Wen, Shu-Hui Department of Public Health

Tzu-Chi University

Practice for Pharmacoepidemiology

based on NHIRD

2014/6/9 1

2014/6/9 2

Refer to Dr. CJ Hsieh’s handout

2014/6/9 3

Refer to Dr. CJ Hsieh’s handout

2014/6/9 4

Refer to Dr. CJ Hsieh’s handout

2014/6/9 5

Refer to Dr. CJ Hsieh’s handout

2014/6/9 6

Refer to Dr. CJ Hsieh’s handout

2014/6/9 7

2014/6/9 8

Review for PS

Pharmacoepidemiology (藥物流行病學)

The study of the utilization and effects of drugs in large

numbers of people; it provides an estimate of the probability of

adverse/beneficial effects of a drug in a population.

This area focuses on the determinants of both unintended and

expected effects of drugs, vaccines, biologics, medical

procedures, and medical devices.

Confounding by indication :

might lead to the appearance of an association between a drug

and a safety outcome when the association is actually due to

the underlying disease or indication for which the drug is

prescribed.

2014/6/9 9

Confounding by indication

http://www.hsph.harvard.edu/pharma-epi/; http://www.hopkinsmedicine.org/gim/research/content/pharmacoepi.html

Replace the collection of confounding covariates with one

scalar function of these covariates: the propensity score.

If X is an indicator of the exposure of interest, e.g. X=1

means exposed; X=0 non-exposed.

and Z is a vector of potential determinants of drug use,

possibly including both discrete and continuous variables,

then the propensity score is the P(X|Z), the most

commonly estimated by logistic regression,

logit (P(X|Z))=Zβ

2014/6/9 10

Propensity Score Approach

Age , Gender, Medication Comobidity

Propensity Score

2014/6/9 11

Match from overlap; or trimming two

tails of PS (non-overlap)

Glynn et al. 2005 Indications for Propensity Scores and Review of their Use in Pharmacoepidemiology

PS 可視為有無治療者(暴露與否)的機率, 若要公平比較暴露與否兩組,可給定在暴露機率相同下比較兩組治療的效果

PS represents the combined effect of each of the

correlates of exposure(e.g. 有無治療、有無吃藥)

and using this collapsed single variable for

1. Matching or Restriction

2. Stratification

3. Adjustment (i.e. modelling)

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Usage

Case study: 抗高血壓藥物流行病學(collaborate with 吳家樑)

針對高血壓病人，用ARB藥物 vs. 未用ARB

藥物者，追蹤一年以上發生 AD的風險

利用propensity score (PS) 進行分析

PS-match

PS-regression

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個案自2007/1/1至2009/12/31門診有兩次以上高血壓診斷(大於65歲)

暴露組 非暴露組

AD個案 非AD個案 AD個案 非AD個案

1.2006/1/1至2006/12/31曾診斷高血壓、中風、甲狀腺疾病及愛滋病個案

2.高血壓診斷日前或診斷日起一年內，門診或住院曾有一次診斷為失智症

健保資料庫80萬抽樣歸人檔(LHID2005)

研究對象

追蹤(高血壓診斷日起至2010/12/31)

比較暴露組及非暴露組發生AD風險

排除

2014/6/9 14

個案自2007/1/1至2009/12/31門診有兩次以上高血壓診斷(大於65歲),n=31,194

非暴露組,n=2,560

1.2006/1/1至2006/12/31曾診斷 高血壓(n=21,494) 中風、甲狀腺疾病及愛滋病個案(n=150) 2.高血壓診斷日前或診斷日起一年內，門診或住院曾有一次診斷為失智症(n=135)

暴露評估:高血壓診斷日起至2010/12/31期間抗高血壓藥物用量(若期間發生AD或失去追蹤，則採用此時間點為終止日)

健保資料庫80萬抽樣歸人檔(LHID2005)

研究對象,n=9,415

排除

ARB組,n=463

人數 平均年齡(歲)

追蹤人年

平均追蹤時間(年)

AD個案

AD/人年(%)

ARB組 463 73.1 1,254 2.71 10 0.8

控制組 2,56

0

73.8 6,841 2.67 81 1.3

Preliminary analysis

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Method-研究流程圖

16 2014/6/9

Method-研究架構

17

抗高血壓藥物 阿茲海默氏失智症

干擾因子： 人口學特性：年齡、性別、投保地區及投保金額

身體疾病：糖尿病、高血脂、憂鬱症、慢性腎臟衰竭、心臟衰竭、缺血性心臟病、心房顫動。 醫療利用：門診及住院次數

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課程練習重點

Step1: 找出study sample: HTN pt 門診診斷兩次以上的病人 Step2:找出使用高血壓藥物的記錄

Step3: 區分exposure group, non-exposure group (e.g. ARB vs. No-ARB), 建立 propensity score

