ricerca efficiente e sostenibile …his research interests focus on under-standing how people change...

RICERCAEFFICIENTEESOSTENIBILESULL’EFFICACIADELLE PSICOTERAPIEPSICODINAMICHE:

L’ESPERIENZA DELL’ANALISI TRANSAZIONALEEnricoBenelli

FISPPADipartimentodiFilosofia,Sociologia,PedagogiaePsicologiaApplicata

L’analisi transazionale nellacomunità scientifica della ricercainpsicoterapia


Analisi Transazionale

• Modello diffuso intutto il mondo• Piu di7,000clinici associati inEuropa• Haispirato diversi modelli dipsicoterapia empiricamente supportati(senza essere citata,e.g.Schematherapy)• Supportata dauna ampia letteratura clinica (TAJ,IJTARP)• Nonancora pienamente riconosciuta comeEmpiricallySupportedTreatmentperi CommonMentalDisorders(e.g.Depression,Anxiety)

International Journal of Transactional Analysis Research Vol 1 No 1, July 2010 www.ijtar.org

Scientific evidence base for transactional analysis in the year 2010

© 2010 Thomas Ohlsson

Abstract The International Journal of Transactional Analysis Research, IJTAR, has been created to stimulate research and support the continued effort to build a scientific evidence base for transactional analysis (TA). This article is an attempt to locate the starting point for the journal, to identify, evaluate and draw conclusions from what has already been done, and to articulate the existing scientific evidence base for TA in the year 2010.

Key Words Transactional analysis, research, psychotherapy

Aims One purpose of this article is to facilitate new research by making a comprehensive list of existing TA-research available. It has been possible to identify 326 studies between 1963 (Albert Hall on prediction of interpersonal behaviour) and 2010 (including two of those appearing in this issue of IJTAR). The reference list of 326 studies constitutes the bulk of the article. Another purpose is to make the present scientific knowledge about TA visible and understandable. Each included study represents substantial investment of time and scholarship (often years of academic work), and each one deserves careful reading and thought to grasp its conclusions, a task far beyond the ambitions of this article. However, some observations will be presented, especially pertaining to the question of the effectiveness of TA psychotherapy. Generally it may be stated that there already exists a substantial scientific evidence base supporting the usefulness of TA theory and methods in several fields of application, including psychotherapy.

Method In a first step a comprehensive list of references, called the Big List, was created. General inclusion criteria were

that the studies likely were conducted and/or approved by trained PhD level researchers, that TA was a major research focus, and that the studies were published. It was assumed that trained researchers have the necessary skills to use appropriate research designs. While the intention was to make an all-inclusive list, it is recognized, with apologies, that qualified existing research may have been omitted due to inadequate search strategies and efforts. This may be particularly true for research in languages other than English and Swedish. The Big List was compiled from several sources:

1. In 1981 Barbara Wilson made a review of all TA research listed in the Dissertation Abstracts International before December 1980. She presented her analysis and also included a reference list organized according to eleven areas of investigation in the Transactional Analysis Journal. Between 1963 and 1980 altogether 124 doctoral dissertations on TA were written and approved, almost all of them at universities in the United States. Although Wilson gave sufficient identification details, author information was omitted, making recognition somewhat difficult. Through cross checking with other available lists it was possible to identify 48 of “Wilson’s” studies by author. These studies are included in part one of the present list, which is alphabetically organized according to author. The remaining 76 studies appear in part two, which keeps Wilson’s original organization principle.

2. When starting his own dissertation work in the mid 1980s the writer began to compile TA research references from data base searches and other sources. This work was intensified during attempts to get TA therapy officially recognized by the Swedish government in the late 1980s. The resulting lists contained references not available to Wilson.

3. Recently Khalil (2007) searched electronic databases and other sources for evidence of outcomes of TA and identified 97 studies.

Ohlsson,2010

• Khalil(2007)conduceuna reviewsull’efficacia dell’AT eidentifica 97studi.• Khalil,E.,Callaghan,P.,James,N.(2007).Transactionalanalysis:Ascopingexerciseforevidenceofoutcome.ReportpreparedfortheBerneInstitute.TheUniversityofNottingham,SchoolofNursing.

• Khalil(p.20)concluse che perlapsicoterapia AT“theevidence-baseremainsscantandofrelativelypoorquality”

Psychotherapy in 2022: A Delphi Poll on Its Future

John C. Norcross and Rory A. PfundUniversity of Scranton

James O. ProchaskaUniversity of Rhode Island

Repeating and expanding Delphi polls conducted during the past 30 years, the authors empaneled 70psychotherapy experts to forecast psychotherapy trends in the next decade. Mindfulness, cognitive–behavioral, integrative, and multicultural theories were predicted to increase the most, whereas Jungiantherapy, classical psychoanalysis, and transactional analysis were expected to decline the most. Tech-nological, self-change, skill-building, and relationship-fostering interventions were judged to be in theascendancy. Internet programs, telephone therapy, and master’s-level professionals were expected toflourish. Forecast scenarios with the highest likelihood centered on expansion of telepsychology,evidence-based practice, pharmacotherapy, and masters-degree practitioners flooding the job market.Four themes seem to be driving these changes: technology, economy, evidence, and ideas.

Keywords: psychotherapy, future of psychology, Delphi poll, psychologists, theoretical orientations,evidence-based practice

What might psychotherapy look like in the next decade? Whereare the growth opportunities and the probable dinosaurs for psy-chologists? How can seasoned practitioners, early career psychol-ogists, and graduate students best prepare themselves for thatevolving future? What will prove hot—and not—in 2022?

As we transition from the industrial era to an information era, itis imperative that we remain knowledgeable of how changes willimpact psychotherapy, psychologists, and our patients (Lesse,1987). The existence of time and the laws of physics make thefuture inevitable, and it will prove advantageous to reflect on andplan for where psychotherapy is heading. What does the future holdin terms of theories, methods, providers, formats, and developments?All of us—practitioners, educators, researchers, policymakers, andstudents—have a profound stake in the future of the discipline.

