms The correlation structure is maintained as the time window increases

Discussion Algorithm effective because at least one measurement during follow-up Same results with MAR Future work… –Introduce censoring –Change smoking status to be time-dependent –Interactions