Using gender, age, Index-year

Step4: 利用 PS-match or PS-regression 分析與AD的相關

Note: No-ARB 可更嚴謹的定義，此次課程簡單以未拿ARB者為No-ARB (拿其他藥者會被納入)

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Su C-Y et al. (2013) PLoS ONE 8(2): e53844.

The definition of chronic HTN being based upon the presence of ICD-9-CM codes 401–405 at either outpatient diagnosis or inpatient discharge diagnosis.

One of these sub-groups was an untreated group (comprising of those within the HTN group who had not used any anti-hypertensive drugs);

Chiang YY et al. (2014) Lowered Cancer Risk With ACE Inhibitors/ARBs: A Population-Based Cohort Study The Journal of Clinical Hypertension 16, p 27–33

According to their antihypertensive prescription patterns, the individuals were assigned into one of the following three groups: the ACE inhibitor group, comprising patients who received ACE inhibitors (ATC C09A and C09B) for at least 80% of the days every year (which was 292 days) during observation and who never received ARBs (ATC C09C, C09DA, C09DB, C09DX01, and C09DX03); the ARB group, comprising patients who received ARBs for at least 80% of the 365 days annually since the year after inclusion until the end of the study and who had never received ACE inhibitors; and the control group, comprising patients who took other antihypertensive medications (ATC C02, C03, C07, and C08 excluding combinations containing ACE inhibitors/ARBs) for at least 80% of the days every year during observation. Threshold of compliance was set as medication available during ≥80% of the observation period according to Caro's classification.[13]

Step1: 找出study sample: HTN pt

抓出高血壓診斷的人次

練習檔案為台大提供的模擬數據檔案 data CASE.htn;

set CASE.NTU_CD0609;

if substr(ICD9CM_1,1,3) in ('401','402','403','404','405') or

substr(ICD9CM_2,1,3) in ('401','402','403','404','405') or

substr(ICD9CM_3,1,3) in ('401','402','403','404','405')

THEN HTN=1;

IF AGE1>=18 and HTN=1 THEN output; /*輸出*/

run;

*(1)針對CD合併後尚未歸人之資料,利用ACODE_ICD9_1~3判斷是否出現HTN(401-405之診斷) */

/*Note:台大模擬檔門診之ICD9診斷欄位名稱為icd9cm_1~3,而且欄位長度僅有3碼*/

/*CD合併未歸人之資料中,共有N= 363163 observations; age>=18 n= 327699 observations*/

* ICD9CM 如同 Acode ; 2014/6/9 20

There were 33684 observations read from the data set CASE.HTNCT.

proc means data=CASE.htn NOPRINT;

var HTN;

by ID;

output out=CASE.htnct N=count; run;

data CASE.htnct;

set CASE.htnct (keep=ID count);;

if count=1 then ct=1; else if count=2 then ct=2;

else if count=3 then ct=3; else ct=4;

PROC FREQ data=CASE.htnct;

table ct;

run;

ct 次數 百分比

1 10964 32.55

2 4773 14.17

3 2539 7.54

4 15408 45.74

327699 高血壓門診人次

33684 位高血壓診斷之病人

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歸人後 htnpt 檔案

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Step2:找出使用高血壓藥物的記錄

/*合併2006-2009 OO 檔，尚未歸人，has 17930527

observations and 15 variables;

data CASE.NTU_OO0609;

set NTU.h_nhi_opdto95-NTU.h_nhi_opdto98;

Run;

*CASE.drugcode是從健保局整理出來

高血壓藥物檔(先有ATC code再找出drug_no )

自健保署提供的用藥品項以及ATC code 找出研究用藥的資訊 命名為 drugcode 檔案 再跟醫令檔merge 找出研究用藥

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http://www.nhi.gov.tw/webdata/webdata.aspx?

menu=21&menu_id=713&webdata_id=3508&WD_ID=713

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http://www.nhi.gov.tw/webdata/webdata.aspx?menu=2

