安慰剂详细资料大全
安慰剂检验介绍、操作及举例
安慰剂检验介绍（Placebo test）安慰剂是一种附加实证检验的思路，并不存在一个具体的特定的操作方法。
一般存在两种寻找安慰剂变量的方法。
比如，在已有的实证检验中，发现自变量Xi会影响自变量Zi与因变量Yi之间存在相关关系。
在其后的实证检验中，采用其他主体（国家，省份，公司）的Xj变量作为安慰剂变量，检验Xj是否影响Zi与Yi之间的相关关系。
如果不存在类似于Xi的影响，即可排除Xi 的安慰剂效应，使得结果更为稳健。
另一种寻找安慰剂变量的方法。
已知，Xi是虚拟变量，Xi=1，if t>T；Xi=0 if t<T；Xi对Zi对Yi的影响的影响在T时前后有显著差异（DID）。
在其后的实证检验中，将Xi`设定为Xi`=1，if t>T+n；Xi`=0 if t<T+n，其中n根据实际情况取值，可正可负。
检验Xi`是否影响Zi与Yi之间的相关关系。
如果不存在类似于Xi的影响，即可排除Xi的安慰剂效应，使得结果更为稳健。
举例：以美国市场某种政策冲击识别策略的因果关系考察，在最后部分选取英国同期的因变量，检验是否有类似的特征，就是安慰剂检验。
以中国2007年所得税改革作为减税的政策冲击以验证减税对企业创新的影响。
亦可以通过把虚拟的政策实施时间往前往后推几年，作为虚拟的政策时点，如果检验发现没有类似的因果，文章的主要结论就更加可信了。
以下是详细的例题，安慰剂检验在最后。
Surviving Graduate Econometrics with R:Difference-in-Differences Estimation — 2 of 8The following replication exercise closely follows the homework assignment #2 in ECNS 562. The data for this exercise can be found here.The data is about the expansion of the Earned Income Tax Credit. This is a legislation aimed at providing a tax break for low income individuals. For some background on the subject, seeEissa, Nada, and Jeffrey B. Liebman. 1996. Labor Supply Responses to the Earned Income Tax Credit. Quarterly Journal of Economics. 111(2): 605-637.The homework questions (abbreviated):1.Describe and summarize data.2.Calculate the sample means of all variables for (a) single women with nochildren, (b) single women with 1 child, and (c) single women with 2+ children.3.Create a new variable with earnings conditional on working (missing fornon-employed) and calculate the means of this by group as well.4.Construct a variable for the “treatment” called ANYKIDS and a variable for afterthe expansion (called POST93—should be 1 for 1994 and later).5.Create a graph which plots mean annual employment rates by year(1991-1996) for single women with children (treatment) and without children (control).6.Calculate the unconditional difference-in-difference estimates of the effect ofthe 1993 EITC expansion on employment of single women.7.Now run a regression to estimate the conditional difference-in-differenceestimate of the effect of the EITC. Use all women with children as the treatment group.8.Reestimate this model including demographic characteristics.9.Add the state unemployment rate and allow its effect to vary by the presence ofchildren.10.A llow the treatment effect to vary by those with 1 or 2+ children.11.Estimate a “placebo” treatment model. Take data from only the pre-reformperiod. Use the same treatment and control groups. Introduce a placebo policy that begins in 1992 (so 1992 and 1993 both have this fake policy).A review: Loading your dataRecall the code for importing your data:STATA:/*Last modified 1/11/2011 */**************************************************************************The following block of commands go at the start of nearly all do files*/*Bracket comments with /* */ or just use an asterisk at line beginningclear /*Clears memory*/set mem 50m /*Adjust this for your particular dataset*/cd "C:\DATA\Econ 562\homework" /*Change this for your file structure*/log using stata_assign2.log, replace /*Log file records all commands & results*/display "$S_DATE $S_TIME"set more offinsheet using eitc.dta, clear*************************************************************************R:1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 # Kevin Goulding # ECNS 562 - Assignment 2 ########################################################################## # Load the foreign package require(foreign) # Import data from web site # update: first download the file eitc.dta from this link: # https:///open?id=0B0iAUHM7ljQ1cUZvRWxjUmpfVXM # Then import from your hard drive: eitc = read.dta("C:/link/to/my/download/folder/eitc.dta")</pre> Note that any comments can be embedded into R code, simply by putting a <code> # </code> to the can download the data file, and import it from your hard drive: eitc = read.dta("C:\DATA\Courses\Econ 562\homework\eitc.dta")Describe and summarize your dataRecall from part 1 of this series, the following code to describe and summarizeyour data:STATA:dessumR:In R, each column of your data is assigned a class which will determine how your data is treated in various functions. To see what class R has interpreted for all your variables, run the following code:1 2 3 4 sapply(eitc,class) summary(eitc)source('sumstats.r') sumstats(eitc)To output the summary statistics table to LaTeX, use the following code:1 2 require(xtable) # xtable package helps create LaTeX code xtable(sumstats(eitc))Note: You will need to re-run the code for sumstats() which you can find in an earlier post.Calculate Conditional Sample MeansSTATA:summarize if children==0summarize if children == 1summarize if children >=1summarize if children >=1 & year == 1994mean work if post93 == 0 & anykids == 1R:1 2 3 4 5 6 7 8 91011121314 # The following code utilizes the sumstats function (you will need to re-run this code) sumstats(eitc[eitc$children == 0, ])sumstats(eitc[eitc$children == 1, ])sumstats(eitc[eitc$children >= 1, ])sumstats(eitc[eitc$children >= 1 & eitc$year == 1994, ])# Alternately, you can use the built-in summary functionsummary(eitc[eitc$children == 0, ])summary(eitc[eitc$children == 1, ])summary(eitc[eitc$children >= 1, ])summary(eitc[eitc$children >= 1 & eitc$year == 1994, ])# Another example: Summarize variable 'work' for women with one child from 1993 onwards. summary(subset(eitc, year >= 1993 & children == 1, select=work))The code above includes all summary statistics – but say you are only interested in the mean. You could then be more specific in your coding, like this:1 2 3 mean(eitc[eitc$children == 0, 'work']) mean(eitc[eitc$children == 1, 'work']) mean(eitc[eitc$children >= 1, 'work'])Try out any of the other headings within the summary output, they should also work: min() for minimum value, max() for maximum value, stdev() for standard deviation, and others.Create a New VariableTo create a new variable called “c.earn” equal to earnings conditional on working (if “work” = 1), “NA” otherwise (“work” = 0) – use the following code:STATA:gen cearn = earn if work == 1R:1 2 3 4 5 6 7 eitc$c.earn=eitc$earn*eitc$workz = names(eitc)X = as.data.frame(eitc$c.earn)X[] = lapply(X, function(x){replace(x, x == 0, NA)}) eitc = cbind(eitc,X)eitc$c.earn = NULLnames(eitc) = zConstruct a Treatment VariableConstruct a variable for the treatment called “anykids” = 1 for treated individual (has at least one child); and a variable for after the expansion called “post93” = 1 for 1994 and later.STATA:gen anykids = (children >= 1)gen post93 = (year >= 1994)R:1 2 eitc$post93 = as.numeric(eitc$year >= 1994) eitc$anykids = as.numeric(eitc$children > 0)Create a plotCreate a graph which plots mean annual employment rates by year (1991-1996) for single women with children (treatment) and without children (control).STATA:preservecollapse work, by(year anykids)gen work0 = work if anykids==0label var work0 "Single women, no children"gen work1 = work if anykids==1label var work1 "Single women, children"twoway (line work0 year, sort) (line work1 year, sort), ytitle(Labor Force Participation Rates)graph save Graph "homework\eitc1.gph", replaceR:1 2 3 4 5 6 7 8 9101112131415 # Take average value of 'work' by year, conditional on anykidsminfo = aggregate(eitc$work, list(eitc$year,eitc$anykids == 1), mean)# rename column headings (variables)names(minfo) = c("YR","Treatment","LFPR")# Attach a new column with labelsminfo$Group[1:6] = "Single women, no children"minfo$Group[7:12] = "Single women, children"minforequire(ggplot2) #package for creating nice plotsqplot(YR, LFPR, data=minfo, geom=c("point","line"), colour=Group,xlab="Year", ylab="Labor Force Participation Rate") The ggplot2 package produces some nice looking charts.Calculate the D-I-D Estimate of the Treatment EffectCalculate the unconditional difference-in-difference estimates of the effect of the 1993 EITC expansion on employment of single women.STATA:mean work if post93==0 & anykids==0 mean work if post93==0 & anykids==1 mean work