Types of Randomization Schemes

  • Simple randomization 

  • Restricted randomization 

  • Adaptive randomization 

Simple randomization : Advantages

Complete randomization, e.g., coin toss 

- Each new assignment is independent 


- Each assignment is completely unpredictable 

- In the long run, number of patients assigned to each group should be about equal 

Simple randomization : Risks


- Imbalances 

Number of patients assigned/treatment group 

Confounding factor by treatment group 

Both lower statistical power 

- May diminish credibility of results 

- Inversely associated with number of participants 

Restricted randomization 

Scheme with constraints to produce expected assignment ratio according to time and/or speci!ed covariate(s) 

- Blocking 

- Strati!cation 

Restricted Rz: Blocking

Block is a list of treatment assignments that achieve the treatment allocation ratio, e.g., A:B = 1:1 

- Block of size 4: 2As and 2Bs 

- List of possible permutations: AABB, ABAB, ABBA, BABA, BAAB, BBAA 

Size of the smallest possible block is the sum of the integers de!ned in treatment allocation ratio 

- 2 for 1:1 ratio (1+1), 3 for 2:1 ratio (2+1) 

- Larger block sizes are multiples of smallest one: 3, 6, 9, 12 

All possible block sequences are randomly permuted (to arrange in all possible ways) 

- randomly chooses 1 of X permuted blocks 

Ensure balance of treatments over time 

Block is a list of treatment assignments that achieve the treatment allocation ratio, e.g., A:B = 1:1 

- Block of size 4: 2As and 2Bs 

- List of possible permutations: AABB, ABAB, ABBA, BABA, BAAB, BBAA 

Size of the smallest possible block is the sum of the integers de!ned in treatment allocation ratio 

- 2 for 1:1 ratio (1+1), 3 for 2:1 ratio (2+1) 

- Larger block sizes are multiples of smallest one: 3, 6, 9, 12 

All possible block sequences are randomly permuted (to arrange in all possible ways) 

- randomly chooses 1 of X permuted blocks 

Ensure balance of treatments over time 

How Many Blocks (Blk)?

Blocking Implementation

Fixed allocation ratio throughout trial 

It is a secret! 

- Block size(s) are on a need-to-know basis 

Use more than one block size 
- Overall sequence appears more random 
- Protects against discovery 
Especially in an unmasked trial 

Blocking Pros/Cons
- Overall balance, especially in smaller trials 
- Protects against time-related changes 
In the composition of study population 
Data collection procedures 
External forces being differential across treatment groups, i.e., secular trends, chronological bias 
- If trial is stopped early, have balanced groups 
- Analyses are more powerful 
- Can facilitate prediction of future assignments 
- More problematic for unmasked trials or poorly masked trials 

Restrictive Rz: Stratification

Ensure balance in treatment assignments within subgroups de!ned before Rz 
- Clinic, gender, risk level 
Subgroup should be related to outcome—strong confounder or effect modi!er 
Requires a separate set of treatment assignments schedules for each category of each stratum 

Stratification and Blocking Example

Practical Aspects of Stratification

Limit to a few (1–2) variables 

- Highly related to outcome 

- Logistical 

Typical ones 

- Clinic in a multicenter trial 

- Surgeon 

- Stage of disease 

- Demographic characteristics (gender, age) 

Too many strata may lead to imbalances in overall treatment group allocation

Adaptive Randomization—Definition

A process in which the probability of assignment to the treatments . . . does not remain constant, but is determined by the current balance and/or composition of the groups (Piantadosi, 1997) 

Minimization—after Rz the !rst patient, the treatment assignment that yields the smallest imbalance is chosen 

- Balance marginal treatment totals across prognostic factors 

- Handle many more factors than strati!cation 

Need to know their value before randomization 

- Allocation sequence can not be determined in advance 

Play the winner—change treatment allocation ratio to favor the better treatment based on the primary outcome 

- Preferentially assigns patients to better treatment 

- Need to evaluate outcomes relatively quickly 

May implement in stages 

저작자 표시 비영리 변경 금지

Phase I

- First stage in testing a new intervention in humans

- Usually 10-30 people

- Identify tolerable dose, provide information on drug metabolism, excretion, and toxicity 

- Often not controlled

Phase II

- Usually 30-100 people

- Preliminary information on efficacy, additional information on safety and side effects

Phase III

- Usually 100+ people

- Assess efficacy and safety

- Controlled, usually randomized

Parallel Design

- Simultaneous treatment and control groups

- Each person is randomly assigned to one treatment group

- Randomization removes treatment selection bias and promotes comparability of treatment groups

- Statistical comparisons made between treatment groups

Crossover Design

- Randomization of order in which treatments are received

- AB or BA

- Randomization promotes balance between treatment groups in timing of exposure

- Testing of both treatments in each patient

- Each patient serves as his/her own control

- Variability reduced because less variability within patient than between patients 

- Fewer patients needed


Crossover Design: Disadvantages

- Treatment can't have permanent effects or cures

- Potential carry-over effects of first-period treatment to second period

- Washout needs to be long enough

- Unequal carry-over effects

- Treatment during washout

- Test for period by treatment interactions not powerful

- Dropouts more significant

- Analysis may be more difficult

Crossover Design: Uses

- Constant intensity of underlying disease

- Chronic diseases-asthma, hypertension, arthritis

- Short-term treatment effects

- Relief of signs or symptoms of disease

- Metabolic, bioavailability, or tolerability studies

Crossover Design: Examples

- Evening-dose vs. morning-dosed travoprost in open-angle glaucoma for 24-hour intraocular pressure control