1&menu_id=713&WD_ID=713&webdata_id=873

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查詢藥物的ATC code (WHO) 再利用健保署網站上的藥物檔查詢 drug no

http://www.nhi.gov.tw/webdata/webdata.aspx?menu=21&men

u_id=713&webdata_id=1139

藥品代碼與ATC對照(僅供醫療院所參考使用，不建議作為研究)(102.12.31更新)

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以Warfarin 為例

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自Excel檔中篩選ATC code 含有

Ｂ０１ＡＡ０３

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整理好就存為 drugcode 檔案

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請查詢高血壓藥物的 drug file

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醫令檔與高血壓藥物檔合併 by drug_no=

健保代碼

*CASE.htndrug with 729113 rows and 26 columns;

proc sql;

create table CASE.htndrug as

select a.*, b.* from CASE.NTU_OO0609 as a inner join

CASE.drugcode as b

on a.drug_no=b._COL0;

quit;

Inner Join

2014/6/9 32

CASE.htn 門診檔高血壓診斷的人次

CASE.htndrug 有高血壓藥物醫令的申報次

門診檔：一年約105萬，每一筆代表一個門診人次(一個人可能有 0, 1, 2+次)

門診醫令：一種藥是一個醫令，每一門診人次的醫令檔可能是(0, 1, 2+ 醫令)

e.g. 看診未拿藥 0個醫令

看診拿三種藥 3個醫令

併檔? 以CASE.htn為主要檔合併 CASE.htndrug

2014/6/9 33

*以高血壓人次的門診檔為主檔 : 合併有高血壓診斷的人次(CD),n= 327699 observations;* 有用高血壓藥的人 (OO), 729113 rows,

Htnfinal: with 425385 rows and 68 columns ;

proc sql;

create table CASE.htnfinal as

select a.*, b.* from CASE.htn as a left join CASE.htndrug as b

on (a.fee_ym=b.fee_ym and a.appl_type=b.appl_type and

a.appl_date=b.appl_date and a.case_type=b.case_type and

a.seq_no=b.seq_no and a.hosp_id=b.hosp_id) order by a.ID;

quit;

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細分高血壓藥物(根據ATC code分類)

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*根據ATC code 將高血壓藥物分成六大類ARB CCB ACEI Diuretics alphab betab ;

Data CASE.htnfinal;

set CASE.htnfinal;

IF substr(_COL7,1,5) in ('C09CA') then ARB=1; else ARB=0;

IF substr(_COL7,1,5) in ('C08CA', 'C08DA', 'C08DB') then CCB=1; else CCB=0;

IF substr(_COL7,1,5) in ('C09AA') then ACEI=1; else ACEI=0;

IF substr(_COL7,1,3) in ('C03') then Diuretics=1;

else Diuretics=0;

IF substr(_COL7,1,5) in ('C02CA') then alphab=1; else alphab=0;

IF substr(_COL7,1,5) in ('C07AA', 'C07AB') then betab=1;

else betab=0;

Run; 2014/6/9 36

累加同一人的多筆門診醫令(多數是藥物) 合併成一筆

DATA CASE.htnf1;

SET CASE.htnfinal;

IF Final_DDD=. THEN Final_DDD=0;

BY id;

IF first.id then do

in_q1=0; in_p1=0; in_FinalDDD=0; in_ARB=0; in_CCB=0; in_ACEI=0; in_Diuretics=0; in_alphab=0; in_betab=0;

end;

in_q1+1; in_p1+drug_day; in_FinalDDD+Final_DDD;

in_ARB+ARB; in_CCB+CCB; in_ACEI+ACEI;

in_Diuretics+Diuretics; in_alphab+alphab; in_betab+betab;

IF last.id then output; 2014/6/9 37

*保留count為HTN 門診診斷次數 ;

PROC SQL;

CREATE TABLE CASE.htnf2 AS

SELECT a.*, b.count, b.ct

FROM CASE.htnf1 as a LEFT JOIN CASE.htnpt as b

ON a.ID=b.ID

ORDER BY ID;

QUIT;

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DATA CASE.htnf3;

set CASE.htnf2;

if in_ARB>0 then arbg=1; else arbg=0;

if in_CCB>0 then ccbg=1; else ccbg=0;

if in_ACEI>0 then aceig=1; else aceig=0;

if in_Diuretics>0 then diug=1; else diug=0;

if in_alphab>0 then alphag=1; else alphag=0;

if in_betab>0 then betag=1; else betag=0;

htndrug=in_ARB+in_CCB+ in_ACEI+ in_Diuretics+

in_alphab+ in_betab;

if htndrug>0 then htndrugg=1; else htndrugg=0;

if ct>1; *取兩次以上HTN門診診斷者 共22720人;

run;