Every 10 years, starting in 1980 (Norcross, Alford, & DeMi-chele, 1992; Norcross, Hedges, & Prochaska, 2002; Prochaska &Norcross, 1982), we have conducted a Delphi poll on the future ofpsychotherapy. The 36 experts in the initial poll anticipated avariety of changes in psychotherapy, such as the shift in theoreticalorientation from psychoanalytic to cognitive–behavioral and thereplacement of long-term therapy with briefer therapy. Their op-timistic and correct forecasts included an increase in female andethnic minority therapists as well as accelerated services to under-served populations, but they erroneously foresaw the establishmentof national health insurance. The 75 experts in our second Delphipoll, 10 years later, opined that self-help groups and social workerswould proliferate and that the proportion of psychotherapy pro-vided by psychiatrists would diminish. In 2001, a panel of 62

JOHN C. NORCROSS, PhD, ABPP, is Distinguished Professor of Psychologyat the University of Scranton, Adjunct Professor of Psychiatry at SUNYUpstate Medical University, and a board-certified clinical psychologist inpart-time independent practice. His most recent books are Self-Help thatWorks, Changeology, Psychotherapy Relationships That Work, and Insid-er’s Guide to Graduate Programs in Clinical and Counseling Psychology.RORY A. PFUND, BS, is a research assistant in the Department of Psychol-ogy at the University of Scranton. His research interests focus on under-standing how people change their addictive and health-related behaviors.JAMES O. PROCHASKA, PhD, is Professor of Psychology and director of theCancer Prevention Research Consortium at the University of Rhode Island.His 50 book chapters and over 250 scholarly articles focus on self-change,health promotion, and psychotherapy from a transtheoretical perspective,the subject of his coauthored textbook, Systems of Psychotherapy: ATranstheoretical Analysis and his self-help book, Changing for Good (withNorcross and DiClemente).IT IS STANDARD PRACTICE in a Delphi poll to acknowledge the panel ofexperts. We are indebted to the following 63 panelists who allowed us toreport their names and acknowledge their participation: Norman Abeles,PhD; Diane B. Arnkoff, PhD; Alan S. Bellack, PhD; Larry Beutler, PhD;Laura S. Brown, PhD; Ronald T. Brown, PhD; Robin Cautin, PhD; David

R. Chabot, PhD; Paul Crits-Cristoph, PhD; Lillian Comas-Diaz, PhD; RayDiGiuseppe, PhD; Paul M. G. Emmelkamp, PhD; Barry Farber, PhD; GaryM. Farkas, PhD; Dan Fishman, PhD; Donald K. Freedheim, PhD; BeverlyFunderburk, PhD; Carol R. Glass, PhD; Marv Goldfried, PhD; Paul Gray-son, PhD; Alan S. Gurman, PhD; Gillian Hardy, PhD; Clara E. Hill, PhD;Mark J. Hilsenroth, PhD; Stefan G. Hofmann, PhD; Steve Hollon, PhD; MardiHorowitz, MD; Judith V. Jordan, PhD; Ellyn Kaschak, PhD; Nadine J.Kaslow, PhD; Elizabeth A. Klonoff, PhD; Gerald P. Koocher, PhD; MichaelJ. Lambert, PhD; Arnold A. Lazarus, PhD; Stanley Messer, PhD; J. Christo-pher Muran, PhD; Peter E. Nathan, PhD; Greg Neimeyer, PhD; Arthur M.Nezu, PhD; Spencer G. Niles, EdD; Penelope Norton, PhD; David Orlinsky,PhD; Fred Piercy, PhD; Mark B. Powers, PhD; Andrew Reeves, PhD; EmilRodolfa, PhD; Ronald H. Rozensky, PhD; Shawn Rubin, PsyD; MorganSammons, PhD; Jack Schaffer, PhD; Golan Shahar, PhD; George Stricker,PhD; Stanley Sue, PhD; Jennifer M. Taylor; Patrick H. Tolan, PhD; Gary R.VandenBos, PhD; Bruce E. Wampold, PhD; Carol Webb, PhD; Danny Wed-ding, PhD; Maureen Whittal, PhD; Barry E. Wolfe, PhD; David Wolitzky,PhD; and Everett Worthington, PhD.CORRESPONDENCE CONCERNING THIS ARTICLE should be addressed to John C.Norcross, PhD, ABPP, Department of Psychology, University of Scranton,Scranton, PA 18510-4596. E-mail: [email protected]

Thi

sdo

cum

ent

isco

pyri

ghte

dby

the

Am

eric

anPs

ycho

logi

cal

Ass

ocia

tion

oron

eof

itsal

lied

publ

ishe

rs.

Thi

sar

ticle

isin

tend

edso

lely

for

the

pers

onal

use

ofth

ein

divi

dual

user

and

isno

tto

bedi

ssem

inat

edbr

oadl

y.

Professional Psychology: Research and Practice © 2013 American Psychological Association2013, Vol. 44, No. 5, 363–370 0735-7028/13/$12.00 DOI: 10.1037/a0034633

363

Comeè percepita l’AT nella comunitàscientifica della ricerca inpsicoterapia?

and medical schools; it is possible that the predictions were slantedtoward teaching, training, and gatekeeping functions (e.g., journalediting) as opposed to practice considerations. Our analyses didnot reveal any pattern of consistent differences due to employmentsetting, however. Fourth, our experts’ predictions do not reflectabsolute changes but rather relative increases and decreases. Atheoretical orientation or a clinical method could increase substan-tially in the next decade but still not be a common event. Fifth, oursample included only one psychiatrist; these are forecasts by andfor psychologists. And sixth, readers should be mindful of thepitfalls of prediction and probability. Many predictions fail be-cause of a poor understanding of probability and uncertainty;overconfidence is often the reason for inaccuracy (Silver, 2012).

The following tables present the item means and standard de-viations from both data waves, but the items are rank-ordered interms of the results of the second wave. Lines in the tables dividethe items into three rationally created categories: those items theexperts expect to increase (item mean of 4.5 and greater), thoseitems predicted to remain about the same (mean ranging from 3.5to 4.49), and those predicted to decrease (mean of 3.49 and less) inthe next decade.

Theoretical Orientations

Our experts rated the extent to which a variety of psychotherapysystems will be used over the next decade. As presented in Figure1, 13 systems were predicted to increase, 8 to stay about the same,and 10 to decrease. Those orientations expected to increase themost were mindfulness, cognitive–behavioral, integrative, multi-cultural, motivational interviewing, dialectical behavior, eclectic,and exposure therapies. By contrast, transactional analysis, Adle-

rian therapy, Jungian therapy, and classical psychoanalysis wereexpected to decrease the most.