- Montelukast vs. salmeterol as adjuvant to inhaled fluticasone for exercise-induced asthma in children

- Topical oil vs. placebo for neuropathic pain

Group Allocation Design

- Also known as "cluster randomization"

- Randomization unit is a group of individuals (community, school, clinic)

- Individual randomization and intervention is not feasible or is unacceptable 

- Tracking

- Contamination

- If there is a correlation in the responses within a group, design loses some efficiency (more individual required)

Group Allocation Example: Sommer Vit A trial

- Population

- Preschool children in northern Sumatra in 1982-83

- Treatments

- Vitamin A supplementation during study

- Vitamin A supplementation after study

- Clusters

- Villages (450) selected using survey sampling method

- Each randomly allocated to one treatment

저작자 표시 비영리 변경 금지

DICOM 파일에 있는 pixel data는 matrix로 되어있으며 하나의 DICOM 파일은 brain의 slice 한장을 나타낸다. 

oro.dicom 패키지를 설치하면 R에서 DICOM 데이터를 직접 읽을 수 있다. 

setwd로 데이터파일이 있는 디렉토리를 지정해준다. 

> slice=readDICOM("IM-0001-0011.dcm")

> class(slice)

[1] "list"

DICOM 파일을 불러보면 2개의 elements가 있는걸 알 수 있는데 바로 DICOM header (hdr) 과 이미지 (img)이다. 

> names(slice)

[1] "hdr" "img"

> class(slice$hdr)

[1] "list"

> class(slice$hdr[[1]])

[1] "data.frame"

> class(slice$img)

[1] "list"

> class(slice$img[[1]])

[1] "matrix"

> dim(slice$img[[1]])

[1] 288 288

> d=dim(t(slice$img[[1]]))

> image(1:d[1],1:d[2],t(slice$img[[1]]),col=gray(0:64/64))

> slice$img[[1]][101:105,121:125]

     [,1] [,2] [,3] [,4] [,5]

[1,]    4   34   36   75  222

[2,]    9   44   33  117  248

[3,]   19   47   54  167  274

[4,]   27   28   98  239  286

[5,]   12   45  170  288  307

> hist(slice$img[[1]][,],breaks=50, xlab="FLAIR", prob=T, col=rgb(0,0,1,1/4),main="")

> hdr=slice$hdr[[1]]

> names(hdr)

[1] "group"    "element"  "name"     "code"     "length"   "value"    "sequence"

> hdr$name

  [1] "GroupLength"                              "FileMetaInformationVersion"              

  [3] "MediaStorageSOPClassUID"                  "MediaStorageSOPInstanceUID"              

  [5] "TransferSyntaxUID"                        "ImplementationClassUID"                  

  [7] "ImplementationVersionName"                "SourceApplicationEntityTitle"            

  [9] "SpecificCharacterSet"                     "ImageType"                               

 [11] "InstanceCreationDate"                     "InstanceCreationTime"                    

 [13] "InstanceCreatorUID"                       "SOPClassUID"                             

 [15] "SOPInstanceUID"                           "StudyDate"                               

 [17] "SeriesDate"                               "AcquisitionDate"                         

 [19] "ContentDate"                              "StudyTime"                               

 [21] "SeriesTime"                               "AcquisitionTime"                         

 [23] "ContentTime"                              "AccessionNumber"                         

 [25] "Modality"                                 "Manufacturer"                            

 [27] "InstitutionName"                          "ReferringPhysiciansName"                 

 [29] "StationName"                              "StudyDescription"                        

 [31] "ProcedureCodeSequence"                    "Item"                                    

 [33] "CodeValue"                                "CodingSchemeDesignator"                  

 [35] "CodeMeaning"                              "ContextGroupExtensionFlag"               

 [37] "ItemDelimitationItem"                     "SequenceDelimitationItem"                

 [39] "SeriesDescription"                        "InstitutionalDepartmentName"             

 [41] "ManufacturersModelName"                   "ReferencedStudySequence"                 

 [43] "Item"                                     "ReferencedSOPClassUID"                   

 [45] "ReferencedSOPInstanceUID"                 "ItemDelimitationItem"                    

 [47] "SequenceDelimitationItem"                 "ReferencedPerformedProcedureStepSequence"

 [49] "Item"                                     "InstanceCreationDate"                    

 [51] "InstanceCreationTime"                     "InstanceCreatorUID"                      

 [53] "ReferencedSOPClassUID"                    "ReferencedSOPInstanceUID"                

 [55] "InstanceNumber"                           "ItemDelimitationItem"                    

 [57] "SequenceDelimitationItem"                 "ReferencedImageSequence"                 

 [59] "Item"                                     "ReferencedSOPClassUID"                   

 [61] "ReferencedSOPInstanceUID"                 "ItemDelimitationItem"                    

 [63] "Item"                                     "ReferencedSOPClassUID"                   

 [65] "ReferencedSOPInstanceUID"                 "ItemDelimitationItem"                    

 [67] "Item"                                     "ReferencedSOPClassUID"                   

 [69] "ReferencedSOPInstanceUID"                 "ItemDelimitationItem"                    

 [71] "SequenceDelimitationItem"                 "PatientsName"                            

 [73] "PatientID"                                "PatientsBirthDate"                       

 [75] "PatientsSex"                              "PatientsWeight"                          

 [77] "PregnancyStatus"                          "ScanningSequence"                        

 [79] "SequenceVariant"                          "SliceThickness"                          