if post93==1 & anykids==0 mean work if post93==1 & anykids==1 R:1 2 3 4 5 a = colMeans(subset(eitc, post93 == 0 & anykids == 0, select=work))b = colMeans(subset(eitc, post93 == 0 & anykids == 1, select=work))c = colMeans(subset(eitc, post93 == 1 & anykids == 0, select=work))d = colMeans(subset(eitc, post93 == 1 & anykids == 1, select=work)) (d-c)-(b-a)Run a simple D-I-D RegressionNow we will run a regression to estimate the conditional difference-in-difference estimate of the effect of the Earned Income Tax Credit on “work”, using all women with children as the treatment group. The regression equation is as follows:Where is the white noise error term.STATA:gen interaction = post93*anykidsreg work post93 anykids interactionR:1 2 reg1 = lm(work ~ post93 + anykids + post93*anykids, data = eitc) summary(reg1)Include Relevant Demographics in RegressionAdding additional variables is a matter of including them in your coded regression equation, as follows:STATA:gen age2 = age^2 /*Create age-squared variable*/gen nonlaborinc = finc - earn /*Non-labor income*/reg work post93 anykids interaction nonwhite age age2 ed finc nonlaborincR:1 2 3 reg2 = lm(work ~ anykids + post93 + post93*anykids + nonwhite+ age + I(age^2) + ed + finc + I(finc-earn), data = eitc) summary(reg2)Create some new variablesWe will create two new interaction variables:1.The state unemployment rate interacted with number of children.2.The treatment term interacted with individuals with one child, or more than onechild.STATA:gen interu = urate*anykidsgen onekid = (children==1)gen twokid = (children>=2)gen postXone = post93*onekidgen postXtwo = post93*twokidR:1 2 3 4 5 6 7 8 9101112 # The state unemployment rate interacted with number of childreneitc$urate.int = eitc$urate*eitc$anykids### Creating a new treatment term:# First, we'll create a new dummy variable to distinguish between one child and 2+. eitc$manykids = as.numeric(eitc$children >= 2)# Next, we'll create a new variable by interacting the new dummy# variable with the original interaction term.eitc$tr2 = eitc$p93kids.interaction*eitc$manykidsEstimate a Placebo ModelTesting a placebo model is when you arbitrarily choose a treatment time before your actual treatment time, and test to see if you get a significant treatment effect.STATA:gen placebo = (year >= 1992)gen placeboXany = anykids*placeboreg work anykids placebo placeboXany if year<1994In R, first we’ll subset the data to exclude the time period after the real treatment (1993 and later). N ext, we’ll create a new treatment dummy variable, and run a regression as before on our data subset.R:1 2 3 4 5 6 7 8 9 10 # sub set the data, including only years before 1994.eitc.sub = eitc[eitc$year <= 1993,]# Create a new "after treatment" dummy variable# and interaction termeitc.sub$post91 = as.numeric(eitc.sub$year >= 1992)# Run a placebo regression where placebo treatment = post91*anykids reg3 <- lm(work ~ anykids + post91 + post91*anykids, data = eitc.sub) summary(reg3)The entire code for this post is available here (File –> Save As). If you have any questions or find problems with my code, you can e-mail me directlyat kevingoulding {at} gmail [dot] com.To continue on to Part 3 of our series, Fixed Effects estimation, click here.。
安慰剂简介
安慰剂简介目录•1拼音•2英文参考•3科研应用•4治疗应用1拼音ān wèi jì2英文参考placebo安慰剂是用以安慰病人而无直接治疗作用的药剂．有时作为药物疗效鉴定的对照剂，即在现场试验中为免除实验组与对照组因是否接受治疗或预防药物而引起精神上的偏见，而给对照组以外表类似试验用药的制剂。
由于此药物并非真的试验用药，又称空白剂．安慰剂是无药理活性的伪药剂，外观相同于药物，为无活性成分的糖类或淀粉剂型。
安慰剂可作为药物对照剂用于科研中；此外，也可用于某些特殊情况，如医生在无适合的药选时用以减轻病人的症状。
安慰剂效应是在接受未被证明有效的治疗方法后出现的症状改善，可发生在任何治疗过程中，包括药物治疗、手术和心理疗法。
安慰剂可引起许多变化包括正面的和负面的。
有两种因素影响安慰剂的效应，一是用药后其结果是可以预知的(通常是期望出现的)，又叫做暗示作用，二是自发效应，有时这种作用是很重要的，病人有时可能不治自愈，若在服安慰剂之后发生此种情况，安慰剂的作用可能被错误地肯定；相反，若用安慰剂后发生自发性头痛或皮疹等，安慰剂的作用可能会不恰当地受到谴责。
病人的个性特点决定安慰剂的效果差异，实际上，由于每个人受不同环境的影响，安慰剂作用程度不同，有些人更敏感。
敏感人群比不敏感人群表现出更多的药物依赖性：如需要加大剂量、有服药的强迫愿望、停药后出现反跳现象等。
3科研应用任何药物都可产生安慰剂效应，无论好的作用和不好的作用。
为确定一个药物作用是否为安慰剂效应，科研中要用安慰剂作为对照，半数受试者给受试药物，半数给安慰剂，理想的是医护人员和病人均不知受试药物和安慰剂的区别，此为双盲法。
试验结束，比较两制剂的效果，去除安慰剂的影响才能确定受试药疗效是否可靠。
如在研究一种新的抗心绞痛药物时，服用安慰剂者中有50%的人症状得到缓解，这种结果表明，这种新药的疗效值得怀疑。
4治疗应用任何治疗都有安慰剂效应。
安慰剂效应Placebo Effect
安慰劑效應(Placebo Effect)安慰劑效應的概述 安慰劑效應，又名偽藥效應、假藥效應、代設劑效應（英文：Placebo Effect，源自拉丁文placebo解「我將安慰」）指病人雖然獲得無效的治療，但卻「預料」或「相信」治療有效，而讓病患症狀得到舒緩的現象。
有人認為這是一個值得注意的人類生理反應，但亦有人認為這是醫學實驗設計所產生的錯覺。
這個現象是否真的存在，科學家至今仍未能完全破解。
[1] 安慰劑效應於1955年由畢闕博士（Henry K. Beecher）提出[2]，亦理解為「非特定效應」（non-specific effects）或受試者期望效應。
一個性質完全相反的效應亦同時存在——反安慰劑效應（Nocebo effect）：病人不相信治療有效，可能會令病情惡化。
反安慰劑效應（拉丁文nocebo解「我將傷害」）可以使用檢測安慰劑效應相同的方法檢測出來。
例如一組服用無效藥物的對照群組（control group），會出現病情惡化的現象。
這個現象相信是由於接受藥物的人士對於藥物的效力抱有負面的態度，因而抵銷了安慰劑效應，出現了反安慰劑效應。
這個效應並不是由所服用的藥物引起，而是基於病人心理上對康復的期望。
安慰劑對照研究畢闕博士的研究（1955年） 有報告[3]紀錄到大約四分之一服用安慰劑的病人，例如聲稱可以醫治背痛的安慰劑，表示有關痛症得到舒緩。
而觸目的是，這些痛症的舒緩，不單是靠病人報稱，而是可以利用客觀的方法檢測得到。
這個痛症改善的現象，並沒有出現於非接受安慰劑的病人身上。
由於發現了這個效應,政府管制機關規定新藥必須通過臨床的安慰劑對照（placebo-controlled）測試，方能獲得認可。
測試結果不單要證明患者對藥物有反應，而且測試結果要與服用安慰劑的對照群組作比較，證明該藥物比安慰劑更為有效（「有效」是指以下2項或其中1項：1)該藥物比安慰劑能影響更多病人，2)病人對該藥物比安慰劑有更強反應）。
安慰剂
Oral Bacteriotherapy as Maintenance Treatment in Patients With Chronic Pouchitis:A Double-Blind,Placebo-Controlled TrialPAOLO GIONCHETTI,*FERNANDO RIZZELLO,*ALESSANDRO VENTURI,*PATRIZIA BRIGIDI,‡DIEGO MATTEUZZI,‡GABRIELE BAZZOCCHI,*GILBERTO POGGIOLI,§MARIO MIGLIOLI,*and MASSIMO CAMPIERI**Department of Internal Medicine and Gastroenterology,‡Department of Pharmaceutical Sciences,and§Department of Clinical Surgery, University of Bologna,Bologna,ItalySee editorial on page584. Background&Aims:Pouchitis is the major long-term complication after ileal pouch–anal anastomosis for ul-cerative colitis.Most patients have relapsing disease, and no maintenance treatment study has been per-formed.We evaluated the efficacy of a probiotic prepa-ration(VSL#3)containing5؋1011per gram of viable lyophilized bacteria of4strains of lactobacilli,3strains of bifidobacteria,and1strain of Streptococcus sali-varius subsp.thermophilus compared with placebo in maintenance of remission of chronic pouchitis. Methods:Forty patients in clinical and endoscopic re-mission were randomized to receive either VSL#3,6 g/day,or an identical placebo for9months.Patients were assessed clinically every month and endoscopically and histologically every2months or in the case of a relapse.Fecal samples were collected for stool culture before and after antibiotic treatment and each month during maintenance treatment.Results:Three patients (15%)in the VSL#3group had relapses within the 9-month follow-up period,compared with20(100%)in the placebo group(P<0.001).Fecal concentration of lactobacilli,bifidobacteria,and S.thermophilus in-creased significantly from baseline levels only in the VSL#3-treated group(P<0.01).Conclusions:These results suggest that oral administration of this new pro-biotic preparation is effective in preventingflare-ups of chronic pouchitis.P ouchitis,a nonspecific inflammation of the ileal res-ervoir,is the most common long-term complication after pouch surgery for ulcerative colitis.Its cumulative frequency depends largely on the duration of the fol-low-up and is approximately50%after10years at the major referral centers.1–4Pouchitis is characterized clinically by increased stool frequency,urgency,abdominal cramping,and discom-fort.Bleeding,low-grade fever,and extraintestinal man-ifestations may also occur.5,6Endoscopicfindings of in-flammation in the pouch include edema,granularity,loss of vascular pattern,contact bleeding,erosions,and ul-cerations7;biopsies show an acute neutrophilic inflam-matory infiltrate with crypt abscesses and ulceration in addition to the normal chronic inflammatory infiltrate, the latter of which is almost universal and probably represents an unavoidable response to fecal stasis.8,9 The cause of pouchitis is still unknown,but it seems that a history of ulcerative colitis and increased bacterial concentration are main factors.10–12The importance of bacteria is further emphasized by the evident efficacy of antibiotics.10In most cases,patients have multiple attacks.3,13,14So far,no studies have focused on the maintenance of re-mission.Probiotics are living microorganisms that belong to the naturalflora and are important to the health and well-being of the host.15Recent observations support their role in the treatment of inflammatory bowel dis-eases.The administration of Lactobacillus spp.prevented the development of spontaneous colitis in interleukin (IL)-10–deficient mice,and continuous feeding with Lactobacillus plantarum attenuated established colitis in the same knockout model.16,17Pouchitis has recently been shown to be associated with reduced counts of lactobacilli and bifidobacteria, suggesting that this syndrome may be the result of an unstable microflora.18The aim of this study was to evaluate the efficacy of a new oral probiotic preparation,containing very high bacterial concentrations of8different bacterial strains Abbreviations used in this paper:GI,gastrointestinal;IL,interleukin; PDAI,Pouchitis Disease Activity Index.