記錄高血壓用藥：凡用過一次以上的用藥 就歸類為1

2014/6/9 39

高血壓診斷不見得取藥? 一次以上HTN

htndrugg 次數 百分比

0 15814 46.95

1 17870 53.05

arbg 次數 百分比 ccbg 次數 百分比 0 24844 73.76 0 19863 58.97 1 8840 26.24 1 13821 41.03

aceig 次數 百分比 diug 次數 百分比 0 24360 72.32 0 23801 70.66 1 9324 27.68 1 9883 29.34

alphag 次數 百分比 betag 次數 百分比 0 29490 87.55 0 21582 64.07 1 4194 12.45 1 12102 35.93 2014/6/9 40

高血壓診斷不見得取藥? 2次以上HTN htndrugg 次數 百分比

0 6413 28.23

1 16307 71.77

arbg 次數 百分比 ccbg 次數 百分比 0 14032 61.76 0 9504 41.83

1 8688 38.24 1 13216 58.17

aceig 次數 百分比 diug 次數 百分比 0 13642 60.04 0 13112 57.71

1 9078 39.96 1 9608 42.29

alphag 次數 百分比 betag 次數 百分比 0 18562 81.7 0 11146 49.06

1 4158 18.3 1 11574 50.94 2014/6/9 41

Check if ICD9 code is filled?

DATA work.htn1;

set CASE.htnf2;

if ct<=1;

run;

proc freq;

table ICD9CM_3;

run;

There were 10964 observations;

ICD9CM_3 遺漏次數 = 6566 (59.9%)

只有一次診斷HTN的人裏頭 有60%的人只用到 ICD9CM_2 不太有 欄位不夠下診斷的問題(i.e., HTN 沒足夠欄位可下診斷)

2014/6/9 42

Definition of HTN? >=2 HTN

>=2 HTN 22720人

htndrugg 次數 百分比

0 6413 28.23

1 16307 71.77

>=1 HTN 33684 人

htndrugg 次數 百分比

0 15814 46.95

1 17870 53.05

2014/6/9 43

22720人 (HTN>=2) 拿藥狀況

2014/6/9 44

HTN_year n 變數 平均值 標準差 最小值 最大值

2005 1082 醫令次 49.9 32.2 2 214

ARB次數 6.1 6.5 0 37

2006 13176 in_q1 23.1 24.7 2 276

in_ARB 2.0 3.6 0 37

2007 4194 in_q1 8.2 10.1 2 128

in_ARB 0.5 1.4 0 18

2008 2730 in_q1 5.8 6.3 2 82

in_ARB 0.3 0.9 0 12

2009 1538 in_q1 3.8 3.0 2 26

in_ARB 0.2 0.6 0 5

小結

2014/6/9 45

疾病的定義條件 門診診斷>=2次?

住院 >=1次?

拿藥記錄?

藥物檔請先準備好 再與統計或資料處理人員討論 留 DDD, drug-day 等資訊

用多種藥?

Step3: 區分exposure group, non-exposure

group (e.g. ARB vs. No-ARB), 建立 PS

data work.aa;

set CASE.htnf3;

if ID_S <=2; *remove ID_S=9;

if HTN_year>2005; *remove func_year=2005;

Run;

proc ttest data=aa;

var age1;

class arbg;

Run;

proc freq data=aa;

table (HTN_year ID_S )*arbg/chisq norow nopercent;

Run;

2014/6/9 46

Simple example: 有無使用ARB藥物的兩群人，追蹤是否發生AD

n =20623

HTN_year arbg 0 1 P-value

2006 6775 6007 <0.0001

51.55 80.31 2007 3074 846

23.39 11.31 2008 2070 436

15.75 5.83 2009 1224 191

9.31 2.55 2014/6/9 47

高血壓診斷年在兩組顯著

年齡在ARB兩組差14.4歲 顯著

性別在兩組也顯著

arbg N 平均值 標準差 最小值 最大值 p-value

0 13143 47.3127 17.3524 18 101 <0.001 1 7480 61.7095 13.1304 18 97

ID_S arbg 0 1 P-value

1 6049 3820 <0.0001 46.02 51.07

2 7094 3660 53.98 48.93

2014/6/9 48

計算 propensity score

以arbg為 dependent variable

用ARB與否在 gender, age, HTN_year 皆有顯著差異

Logit(P(ARB|X))=a+b*gender+c*age+d*HT

N_year

估計 PS

2014/6/9 49

Replace the collection of confounding covariates with one

scalar function of these covariates: the propensity score.