 [81] "RepetitionTime"                           "EchoTime"                                

 [83] "InversionTime"                            "NumberOfAverages"                        

 [85] "ImagingFrequency"                         "ImagedNucleus"                           

 [87] "EchoNumbers"                              "MagneticFieldStrength"                   

 [89] "SpacingBetweenSlices"                     "NumberOfPhaseEncodingSteps"              

 [91] "EchoTraInLength"                          "PercentSampling"                         

 [93] "PercentPhaseFieldOfView"                  "DeviceSerialNumber"                      

 [95] "SoftwareVersions"                         "ProtocolName"                            

 [97] "LowRRValue"                               "HighRRValue"                             

 [99] "IntervalsAcquired"                        "IntervalsRejected"                       

[101] "HeartRate"                                "ReconstructionDiameter"                  

[103] "ReceiveCoilName"                          "TransmitCoilName"                        

[105] "AcquisitionMatrix"                        "InPlanePhaseEncodingDirection"           

[107] "FlipAngle"                                "PatientPosition"                         

[109] "StudyInstanceUID"                         "SeriesInstanceUID"                       

[111] "StudyID"                                  "SeriesNumber"                            

[113] "AcquisitionNumber"                        "InstanceNumber"                          

[115] "ImagePositionPatient"                     "ImageOrientationPatient"                 

[117] "FrameOfReferenceUID"                      "TemporalPositionIdentifier"              

[119] "NumberOfTemporalPositions"                "SliceLocation"                           

[121] "SamplesperPixel"                          "PhotometricInterpretation"               

[123] "Rows"                                     "Columns"                                 

[125] "PixelSpacing"                             "PixelAspectRatio"                        

[127] "BitsAllocated"                            "BitsStored"                              

[129] "HighBit"                                  "PixelRepresentation"                     

[131] "WindowCenter"                             "WindowWidth"                             

[133] "LossyImageCompression"                    "RequestingService"                       

[135] "RequestedProcedureDescription"            "PerformedStationAETitle"                 

[137] "PerformedProcedureStepStartDate"          "PerformedProcedureStepStartTime"         

[139] "PerformedProcedureStepEndDate"            "PerformedProcedureStepEndTime"           

[141] "PerformedProcedureStepID"                 "PerformedProcedureStepDescription"       

[143] "PerformedProtocolCodeSequence"            "Item"                                    

[145] "CodeValue"                                "CodingSchemeDesignator"                  

[147] "CodeMeaning"                              "ContextGroupExtensionFlag"               

[149] "ItemDelimitationItem"                     "SequenceDelimitationItem"                

[151] "RequestAttributesSequence"                "Item"                                    

[153] "ScheduledProcedureStepDescription"        "ScheduledProcedureStepID"                

[155] "RequestedProcedureID"                     "ItemDelimitationItem"                    

[157] "SequenceDelimitationItem"                 "FilmConsumptionSequence"                 

[159] "SequenceDelimitationItem"                 "RequestedProcedureID"                    

[161] "PresentationLUTShape"                     "PixelData"                               

> hdr[hdr$name == "PixelSpacing", "value"]

[1] "0.79861110448837 0.79861110448837"

> hdr[hdr$name == "FlipAngle",]

    group element      name code length value sequence

107  0018    1314 FlipAngle   DS      4  90.0   

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'Neuroimaging > R' 카테고리의 다른 글

Data Structures and Operations  (0) 2016.10.14

USAGE: make_average_subject

Required Arguments

  • --subjects <subj1> <subj2> ... <subjN>

    • : or declare subjects in SUBJECTS env var
    --fsgd fsgdfile : get subject list from fsgd


Example 1:

  • make_average_subject --out avgsubject --subjects subj1 subj2 subj3 subj4

will create $SUBJECTS_DIR/avgsubject with average surfaces for orig, white, pial, inflated for each hemi. It will also create average volumes for orig, brain, and T1. Notice that the '--out avgsubject' is merely overriding the default output name 'average'.

Example 2:

  • setenv SUBJECTS = (subj1 subj2 subj3 subj4) make_average_subject --out avgsubject

will do the same as Example 1.

Example 3: check that the average subject volume aligns with the talairach subject:

  • tkregister2 --fstal --s avgsubject --mgz

Example 4: check that the average subject surfaces align with the volume:

  • tkmedit avgsubject orig.mgz lh.white

You should see that the surfaces more-or-less align with the folds. Remember this is talairach, so the volume will be blurry.


make_average_volume, make_average_surface, recon-all, make_final_surfaces, morph_subject

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'Neuroimaging > FreeSurfer' 카테고리의 다른 글

make_average_subject  (0) 2016.10.12

BACKGROUND: Neuroinflammation and white matter pathology have each been independently associated with schizophrenia, and experimental studies have revealed mechanisms by which the two can interact in vitro, but whether these abnormalities simultaneously co-occur in people with schizophrenia remains unclear. 

METHOD: We searched MEDLINE, EMBASE, PsycINFO and Web of Science from inception through 12 January 2014 for studies reporting human data on the relationship between microglial or astroglial activation, or cytokines and white matter pathology in schizophrenia. 