©2000by the American Gastroenterological Association0016-5085/00/$10.00doi:10.1053/gast.2000.9370GASTROENTEROLOGY2000;119:305–309compared with placebo in the maintenance treatment of chronic relapsing pouchitis.Patients and MethodsPatientsThe study was performed in accordance with the Dec-laration of Helsinki and was approved by the ethical commit-tee of our hospital;written,informed consent was obtained from the patients.Eligible patients were between18and65 years old and had chronic relapsing pouchitis,defined as at least3relapses per year.In addition,patients were in clinical and endoscopic remission,defined as score0after1month of combined antibiotic treatment,in the clinical and endoscopic portion of the Pouchitis Disease Activity Index(PDAI)by Sandborn et al.,19which includes clinical,endoscopic,and acute histologic criteria(Table1).No concurrent treatments were allowed.Patients with perianal disease,including abscess,fistula,fissure,stricture,or anal sphincter weakness,were excluded.Study MedicationVSL#3(Yovis;Sigma-Tau,Pomezia,Italy)consisted of 3-g bags each containing300billion viable lyophilized bac-teria per gram of4strains of Lactobacillus(L.casei,L.plantarum,L.acidophilus,and L.delbrueckii subsp.bulgaricus),3strains of Bifidobacterium(B.longum,B.breve,and B.infantis),and1strain of Streptococcus salivarius subsp.thermophilus.The placebo consisted of identical bags each containing3g of maize starch.The VSL#3and placebo were administered orally twice a day.The taste and smell of the active drug were not readily identifiable.Study DesignThis was a randomized,double-blind,placebo-con-trolled study.Patients,whose conditions were in clinical and endoscopic remission(with a score of0in the clinical and endoscopic portion of PDAI)after1month of antibiotic treatment with 1g ciprofloxacin plus2g rifaximin daily(Alfa-Wasserman, Bologna,Italy),were randomized to receive VSL#3(6g/day)or placebo for9months.Assignment to therapy or placebo was determined according to a computer-generated randomization scheme.20Randomiza-tion was done by the clinical trial’s pharmacist,who kept the codes until completion of the study.None of the staff or patients had access to the randomization codes during the study.The medications were dispensed by the investigator at each visit;compliance was assessed by counting returned bags and questioning the patients.Evaluation and SchedulingSymptoms were assessed,medical histories were taken, and physical examinations were performed at baseline and every month thereafter.Endoscopic examination of the ileal pouch and the ileum for a few centimeters proximal to the pouch,with mucosal biopsies,was performed at baseline and every2months thereafter,and histologic assessment of biopsy specimens was performed at entry and every2months boratory studies,including a complete blood count and blood chemistry measurements,were performed at base-line and at the end of treatment.Relapse was defined as an increase of at least2points in the clinical portion of PDAI,confirmed by endoscopy and histol-ogy.Microbiological DeterminationsStool cultures were performed before and after antibi-otic treatment and every month during maintenance treat-ment.Collection of specimens,anaerobic culture techniques, isolation procedures,and identification methods were per-formed according to the Wadsworth Anaerobic Bacteriology Man-ual(5th edition).21Fecal specimens were collected into sterile plastic containers and stored atϪ20°C until they were assayed (within7days).Fecal samples were homogenized and serially diluted in an anaerobic cabinet(Anaerobic System,model 2028;Forma Scientific Co,Marietta,OH)with half-strength Wilkins Chalgreen anaerobic broth(Oxoid,Basingstoke,En-gland).Plates were incubated in triplicate using the appropri-ate media for enumeration of total aerobes(nutrient agar; Oxoid),total anaerobes(Schaedler agar;Oxoid),enterococciTable1.Pouchitis Disease Activity IndexCriteria ScoreClinicalPostoperative stoolfrequencyUsual01–2stools/day more thanusual13or more stools/day morethan usual2Rectal bleeding None or rare0Present daily1Fecal urgency/abdominalcrampsNone0Occasional1Usual2Fever(temperatureϾ100°F)Absent0Present1EndoscopicEdema1Granularity1Friability1Loss of vascular pattern1Mucus exudate1Ulcerations1Acute histologicalPolymorph infiltration Mild1Moderateϩcrypt abscess2Severeϩcrypt abscess3Ulcerations per low-powerfield(average)Ͻ25%125%–50%2Ͼ50%3306GIONCHETTI ET AL.GASTROENTEROLOGY Vol.119,No.2(Azide maltose agar;Biolife,Milan,Italy),coliforms(Mac-Konkey agar;Merck,Darmstadt,Germany),Bacteroides(Schaed-ler agar plus vancomycin and gentamycin;Oxoid),bifidobac-teria(PYG,plus polymyxin[50mg/mL]and kanamycin[50 mg/mL]),and Clostridium perfringens(O.P.S.P.;Oxoid).Plates were incubated aerobically or anaerobically as appropriate.The lower limit of detection was1000microorganisms per gram of feces.Statistical AnalysisBased on their experience,clinical investigators thought it was reasonable to expect a25%response in the placebo group and a75%response in the therapy group,and such difference is relevant from a clinical point of view. Accordingly,for␣ϭ0.05(2-tailed test)andϭ0.20,a sample size of more than19patients per group was estimated. Baseline characteristics of patients after randomization in the2groups were compared using the2test or the Student t test for independent samples as appropriate.The primary study variable(number of patients who relapsed)was tested using the2test with the Yates correction.Survival analysis was used to analyze the data set with respect to relapse.The Kaplan–Meier method was used to estimate the survivor function,and comparison of cumulative relapse rates between treatment groups was tested by the log-rank test. The results of microbiological tests(secondary study vari-able)have been submitted to comparative multivariate analy-ses of variance.The significance of contrasts and multiple pairwise comparisons was tested using the2-tailed Student t test.The level of significance was adjusted using the Bonfer-roni correction for multiple comparisons.ResultsPatient CharacteristicsForty-three patients were screened,and40were eligible;20were randomly assigned to receive VSL#3 and20to receive placebo;and3patients were excluded because they refused consent.Study groups were well matched with respect to age,sex,duration of follow-up, duration of pouchitis,and number of yearly relapses (Table2).The basal median PDAI score was0(range,0–1)in both groups(median clinical portion score,0[range,0–0];median endoscopic portion score0[range,0–0]; and median histologic portion score,0[range,0–1]). Median stool frequency was10(range,8–13)before antibiotic treatment and4(range,3–7)after antibiotic treatment.Clinical ResultsLife-table analysis of the relapses in the2groups is shown in Figure1.Of the20patients who received the placebo,all had relapses,8within2months,7within3months,and5 within4months.Of the20patients treated with VSL#3, 17(85%)were still in remission after9months(PϽ0.001)(Figure2);all17of these patients had relapses within