Effect size calculations for the clinician: Methods and comparability

Seidel, Jasón A.; Miller, Scott D.; Chow, Daryl

doi:10.1080/10503307.2013.840812

Cited by 28 publications

(26 citation statements)

References 41 publications

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“…As ΔT is based on T-scores with SD pretest = 10, a 0.50 ΔT-point implies a 0.05 shift in standardized pre-post difference or an effect size of ES = 0.05 (Cohen, 1988;Seidel, Miller, & Chow, 2013). As ΔT is based on T-scores with SD pretest = 10, a 0.50 ΔT-point implies a 0.05 shift in standardized pre-post difference or an effect size of ES = 0.05 (Cohen, 1988;Seidel, Miller, & Chow, 2013).…”

Section: Threshold Values For Selective Inclusion Bias and Attritiomentioning

confidence: 99%

“…For selective inclusion or attrition bias, a critical cut-off value was set at 0.50 ΔT points. As ΔT is based on T-scores with SD pretest = 10, a 0.50 ΔT-point implies a 0.05 shift in standardized pre-post difference or an effect size of ES = 0.05 (Cohen, 1988;Seidel, Miller, & Chow, 2013). A critical cut-off value of 0.50 implies that we deem a difference between two providers larger than 1.0 points as not attributable to inclusion or attrition bias.…”

Section: Threshold Values For Selective Inclusion Bias and Attritiomentioning

confidence: 99%

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Bias through selective inclusion and attrition: Representativeness when comparing provider performance with routine outcome monitoring data

Beurs

Warmerdam²,

Twisk

2019

Clin Psychology and Psychoth

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Background Observational research based on routine outcome monitoring is prone to missing data, and outcomes can be biased due to selective inclusion at baseline or selective attrition at posttest. As patients with complete data may not be representative of all patients of a provider, missing data may bias results, especially when missingness is not random but systematic. Methods The present study establishes clinical and demographic patient variables relevant for representativeness of the outcome information. It applies strategies to estimate sample selection bias (weighting by inclusion propensity) and selective attrition bias (multiple imputation based on multilevel regression analysis) and estimates the extent of their impact on an index of provider performance. The association between estimated bias and response rate is also investigated. Results Provider‐based analyses showed that in current practice, the effect of selective inclusion was minimal, but attrition had a more substantial effect, biasing results in both directions: overstating and understating performance. For 22% of the providers, attrition bias was estimated to be in excess of 0.05 ES. Bias was associated with overall response rate (r = .50). When selective inclusion and attrition bring providers' response below 50%, it is more likely that selection bias increased beyond a critical level, and conclusions on the comparative performance of such providers may be misleading. Conclusions Estimates of provider performance were biased by selection, especially by missing data at posttest. Results on the extent and direction of bias and minimal requirements for response rates to arrive at unbiased performance indicators are discussed.

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Section: Threshold Values For Selective Inclusion Bias and Attritiomentioning

confidence: 99%

Section: Threshold Values For Selective Inclusion Bias and Attritiomentioning

confidence: 99%

Bias through selective inclusion and attrition: Representativeness when comparing provider performance with routine outcome monitoring data

Beurs

Warmerdam²,

Twisk

2019

Clin Psychology and Psychoth

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“…in randomized clinical trials, the magnitude of improvement is calculated as the mean change from pre-test to post-test in standard units (effect size [ES] or the standardized mean difference, also known as Glass's Δ, computed as the pre-test score minus the post-test score divided by the pre-test standard deviation [SD]). A limitation of ES is that variability in the variance of baseline scores may strongly influence the ES (Seidel, Miller, & Chow, 2013). Institutes with a homogeneous patient population with low variance in pre-test scores would be at an advantage compared with institutes with a heterogeneous population.…”

Section: Key Practitioners Messagesmentioning

confidence: 99%

Comparing Methods to Denote Treatment Outcome in Clinical Research and Benchmarking Mental Health Care

Beurs

Barendregt

Heer

et al. 2015

Clin Psychology and Psychoth

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When comparing various indicators or treatment outcome, statistical considerations designate continuous outcomes, such as the effect size of the pre-post change (effect size or ΔT) as the optimal choice. Expressing outcome in proportions of recovered, changed, unchanged or deteriorated patients has supplementary value, as it is more easily interpreted and appreciated by clinicians, managerial staff and, last but not the least, by patients. If categorical outcomes are used with small datasets, true differences in institutional performance may get obscured due to diminished power to detect differences. With sufficient data, outcome according to continuous and categorical indicators converge and lead to similar rankings of institutes' performance.