RESULTS: Fifteen studies totaling 792 subjects (350 with schizophrenia, 346 controls, 49 with bipolar disorder, 37 with major depressive disorder and 10 with Alzheimer's disease) met all eligibility criteria. Five neuropathological and two neuroimaging studies collectively yielded consistent evidence of an association between schizophrenia and microglial activation, particularly in white rather than gray matter regions. Ultrastructural analysis revealed activated microglia near dystrophic and apoptotic oligodendroglia, demyelinating and dysmyelinating axons and swollen and vacuolated astroglia in subjects with schizophrenia but not controls. Two neuroimaging studies found an association between carrier status for a functional single nucleotide polymorphism in the interleukin-1β gene and abnormal white as well as gray matter volumes in schizophrenia but not controls. A neuropathological study found that orbitofrontal white matter neuronal density was increased in schizophrenia cases exhibiting high transcription levels of pro-inflammatory cytokines relative to those exhibiting low transcription levels and to controls. Schizophrenia was associated with decreased astroglial density specifically in subgenual cingulate white matter and anterior corpus callosum, but not other gray or white matter areas. Astrogliosis was consistently absent. Data on astroglial gene expression, mRNA expression and protein concentration were inconsistent. 

CONCLUSION: Neuroinflammation is associated with white matter pathology in people with schizophrenia, and may contribute to structural and functional disconnectivity, even at the first episode of psychosis. 

3.3. Cytokines 

Two neuropathological studies and three neuroimaging studies (349 subjects) yielded data on the relationship between cytokines and white matter pathology (see Table 3). One study found a significant positive correlation between microglial density and interleukin-1β (IL-1β) mRNA expression in the dorsolateral prefrontal white – but not gray matter among schizophrenia subjects (Fillman et al., 2013). In a follow-up analysis, the density of neuronal nuclear antigen (NeuN)-immunoreactive orbitofrontal white matter neurons was almost 50% higher in schizophrenia subjects with high transcription levels of interleukin (IL)-6, IL-8, IL-1β, and serpin peptidase inhibitor, clade A (α-1 antiproteinase, antitrypsin) and member 3 (SERPINA3) (henceforth “high-inflammation subgroup”) compared with controls. Significant differences were also observed in the density of NeuN-immunoreactive dorsolateral prefrontal white matter neurons (18.9% higher in the high-inflammation subgroup compared with controls), and the density of NeuN-immunoreactive orbitofrontal, but not dorsolateral prefrontal, white matter neurons (14.9% higher in the low-inflammation subgroup compared with controls, and 25.0% higher in the high-inflammation subgroup compared with the low-inflammation subgroup). The density of glutamic acid decarboxylase 65/67 kDa GAD65/67)-immunoreactive orbitofrontal, but not dorsolateral prefrontal, white matter neurons was also significantly higher in both high-inflammation and low-inflammation subgroups compared with controls; however, there was no statistically significant difference between the high-inflammation and low-inflammation subgroups. Among the high-inflammation subgroup, a significant negative correlation was also observed between the densities of NeuN-immunoreactive white matter neurons and GAD67-immunoreactive gray matter neurons in the dorsolateral prefrontal region (Fung et al., 2014). Two studies assessed gray and white matter volumes of schizophrenia and control participants in relation to carrier status for a functional single nucleotide polymorphism in the IL-1β gene, which involves a C–T transposition at position − 511. Participants were genotyped to es- tablish whether they were allele 2 (A2) carriers (genotype T/T or C/T) or A1 non-carriers (genotype C/C). In the first study, A2 carrier subjects with schizophrenia had significantly decreased total white matter, fron- tal gray matter, and temporal gray matter volumes compared with A2 carrier controls (Meisenzahl et al., 2001). The second study found significantly increased volumes of frontal white but not gray matter, occipital white matter, bilateral arcuate fasciculi and left inferior tempo- ral gray matter in the A2 carrier schizophrenia subjects compared with A1 non-carrier schizophrenia subjects. Gray matter volumes in the right lateral prefrontal cortex, thalamus, insula and right medial temporal lobe were significantly lower among schizophrenia subjects compared with controls. Gray and white matter volumes did not differ significantly between A2 versus A1 controls in any regions (Maitra et al., 2009). A conventional DTI study found significantly decreased FA in the superior longitudinal, arcuate and uncinate fasciculi and forceps minor among those with schizophrenia compared with controls. A significant positive correlation was also observed between serum concentrations of IL-6, but not C-reactive protein, and FA in the forceps minor among those with schizophrenia (Prasad et al., 2013).

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Objective Becauseofthepressurefortimely,informeddecisionsinpublichealthand clinical practice and the explosion of information in the scientific literature, research results must be synthesized. Meta-analyses are increasingly used to address this prob- lem, and they often evaluate observational studies. A workshop was held in Atlanta, Ga, in April 1997, to examine the reporting of meta-analyses of observational studies and to make recommendations to aid authors, reviewers, editors, and readers.

Participants Twenty-sevenparticipantswereselectedbyasteeringcommittee,based on expertise in clinical practice, trials, statistics, epidemiology, social sciences, and biomedi- cal editing. Deliberations of the workshop were open to other interested scientists. Fund- ing for this activity was provided by the Centers for Disease Control and Prevention.

Evidence Weconductedasystematicreviewofthepublishedliteratureonthecon- duct and reporting of meta-analyses in observational studies using MEDLINE, Educa- tional Research Information Center (ERIC), PsycLIT, and the Current Index to Statistics. We also examined reference lists of the 32 studies retrieved and contacted experts in the field. Participants were assigned to small-group discussions on the subjects of bias, searching and abstracting, heterogeneity, study categorization, and statistical methods.

Consensus Process From the material presented at the workshop, the authors developed a checklist summarizing recommendations for reporting meta-analyses of ob- servational studies. The checklist and supporting evidence were circulated to all confer- ence attendees and additional experts. All suggestions for revisions were addressed.