the4months after the conclusion of active treat-ment,and the median duration of remission was2 months(range,1–4).The median total PDAI score of the20relapsed patients treated with placebo was12(range,8–18);this score was the result of a significant increase in clinical (median4[range,3–6]),endoscopic(median4[range, 3–6]),or histologic(median4[range,3–5])scores on the PDAI;median stool frequency was9(range,7–11).In the group treated with VSL#3,the3patients who had relapses during the9months of follow-up had a median total PDAI score of11(range,9–17;median clinical portion score3[range,2–5];median endoscopic portion score4[range,3–5];and median histologic portion score4[range,3–5]).Median stool frequency in these patients was8(range,6–11)at the time of relapse. The17patients who remained in remission had a median total PDAI score of0(range,0–1;median clinical por-tion score0[range,0–0];median endoscopic portion score0[range,0–0];and median histologic portion score0[range,0–1]).The median stool frequency in these patients did not increase significantly compared with that obtained after antibiotic treatment(4[range, 3–6]).Median stool frequency increased slightly within 15days after cessation of active treatment(5[range, 2–6])and was7(range,6–11)at the time ofrelapse. Figure1.Kaplan–Meier estimates of relapse during treatment with VSL#3(A)or placebo(B).Table2.Demographic and Clinical CharacteristicsVSL#3 nϭ20Placebo nϭ20Mean age(yr)32.834.2Sex(M/F)11/912/8Months of pouch function;median(range)46(8–108)49(5–134)Duration of disease(mo);median(range)37(4–96)43(3–118)No.of yearly relapses;mean 3.8 3.5August2000PROBIOTIC THERAPY IN POUCHITIS307Microbiological ResultsIn patients treated with VSL#3,fecal concentra-tions of lactobacilli,bifidobacteria,and Streptococcus sali-varius increased significantly (P Ͻ0.001)compared with concentrations present both before and after antibiotic treatment and remained stable throughout the study (Figure 3).No significant changes were registered for concentrations of Bacteroides ,coliforms,clostridia,entero-cocci,and total aerobes and anaerobes compared with basal levels.One month after discontinuation of VSL#3,fecal concentrations of lactobacilli,bifidobacteria,and Streptococcus salivarius subsp.thermophilus had reached lev-els similar to basal levels again.In the group treated with placebo,fecal concentrations of all species evaluated remained similar at all intervals to those measured before antibiotic treatment.SafetyNo side effects and no significant changes from baseline values in any of the laboratory parameters ex-amined were registered in either group of patients.DiscussionThis is the first controlled trial of maintenance treatment of pouchitis.Oral administration of VSL#3was effective in the prevention of relapses in patients with chronic pouchitis;the efficacy of this new probiotic preparation may be related to the increase in concentra-tions of protective bacteria,as shown by the microbio-logical data,and in their metabolic activities.The cumulative risk of developing pouchitis increases with time and,in series from centers with the largest experience and the longest follow-up,approaches nearly 50%by 10years.3More than two thirds of patients experience multiple episodes,but most cases will re-spond to oral antibiotics.The cause is still unknown and is likely to be multifactorial;however,the immediate response to antibiotic treatment suggests a pathogenetic role for the microflora,and recently pouchitis was asso-ciated with a decreased ratio of anaerobic to aerobic bacteria,reduced fecal concentrations of lactobacilli and bifidobacteria,and an increase in luminal pH.18Treat-ment of pouchitis is largely empiric,and only a few small placebo-controlled trials have been conducted.Antibiot-ics have become the mainstay of treatment;metronida-zole is the common initial therapeutic approach,and most patients have a dramatic response within a few days,whereas treatment of chronic refractory pouchitis is often difficult and disappointing and may require a pro-longed course of antibiotics.Other medical therapies reported to be of benefit in uncontrolled trials include other antibacterial agents such as ciprofloxacin,amox-icillin/clavulanic acid,erythromycin and tetracycline,topical and oral mesalamine,conventional corticosteroid enemas,budesonide enemas,cyclosporine enemas,azathio-prine,bismuth carbomer enemas,bismuth subsalicylate tablets,and short-chain fatty acid enemas or suppositories.22The probiotic preparation we used has 2main inno-vative characteristics:a very high bacterial concentration (300billion viable bacteria per gram)and the presence of a mixture of different bacterial species with potential synergistic relationships to enhance suppression of po-tential pathogens.23Various strains of probiotics can have very different and specialized metabolic activities,24such that claims made for one strain of an organism cannot necessarily be applied to another.Theoretically,a composite mixture of a large num-ber of probiotic strains should be most effective.Experi-ments using anaerobic continuous-flow chemostats that duplicate the normal gastrointestinal (GI)microecology have suggested that a single strain or even a few probiotic strains are unlikely to colonize the GI tract or determine important modifications in the GI microecology.25Figure 2.Clinical outcome of patients according to treatment re-ceived.Figure 3.Fecal concentration of bifidobacteria,lactobacilli,and Streptococcus salivarius subsp.thermophilus before (Ϫ1)and after (0)antibiotic treatment and during maintenance treatment in the group treated with VSL#3.308GIONCHETTI ET AL.GASTROENTEROLOGY Vol.119,No.2Recent studies have supported the potential role of oral bacteriotherapy in inflammatory bowel disease (IBD).Lactobacillus spp.and Lactobacillus plantarum have been shown to be able to prevent the development of spontaneous colitis and to attenuate established colitis in IL-10knockout mice,respectively.16,17In2controlled studies,patients with ulcerative colitis were given oral mesalamine or capsules containing a nonpathogenic strain of Escherichia coli as a maintenance treatment;no significant difference in relapse rates was observed between the2 treatments.26,27Moreover,in an open study VSL#3was effective in the prevention of relapses in patients with ulcerative colitis who were intolerant or allergic to sulfasala-zine or mesalamine.28The mechanisms by which probiotics exert their beneficial effects in the host in vivo have not been fully defined;we showed recently that continuous treatment with VSL#3determines a significant increase of tissue levels of IL-10in patients with chronic pouchitis.29 In conclusion,the results of this study indicate that the use of a highly concentrated mixture of probiotic bacterial strains is effective in maintenance treatment of chronic relapsing pouchitis,further supporting the po-tential role of probiotics in IBD therapy.30References1.Svaninger G,Nordgren S,Oresland T,Hulten L.Incidence andcharacteristics of pouchitis in the Koch continent ileostomy and the pelvic pouch.Scand J Gastroenterol1993;28:695–700. 2.Penna C,Dozois R,Tremaine W,Sandborn W,LaRusso N,Schleck C,Ilstrup D.Pouchitis after ileal pouch–anal anastomo-sis for ulcerative colitis occurs with increased frequency in pa-tients with associated primary sclerosing cholangitis.Gut1996;38:234–239.3.Stahlberg D,Gullberg K,Liljeqvist L,Hellers G,Lo¨fberg R.Pou-chitis following pelvic pouch operation for ulcerative colitis.Inci-dence,cumulative risk,and risk factors.Dis Colon Rectum1996;39:1012–1018.4.Meagher AP,Farouk R,Dozois RR,Kelly KA,Pemberton JH.J ilealpouch–anal anastomosis for chronic ulcerative colitis:complica-tions and long-term outcome in1310patients.Br J Surg1998;85:800–803.5.Madden MV,Farthing MJ,Nicholls RJ.Inflammation in the ilealreservoir:“pouchitis.”Gut1990;31:247–249.6.Lohmuller JL,Pemberton JH,Dozois RR,Ilstrup DM,van HeerdenJ.Pouchitis and extraintestinal manifestations of inflammatory bowel disease after ileal pouch–anal anastomosis.Ann Surg 1990;211:622–629.7.Tytgat GN,van Deventer SJ.Pouchitis.Int J Colorectal Dis1988;3:226–228.8.Moskowitz RL,Sheperd NA,Nicholls RJ.An assessment of in-flammation in the reservoir after restorative proctocolectomy with ileoanal ileal reservoir.Int J Colorectal Dis1986;1:167–174. 