We repeatedly emphasized that panelists should predict whatwould happen rather than what they would like to happen. But,in addition to being experts, our observers might be prone topresent their own preferred theories in a more favorable light.To investigate the possibility of a rating bias due to theoreticalorientation, we compared the average predictions of endorsersof three superordinate orientations: integrative/eclectic (n !20), cognitive– behavioral (n ! 18), and insight-oriented (psy-chodynamic, psychoanalytic, and humanistic; n ! 11). Panelistswho identified themselves as insight-oriented rated the future ofpsychodynamic therapy significantly more favorably (M of 4.60vs. 2.83 and 4.29, p " .05) than did the cognitive– behavioristsand integrative/eclectics. Integrative/eclectic therapists, in turn,rated the future of integrative therapy significantly higher (M of5.69 vs. 4.72, p " .05) than did the insight-oriented. However,no differential ratings were made on the future of cognitive–behavioral therapy. When we compared ratings on the 31 the-oretical orientations, we determined that only six were statisti-cally different due to the experts’ orientations. Thus, there wasrobust convergence in predictions but modest allegiance biaswith regard to the experts’ favored theories.

Therapeutic Interventions

As Table 1 shows, 19 of the 45 listed interventions werepredicted to increase in the next decade. Computer technology(online self-help, smartphone applications, virtual reality, socialnetworking interventions), client self-change (self-help re-sources, bibliotherapy, self-help techniques, self-control proce-

Figure 1. Predicted changes in theoretical orientations in rank order. 1 ! great decrease, 4 ! remain the same,7 ! great increase. EMDR ! eye movement desensitization and reprocessing.

Thi

sdo

cum

ent

isco

pyri

ghte

dby

the

Am

eric

anPs

ycho

logi

cal

Ass

ocia

tion

oron

eof

itsal

lied

publ

ishe

rs.

Thi

sar

ticle

isin

tend

edso

lely

for

the

pers

onal

use

ofth

ein

divi

dual

user

and

isno

tto

bedi

ssem

inat

edbr

oadl

y.

365PSYCHOTHERAPY IN 2022

Council conclusions on'TheEuropean Pact forMental HealthandWell-being:results andfutureaction'

• EUCouncil(6giugno 2011)3095thEMPLOYMENT,SOCIALPOLICY,HEALTHandCONSUMERAFFAIRS

• Councilconclusionson“TheEuropeanPactforMentalHealthandWell-being:resultsandfutureaction”.• Retrievedfromhttp://www.consilium.europa.eu/uedocs/cms_data/docs/pressdata/en/lsa/122389.pdf

• Articolo 22:“...toidentifyevidencebasedbestpolicyapproachesandpracticesandanalyse activitiesinparticularinthefollowingareas:• Tacklingmentaldisordersthroughhealthandsocialsystems;• Takingevidencebasedmeasuresagainstdepression;

Comeè riconosciuta l’AT nei SSNeuropei?

• Svezia:esclusa dalla lista dei trattamenti riconosciuti negli anni 90• GranBretagna:noncontemplata dalle NICEGuidelinesperladepressione• Germania:nonhaottenuto perlaseconda volta il riconoscimentoministeriale• Svizzera:il paradigma nonè stato riconosciuto elescuole diATsonostatechiuse nel 2018• Spagna:nel 2019è stata equiparata alle pseudoscienze comel’omeopatia ed è stata avviata una procedura divalutazione delleprovediefficacia

LivellidievidenzarichiestidalSSNsvizzero:Leichsenring &Rueger Criteria

Level ofevidence

Question 1:Efficacystudies(laboratory,experimental)

Question2:Effectivenessstudies(fields,observational)

Level1 1. Prospective studiesofarandomized-controlledtype;

2. Randomizedcontrolgroup;3. Blindrating;clearinclusion

andexclusioncriteria;Currentdiagnosticmethods;

4. adequatesamplesizewithrespecttothetestreliability;

5. statisticalmethodsclearlydescribed

1. Naturalistic,quasi-experimentalprospective studies;2. non-randomizedcontrolgroup(e.g.,matching,

stratification);3. Blindrating;clearinclusionandexclusioncriteria;Current

diagnosticmethods;4. adequatesamplesizewithrespecttothetestreliability;5. statisticalmethodsclearlydescribed;6. Guaranteeofinternalvalidity(e.g.,additionaldesign

elements,predictionofcomplexpatternsattheresultslevel);

7. Clinicallyrepresentativeanddefinedtreatments;8. Patientwithdefineddisorders

Level2 Clinicalstudiesmissingsomelevel1characteristics (e.g.,withoutdoubleblindingorwithoutrandomization)

Clinicalstudies missacontrolgroupbutmeettheessentialcriteriaofLevel1studies

(Leichsenring &Rueger,2004)

Level ofevidence

Question 1:Efficacystudies Question2:Effectiveness

Level3 Openpilot studyorcasecontrolstudiesinwhichtheoutcomewascollectedafterthetreatment

Clinicalstudiesmissingseveralcharacteristics ofthelevel1(e.g.,without:pre-inquiry,comparisongroup,blindrating)

Level 4 Reviewswith secondarydataanalysis

Reviewswith secondarydataanalysis

Level 5 Reviewswithoutsecondarydataanalysis

Reviewswithoutsecondarydataanalysis

Level6 Casestudies,essays,opinionarticles

Casestudies,essays,opinion articles

Empirically Supported Treatment:comeesserericonosciuti


Cosasignifica “empiricalsupport”?

TobecomeanESTweneed...

“onlywhenatreatmenthasbeenfoundefficaciousinatleasttwostudiesbytwo independentresearchteamsdoweconsideritsefficacytohavebeenestablishedandlabelitanefficacioustreatment.Ifthereisonlyone studysupportingatreatment'sefficacy,orifalloftheresearchhasbeenconductedbyone team,weconsiderthefindingspromisingbutwouldlabelsuchtreatmentsaspossiblyefficacious,pendingreplication.“

(Chambless &Hollon,1998,p8)


Diventare EST:soloRCT?

“efficacyisbestdemonstratedinrandomizedclinicaltrials (RCTs),groupdesignsinwhichpatientsarerandomlyassignedtothetreatmentofinterestoroneormorecomparisonconditions...