Conclusions Theproposedchecklistcontainsspecificationsforreportingofmeta- analyses of observational studies in epidemiology, including background, search strat- egy, methods, results, discussion, and conclusion. Use of the checklist should improve the usefulness of meta-analyses for authors, reviewers, editors, readers, and decision makers. An evaluation plan is suggested and research areas are explored.

Table. A Proposed Reporting Checklist for Authors, Editors, and Reviewers of Meta-analyses of Observational Studies

Reporting of background should include 

  • Problem definition
  • Hypothesis statement
  • Description of study outcome(s)
  • Type of exposure or intervention used 
  • Type of study designs used
  • Study population

Reporting of search strategy should include

  • Qualifications of searchers (eg, librarians and investigators)
  • Search strategy, including time period included in the synthesis and keywords
  • Effort to include all available studies, including contact with authors
  • Databases and registries searched
  • Search software used, name and version, including special features used (eg, explosion) 
  • Use of hand searching (eg, reference lists of obtained articles)
  • List of citations located and those excluded, including justification
  • Method of addressing articles published in languages other than English
  • Method of handling abstracts and unpublished studies
  • Description of any contact with authors

Reporting of methods should include

  • Description of relevance or appropriateness of studies assembled for assessing the hypothesis to be tested
  • Rationale for the selection and coding of data (eg, sound clinical principles or convenience) 
  • Documentation of how data were classified and coded (eg, multiple raters, blinding, and interrater reliability)
  • Assessment of confounding (eg, comparability of cases and controls in studies where appropriate)
  • Assessment of study quality, including blinding of quality assessors; stratification or regression on possible predictors of study results
  • Assessment of heterogeneity
  • Description of statistical methods (eg, complete description of fixed or random effects models, justification of whether the chosen models account for predictors of study results, dose-response models, or cumulative meta-analysis) in sufficient detail to be replicated 
  • Provision of appropriate tables and graphics

Reporting of results should includeReporting of discussion should include

  • Graphic summarizing individual study estimates and overall estimate 
  • Table giving descriptive information for each study included
  • Results of sensitivity testing (eg, subgroup analysis)
  • Indication of statistical uncertainty of findings

Reporting of discussion should include

  • Quantitative assessment of bias (eg, publication bias)
  • Justification for exclusion (eg, exclusion of non–English-language citations) 
  • Assessment of quality of included studies

Reporting of conclusions should include

  • Consideration of alternative explanations for observed results
  • Generalization of the conclusions (ie, appropriate for the data presented and within the domain of the literature review) Guidelines for future research 
  • Disclosure of funding source 

저작자 표시 비영리 변경 금지

What Is a Clinical Study?

A clinical study involves research using human volunteers (also called participants) that is intended to add to medical knowledge. There are two main types of clinical studies: clinical trials (also called interventional studies) and observational studies. ClinicalTrials.gov includes both interventional and observational studies.

  • Clinical Trials

    In a clinical trial, participants receive specific interventions according to the research plan or protocol created by the investigators. These interventions may be medical products, such as drugs or devices; procedures; or changes to participants' behavior, such as diet. Clinical trials may compare a new medical approach to a standard one that is already available, to a placebo that contains no active ingredients, or to no intervention. Some clinical trials compare interventions that are already available to each other. When a new product or approach is being studied, it is not usually known whether it will be helpful, harmful, or no different than available alternatives (including no intervention). The investigators try to determine the safety and efficacy of the intervention by measuring certain outcomes in the participants. For example, investigators may give a drug or treatment to participants who have high blood pressure to see whether their blood pressure decreases.

    Clinical trials used in drug development are sometimes described by phase. These phases are defined by the Food and Drug Administration (FDA).

    Some people who are not eligible to participate in a clinical trial may be able to get experimental drugs or devices outside of a clinical trial through an Expanded Access Program. See more information on expanded access from the National Library of Medicine.

  • Observational Studies

    In an observational study, investigators assess health outcomes in groups of participants according to a research plan or protocol. Participants may receive interventions (which can include medical products such as drugs or devices) or procedures as part of their routine medical care, but participants are not assigned to specific interventions by the investigator (as in a clinical trial). For example, investigators may observe a group of older adults to learn more about the effects of different lifestyles on cardiac health.

Who Conducts Clinical Studies?

Every clinical study is led by a principal investigator, who is often a medical doctor. Clinical studies also have a research team that may include doctors, nurses, social workers, and other health care professionals.

Clinical studies can be sponsored, or funded, by pharmaceutical companies, academic medical centers, voluntary groups, and other organizations, in addition to Federal agencies such as the National Institutes of Health, the U.S. Department of Defense, and the U.S. Department of Veterans Affairs. Doctors, other health care providers, and other individuals can also sponsor clinical research.

Where Are Clinical Studies Conducted?

Clinical studies can take place in many locations, including hospitals, universities, doctors' offices, and community clinics. The location depends on who is conducting the study.

How Long Do Clinical Studies Last?

The length of a clinical study varies, depending on what is being studied. Participants are told how long the study will last before they enroll.

Reasons for Conducting Clinical Studies

In general, clinical studies are designed to add to medical knowledge related to the treatment, diagnosis, and prevention of diseases or conditions. Some common reasons for conducting clinical studies include:

  • Evaluating one or more interventions (for example, drugs, medical devices, approaches to surgery or radiation therapy) for treating a disease, syndrome, or condition

  • Finding ways to prevent the initial development or recurrence of a disease or condition. These can include medicines, vaccines, or lifestyle changes, among other approaches.