9.Sheperd NA,Jass JR,Duval I,Moskowitz RL,Nicholls RJ,MorsonBC.Restorative proctocolectomy with ileal reservoir:pathological and histochemical study of mucosal biopsy specimens.J Clin Pathol1987;40:601–607.10.Sanborn WJ.Pouchitis following ileal pouch–anal anstomosis:definition,pathogenesis,and treatment.Gastroenterology1994;107:1856–1860.11.Keighley MRB.Review article:the management of pouchitis.Aliment Pharmacol Ther1996;10:449–457.12.Nicholls RJ,Banerjee AK.Pouchitis:risk factors,etiology,andtreatment.Word J Surg1998;22:347–351.13.Hurst RD,Molinari M,Chung TP,Rubin M,Michelassi F.Prospec-tive study of the incidence,timing and treatment of pouchitis in 104consecutive patients after restorative proctocolectomy.Arch Surg1996;131:497–502.14.Sachar DB.How do you treat refractory pouchitis and when doyou decide to remove the pouch?Inflamm Bowel Dis1998;4: 165–166.15.Fuller R.Probiotics in human medicine.Gut1991;32:439–442.16.Madsen KL,Doyle JSG,Jewell LD,Tavernini MM,Fedorak RN.Lactobacillus species prevents colitis in interleukin-10gene–deficient mice.Gastroenterology1997;116:1107–1114.17.Schultz M,Veltkamp C,Dieleman LA,Wyrick PB,Tonkonogy SL,Sartor RB.Continuous feeding of Lactobacillus plantarum atten-uates established colitis in interleukin-10deficient mice(abstr).Gastroenterology1998;114:A1081.18.Ruseler-van-Embden JGH,Schouten WR,van Lieshout LMC.Pou-chitis:result of microbial imbalance?Gut1994;35:658–664.19.Sandborn WJ,Tremaine WJ,Batts KP,Pemberton JH,Phillips SF.Pouchitis after ileal pouch–anal anastomosis:a pouchitis dis-ease activity index.Mayo Clin Proc1994;69:409–415.20.Tiplady B.A basic program for constructing a dispensing list for arandomized clinical trial.Br J Clin Pharmacol1981;11:617–618.21.Summanen P,Baron EJ,Citron DM,Strong C,Wexler HH,Fine-gold SM,eds.Wadsworth anaerobic bacteriology manual.5th ed.Singapore:Star,1993.22.Sandborn WJ,McLeod R,Jewell DP.Medical therapy for inductionand maintenance of remission in pouchitis:a systematic review.Inflamm Bowel Dis1999;5:33–39.23.Mackowiak PA.The normal microbialflora.N Engl J Med1982;307:83–93.24.Bengmark S.Econutrition and health maintenance.A new con-cept to prevent GI inflammation,ulceration and sepsis.Clin Nutr 1996;15:1–10.25.Berg RD.Probiotics,prebiotics or“conbiotics”?Trends Microbiol1998;6:89–92.26.Kruis W,Schuts E,Fric P,Fixa B,Judmaier G,Stolte M.Double-blind comparison of an oral Escherichia coli preparation and mesalazine in maintaining remission of ulcerative colitis.Aliment Pharmacol Ther1997;11:853–858.27.Rembacken BJ,Snelling AM,Hawkey PM,Chalmers DM,AxonATR.Non pathogenic Escherichia coli versus mesalazine for the treatment of ulcerative colitis:a randomised ncet1999;354:635–639.28.Venturi A,Gionchetti P,Rizzello F,Johansson R,Zucconi E,BrigidiP,Matteuzzi D,Campieri M.Impact on the fecalflora composition by a new probiotic preparation:preliminary data on maintenance treatment of patients with ulcerative colitis.Aliment Pharmacol Ther1999;13:1103–1108.29.Gionchetti P,Rizzello F,Cifone G,Venturi A,D’Alo’S,Peruzzo S,Bazzocchi G,Miglioli M,Campieri M.In vivo effect of a highly concentrated probiotic on IL-10pelvic pouch tissue levels(abstr).Gastroenterology1999;116:A723.30.Campieri M,Gionchetti P.Probiotics in inflammatory bowel dis-ease:new insight to pathogenesis or a possible therapeutic alternative?Gastroenterology1999;116:1246–1249. Received November3,1999.Accepted March15,2000. Address requests for reprints to:Paolo Gionchetti,M.D.,Diparti-mento di Medicina Interna e Gastroenterologia,Policlinico S.Orsola, Via Massarenti9,40138Bologna,Italy.e-mail:paolo@med.unibo.it; fax:(39)51-392538.August2000PROBIOTIC THERAPY IN POUCHITIS309。
安慰剂对照审查参考.doc
安慰剂对照审查参考临床试验中对照组的设置通常包括五种类型，即安慰剂对照、空白对照、剂量对照、阳性药物对照和外部对照。
而对照药分为阴性对照药（安慰剂）和阳性对照药（有活性的药物）。
在临床试验设计中如何合理地选择安慰剂或阳性对照药物，审查参考如下：一、临床研究中的安慰剂选择安慰剂是一种“模拟”药，其物理特性如外观、大小、颜色、剂型、重量、味道和气味都要尽可能与试验药物相同，但不能含有试验药的有效成份。
安慰剂常用于安慰剂对照的临床试验。
因能可靠地证明受试药物的疗效，并可反映受试药的“绝对”有效性和安全性，所以在很多需要证明受试药绝对作用大小的临床试验中选择安慰剂作对照，只有证实受试药显著优于安慰剂时，才能确定受试药本身的药效作用。
有时，安慰剂可用于阳性药物对照试验中。
为了保证双盲试验的执行，常采用双模拟技巧，受试药和阳性对照药都制作了安慰剂以利于设盲；另外，在阳性药物对照试验中加入安慰剂，可提高临床试验的效率。
临床研究中采用安慰剂最大的问题是伦理学方面的原因。
一般认为，安慰剂适用于轻症或功能性疾病患者。
在急性、重症或有较严重器质性病变的患者，通常不用安慰剂进行对照；当一种现行治疗已知可以防止受试者疾病发生进展时，一般也不宜用安慰剂进行对照。
一种新药用于尚无已知有效药物可以治疗的疾病进行临床试验时，对新药和安慰剂进行比较试验通常不存在伦理学问题，可以选择以安慰剂作为对照药；在一些情况下，停用或延迟有效治疗不会造成受试者较大的健康风险时，即使可能会导致患者感到不适，但只要他们参加临床试验是非强迫性的，而且他们对可能有的治疗及延迟治疗的后果完全知情，要求患者参加安慰剂对照试验可以认为是合乎伦理的。
对新药选择安慰剂进行对照是否能被受试者和研究者接受是一个由研究者、患者和机构审查委员会或独立伦理委员会（IRB/IEC）判断的问题。
另外，在早期脱离、随机撤药试验中也经常选择安慰剂作对照。
在随机撤药试验中接受一定时间试验药物治疗的受试者被随机分配继续使用受试药治疗或安慰剂治疗。
安慰剂检验介绍、操作及举例
安慰剂检验介绍（Placebo test）安慰剂是一种附加实证检验的思路，并不存在一个具体的特定的操作方法。
一般存在两种寻找安慰剂变量的方法。
比如，在已有的实证检验中，发现自变量Xi会影响自变量Zi与因变量Yi之间存在相关关系。
在其后的实证检验中，采用其他主体（国家，省份，公司）的Xj变量作为安慰剂变量，检验Xj是否影响Zi与Yi之间的相关关系。
如果不存在类似于Xi的影响，即可排除Xi 的安慰剂效应，使得结果更为稳健。
另一种寻找安慰剂变量的方法。
已知，Xi是虚拟变量，Xi=1，if t>T；Xi=0 if t<T；Xi对Zi对Yi的影响的影响在T时前后有显著差异（DID）。
在其后的实证检验中，将Xi`设定为Xi`=1，if t>T+n；Xi`=0 if t<T+n，其中n根据实际情况取值，可正可负。
检验Xi`是否影响Zi与Yi之间的相关关系。
如果不存在类似于Xi的影响，即可排除Xi的安慰剂效应，使得结果更为稳健。
举例：以美国市场某种政策冲击识别策略的因果关系考察，在最后部分选取英国同期的因变量，检验是否有类似的特征，就是安慰剂检验。
以中国2007年所得税改革作为减税的政策冲击以验证减税对企业创新的影响。
亦可以通过把虚拟的政策实施时间往前往后推几年，作为虚拟的政策时点，如果检验发现没有类似的因果，文章的主要结论就更加可信了。
以下是详细的例题，安慰剂检验在最后。
Surviving Graduate Econometrics with R:Difference-in-Differences Estimation — 2 of 8The following replication exercise closely follows the homework assignment #2 in ECNS 562. The data for this exercise can be found here.The data is about the expansion of the Earned Income Tax Credit. This is a legislation aimed at providing a tax break for low income individuals. For some background on the subject, seeEissa, Nada, and Jeffrey B. Liebman. 1996. Labor Supply Responses to the Earned Income Tax Credit. Quarterly Journal of Economics. 111(2): 605-637.The homework questions (abbreviated):1.Describe and summarize data.2.Calculate the sample means of all variables for (a) single women with nochildren, (b) single women with 1 child, and (c) single women with 2+ children.3.Create a new variable with earnings conditional on working (missing fornon-employed) and calculate the means of this by group as well.4.Construct a variable for the “treatment” called ANYKIDS and a variablefor after the expansion (called POST93—should be 1 for 1994 and later).5.Create a graph which plots mean annual employment rates by year(1991-1996) for single women with children (treatment) and without children (control).6.Calculate the unconditional difference-in-difference estimates of theeffect of the 1993 EITC expansion on employment of single women.7.Now run a regression to estimate the conditional difference-in-differenceestimate of the effect of the EITC. Use all women with children as the treatment group.8.Reestimate this model including demographic characteristics.9.Add the state unemployment rate and allow its effect to vary by thepresence of children.10.Allow the treatment effect to vary by those with 1 or 2+ children.11.Estimate a “placebo” treatment model. Take data from only thepre-reform period. Use the same treatment and control groups. Introduce a placebo policy that begins in 1992 (so 1992 and 1993 both have this fake policy).A review: Loading your dataRecall the code for importing your data: STATA:/*Last modified 1/11/2011 */************************************************************************* *The following block of commands go at the start of nearly all do files*/ *Bracket comments with /* */ or just use an asterisk at line beginningclear /*Clears memory*/set mem 50m /*Adjust this for your particular dataset*/ cd "C:\DATA\Econ 562\homework" /*Change this for your file structure*/ log using stata_assign2.log, replace /*Log file records all commands & results*/ display "$S_DATE $S_TIME" set more offinsheet using eitc.dta, clear************************************************************************* R:12 3456 7891011121314 15# Kevin Goulding # ECNS 562 - Assignment 2 ########################################################################## # Load the foreign package require(foreign) # Import data from web site # update: first download the file eitc.dta from this link: # https:///open?id=0B0iAUHM7ljQ1cUZvRWxjUmpfVXM # Then import from your hard drive: eitc = read.dta("C:/link/to/my/download/folder/eitc.dta")</pre> Note that any comments can be embedded into R code, simply by putting a <code> # </code> to the can download the data file, and import it from your hard drive: eitc = read.dta("C:\DATA\Courses\Econ 562\homework\eitc.dta") Describe and summarize your dataRecall from part 1 of this series, the following code to describe and summarize your data: STATA:dessumR:In R, each column of your data is assigned a class which will determine how your data is treated in various functions. To see what class R has interpreted for all your variables, run the following code:1 2 3 4 sapply(eitc,class) summary(eitc)source('sumstats.r') sumstats(eitc)To output the summary statistics table to LaTeX, use the following code:1 2 require(xtable) # xtable package helps create LaTeX code xtable(sumstats(eitc))Note: You will need to re-run the code for sumstats() which you can find in an earlier post.Calculate Conditional Sample MeansSTATA:summarize if children==0summarize if children == 1summarize if children >=1summarize if children >=1 & year == 1994mean work if post93 == 0 & anykids == 1R:1 2 3 4 5 6 7 8 91011121314 # The following code utilizes the sumstats function (you will need to re-run this code) sumstats(eitc[eitc$children == 0, ])sumstats(eitc[eitc$children == 1, ])sumstats(eitc[eitc$children >= 1, ])sumstats(eitc[eitc$children >= 1 & eitc$year == 1994, ])# Alternately, you can use the built-in summary functionsummary(eitc[eitc$children == 0, ])summary(eitc[eitc$children == 1, ])summary(eitc[eitc$children >= 1, ])summary(eitc[eitc$children >= 1 & eitc$year == 1994, ])# Another example: Summarize variable 'work' for women with one child from 1993 onwards. summary(subset(eitc, year >= 1993 & children == 1, select=work))The code above includes all summary statistics – but say you are only interested in the mean. You could then be more specific in your coding, like this:1 2 3 mean(eitc[eitc$children == 0, 'work']) mean(eitc[eitc$children == 1, 'work']) mean(eitc[eitc$children >= 1, 'work'])Try out any of the other headings within the summary output, they should also work: min() for minimum value, max() for maximum value, stdev() for standard deviation, and others.Create a New VariableTo create a new variable called “c.earn” equal to earnings conditional on working (if “work” = 1), “NA” otherwise (“work” = 0) – use the following code:STATA:gen cearn = earn if work == 1R:1 2 3 4 5 6 7 eitc$c.earn=eitc$earn*eitc$workz = names(eitc)X = as.data.frame(eitc$c.earn)X[] = lapply(X, function(x){replace(x, x == 0, NA)}) eitc = cbind(eitc,X)eitc$c.earn = NULLnames(eitc) = zConstruct a Treatment VariableCons truct a variable for the treatment called “anykids” = 1 for treated individual (has at least one child); and a variable for after the expansion called “post93” = 1 for 1994 and later.STATA:gen anykids = (children >= 1)gen post93 = (year >= 1994)R:1 2 eitc$post93 = as.numeric(eitc$year >= 1994) eitc$anykids = as.numeric(eitc$children > 0)Create a plotCreate a graph which plots mean annual employment rates by year (1991-1996) for single women with children (treatment) and without children (control). STATA: preservecollapse work, by(year anykids) gen work0 = work if anykids==0label var work0 "Single women, no children" gen work1 = work if anykids==1label var work1 "Single women, children"twoway (line work0 year, sort) (line work1 year, sort), ytitle(Labor Force Participation Rates)graph save Graph "homework\eitc1.gph", replace R: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15# Take average value of 'work' by year, conditional on anykidsminfo = aggregate(eitc$work, list(eitc$year,eitc$anykids == 1), mean)# rename column headings (variables)names(minfo) = c("YR","Treatment","LFPR")# Attach a new column with labelsminfo$Group[1:6] = "Single women, no children" minfo$Group[7:12] = "Single women, children" minforequire(ggplot2) #package for creating nice plotsqplot(YR, LFPR, data=minfo, geom=c("point","line"), colour=Group,xlab="Year", ylab="Labor Force Participation Rate")The ggplot2 package produces some nice looking charts.Calculate the D-I-D Estimate of the Treatment EffectCalculate the unconditional difference-in-difference estimates of the effect of the 1993 EITC expansion on employment of single women.STATA:mean work if post93==0 & anykids==0mean work if post93==0 & anykids==1mean work if post93==1 & anykids==0mean work if post93==1 & anykids==1R:1 2 3 4 5 a = colMeans(subset(eitc, post93 == 0 & anykids == 0, select=work))b = colMeans(subset(eitc, post93 == 0 & anykids == 1, select=work))c = colMeans(subset(eitc, post93 == 1 & anykids == 0, select=work))d = colMeans(subset(eitc, post93 == 1 & anykids == 1, select=work)) (d-c)-(b-a)Run a simple D-I-D RegressionNow we will run a regression to estimate the conditional difference-in-difference est imate of the effect of the Earned Income Tax Credit on “work”, using all women with children as the treatment group. The regression equation is asfollows:Where is the white noise error term.STATA:gen interaction = post93*anykidsreg work post93 anykids interactionR:1 2 reg1 = lm(work ~ post93 + anykids + post93*anykids, data = eitc) summary(reg1)Include Relevant Demographics in RegressionAdding additional variables is a matter of including them in your coded regression equation, as follows:STATA:gen age2 = age^2 /*Create age-squared variable*/gen nonlaborinc = finc - earn /*Non-labor income*/reg work post93 anykids interaction nonwhite age age2 ed finc nonlaborinc R:1 2 3 reg2 = lm(work ~ anykids + post93 + post93*anykids + nonwhite+ age + I(age^2) + ed + finc + I(finc-earn), data = eitc) summary(reg2)Create some new variablesWe will create two new interaction variables:1.The state unemployment rate interacted with number of children.2.The treatment term interacted with individuals with one child, or morethan one child.STATA:gen interu = urate*anykidsgen onekid = (children==1)gen twokid = (children>=2)gen postXone = post93*onekidgen postXtwo = post93*twokidR:1 2 3 4 5 6 7 8 9101112 # The state unemployment rate interacted with number of childreneitc$urate.int = eitc$urate*eitc$anykids### Creating a new treatment term:# First, we'll create a new dummy variable to distinguish between one child and 2+. eitc$manykids = as.numeric(eitc$children >= 2)# Next, we'll create a new variable by interacting the new dummy# variable with the original interaction term.eitc$tr2 = eitc$p93kids.interaction*eitc$manykidsEstimate a Placebo ModelTesting a placebo model is when you arbitrarily choose a treatment time before your actual treatment time, and test to see if you get a significant treatment effect.STATA:gen placebo = (year >= 1992)gen placeboXany = anykids*placeboreg work anykids placebo placeboXany if year<1994In R, first we’ll subset the data to exclude the time period after the real treatment (1993 and later). Next, we’ll create a new treatment dummy variable, and run a regression as before on our data subset.R:1 2 3 4 5 6 7 8 9 10 # sub set the data, including only years before 1994.eitc.sub = eitc[eitc$year <= 1993,]# Create a new "after treatment" dummy variable# and interaction termeitc.sub$post91 = as.numeric(eitc.sub$year >= 1992)# Run a placebo regression where placebo treatment = post91*anykids reg3 <- lm(work ~ anykids + post91 + post91*anykids, data = eitc.sub) summary(reg3)The entire code for this post is available here (File –> Save As). If you have any questions or find problems with my code, you can e-mail me directlyat kevingoulding {at} gmail [dot] com.To continue on to Part 3 of our series, Fixed Effects estimation, click here.。