...orcarefullycontrolledsinglecaseexperiments(i.e.,SCED) andtheirgroupanalogues(i.e.,multiplebaselineseriesacrosspatients)”

Clinica eRicerca nellePsicoterapiePsicodinamichePadova,5 Maggio2018


AssociazioneItalianaAnalisiTransazionale

Diventare ESTconlaricerca singlecase

“Weconsideratreatmenttobepossiblyefficacious ifithasprovedbeneficialtoatleastthree participantsinresearchbyone group.Multiplereplications(atleastthree each)bytwo(thenthree) ormoreindependentresearchgroupsarerequiredbeforeweconsideratreatment'sefficacy asestablished(eachintheabsenceofconflictingdata).”




Chambless andHollon criteriaperdiventareEmpiricallySupportedTreatment

Between groupsdesignRCT- RandomizedClinicalTrials

Within subjectdesignSCED- SingleCaseExperimentalDesign

Efficacious 2RCTsconductedby2researchgroups

6(9)SCEDsby2(3)researchgroups(3each)

Probablyefficacious 1RCTormoreRCTsconductedby1researchgroup

3ormoreSCEDsconductedby1group

The Empirical Status of Empirically Supported Psychotherapies:Assumptions, Findings, and Reporting in Controlled Clinical Trials

Drew WestenEmory University

Catherine M. NovotnyVeterans Affairs Medical Center, San Francisco, California

Heather Thompson-BrennerBoston University

This article provides a critical review of the assumptions and findings of studies used to establishpsychotherapies as empirically supported. The attempt to identify empirically supported therapies (ESTs)imposes particular assumptions on the use of randomized controlled trial (RCT) methodology that appearto be valid for some disorders and treatments (notably exposure-based treatments of specific anxietysymptoms) but substantially violated for others. Meta-analytic studies support a more nuanced view oftreatment efficacy than implied by a dichotomous judgment of supported versus unsupported. Theauthors recommend changes in reporting practices to maximize the clinical utility of RCTs, describealternative methodologies that may be useful when the assumptions underlying EST methodology areviolated, and suggest a shift from validating treatment packages to testing intervention strategies andtheories of change that clinicians can integrate into empirically informed therapies.

When the results of scientific studies are applied to new and importantquestions that may directly or indirectly affect clinical training, clin-ical treatment, and financial decisions about how to treat, it is usefulfor us to return to our roots in empirical science and to carefullyconsider again the nature of our scientific methods and what they doand do not provide in the way of possible conclusions relevant tothose questions. (Borkovec & Castonguay, 1998, p. 136)

Robert Abelson (1995) has argued that the function of statisticsis not to display “the facts” but to tell a coherent story—to makea principled argument. In recent years, a story has been told in theclinical psychology literature, in graduate programs in clinicalpsychology, in psychiatry residency programs, and even in thepopular media that might be called “The Tale of the EmpiricallySupported Therapies (ESTs).” The story goes something like this.

Once upon a time, in the Dark Ages, psychotherapists practicedhowever they liked, without any scientific data guiding them. Then agroup of courageous warriors, whom we shall call the Knights of theContingency Table, embarked upon a campaign of careful scientifictesting of therapies under controlled conditions.Along the way, the Knights had to overcome many obstacles.

Among the most formidable were the wealthy Drug Lords whodwelled in Mercky moats filled with Lilly pads. Equally treacherouswere the fire-breathing clinician-dragons, who roared, without anybasis in data, that their ways of practicing psychotherapy were better.After many years of tireless efforts, the Knights came upon a set of

empirically supported therapies that made people better. They beganto develop practice guidelines so that patients would receive the bestpossible treatments for their specific problems. And in the end,Science would prevail, and there would be calm (or at least lessnegative affect) in the land.

In this article we tell the story a slightly different way, with afew extra twists and turns to the plot. Ours is a sympathetic butcritical retelling, which goes something like this.

Once upon a time, psychotherapists practiced without adequate em-pirical guidance, assuming that the therapies of their own persuasionwere the best. Many of their practices were probably helpful to manyof their patients, but knowing which were helpful and which wereinert or iatrogenic was a matter of opinion and anecdote.Then a group of clinical scientists developed a set of procedures

that became the gold standard for assessing the validity of psycho-therapies. Their goal was a valorous one that required tremendouscourage in the face of the vast resources of the Drug Lords and thenonempirical bent of mind of many clinician-dragons, who tended tobreathe some admixture of hot air, fire, and wisdom. In their quest, theKnights identified interventions for a number of disorders that showedsubstantial promise. The treatments upon which they bestowed Em-pirical Support helped many people feel better—some considerablyso, and some completely.

Drew Westen, Department of Psychology and Department of Psychiatryand Behavioral Sciences, Emory University; Catherine M. Novotny, Vet-erans Affairs Medical Center, San Francisco, California; HeatherThompson-Brenner, Center for Anxiety and Related Disorders and Depart-ment of Psychology, Boston University.Catherine M. Novotny is now at the Department of Mental Health

Services, Opportunities for Technology Information Careers, Antioch,California.Preparation of this article was supported in part by National Institute of

Mental Health Grants MH62377 and MH62378 and by a Glass FoundationGrant to Drew Westen. We thank Hal Arkowitz, David Barlow, RebekahBradley, Glen Gabbard, Robert Rosenthal, Laura Westen, and SherwoodWaldron for their very useful comments on earlier drafts of this article.Correspondence concerning this article should be addressed to Drew

Westen, Department of Psychology and Department of Psychiatry andBehavioral Sciences, Emory University, 532 North Kilgo Circle, Atlanta,GA 30322. E-mail: [email protected]

Psychological Bulletin Copyright 2004 by the American Psychological Association2004, Vol. 130, No. 4, 631–663 0033-2909/04/$12.00 DOI: 10.1037/0033-2909.130.4.631

631

RCTevaliditàinterna

• Itrattamentisonodisegnatiperunsingolodisturbo• Ipazientisonoselezionatipermassimizzarel’omogeneitàdelladiagnosi(nocomorbidità)• Itrattamentisonomanualizzatiedibrevee/ofissaduratadurataperminimizzarelavarianza• Lavalutazionedell’esitoèbasatasullariduzionedelsintomo