  • Evaluating one or more interventions aimed at identifying or diagnosing a particular disease or condition

  • Examining methods for identifying a condition or the risk factors for that condition

  • Exploring and measuring ways to improve the comfort and quality of life through supportive care for people with a chronic illness

Participating in Clinical Studies

A clinical study is conducted according to a research plan known as the protocol. The protocol is designed to answer specific research questions and safeguard the health of participants. It contains the following information:

  • The reason for conducting the study

  • Who may participate in the study (the eligibility criteria)

  • The number of participants needed

  • The schedule of tests, procedures, or drugs and their dosages

  • The length of the study

  • What information will be gathered about the participants

Who Can Participate in a Clinical Study?

Clinical studies have standards outlining who can participate. These standards are called eligibility criteria and are listed in the protocol. Some research studies seek participants who have the illnesses or conditions that will be studied, other studies are looking for healthy participants, and some studies are limited to a predetermined group of people who are asked by researchers to enroll.

Eligibility. The factors that allow someone to participate in a clinical study are called inclusion criteria, and the factors that disqualify someone from participating are called exclusion criteria. They are based on characteristics such as age, gender, the type and stage of a disease, previous treatment history, and other medical conditions.

How Are Participants Protected?

Informed consent is a process used by researchers to provide potential and enrolled participants with information about a clinical study. This information helps people decide whether they want to enroll or continue to participate in the study. The informed consent process is intended to protect participants and should provide enough information for a person to understand the risks of, potential benefits of, and alternatives to the study. In addition to the informed consent document, the process may involve recruitment materials, verbal instructions, question-and-answer sessions, and activities to measure participant understanding. In general, a person must sign an informed consent document before joining a study to show that he or she was given information on the risks, potential benefits, and alternatives and that he or she understands it. Signing the document and providing consent is not a contract. Participants may withdraw from a study at any time, even if the study is not over. See the Questions to Ask section on this page for questions to ask a health care provider or researcher about participating in a clinical study.

Institutional review boards. Each federally supported or conducted clinical study and each study of a drug, biological product, or medical device regulated by FDA must be reviewed, approved, and monitored by an institutional review board (IRB). An IRB is made up of doctors, researchers, and members of the community. Its role is to make sure that the study is ethical and that the rights and welfare of participants are protected. This includes making sure that research risks are minimized and are reasonable in relation to any potential benefits, among other responsibilities. The IRB also reviews the informed consent document.

In addition to being monitored by an IRB, some clinical studies are also monitored by data monitoring committees (also called data safety and monitoring boards).

Various Federal agencies, including the Office of Human Subjects Research Protection and FDA, have the authority to determine whether sponsors of certain clinical studies are adequately protecting research participants.

Relationship to Usual Health Care

Typically, participants continue to see their usual health care providers while enrolled in a clinical study. While most clinical studies provide participants with medical products or interventions related to the illness or condition being studied, they do not provide extended or complete health care. By having his or her usual health care provider work with the research team, a participant can make sure that the study protocol will not conflict with other medications or treatments that he or she receives.

Considerations for Participation

Participating in a clinical study contributes to medical knowledge. The results of these studies can make a difference in the care of future patients by providing information about the benefits and risks of therapeutic, preventative, or diagnostic products or interventions.

Clinical trials provide the basis for the development and marketing of new drugs, biological products, and medical devices. Sometimes, the safety and the effectiveness of the experimental approach or use may not be fully known at the time of the trial. Some trials may provide participants with the prospect of receiving direct medical benefits, while others do not. Most trials involve some risk of harm or injury to the participant, although it may not be greater than the risks related to routine medical care or disease progression. (For trials approved by IRBs, the IRB has decided that the risks of participation have been minimized and are reasonable in relation to anticipated benefits.) Many trials require participants to undergo additional procedures, tests, and assessments based on the study protocol. These requirements will be described in the informed consent document. A potential participant should also discuss these issues with members of the research team and with his or her usual health care provider.

Questions to Ask

Anyone interested in participating in a clinical study should know as much as possible about the study and feel comfortable asking the research team questions about the study, the related procedures, and any expenses. The following questions may be helpful during such a discussion. Answers to some of these questions are provided in the informed consent document. Many of the questions are specific to clinical trials, but some also apply to observational studies.

  • What is being studied?

  • Why do researchers believe the intervention being tested might be effective? Why might it not be effective? Has it been tested before?

  • What are the possible interventions that I might receive during the trial?

  • How will it be determined which interventions I receive (for example, by chance)?

  • Who will know which intervention I receive during the trial? Will I know? Will members of the research team know?

  • How do the possible risks, side effects, and benefits of this trial compare with those of my current treatment?

  • What will I have to do?

  • What tests and procedures are involved?

  • How often will I have to visit the hospital or clinic?

  • Will hospitalization be required?

  • How long will the study last?

  • Who will pay for my participation?

  • Will I be reimbursed for other expenses?

  • What type of long-term follow-up care is part of this trial?

  • If I benefit from the intervention, will I be allowed to continue receiving it after the trial ends?

  • Will results of the study be provided to me?

  • Who will oversee my medical care while I am participating in the trial?

  • What are my options if I am injured during the study?

저작자 표시 비영리 변경 금지

'Clinical Research > Clinicaltrials.gov' 카테고리의 다른 글

What Is a Clinical Study?  (0) 2016.09.29

Chapter 7 단면조사 연구 및 코호트 연구의 설계

단면조사 연구(cross-sectional studies)

단면조사 연구의 장점과 단점 

- 단면조사 연구는 발병률이 아닌 유병률을 측정하므로, 어떤 질병의 원인, 예후, 자연적 경과에 대한 추론을 도출할 때 조심해야 한다. 