安慰剂检验介绍、操作及举例
安慰剂检验介绍（Placebo test）安慰剂是一种附加实证检验的思路，并不存在一个具体的特定的操作方法。
一般存在两种寻找安慰剂变量的方法。
比如，在已有的实证检验中，发现自变量Xi会影响自变量Zi与因变量Yi之间存在相关关系。
在其后的实证检验中，采用其他主体（国家，省份，公司）的Xj变量作为安慰剂变量，检验Xj是否影响Zi与Yi之间的相关关系。
如果不存在类似于Xi的影响，即可排除Xi的安慰剂效应，使得结果更为稳健。
另一种寻找安慰剂变量的方法。
已知，Xi是虚拟变量，Xi=1，if t>T；Xi=0 if t<T；Xi对Zi对Yi的影响的影响在T时前后有显著差异（DID）。
在其后的实证检验中，将Xi`设定为Xi`=1，if t>T+n；Xi`=0 if t<T+n，其中n根据实际情况取值，可正可负。
检验Xi`是否影响Zi与Yi之间的相关关系。
如果不存在类似于Xi的影响，即可排除Xi的安慰剂效应，使得结果更为稳健。
举例：以美国市场某种政策冲击识别策略的因果关系考察，在最后部分选取英国同期的因变量，检验是否有类似的特征，就是安慰剂检验。
以中国2007年所得税改革作为减税的政策冲击以验证减税对企业创新的影响。
亦可以通过把虚拟的政策实施时间往前往后推几年，作为虚拟的政策时点，如果检验发现没有类似的因果，文章的主要结论就更加可信了。
以下是详细的例题，安慰剂检验在最后。
Surviving Graduate Econometrics with R: Difference-in-Differences Estimation — 2 of 8The following replication exercise closely follows thehomework assignment #2 in ECNS 562. The data for this exercise can be found here.The data is about the expansion of the Earned Income Tax Credit. This is a legislation aimed at providing a tax break for low income individuals. For some background on the subject, seeEissa, Nada, and Jeffrey B. Liebman. 1996. Labor Supply Responses to the Earned Income Tax Credit. Quarterly Journal of Economics. 111(2): 605-637.The homework questions (abbreviated):1.Describe and summarize data.2.Calculate the sample means of all variables for (a) single women with nochildren, (b) single women with 1 child, and (c) single women with 2+ children.3.Create a new variable with earnings conditional on working (missing fornon-employed) and calculate the means of this by group as well.4.Construct a varia ble for the “treatment” called ANYKIDS and a variable forafter the expansion (called POST93—should be 1 for 1994 and later).5.Create a graph which plots mean annual employment rates by year (1991-1996)for single women with children (treatment) and without children (control).6.Calculate the unconditional difference-in-difference estimates of theeffect of the 1993 EITC expansion on employment of single women.7.Now run a regression to estimate the conditional difference-in-differenceestimate of the effect of the EITC. Use all women with children as the treatment group.8.Reestimate this model including demographic characteristics.9.Add the state unemployment rate and allow its effect to vary by the presenceof children.10.Allow the treatment effect to vary by those with 1 or 2+ children.11. Estimate a “placebo” treatment model. Take data from only the pre -reformperiod. Use the same treatment and control groups. Introduce a placebo policy that begins in 1992 (so 1992 and 1993 both have this fake policy). A review: Loading your dataRecall the code for importing your data: STATA:/*Last modified 1/11/2011 */************************************************************************* *The following block of commands go at the start of nearly all do files*/ *Bracket comments with /* */ or just use an asterisk at line beginningclear /*Clears memory*/set mem 50m /*Adjust this for your particular dataset*/ cd "C:\DATA\Econ 562\homework" /*Change this for your file structure*/ log using , replace /*Log file records all commands & results*/ display "$S_DATE $S_TIME" set more offinsheet using , clear************************************************************************* R:1 2 3 456 7 8 9 10 # Kevin Goulding# ECNS 562 - Assignment 2########################################################################## # Load the foreign package require(foreign)# Import data from web site# update: first download the file from this link: # Then import from your hard drive:eitc = ("C:/link/to/my/download/folder/")</pre>1112 1314 15Note that any comments can be embedded into R code, simply by putting a <code> # </code> to the download the data file, and import it from your hard drive:eitc = ("C:\DATA\Courses\Econ 562\homework\") Describe and summarize your dataRecall from part 1 of this series, the following code to describe and summarize your data: STATA: des sum R:In R, each column of your data is assigned a class which will determine how your data is treated in various functions. To see what class R has interpreted for all your variables, run the following code: 1 2 3 4 sapply(eitc,class) summary(eitc) source('') sumstats(eitc)To output the summary statistics table to LaTeX, use the following code:1 2 require(xtable) # xtable package helps create LaTeX code xtable(sumstats(eitc))Note: You will need to re-run the code for sumstats() which you can find in an earlier post.Calculate Conditional Sample Means STATA:summarize if children==0 summarize if children == 1summarize if children >=1summarize if children >=1 & year == 1994 mean work if post93 == 0 & anykids == 1 R:1 2 3 4 5 6 7 8 9 10 11 12 13 14# The following code utilizes the sumstats function (you will need to re-run this code) sumstats(eitc[eitc$children == 0, ])sumstats(eitc[eitc$children == 1, ])sumstats(eitc[eitc$children >= 1, ])sumstats(eitc[eitc$children >= 1 & eitc$year == 1994, ])# Alternately, you can use the built-in summary functionsummary(eitc[eitc$children == 0, ])summary(eitc[eitc$children == 1, ])summary(eitc[eitc$children >= 1, ])summary(eitc[eitc$children >= 1 & eitc$year == 1994, ])# Another example: Summarize variable 'work' for women with one child from 1993 onwards. summary(subset(eitc, year >= 1993 & children == 1, select=work))The code above includes all summary statistics – but say you are only interested in the mean. You could then be more specific in your coding, like this:1 2 3mean(eitc[eitc$children == 0, 'work']) mean(eitc[eitc$children == 1, 'work']) mean(eitc[eitc$children >= 1, 'work'])Try out any of the other headings within the summary output, they should also work: min() for minimum value, max() for maximum value, stdev() for standard deviation, and others.Create a New VariableTo create a new variable called “” equal to earnings conditional on working (if “work” = 1), “NA” otherwise (“work” = 0) – use the following code:STATA:gen cearn = earn if work == 1 R:12 3 4 5 6 7eitc$=eitc$earn*eitc$workz = names(eitc)X = = lapply(X, function(x){replace(x, x == 0, NA)}) eitc = cbind(eitc,X)eitc$ = NULLnames(eitc) = zConstruct a Treatment VariableCon struct a variable for the treatment called “anykids” = 1 for treated individual (has at least one child); and a variable for after the expansion called “post93” = 1 for 1994 and later.STATA:gen anykids = (children >= 1)gen post93 = (year >= 1994)R:1 2eitc$post93 = (eitc$year >= 1994) eitc$anykids = (eitc$children > 0)Create a plotCreate a graph which plots mean annual employment rates by year (1991-1996) for single women with children (treatment) and without children (control).STATA:preservecollapse work, by(year anykids)gen work0 = work if anykids==0label var work0 "Single women, no children"gen work1 = work if anykids==1label var work1 "Single women, children"twoway (line work0 year, sort) (line work1 year, sort), ytitle(Labor Force Participation Rates)graph save Graph "homework\", replaceR:12 3 4 5 6 7 8 9 10 11 12 13 14 15# Take average value of 'work' by year, conditional on anykidsminfo = aggregate(eitc$work, list(eitc$year,eitc$anykids == 1), mean)# rename column headings (variables)names(minfo) = c("YR","Treatment","LFPR")# Attach a new column with labelsminfo$Group[1:6] = "Single women, no children"minfo$Group[7:12] = "Single women, children"minforequire(ggplot2) #package for creating nice plotsqplot(YR, LFPR, data=minfo, geom=c("point","line"), colour=Group,xlab="Year", ylab="Labor Force Participation Rate") The ggplot2 package produces some nice looking charts.Calculate the D-I-D Estimate of the Treatment EffectCalculate the unconditional difference-in-difference estimates of the effect of the 1993 EITC expansion on employment of single women.STATA:mean work if post93==0 & anykids==0mean work if post93==0 & anykids==1mean work if post93==1 & anykids==0mean work if post93==1 & anykids==1R:1 