AssunzionisottostantigliRCT

• Lapsicopatologiaèaltamentemalleabile• Ipazientipossonoesseretrattatiperunsingolosintomo• Idisturbipsichiatricipossonoesseretrattatiinmodoindipendentedallapersonalità• Ilmetodosperimentaleèilmigliorstandardperidentificarelepsicoterapieutili

SingleCaseExperimentalDesign(SCED)

• Multiplebaseline(69%)– Acrossbehaviours,settingsorpatients– Concurrent andnonconcurrent

• Reversal design(17%)– ABABphases

• Alternating/simultaneousdesign(6%)– Twoormoretreatments

• Changingcriterion(4%)– Stepwise changeandcriterionshift

• MixedDesign(10%)

SCED– reversaldesign

• Withdrawalisnotapplicableinpsychotherapy• PhaseA:Notreatment- Baseline• PhaseB:Treatment• PhaseA:Notreatment- Followup

0

2

4

6

8

10

12

pre1 pre2 pre3 session1

session2

session3

session4

session5

session6

session7

session8

session9

session10

Followup1

Followup2

Followup3

SCORE

ABAdesign

Clinicalcutoff

patientA

SCED:multiplebaselineacrosspatients

0

1

2

3

4

5

6

7

8

9

10

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Weeks

SCORE

MultipleconcurrentBaseline

Clinicalcutoff

patientlag3

patientlag6

patientlag9

SCED:multiplebaselineacrosspatients

0

1

2

3

4

5

6

7

8

9

10

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

Weeks

SCORE

MultiplenonconcurrentBaseline

Clinicalcutoff

patientlag3

patientlag6

patientlag9

DagliSCEDagliHSCED


SingleCaseExperimental DesigneHermeneutic SingleCaseEfficacy Design

• HermeneuticSingleCaseEfficacyDesign (HSCED)isconsideredoneofthemostadvancedresearchdesignforsystematiccollectionofevidenceonefficacyandeffectivenessofpsychotherapies

(McLeod,2010)

FromSCEDtoHSCED

Caratteristiche degli HSCED

– Timeseriesanalysisdivariabili quantitativediesito,comegli SCED– Analisiincrociatedeidatiquantitativiconidatiqualitativiricavatidafontimultiple(patient,therapist,supervisor,researcher)– Diversi valutatori coinvolti nell’analisi delcaso,sia nelle analisi ermeneutiche,sia nel giudizio finalesull’efficacia deltrattamento.–Maggioresensibilità acambiamenti causali complessi– Permette l’analisi elosviluppo della teoria (theorybuildingcasestudy)– Coinvolge l’interesse dei clinici

Marginalized andEmergingPsychotherapies (MEPs)


Ilproblema dell’attuale sistema basato sugliRCTcomeGoldenStandard• Isostenitori ditutti gli approcci dipsicoterapia sono pressati acondurre sempre più esempre migliori RCTs• Approcci senza supporto diRCTsono marginalizzati edisincentivati• Questaideologia favorisce losviluppo ditrattamenti più facili dastudiare,conpoche sedute,manualizzabili,conobiettivi più sempliciemisurabili• Lalista degli ESTdiventa autoperpetuante

RCTcriticità

• RCTssono costosi erichiedono molto tempoespesso nonsonoutilizzabili persupportare:1)Modelli emergenti dipsicoterapia;2)Applicazione dimodelli benvalidati ericonosciuti anuove patologie;3)Modelli dipsicoterapia tradizionali,che tuttavia nonsono empiricamentesupportati eperciò marginalizzati inalcuni Stati

• EmergingandMarginalizedPsychotherapiesnonpossono affrontaregli RCT

Ilpercorso proposto daStiles,Hill&Elliott,2015• “AfoursteppathwaytobringMarginalizedandEmergingPsychotherapiestowardrecognition”

• 1.condurre una serie distudi sistematici sul caso singolo• Sensibilizzare lacomunità clinica

• 2.Costruire unPractitionerResearchNetwork• Raccolta didati Onlineabassocosto,direttamente dalla pratica clinica,che possonoessere aggragati epubblicati comeopenclinicaltrials

• Comparazione degli outcomedegli studi pre-postconi benchmarkspresenti inletteratura perstimare l’efficacia dei trattamenti MEP

ThepathwayproposedbyStiles,HillandElliott• 3.IPRCpossono condurre piccoli-medi RCTcomparando il MEPconaltritrattamenti• Diffusione dimanuali ditrattamento emisure dell’aderenza• Practicebasedrandomizedstudies(pragmatictrials)

• 4.Politiche direteperil riconoscimento el’inclusione nelle linee guida

Versoil riconoscimento dell’AT comeunEST

• aseriesofmixedmethodssystematiccasestudies– HSCED(Elliott,2002)– Significance is based onmeasures Reliable andClinically Significant Change(RCSC)(Jacobson& Truax,1991)• ReliableChange• Movementtothefunctionalpopulation

– Benchmarking

Statisticalandclinicalsignificance• Statisticalsignificance– Providesnoinformationontheimpactofthetherapy– ormeaningfulnessofchange

• ReliableandClinicallySignificantChange(RCSC)– Allowtocompareproportion ofchangeincaseseriesoutcomeresearch,againstnormativevaluesorbenchmarksfromRandomizedClinicalTrials

Change CORE(Italiannormativescores)

Reliableimprovement - 5.1Reliabledeterioration +5.1

Movingfrompopulation ≤10.9(M)/≤12.2(F)Clinicalsignificant(RCSI) Bothreliableimprovementandfunctionalpopulation

CaseStudyResearchforpolicymakers(McLeod,2011)• Authoritativepolicy-makinggroupshaveaccepted,withintheirguidelines,thatcasestudiesdohaveavaluablecontributiontomake• Atthepresenttime,itisnotpossibletoidentifyanymodelofpsychotherapyofficiallyapprovedonthebasisofcasestudyevidence• Becausetheredoesnotexistsufficientgood-qualityevidencethatcanbeputforward• Thissituationcanbecontrastedwithotherfields,suchasmanagementstudies,education,wherecasestudyevidencehasoftenhadadecisiveimpactonpolicyandpractice

Practice-Oriented Research (POR)andPractioner Researcher Network(PRN)

• Practice-OrientedResearch (POR)allowsgatheringPractice-BasedEvidences(PBEs)thatarecomplementarytotheindicationsoftheEvidenceBasePractice(EBP).