If X is an indicator of the exposure of interest, e.g. X=1

means exposed; X=0 non-exposed.

and Z is a vector of potential determinants of drug use,

possibly including both discrete and continuous variables,

then the propensity score is the P(X|Z), the most

commonly estimated by logistic regression, e.g. logit

(P(X|Z))=Zβ

2014/6/9 50

Propensity Score Approach

Age , Gender, Medication Comobidity

Propensity Score

2014/6/9 51

Match from overlap; or trimming two

tails of PS (non-overlap)

Glynn et al. 2005 Indications for Propensity Scores and Review of their Use in Pharmacoepidemiology

*估計PS 建立模型的機率是 arbg=1;

PROC LOGISTIC DATA =CASE.aa descending;

CLASS ID_S HTN_year;

MODEL arbg =ID_S HTN_year age1;

OUTPUT out=CASE.a2 prob=ps xbeta=logit_ps;

RUN;

注意 output 建立模型的機率是 arbg=1

2014/6/9 52

PS 分成五個分類

/*quintiles of propensity scores*/

PROC RANK DATA=CASE.a2 OUT=CASE.a3

GROUPS=5;

RANKS QUINTILES_PS;

VAR PS;

RUN;

2014/6/9 53

PS以及PS的分組

2014/6/9 54

確認PS在ARB用藥與否兩組的分佈

2014/6/9 55

2014/6/9 56

確認是comparable group?

前兩組顯著差異(差9歲與2歲)

2014/6/9 57

*檢查PS分5組之後 Gender, Age在ARBG兩組的差異 ;

proc freq data=CASE.a4;

table QUINTILES_PS*ID_S*arbg/norow nopercent chisq;

run;

PROC ttest DATA =CASE.a4;

CLASS arbg;

VAR AGE1;

BY QUINTILES_PS;

Run;

性別在5組皆未達統計顯著，年齡僅在前兩組達顯著

2014/6/9 58

PS可視為有無治療者(暴露與否)的機率, 若要公平比較暴露與否兩組,可給定在暴露機率相同下比較兩組治療的效果

PS represents the combined effect of each of the correlates of

exposure(e.g. 有無治療、有無吃藥) and using this collapsed

single variable for

1. Matching or Restriction

2. Stratification

3. Adjustment (i.e. modelling)

2014/6/9 59

Raw data 原始資料不考慮 PS

請準備好 outcome variable (ad) 也就是高血壓診斷後發生 ad 的資訊

必須排除290.4 Arteriosclerot dementia 血管型失智 但台大模擬檔只有三碼無法辨識;

/* unadjusted treatment effects*/

PROC LOGISTIC DATA=CASE.aa descending;

CLASS arbg/ref=first;

MODEL ad = arbg;

RUN;

2014/6/9 60

STRATIFYING ON PROPENSITY SCORES

ESTIMATE

/* treatment effects by stratifying on propensity scores*/

TITLE 'STRATIFYING ON PROPENSITY SCORES ESTIMATE';

proc sort data=CASE.a3;

by descending arbg descending ad;

run;

PROC FREQ DATA=Case.a3 order=data;

TABLE QUINTILES_PS*arbg*ad / NOCOL CMH relrisk ;

RUN;

2014/6/9 61

PS regression: ps quantiles

/* treatment effects by regression adjusting for quintiles of

propensity scores*/

PROC LOGISTIC DATA=CASE.a3 descending;

CLASS arbg QUINTILES_PS/ref=first;

MODEL ad = arbg QUINTILES_PS;

RUN;

2014/6/9 62

PS regression: ps score

/* treatment effects by regression adjusting for propensity

score as a continuous covariate*/

PROC LOGISTIC DATA=CASE.a3 descending;

CLASS arbg/ref=first;

MODEL ad = arbg PS;

RUN;

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PS-stratification

2014/6/9 64

PS分層分析 (列 1/列 2)

研究類型 方法 值 95% 信賴界限

個案控制 Mantel-Haenszel 1.9587 1.8055 2.1248

原始資料 ( 3473 ARB vs. 17150 non-ARB)

效果 勝算比 95% Wald信賴界限

arbg (1 vs. 0) 3.483 3.229 3.756

PS-regression

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效果 點估計值 95% Wald信賴界限

arbg 2.044 1.883 2.218

PS 8.332 6.892 10.