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“…Daarvoor maakten we gebruik van de Effect Size voor herhaalde metingen binnen eenzelfde onderzoeksgroep (EffectSize repeated-measures correction for paired t-test: ESrmc). 19 De effectgroottes in de sociale wetenschappen worden gedefinieerd door Cohen van 0,2 als klein, 0,5 als middelgroot en 0,8 als groot. 20 Met subgroepanalyses onderzochten we ten slotte nog of verschillen tussen voor-en nameting varieerden voor leeftijd en geslacht.…”

Section: Analyseunclassified

“…21 De score is op vier niveaus: van 1 'op geen enkel gebied van mijn leven in staat zijn om', tot 4 'op alle gebieden van mijn leven in staat zijn om'. De score op elk van de vijf attributen worden opgeteld tot een totaalscore (score [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. De ICECAP-A is vertaald en gevalideerd in het Nederlands en er is toestemming gevraagd om het instrument te gebruiken voor het huidige onderzoek.…”

Section: Instrumentunclassified

De bijdrage van BigMove GGZ aan capabilities, ervaren gezondheid en kwaliteit van leven

Veen¹,

Harting

2016

Tijdschr Gezondheidswet

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BigMove GGZ is ontwikkeld voor mensen met meervoudige gezondheidsproblemen. Het programma beoogt capabilities te versterken en de ervaren gezondheid en kwaliteit van leven te verbeteren. Doel van dit onderzoek was te exploreren in welke mate en op welke wijze het programma deze doelen bereikt. Gekozen is voor een mixed-methods onderzoek. Voor en na deelname vulden deelnemers (N=163) een vragenlijst in over ervaren gezondheid, kwaliteit van leven en coping. Tien respondenten zijn, aan de hand van een korte vragenlijst, geïnterviewd over hun capabilities en de bijdrage daaraan door het programma. Deelnemers rapporteerden verbeteringen op ervaren gezondheid (gemiddelde [M] voor-na = 2,80-3,29; effectgrootte [ES] = 0,62), kwaliteit van leven (M voor-na = 4,21-4,82; ES = 0,66) en actief copinggedrag (M voor-na = 2,40-2,58; ES = 0,24). Respondenten vertelden dat hun capabilities waren toegenomen. Hun zelfkennis en vertrouwen in eigen kunnen waren vergroot en er ontstond een intrinsieke motivatie om gedrag te veranderen. Daardoor ondernamen ze meer activiteiten, onderhielden ze meer sociale contacten en participeerden ze actiever in de maatschappij door (vrijwilligers)werk. Als belangrijke elementen van het programma benoemden respondenten de individuele gesprekken met de BigMove psycholoog alsmede met de BigMove begeleider, het stellen van doelen, het bewegen, het spel en plezier, en de steunfunctie van de groep. Deze exploratie laat zien dat en hoe BigMove GGZ een relevante bijdrage kan leveren aan het verbeteren van de ervaren gezondheid, kwaliteit van leven en capabilities. Trefwoorden: kwaliteit van leven, ervaren gezondheid, Capability Approach, ICECAP-A, GGZ 1 BigMove Institute, Amsterdam 2 Afdeling Sociale Geneeskunde, AMC, Universiteit van Amsterdam, Amsterdam tsg jaargang 94 / 2016 nummer 2 De bijdrage van BigMove GGZ aan capabilities, ervaren gezondheid en kwaliteit van leven -pagina 64 / www.tsg.bsl.nl

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Effect size calculations for the clinician: Methods and comparability

Abstract: Three methods for calculating effect sizes are recommended for moderating these differences, including two equations that show promise as valid and practical methods for use by clinicians in professional practice.

Cited by 28 publications

References 41 publications

Bias through selective inclusion and attrition: Representativeness when comparing provider performance with routine outcome monitoring data

Bias through selective inclusion and attrition: Representativeness when comparing provider performance with routine outcome monitoring data

Comparing Methods to Denote Treatment Outcome in Clinical Research and Benchmarking Mental Health Care

De bijdrage van BigMove GGZ aan capabilities, ervaren gezondheid en kwaliteit van leven

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