- 질병의 유병률과 연관된 요인은 그 질병의 원인일 수도 있지만, 그저 그 질병의 과정과 연관된 것일 수도 있다. 

순차적 설문조사

코호트 연구(cohort studies)

전향적 코호트 연구(prospective cohort studies)

전향적 코호트 연구의 장점과 단점 

후향적 코호트 연구(retrospective cohort studies)

후향적 코호트 연구의 장점과 단점

다중 코호트 연구(multiple-cohort studies)와 외부 대조군(external controls)

코호트 연구에 대한 통계학적 접근

기존 코호트 연구와 관련된 문제들 

표7.3 추적관찰 동안 손실을 취소화하기 위한 전략


 1. 다음과 같은 손실 가능성을 배제 

    a) 이사 계획이 있는 경우 

    b) 회신의 의지가 불분명한 경우 

    c) 연구 질문과 연관이 없는 병약 상태이거나 치명적인 질병이 있는 경우 

2. 향후 추적이 가능한 정보를 취합 

    a) 대상자의 주소, 전화번호, 이메일 주소 

    b) 주민등록번호/의료보험증 번호 

    c) 대상자와 동거 중이지 않은 친척이나 친구 한 두명의 이름, 주소, 전화번호, 이메일 주소 

    d) 주치의의 이름, 이메일, 주소, 전화번호 


 1. 정보를 수집하고, 결과를 제공하며, 관심을 표현하기 위해 대상자와 주기적으로 접촉 

    a) 전화: 주말과 저녁에 전화요망 

    b) 편지: 이메일, 우편, 반송용 카드를 이용하여 반복전달 

    c) 기타: 소식지, 상품권 

2. 전화나 우편으로 연락이 닿지 않는 경우 

    a) 친구, 친척, 주치의와 연락 

    b) 우체국에서 새주소 파악 

    c) 전화번호부, 인터넷, 신용조사기관등의 공적 자료를 통한 주소 파악 

    d) 의료보험 혜택을 받는 대상자의 경우, 사회보장국을 통해 병원 퇴원 기록을 수집 

    e) 보건복지부나 통계청 국가사망기록 등을 통해 생존여부 확인 


 1. 감사와 친절, 존경의 마음으로 연구대상자들을 대하고, 그들이 연구에 성공적인 파트너로 참여를 원할 수 있게 연구 질문을 이해하는 데 도움을 주어야 한다. 

저작자 표시 비영리 변경 금지

5. 표본크기 산출을 위한 준비: 가설과 기본 원칙


좋은 가설의 특징

단순함 vs 복잡함

특정함 vs 모호함

사전(in-advance) vs 사후(after-the-fact)

귀무가설과 대립가설 

기본통계의 원칙 


대립가설의 측면

통계적 검정의 종류 

추가적으로 검토할 사항 


다중, 사후가설 

1차 및 2차 가설 


1. 분석적 및 서술적 연구에서 표본 크기를 정하는 것은 중요한 과정이다. 연구 설계과정 초기에 표본크기를 정해야 적절히 필요한 수정을 할 수 있다. 

2. 분석적 연구와 실험에서는 주요 예측변수와 결과변수간의 예상되는 연관성에 대하여 명시하는 가설을 수립해야 통게적 검정을 수행할 수 있다. 완전히 서술적인 연구는 비교기법이 없으므로 가설이 필요없다. 

3. 좋은 가설은 상세하게 모집단으로부터의 표본추출방법, 변수 측정법을 명시해야하며, 단순한 가설로(단 하나의 예측변수와 단 하나의 결과변수만 있는 경우), 사전에 미리 수립해야한다.

4. 귀무가설은 예측변수와 결과변수가 서로 연관되어있지 않다고 가정하며 통계적 유효도 검정의 기반이 된다. 대립가설은 예측 변수와 결과변수가 서로 연관되어있다고 가정한다. 통계적 검정을 통해 연관성이 없음을 가정하는 귀무가설을 부정하고 연관성이 있음을 주장하는 대립가설을 수용하려 시도하게 된다. 

5. 대립가설은 단측(연관성의 한 쪽 방향만 검정함) 혹은 양측(양쪽 방향을 모두 검정함)일 수 있다. 단측가설은 연관성의 한 쪽 방향만 임상적으로나 생물학적으로 의미가 있는 매우 특이한 경우에만 사용해야 한다. 

6. 분석적 연구 및 실험에 있어서 표본크기란, 주어진 효과크기(effect size)와 분산(variability) 상황에서 제 1종(위양성) 및 제 2종(위음성) 오류를 범할 가능성을 일정부분 지닌 채, 연관성을 찾아내기 위해 필요한 피험자의 수이다. 제 1종 오류를 범할 최대 확률을 α 라고 부르며, 제 2종 오류를 범할 최대 확율은 β 라고 한다. 1에서 β를 뺀 값(1-β)을 검정력(power)이라 하며 이는 모집단 내에 실제로 연관성이 있을 경우, 표본에서 주어진 효과크기 혹은 그 이상의 연관성을 찾아낼 수 있는 가능성을 의미한다.