ad 總計次數

1 3473

0 18217

勝算比估計值

效果 點估計值 95% Wald信賴界限 arbg 1 與 0 之間的關係 1.976 1.821 2.145 QUINTILES_PS 1 與 0 1.787 1.503 2.125 QUINTILES_PS 2 與 0 3.000 2.544 3.537 QUINTILES_PS 3 與 0 3.830 3.252 4.509 QUINTILES_PS 4 與 0 4.642 3.942 5.465

PS-match

Refer to Chapter2-3

SAS program download

from the website

Support.sas.com/authors

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請執行psmatch SAS code

%gmatch(

data = out_ps,

group = arbg,

id = ID,

mvars = logit_ps,

wts = 1,

dist = 1,

dmaxk = &stdcal,

ncontls = 1,

seedca = 25102007,

seedco = 26102007,

out = matchpairs,

print = F

);

%gmatch(data=fakereg,group=case, id=id,

mvars=age sex,wts=2 1,dmaxk= 5 0,

transf=0, time=timex, dist=1,

ncontls=2,seedca=234098,seedco=0489,

out=regccout,outnmco=matched,print=Y);

run;

*PS-match: 注意 資料檔案必須包含exposure variable, variables for

constructing PS, outcome variable,

follow-up time;

*估計PS 建立模型的機率是 arbg=1;

存成 out_ps 2014/6/9 67

Gmatch 執行完後 The data set WORK.MATCHPAIRS has

5993 observations and 12 variables.

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Case ID Control ID

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利用 PS 分數做配對的 ARB用藥組 vs. 未用ARB組

年齡在Psmatch “前兩組”就同質了 ps -quantile=0

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arbg N 平均值 標準差 最小值 最大值 P-value

0 1199 46.44 9.84 18. 72 0.99

1 1199 46.44 9.84 18. 72

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arbg N 平均值 標準差 最小值 最大值 P-value

0 1209 51.7 9.01 39 82 0.71

1 1199 51.5 8.71 39 79.

ps –quantile=1

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arbg N 平均值 標準差 最小值 最大值 P-value

0 1224 58.9 9.0 49 88 <0.001

1 1159 56.6 6.8 49 88.

ps –quantile=2

Raw data vs. PS-match sample

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N=20623 OR for AD 95% Wald信賴界限

arbg 1 vs 0 3.483 3.229 3.756

CLR (n=11986) OR for AD 95% Wald信賴界限

arbg 1.870 1.705 2.052

PS-matched cohort (ARB exposed vs. non-ARB)

有用藥與沒用藥兩組在 age, gender, index-year是同質的

Logistic reg OR for AD 95% Wald信賴界

arbg 1 vs 0 1.871 1.710 2.047

PS-stratification

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PS分層分析 (列 1/列 2)

研究類型 方法 值 95% 信賴界限

個案控制 Mantel-Haenszel 1.9587 1.8055 2.1248

PS-regression

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效果 點估計值 95% Wald信賴界限

arbg 2.044 1.883 2.218

PS 8.332 6.892 10.

ad 總計次數

1 3473

0 18217

勝算比估計值

效果 點估計值 95% Wald信賴界限 arbg 1 與 0 之間的關係 1.976 1.821 2.145 QUINTILES_PS 1 與 0 1.787 1.503 2.125 QUINTILES_PS 2 與 0 3.000 2.544 3.537 QUINTILES_PS 3 與 0 3.830 3.252 4.509 QUINTILES_PS 4 與 0 4.642 3.942 5.465

Multiple logistic regression, 高估?

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N=20623 OR for AD 95% Wald信賴界限

arbg 1 vs. 0 2.012 1.852 2.185

ID_S 2 vs. 1 1.099 1.018 1.187

HTN_year 2007 0.521 0.463 0.587

HTN_year 2008 0.350 0.295 0.415

HTN_year 2009 0.195 0.146 0.262

AGE1 1.029 1.027 1.032

Discussion

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PS主要是用來建立 comparable group

建構PS時盡可能考慮會影響用藥(暴露組)的因素

PS無法考慮”未能觀察到的因素”

利用PS進行迴歸分析比起multiple logistic

regression 有的好處為

1. PS-regression model 較簡單 較可能符合模式的假設

2.10次有9次兩種的結果接近，另外一次則以

PS-regression 較為保守

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Thank you for listening

http://circ.ahajournals.org/content/108/10_suppl_1/II-90.full.pdf+html