• PractitionerResearchNetworks(PRN)allowcollaborationbetweencliniciansandresearchers

ATversoil riconoscimento EST

• TransactionalAnalysisPractitionerResearchNetworks– 7Istituti ditraininginAT,associazioni,scuole diformazione,singoli ricercatori– uncasoall’anno

• Replicazione sistematica delle evidenze raccolte inUK

EffectSizesdegli studi finora pubblicati periltrattamento ATdella depressione

N Study DepressiveSymptoms(PHQ9/BDI-II) General Distress(CORE-OM)

1 Peter -1,689702716 -1,967335147

2 Denise -1,802349564 -3,152207452

3 Tom -2,47823065 -3,576972995

4 Linda(9sessions) -2,140290107 -3,129851371

5 Alastair -2,665845369 -3,308700021

6 Sara -2,665845369 -2,705085828

7 Penelope 0 -0,067068244

8 Luisa -3,838817332 -3,129851371

9 Anna -1,066338148 -1,050735817

10 Caterina -1,412898046 -2,481525016

11 Deborah -2,399260832 missing

GROUPEFFECTSIZE -2,00 -2,45

SingleCaseMeta-Analysis


StandardizedMeanDifference(SMD)statistic

• una d-statisticperstudi singoli comegli SCEDegli HSCED• Shadish etal,2014

• Ivantaggi dell’uso della statistica SMD:• Halastessa metrica delle d-statisticusate nei between-subjectsdesigns(RCTs)• Hauno sviluppo statistico formale• Permette una appropriata poweranaliysis

Lad statisticpermette lacomparazione tradisegni HSCEDeRCT

• SingleCaseDesigns(SCD)possono contribuire aldibattito nelmovimento Evidence-BasedPractice(EBP)• Percontribuire aldibattito è utileavere una misura dell’effetto nellastessa metrica usata nei Between-SubjectDesign(BSD),comegli RCT

EffectSizewithd statistic

• Effectsizeisimportantfordifferentreasons:1. ManyreviewofEBPconsiderevidencesfrombothSCDandBSD,both

combiningandseparatelyreportingtheresults.EBPcommunityoftenusestandardizedeffectsizeestimatesasthecommondenominatorforcomparingstudies,thusitisessentialtorepresentevidencefromSCDsonthesamescaleusedinBSDs, becauseacommoneffectsizemeasureforSCDandBSDwouldprovidecomparabilityinthereportingandsynthetizingofevidence

2. RationalplanningofSCDshoulddependonpowercalculationtoensureadequatedesignsensitivity,justasitdoesinBSDs.MethodsforstatisticalpoweranalysisthatdependonastandardizedeffectsizemetricwouldallowSCDresearcherstorationallyplanandjustifyingrantproposalstheirdeignsintermsofthesameeffectsizeparametersusedbyBSDresearchers


3. Thereisagrowinginterestinmeta-analysisofSCDs,butmosteffectsizesproposedforSCDlackaformalstatisticaldevelopment.Withoutplausibledistributiontheory,(e.g.,knowledgeofthesamplingvarianceofaneffectsizestatistic)theseeffectsizescannotbeusedwithcommonmeta-analytictools,suchasforestplots,diagnosticplots(radialandresidual),cumulativemeta-analysis,regressiontests,publicationbiasanalysis

4. APAprompt thatresultsfromBSDsaresummarizedusingeffectsizesandaccompanyingconfidenceintervals





5. ItisofbothintellectualandpracticalinteresttocomparethesizeoftreatmenteffectsfromSCDwiththeeffectsfromothermethodologies:nonrandomizedexperimentscanapproximatetheresultsfromrandomizedexperiments.SCDareaformoftimeseriesanalysis,butthereislittleempiricalevidencethataddresshowtheirresultscomparetoresultsfromrandomizedexperiments.

6. Practically,bothpractitionersandpolicymakerscannotalwayscarryoutrandomizedexperimentstoexamineeverycausalquestion, andareinterestedinwhattheycangetfromalternativedesigns.Thesecomparisonscannotbemadewithoutstatisticsinthesamemetricsforalldesings.




Conclusion

• Thisdmayproveusefulforboththeanalysisandmeta-analysisofdatafromSingleCaseDesignsuchasSingleCaseExperimentalDesignandHermeneuticSingleCaseEfficacyDesign




Singlecasemeta-analysisdiHSCEDpubblicatisull’efficacia deltrattamento ATdella depressione

Meta-analisi distudi randomizzatisull’efficacia della psicoterapia psicodinamica

Autore Anno Rivista StudiRCT N Popolazione Intervento Controllo Outcome ESEnd

ESFollow Up

Abbass,Hancock,

Hendersonetal.

2006 CochcraneLibrary 23 1431

CMDCommon Mental

Disorders

PsicoterapiaPsicodinamicaBreve(<40)

Listad’attesa,TAU

MiglioramentoSintomatologicoGenerale

.97 1.51(>9m)

Sintomisomatici .81 2.21(>9m)

Ansia 1.08 1.35(>9m)

Depressione .59 .98(>9m)

Leichsenring,Rabung,Leibing

2004ArchivesofGeneralPsychiatry

17 CMDPsicoterapiapsicodinamicaBreve(M=21)

TAU Sintomatologia generale

1.17(controlli)1.39(pre-post) 1.57(13m)

Abbas,Kisely,Kroencke 2009 Psychotherapy

Psychosomatic 23 1870 Condizionisomatiche

1)Sintomipsichiatricigenerali.2)Sintomisomatici

.69

.59

Leichsenring,Leibing 2003

AmericanJournalofPsychiatry

14Psicodin.11CBT

Disturbi diPersonalità

Dinamica M=37CBTM=16

1.46(18m)1.0(13w)

Benchmarking


Benchmarking

• Domanda cruciale:i pazienti curati incontesti clinici hanno i beneficiattesi edimostrati nei trialclinici?• Spesso è impossibile comparare il trattamento conunnontrattamento,placeboolista diattesa nei contesti clinici• IlBenchmarkingpermette dicomparare l’esito dei trattamenti neicontesti clinici congli standarddiesito osservati nei trialclinici

Benchmarking

• Benchmarkingpermette dicomparare l’effect sizediuncaso singolo odiuna serie dicasi ai valori dialta ebassa performanceottenuti dalle meta-analisi diRCTpubblicati.• SingleCaseMeta-Analysis(SCMA)fornisce unESnella stessa metrica degliRCTepermette diconfrontare l’efficacia conlesoglie dibassa ed altaefficacia ricavate dagli RCTedaampi studi ecologici• IBenchmarkssono soglie diriferimento che possono essere usate perinterpretare i dati• Permettono dicomparare il caso singolo conIvalori normativi ricavati daaltri studi• Nonpossono verificare lacausalità poichè nonprevedono il controllointerno

Benchmarkingrules

• Reactivityandspecificityoftheoutcomemeasures(e.g.,PHQ9)usedinthemeta-analysisofclinicaltrials,shouldmatchthereactivityandspecificityoftheoutcomemeasuresusedinclinicalstudies

Reactivity:Whomeasuretheoutcome?

