A time stratified to time series data

Transcription

1 Case crossover method A time stratified to time series data ISEE workshop 2010

2 Contents Case crossover? casecross(), a function in R Application to casecross() 1) matching day of the week 2) matching temperature 3) non linear associations

3 Case crossover The case crossover method compares case days with control days to look for differences in exposure. The method uses conditional logistic regression. The parameter estimates t are odds ratios. Control days are selected to be nearby to case days. Only recent changes in the Xs are compared. Long term or seasonal variation in the Y and Xs can be eliminated. This elimination depends on the definition of nearby and on the seasonal and long term patterns in the Xs.

4 Case crossover Control and case days are only compared if they are in the same stratum. In R the default value is 28 days (four week). Smaller stratum lengths provide a closer control for season, but reduce the available number of controls. Control days that are close to the case day may have similar il levels l of the Xs. (correlation) To reduce this correlation, it is possible to place an exclusion around the cases. (The default value is 2.)

5 Data structure Time series data

6 Season package casecross() formular Data stratalength Length of stratum in days, set to 28 (default) exclusion Exclusion period (in days) around cases, set to 2 (default) matchdow Match case and control days using day of the week matchconf Match case and control days using an important confounder (e.g., temperature) confrange Range of the confounder

7 Application to casecross() 1) matching day of the week 2) matching temperature 3) non linear associations

8 1) Matching day of the week S M T W T F F F. F S S To remove any confounding by day of the week This often reduces the number of available controls. This matching is in addition to the strata matching (select control in the same strata). Problem We assumed that the effect of temperature is linear.

9 1) Matching day of the week library(season) Package age installation at # 1) match on day of the week formular m1< casecross(nonacc~meanpm10+meanhumi+meanpress+meantemp, matchdow=t, data=seoul) summary.casecross(m1) Match case and control days using day of the week We are interested in the association between mortality and pm10 adjusted for humidity, air pressure, and temperature.

10 1) Matching day of the week library(season) # 1) match on day of the week m1< casecross(nonacc~meanpm10+meanhumi+meanpress+meantemp, meanpm10 meanh mi meanpress meantemp matchdow=t, data=seoul) summary.casecross(m1)

11 2) Matching temperature ±1.0 ±0.5 To control for the temperature (confounder) The effect on temperature was disappeared. If the range of the temperature is set too narrow then the number of available controls will become too small. Problem The effect on PM10 depends on the range of the temperature.

12 2) Matching temperature # dummy variables for day of the week (seoul$dow==1 : Sunday (reference day)) seoul$mon< ifelse(seoul$dow==2,1,0) seoul$tue< ifelse(seoul$dow==3,1,0) seoul$wed< ifelse(seoul$dow==4,1,0) seoul$thu< ifelse(seoul$dow==5,1,0) seoul$fri< ifelse(seoul$dow==6,1,0) seoul$sat< ifelse(seoul$dow==7,1,0)

13 2) Matching temperature # 2) match on temperature to within 0.5 degree m2< casecross(nonacc~meanpm10+meanhumi+meanpress p +Mon+Tue+Wed+Thu+Fri+Sat, matchconf='meantemp', confrange=0.5, data=seoul) summary.casecross(m2) Range of temperature Match case and control days using temperature (important confounder)

14 2) Matching temperature # 2) match th on temperature t to within 0.5 degree m2< casecross(nonacc_gp1~meanpm10+meanhumi+meanpress +Mon+Tue+Wed+Thu+Fri+Sat, matchconf='meantemp', confrange=0.5, data=seoul) summary.casecross(m2)

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16 The effect on PM10 depends on the range of the temperature. Range β for PM10 SE # of case days # of control days Range = Range of temperature (Celsius) # of case days = The number of case days with available control days # of control days = The average number of control days per case day

17 3) non linear associations In the previous example (1. matching day of the week) we have assumed that the effect of temperature is linear. We can add a non linear effect by including an interaction term (between temperature and season or month). This model probably controls better for the effect of temperature than the previous model assuming a linear risk.

18 31) 3.1) Interaction term without main effect of season # 3) non-linear associations : by including interaction term seoul$season<-as.factor(seoul$season) M3.1<-casecross(nonacc~meanpm10+meanhumi+meanpress +meantemp:season, Interaction term matchdow=t, data=seoul) summary.casecross(m3.1)

19 32) 3.2) Interaction term with main effect of season m3.2<-casecross(nonacc~meanpm10+meanhumi+meanpress 32 +meantemp:season+season, matchdow=t,,data=seoul) Interaction term and summary.casecross(m3.2) main effect of season

20 41) 4.1) Interaction term without main effect of month seoul$mm<-as.factor(seoul$mm) m4.1< 1<-casecross(nonacc~meanpm10+meanhumi+meanpress +meantemp:mm, matchdow=t, data=seoul) Interaction term summary.casecross(m4.1)

21 42) 4.2) Interaction term with main effect of month m4.2<-casecross(nonacc~meanpm10+meanhumi+meanpress +meantemp:mm+mm, Interaction term and matchdow=t, data=seoul) main effect of month

22 Suggestion! Adjustment for temperature as non linear term Adding Interaction term (previous example) Application of spline function in the model (better!) However, the Spline did not work in casecross(). Further studies are needed. library(splines) casecross(nonacc~meanpm10+meanhumi+meanpress +ns(meantemp),data=seoul) seoul) Error!!