2 3 4 5a = colMeans(subset(eitc, post93 == 0 & anykids == 0, select=work))b = colMeans(subset(eitc, post93 == 0 & anykids == 1, select=work))c = colMeans(subset(eitc, post93 == 1 & anykids == 0, select=work))d = colMeans(subset(eitc, post93 == 1 & anykids == 1, select=work)) (d-c)-(b-a)Run a simple D-I-D RegressionNow we will run a regression to estimate the conditional difference-in-difference estimate of the effect of the Earned Income Tax Credit on “work”, using all women with children as the treatment group. The regression equation is as follows:Where is the white noise error term.STATA:gen interaction = post93*anykidsreg work post93 anykids interactionR:1 2reg1 = lm(work ~ post93 + anykids + post93*anykids, data = eitc) summary(reg1)Include Relevant Demographics in RegressionAdding additional variables is a matter of including them in your coded regression equation, as follows:STATA:gen age2 = age^2 /*Create age-squared variable*/gen nonlaborinc = finc - earn /*Non-labor income*/reg work post93 anykids interaction nonwhite age age2 ed finc nonlaborinc R:1 2 3reg2 = lm(work ~ anykids + post93 + post93*anykids + nonwhite+ age + I(age^2) + ed + finc + I(finc-earn), data = eitc) summary(reg2)Create some new variablesWe will create two new interaction variables:1.The state unemployment rate interacted with number of children.2.The treatment term interacted with individuals with one child, or more thanone child.STATA:gen interu = urate*anykidsgen onekid = (children==1)gen twokid = (children>=2)gen postXone = post93*onekidgen postXtwo = post93*twokidR:1 2 3 4 5 6 7# The state unemployment rate interacted with number of childreneitc$ = eitc$urate*eitc$anykids### Creating a new treatment term:# First, we'll create a new dummy variable to distinguish between one child and 2+. eitc$manykids = (eitc$children >= 2)89 10 11 12# Next, we'll create a new variable by interacting the new dummy # variable with the original interaction term.eitc$tr2 = eitc$*eitc$manykidsEstimate a Placebo ModelTesting a placebo model is when you arbitrarily choose a treatment time before your actual treatment time, and test to see if you get a significant treatment effect. STATA:gen placebo = (year >= 1992)gen placeboXany = anykids*placeboreg work anykids placebo placeboXany if year<1994In R, first we’ll subset the data to exclude the time period after the real treatment (1993 and later). Next, we’ll create a new treatment dummy variable, and run a regression as before on our data subset.R:1 2 3 4 5 6 7 8 9 10# sub set the data, including only years before 1994.= eitc[eitc$year <= 1993,]# Create a new "after treatment" dummy variable# and interaction term$post91 = $year >= 1992)# Run a placebo regression where placebo treatment = post91*anykids reg3 <- lm(work ~ anykids + post91 + post91*anykids, data = summary(reg3)The entire code for this post is available here (File –> Save As). If you have any questions or find problems with my code, you can e-mail me directlyat kevingoulding {at} gmail [dot] com.To continue on to Part 3 of our series, Fixed Effects estimation, click here.11。
安慰剂的定义和应用
数据收集与处理
统计分析
运用适当的统计方法对数据进行分析 ，比较试验组和对照组的差异，并评 估安慰剂效应的大小和显著性。
采用统一的标准和方法收集数据，确 保数据的准确性和可比性。
伦理道德考量及规范
受试者权益保护
确保受试者的知情同意权 、隐私权和安全权得到充 分保障。
伦理委员会审查
临床试验方案需经过伦理 委员会的审查和批准，确 保研究符合伦理道德要求 。
条件反射作用
在某些情况下，安慰剂可能与患者 过去的积极经验或条件反射相关联 ，导致患者产生类似的生理反应。
生理反应机制
尽管安慰剂本身没有药理活性，但 它可能通过影响患者的生理反应机 制（如神经递质、荷尔蒙等）来产 生一定的治疗效果。
伦理道德问题探讨
欺骗性质
使用安慰剂可能涉及欺骗患者的 问题，因为患者可能不知道他们 接受的是无效治疗。这需要在临 床试验前充分告知患者并获得其
THANK YOU
感谢观看
同意。
风险与受益平衡
在某些情况下，使用安慰剂可能 对患者造成一定的风险。因此， 需要仔细评估使用安慰剂的潜在 风险和受益，并确保患者的安全
和权益得到保障。
知情同意原则
在使用安慰剂的临床试验中，必 须遵循知情同意原则，确保患者 充分了解试验的目的、过程、潜 在风险和受益，并自愿参与试验
。
02
安慰剂在医学研究中应用
针对不同患者群体心理干预策略
焦虑症患者
提供安全感，建立信任关系，引导患者放松身心 。
抑郁症患者
给予关心和支持，提高患者自尊和自信，激发积 极情绪。
失眠症患者
创造舒适睡眠环境，进行渐进性肌肉松弛训练等 。
注意事项及风险防范
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安慰剂详细资料大全安慰剂（pIacebo）是指没有药物治疗作用的片、丸、针剂。
对长期服用某种药物引起不良后果的人具有替代和安慰作用。
本身没有任何治疗作用。
但因患者对医生信任、患者叫自我暗示以及对某种药物疗效的期望等而起到镇痛、镇蘸或缓解症状的作用。
在临床医学的实验研究中．为观察糊种药物的疗效，也常用于对照试验。
基本介绍•药品名称：安慰剂•别名：假药•外文名称： Placebo•是否处方药：非处方药•剂型：片剂或注射剂•运动员慎用：慎用•是否纳入医保：未纳入•药品类型：化学药品•影响范围：血压、心率、胃分泌、呕吐等定义,药物作用,药物效应,术语解释,解释,产生效应,效应之谜,精神作用,巨大争议,冒牌兴奋剂,科研套用,治疗套用,定义安慰剂（placebo）是一种“模拟药物”。
其物理特性如外观、大小、颜色、剂型、重量、味道和气味都要尽可能与试验药物相同，但不能含有试验药的有效成份（如含乳糖或淀粉的片剂或生理盐水注射剂）。
药物作用具有一定的作用，对有心理因素参与控制的自主神经系统功能如血压、心率、胃分泌、呕吐、性功能等的影响较大。
一、可以稳定病人的紧张情绪。
二、在新药研发时作为实验的空白对照，排除新药可能引发的安慰剂效应。
三、用于缓解癌症晚期患者的痛苦。
药物效应安慰剂的效应，称为“安慰剂效应”（placebo effect）。
据文献报导，由病人高度信赖的医师治疗，安慰剂对胃十二指肠溃疡的短期疗效最高可达约70﹪。
对恶性肿瘤患者，安慰剂对缓解某些症状会产生“安慰剂效应”，但对延缓生命无效。
这样的发现为现有的医疗方法带来了一种新的可能：利用安慰剂效应来强化常规疗法的效果。
科学家正在研究如何通过安慰剂效应使常规治疗的疗效最大化。
有研究表明，安慰剂的剂量和外观很重要，注射剂似乎比内服药更有效，但最重要的是医生的态度。
南安普顿大学的研究人员做了一个实验。
他们随机抽取两组病人，一组病人得到医生的确诊，并被告知他们很快就会好起来——一些人没有接受任何治疗；对另一组病人则给予含糊其词的诊断，也没有保证他们能很快康复。
结果第一组有64%的病人病情出现好转，第二组只有39%、这说明在治疗病人时，医生的态度远比他们开出的处方重要。
如果能把安慰剂效应和常规治疗结合起来，那将使现有的医疗方法发生巨大的变化。
贝内代蒂说：“如果能充分利用安慰剂效应，让病人周一服药，周二服用安慰剂，周三再服药。
这样循环往复，就能减少50%的药物使用量。
”术语解释解释安慰剂，由没有药效、也没有毒副作用的物质制成，如葡萄糖、淀粉等，外形与真药相像。
服用安慰剂，对于那些渴求治疗、对医务人员充分信任的患者，能在心理上产生良好的积极反应，从而改善人的生理状态，达到所希望的药效，这种反应被称为安慰剂效应。
安慰剂的另一个意思是比喻能够产生心理抚慰作用的方法和举措等。
例如北京市出台了新的购房契约，可是有的律师和社区治理专家认为有些条款属于“安慰剂”。
产生效应首先，患者期待药物起作用的心理激发了生理反应；其次，患者对所处的医疗环境引起了生理上的条件反射。
效应之谜早在几百年前，医生们就意识到安慰剂的强大作用。
当患者服用或接受实际上没有药理作用且无毒副作用的药物或疗法，使病情有所改善时，医学上称之为出现了安慰剂效应。
在临床诊治中，这种无毒副作用的替代品对治愈病人起到了积极作用。
但安慰剂效应究竟有多大？它是如何发挥作用的？这些问题依然是萦绕在人们心中的不解之谜。
有人说安慰剂能对大约1/3的病人产生作用。
实际上，这个比例并非一成不变。
对患有精神抑郁症的病人来说，安慰剂的有效率高达80%，而对糖尿病等患者案例说，它的作用可能为零。
精神作用要解开安慰剂效应之谜，让我们先来做一个简单的实验：对比镇静剂和兴奋剂的效果。
研究人员请了60名志愿者参加这个实验，并为他们准备两种药物：蓝色的药片是镇静剂，红色的是兴奋剂。
参与者每人随机领取一种药物服用，然后观察他们是否有昏昏欲睡的症状。
结果很明显：服用镇静剂的人比服用兴奋剂的人更容易出现上述症状，其比例是后者的2倍。
这似乎很正常——直到谜底揭晓：志愿者既没有服用镇静剂，也没有服用兴奋剂。
他们服用的是同一种没有药理作用且无毒副作用的化合物，唯一的不同是药片颜色。
这个实验充分说明了精神对肉体的巨大影响。
在过去几年里，科学家通过各种类似的实验证明了安慰剂的效果——只要相信某种治疗能产生效果就能使病人好起来。
实验证明，安慰剂对有精神抑郁症或疼痛的患者效果最明显。
这种对身体无害的疗法通常能起到和“真正的”药物治疗一样好的效果。
巨大争议那么安慰剂的作用究竟有多大？它是如何产生作用的？能不能用它来改善对病患的治疗？这些问题在医学界引起了巨大争议。
从伦理道德的角度出发，把安慰剂作用常规治疗的替代品一般被认为是不道德的做法。
但这只是近代才出现的观点。
直到20世纪初，医生还经常给病人服用面团做的药片和注射水做的注射液，为的是让他们相信自己的病能好起来。
安慰剂的效果完全取决于病人的心理状态，对头脑简单或容易神经过敏的人效果最好。
然而到了20世纪中期，随着医学治疗的态度日趋严谨，人们对安慰剂的看法也逐渐发生了变化。
很多人认为，安慰剂只能在临床实验中使用，用来控制实验或测试某种尚未推广的治疗方法的有效程度。
他们认为，在临床治疗中适用安慰剂是不道德的行为，只有疗效远胜安慰剂的治疗方法才是医学上可以接受的疗法。
但就像文中提到的实验一样，研究人员惊奇地发现，有时候仅仅使用安慰剂就能让患者的病情获得很大改善。
1955年，美国科学家亨利~比彻发表了具有里程碑意义的论文《强大的安慰剂》。
文中分析了15种安慰剂的临床实验结果，并得出结论：在一般情况下，35%的病人在使用安慰剂后病情能得到有效改善。
那意味着1/3的人可以什么药都不用，仅靠安慰剂来治病。
但比彻在分析中犯了一个致命的错误：他假设安慰剂效应是病人病情得到改善的唯一解释。
实际上，患者病情或症状得到改善可以有无数种可能的解释。
很多病（如背疼）具有“自愈”的特性，那意味着它们自己慢慢会好。
赫尔大学研究安慰剂的专家欧文·基尔希说：“1/3的人能靠安慰剂治病的观点是错误的。
”各抒己见那么真实情况究竟是怎么样的？2001年，哥本哈根大学的两位研究人员发表了一篇论文，旨在揭开安慰剂效应之谜。
他们对上百次临床实验的结果进行分析，比较药物治疗、没有任何治疗和安慰剂治疗之间的效果。
如果真的存在所谓的“安慰剂效应”，那么使用安慰剂的病人应该比不接受任何治疗的病人好得更快。
他们的发现直到今天仍有争议。
通过比较，两位研究人员发现安慰剂在临床治疗上几乎没有任何用处。
除了止痛等一些微不足道的作用外，安慰剂治疗的效果和不治疗相差无几。
也有一些研究人员坚持自己的观点，认为文中得出的结论不可信。
大多数批评人士认为，不能对安慰剂效应一概而论。
基尔希指出：“研究某种特定的治疗方法对某种具有的病有什么作用似乎更合理些。
安慰剂效应在精神抑郁症等症状上效果相当明显，对疼痛之类的症状也能产生作用，但效果相对弱一些，对糖尿病这样的病可能根本不起作用。
” 在辩论安慰剂是否具有“疗效”的同时，一些科学家也对安慰剂效应如何对人体产生作用进行研究。
在一次实验中，都灵大学的发布里奇奥·贝内代蒂和同事给志愿者注射辣椒素，令其产生疼痛感，然后给他们涂抹一种强效止痛膏。
实际上，这种止痛膏根本不含任何药性，但志愿者普遍感觉疼痛感有所减轻。
接着研究人员给他们注射耐勒克松（一种强力解麻醉剂），结果疼痛感又回来了。
通过这个实验，研究人员得出结论：安慰剂效应并非纯粹出于心理作用，患者期待药物起作用的心理也会引起生理上的条件反射。
在这个实验中，安慰剂能促使大脑分泌出缓解疼痛的化学物质。
冒牌兴奋剂研究显示，安慰剂可以激活机体内源性的镇痛成分——阿片样物质。
尤其是，如果某个人接受了吗啡注射，并形成了药物注射与缓解疼痛之间的联想，那么给他注射生理盐水也能缓解他的疼痛。
既然如此，这种方法能在体育比赛时提高运动员的疼痛忍耐力吗？世界反兴奋剂组织的禁药表中规定，在体育比赛中使用吗啡是非法的，但训练时可以使用。
因此运动员可以在赛前注射吗啡，比赛时再用安慰剂替代。
然而，为了确保这一策略有效，运动员需要在比赛前几天就停止注射吗啡，确保比赛那天吗啡不会残留在体内。
然而，直到研究人员才确定，经过一天以上的较长间隔期，这种条件反射仍有效力。
科研套用任何药物都可产生安慰剂效应，无论好的作用和不好的作用。
为确定一个药物作用是否为安慰剂效应，科研中要用安慰剂作为对照，半数受试者给受试药物，半数给安慰剂，理想的是医护人员和病人均不知受试药物和安慰剂的区别，此为双盲法。
试验结束，比较两制剂的效果，去除安慰剂的影响才能确定受试药疗效是否可靠。
如在研究一种新的抗心绞痛药物时，服用安慰剂者中有50%的人症状得到缓解，这种结果表明，这种新药的疗效值得怀疑。
治疗套用任何治疗都有安慰剂效应。
病人对医护人员、药物有信心，安慰剂效果就明显；对治疗消极，则影响其疗效，甚至出现不良反应。
如当医生和病人都相信安慰剂有益时，用对关节无治疗作用的维生素B12治疗关节炎，可减轻症状。
有时一个低活性药也可产生明显的疗效。
除科研用药外，医生应避免刻意地选择安慰剂。
因为这种欺骗行为可损害医生和病人的关系，另外，医生也可能误解病人出现的症状，掩盖了真正的病情。
当有其他医护人员同时参与诊治时，这种欺骗行为会加重病人的不信任感。
然而，医生可以在如慢性疼痛的病人对镇痛药产生依赖性后试用安慰剂。
尽管医生很少使用安慰剂，但大多数医生都认为安慰剂对病人有预防或减轻疾病的作用，虽然没有科学依据。
例如，长期用维生素B12或其他维生素的人停用后出现疾病的症状；疼痛病人用后疼痛明显减轻。
由于文化和心理作用的不同，一些人可受益于这种似无科学依据的治疗方法。
许多医生认为这种治疗方法对医生与病人关系不利，对用药不利，但大多医生仍然相信一些病人对安慰剂作用明显，停药后反而不利。