LowReactivity(selfreport)

HighReactivity(proxyrated)

Sensitivity:

Wha

tad

dressthe

ou

tcom

emeasure?

LowSensitivity(general disease,e.g.globaldistress)

SCL-90/GlobalSeverityIndexCORE-OM

GAF/DSM-IV-TR AxisV

High Sensitivity(specificsymptoms,e.g.depression)

BeckDepression InventoryPHQ-9

HamiltonRatingScaleforDepression

Benchmarks for Psychotherapy Efficacy in Adult Major Depression

Takuya MinamiUniversity of Utah

Bruce E. Wampold and Ronald C. SerlinUniversity of Wisconsin—Madison

John C. KircherUniversity of Utah

George S. (Jeb) BrownCenter for Clinical Informatics

This study estimates pretreatment–posttreatment effect size benchmarks for the treatment of majordepression in adults that may be useful in evaluating psychotherapy effectiveness in clinical practice.Treatment efficacy benchmarks for major depression were derived for 3 different types of outcomemeasures: the Hamilton Rating Scale for Depression (M. A. Hamilton, 1960, 1967), the Beck DepressionInventory (A. T. Beck, 1978; A. T. Beck & R. A. Steer, 1987), and an aggregation of low reactivity–lowspecificity measures. These benchmarks were further refined for 3 conditions: treatment completers,intent-to-treat samples, and natural history (wait-list) conditions. The study confirmed significant effectsof outcome measure reactivity and specificity on the pretreatment–posttreatment effect sizes. The authorsprovide practical guidance in using these benchmarks to assess treatment effectiveness in clinicalsettings.

Keywords: effectiveness, psychotherapy, outcome, benchmarking, depression

Although the efficacy of psychotherapy for adult depression hasclearly been established (e.g., Lambert & Ogles, 2004; Wampold,2001), there has been a consistent concern in the field as towhether or not clients treated in clinical settings receive the ben-efits demonstrated in clinical trials (i.e., effectiveness of treatment;Barlow, 1981; Cohen, 1965; Goldfried & Wolfe, 1998; Luborsky,1972; Seligman, 1995; Strupp, 1989). Despite some evidence fromclinical trials suggesting that efficacy corresponds to effectiveness(e.g., Shadish, Matt, Navarro, & Phillips, 2000; Shadish et al.,1997), there are few outcome data from clinical settings to suggestthat treatments in these settings (i.e., treatment as usual; TAU)attain the benefits observed in clinical trials.

Treatment efficacy is often gauged by comparing treatment withno treatment (i.e., wait-list control groups). This strategy is oftenprecluded in clinical settings, however, because control groups

(i.e., no-treatment controls) rarely exist in naturalistic settings forpractical and ethical reasons. Thus, it is often unclear as to whetherthe effectiveness of TAU is significantly better than the naturalhistory of the disorder or is as effective as treatments provided inclinical trials.

One method to assess effectiveness in clinical settings is bench-marking, a strategy that allows for comparison of outcome dataobtained from clinical settings (i.e., TAU) against a reliable out-come standard observed in clinical trials. In the area of childhooddepression, Weersing and Weisz (2002) conducted a study inwhich they compared the outcome data in six community mentalhealth centers in the Los Angeles area against a clinical trialsbenchmark. Contrary to other benchmarking studies that selected asingle clinical trial to serve as a benchmark (e.g., Merrill, Tolbert,& Wade, 2003; Wade, Treat, & Stuart, 1998) Weersing and Weiszconducted a meta-analysis of 13 cognitive–behavioral therapyclinical trials, aggregating the effect sizes to obtain “a researchstandard of care for comparison—creating a best practice bench-mark from a review of the entire youth depression treatmentliterature” (p. 300).

One factor that Weersing and Weisz (2002) did not explicitlypursue as a potential methodological issue in conducting bench-marking studies was the reactivity and specificity of outcomemeasures, which have repeatedly been shown to significantlyaffect the effect size estimates of treatment outcomes (Lambert &Bergin, 1994; Lambert, Hatch, Kingston, & Edwards, 1986; Rob-inson, Berman, & Neimeyer, 1990; Shadish et al., 1993, 1997,2000; Shapiro & Shapiro, 1982; Smith, Glass, & Miller, 1980).Reactivity is generally concerned with the sensitivity of the mea-sure produced by the rater of the outcome—notably, an observer(either the treating clinician or an independent rater) or the client.Specificity, on the other hand, refers to the extent to which theoutcome measures assess targeted symptoms of a particular disor-

Takuya Minami and John C. Kircher, Department of Educational Psy-chology, University of Utah; Bruce E. Wampold, Department of Counsel-ing Psychology, University of Wisconsin—Madison; Ronald C. Serlin,Department of Educational Psychology, University of Wisconsin—Madison; George S. (Jeb) Brown, Center for Clinical Informatics, SaltLake City, Utah.

Part of this article was based on a doctoral dissertation in partialfulfillment of the requirements for a doctorate in counseling psychologyfrom the University of Wisconsin—Madison, completed by Takuya Mi-nami under the guidance of Bruce E. Wampold and Ronald C. Serlin.Partial funding for this study was provided by the Department of Coun-seling Psychology, University of Wisconsin—Madison as a doctoral re-search award to Takuya Minami. We thank Jason A. Seidel for his critiqueof earlier versions of this article.