7. 하나이상의 가설을 미리 수립하면 바람직한 경우가 많다. 그러나 연구자는 단일한 1차 가설을 정하여 표본 크기는 이를 기준으로 산출해야 한다. 데이터로부터 얻어지는 예상치 못했던 결과물을 포함하여 표본 내의 다중 가설을 검정하여 얻는 결과물을 분석하는 과정은, 그 결과물들이 모집단 내에서 일어나는 현상을 설명하는 사전확률(prior probability)에 대한 판단에 근거한다. 

6. 표본 크기의 산출과 검정력: 응용과 사례 

분석적 연구 및 실험을 위한 표본 크기의 기법

t 검정

카이제곱 검정


기타 고려사항 및 특수한 문제


범주형 변수

생존 분석



다변량 조정 및 기타 특수한 통계분석 

동등성 시험 및 비열등성 시험 

서술적 연구를 위한 표본크기 기법 


이분형 변수 

표본크기가 고정되어 있는 경우 

표본 크기를 최소화하고 검정력을 최대화하기 위한 전략 

연속형 변수의 사용

쌍체 측정법(paired measurements)의 사용

간략한 기술적 언급

정밀도가 높은 변수의 사용

크기가 동일하지 않은 집단의 사용

발현율이 높은 결과를 사용

정보가 충분치 않을 때 표본 크기를 산출하는 방법 

피해야 할 흔한 실수들 


1. 분석적 연구에서 표본크기를 구할 때는 다음 단계를 수행하라

a) 귀무가설과 대립가설을 수립하라. 양측인지 단측인지 명시하라

b) 데이터를 분석할 때 쓸 수 있는 통계적 검정을 선택하라, 예측 변수와 결과 변수의 종류에 근거한다. 

c) 제 1종 및 제 2종 오류를 범하지 않기 위한 중요도를 감안하여 α와 β를 정하라 

2. 분석적 연구에서 표본 크기를 산출할 때 고려할 사항으로는 발생 가능한 탈락분에 대한 조정, 범주형 변수 처리 전략, 생존분석, 군집표본, 다변량 조정, 동등성연구가 있다. 

3. 가설이 필요치 않은 서술적 연구에서 표본크기를 산출하는 방법은 다음 단계를 따른다.

a) 이분적 결과물을 갖는 피험자의 비율이나 연속적 결과물의 표준편차를 구한다.

b) 원하는 정밀도(신뢰구간의 간격)을 정한다.

c) 신뢰도(예를들어 95%)를 정한다. 

4. 표본크기가 미리 결정되어 있다면, 역방향으로 작업하여 검출가능한 효과크기를 산출한다. 혹은 비교적 드문 경우지만 검정력을 산출한다.

5. 연속변수, 보다 정밀한 측정법, 쌍체측정법, 크기가 동일하지 않은 집단, 빈도가 높은 결과물을 활용하면 필요한 표본 크기를 최소화할 수 있다.

6. 표본크기를 산출하기 위한 정보가 부족한 경우, 관련분야의 문헌을 검토하고 동료들에게 자문을 구하여 임상적으로 의미있는 효과크기를 선택하여야 한다.

7. 피해야할 오류들은 다음과 같다. 표본크기를 너무 늦게 산출하는 것; 백분율로 표시된 비율을 연속형으로 잘못 해석하는 것; 빠진 피험자와 데이터를 고려하지 않는 것; 군집 데이터와 쌍을 이룬 데이터를 적절히 설명하지 않는 것. 

저작자 표시 비영리 변경 금지

UCSF의 Biostatistics department에서 내놓은 임상연구디자인 4판. 

과거 날림으로 읽었던 책인데 오늘부터 시간날 때 마다 조금씩 정리해보기로.. 

ref: http://www.dcr-4.net

챕터 1: 임상연구의 모든 것

연구의 구성 

- 연구질문 

- 배경과 중요도 

- 설계 

- 연구대상자 

- 변수 

- 통계적 이슈

연구의 운영  

: 임상연구의 목표는 자연현상에 대한 연구에서 얻어지는 발견사항으로부터 결론을 이끌어 내는 것 

1) 내적타당성 (internal validity) - 연구자가 실제 연구의 결과들로부터 올바른 결과를 이끌어내는 정도 

2) 외적타당성 (external validity) - 이끌어낸 결론이 연구대상 이외의 일반 대중 및 사건들에 적절하게 적용될 수 있는 정도 (일반화가능성 generalizability)

: 추론과정을 위협하는 무작위 오류(우연)와 계통 오류 (치우침) 을 제어할 수 있도록 설계하는 것이 중요 

- 연구설계 

- 연구실행 

- 인과관계 추론 (Causal Inference)

- 연구의 오류


- 연구계획 

- 손익 분석 (Trade-offs)

챕터 2: 연구질문 및 연구계획 

연구 질문의 시작 

- 기존 연구에 대한 완벽한 이해

- 새로운 아이디어와 기술에 대한 적극적인 수용 

- 늘 상상력을 발휘하라 

- 멘토 구하기 

훌륭한 연구 질문의 특성 (FINER)

- 실행가능성 (feasible)

- 흥미 (interest)

- 참신성 (novel)

- 윤리성 (ethical)

- 적절성 (relevant)

연구질문 및 계획안의 개발 

: 초기에 연구질문을 1쪽 분량의 연구개요로 작성해야한다. 연구개요는 필요한 피험자의 수, 피험자 선정방법, 측정항목 등을 상세하게 기술한다. 

- 문제점과 접근법 

- 일차질문과 이차질문 

중계연구 (Translational research)

- 실험실 연구에서 임상연구로의 중계 

- 임상연구에서 모집단연구로의 중계 

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