Correspondence concerning this article should be addressed to TakuyaMinami, Department of Educational Psychology, University of Utah, 1705East Campus Center Drive, Room 327, Salt Lake City, UT 84112. E-mail:[email protected]

Journal of Consulting and Clinical Psychology Copyright 2007 by the American Psychological Association2007, Vol. 75, No. 2, 232–243 0022-006X/07/$12.00 DOI: 10.1037/0022-006X.75.2.232

232

exceed the efficacy benchmark minus dmin for 50 clients, theonly conclusion that could be drawn from this result is that forthese 50 clients, the treatment was as clinically effective as theclinical trials.

As a final but critical caveat, it is important to note that there arenumerous differences between clinical trials and naturalistic set-tings that could render simplistic numerical comparisons problem-atic. In clinical trials, the clients are selected through several

Figure 3. Low reactivity–low specificity outcome measures effect size critical values by clinical data sample size.

Figure 2. Beck Depression Inventory effect size critical values by clinical data sample size.

240 MINAMI, WAMPOLD, SERLIN, KIRCHER, AND BROWNBenchmarksforLowReactivity/HighSensitivityoutcomemeasuresfordepression

exceed the efficacy benchmark minus dmin for 50 clients, theonly conclusion that could be drawn from this result is that forthese 50 clients, the treatment was as clinically effective as theclinical trials.

As a final but critical caveat, it is important to note that there arenumerous differences between clinical trials and naturalistic set-tings that could render simplistic numerical comparisons problem-atic. In clinical trials, the clients are selected through several

Figure 3. Low reactivity–low specificity outcome measures effect size critical values by clinical data sample size.

Figure 2. Beck Depression Inventory effect size critical values by clinical data sample size.

240 MINAMI, WAMPOLD, SERLIN, KIRCHER, AND BROWN

TAsinglecasemetaanalysis

Behavioural and Cognitive Psychotherapy, 2014, 42, 16–30First published online 24 October 2012 doi:10.1017/S135246581200080X

Benchmarking Routine Psychological Services:A Discussion of Challenges and Methods

Jaime Delgadillo

Leeds Community Healthcare NHS Trust, UK

Dean McMillan

University of York, UK

Chris Leach and Mike Lucock

South West Yorkshire Partnership NHS Foundation Trust and University of Huddersfield, UK

Simon Gilbody

University of York, UK

Nick Wood

Leeds Community Healthcare NHS Trust, UK

Background: Policy developments in recent years have led to important changes in the levelof access to evidence-based psychological treatments. Several methods have been used toinvestigate the effectiveness of these treatments in routine care, with different approaches tooutcome definition and data analysis. Aims: To present a review of challenges and methodsfor the evaluation of evidence-based treatments delivered in routine mental healthcare. Thisis followed by a case example of a benchmarking method applied in primary care. Method:High, average and poor performance benchmarks were calculated through a meta-analysis ofpublished data from services working under the Improving Access to Psychological Therapies(IAPT) Programme in England. Pre-post treatment effect sizes (ES) and confidence intervalswere estimated to illustrate a benchmarking method enabling services to evaluate routineclinical outcomes. Results: High, average and poor performance ES for routine IAPT serviceswere estimated to be 0.91, 0.73 and 0.46 for depression (using PHQ-9) and 1.02, 0.78 and 0.52

Reprint requests to Jaime Delgadillo, Leeds Community Healthcare NHS Trust - Primary Care Mental Health, TheReginald Centre, Second Floor, 263 Chapeltown Road, Leeds LS7 3EX, UK. E-mail: [email protected]

© British Association for Behavioural and Cognitive Psychotherapies 2012

Benchm

arkingroutine

psychologicalservices25

0.0 0.4 0.8 1.2

Leeds n=2890 d=0.81 (0.77,0.86)

R30 n=69 d=0.42 (0.18, 0.65)R20 n=1059 d=0.42 (0.36, 0.48)

R18 n=60 d=0.48 (0.22, 0.73)R21 n=820 d=0.50 (0.43, 0.57)R12 n=366 d=0.58 (0.47, 0.69)R15 n=424 d=0.61 (0.51, 0.71)R10 n=449 d=0.62 (0.52, 0.72)

R9 n=477 d=0.63 (0.53, 0.72)R8 n=739 d=0.65 (0.57, 0.72)

R26 n=275 d=0.68 (0.55, 0.81)R19 n=266 d=0.69 (0.56, 0.82)R13 n=148 d=0.69 (0.52, 0.87)

R25 n=1274 d=0.70 (0.64, 0.76)R27 n=1111 d=0.70 (0.64, 0.77)R31 n=1736 d=0.73 (0.68, 0.78)R33 n=1480 d=0.73 (0.68, 0.79)

R5 n=1001 d=0.74 (0.67, 0.81)R11 n=692 d=0.74 (0.66, 0.82)

R23 n=1712 d=0.75 (0.70, 0.80)R3 n=324 d=0.75 (0.63, 0.87)

R16 n=829 d=0.77 (0.70, 0.85)R7 n=1139 d=0.79 (0.72, 0.85)

R36 n=1058 d=0.79 (0.72, 0.86)R17 n=724 d=0.84 (0.75, 0.92)R24 n=135 d=0.85 (0.65, 1.04)R22 n=118 d=0.85 (0.64, 1.05)

R14 n=1529 d=0.88 (0.82, 0.94)R6 n=1552 d=0.91 (0.85, 0.96)

R4 n=956 d=0.95 (0.87, 1.02)R28 n=641 d=0.95 (0.86, 1.04)

Depression effect sizes (PHQ-9)Notes: Solid line = average benchmark (0.73); do!ed lines = low (0.46) and high (0.91) performance benchmarks

Figure 1. Forest plot of PHQ-9 effect sizes (and 95% CI) for IAPT roll-out sites (R), and Leeds site

TAsinglecasemetaanalysis

Benchmarking TAsinglecasemetaanalysis

• IlBenchmarkingnonè adatto adessere utilizzato concampioniinferiori aN=100

• Gli Outcomericavati dapiccoli settingclinici possono comparare illoro effectsizeconi benchmarks,• Mail risultato è valido soloperquella osservazione enonpuò esseregeneralizzato

Conclusioni:

• Peri pazienti inclusi inqueste serie dicasi, il trattamento ATperladepressione risulta più efficace che il miglior centro IAPT

• Ovviamente….Moreresearchneeding….

[email protected]

ricerca efficiente e sostenibile …his research interests focus on under-standing how people change...

Documents