What random assignment does and does not do

Krause, Merton S.; Howard, Kenneth I.

doi:10.1002/jclp.10170

Cited by 53 publications

(46 citation statements)

References 11 publications

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“…Krause and Howard (2003;p. 754) state that bAll clinical trials are quasiexperiments for the foreseeable future, so long as our causal models are not fully specified and all the causal variables are not precisely controlled or accurately measured.Q They go on to demonstrate additional limitations of randomized designs in controlling interactions between treatment and patient variables.…”

Section: Background For Treatment Process Researchmentioning

confidence: 95%

A conceptual framework for drug treatment process and outcomes

Simpson¹

2004

Journal of Substance Abuse Treatment

288

265

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Section: Background For Treatment Process Researchmentioning

confidence: 95%

A conceptual framework for drug treatment process and outcomes

Simpson¹

2004

Journal of Substance Abuse Treatment

288

265

View full text Add to dashboard Cite

“…40 Our fi ndings also have important methodological ramifi cations for both RCTs and observational studies. Regarding RCTs, currently available methods for handling missing data, such as intention-to-treat analysis and last observation carried forward, assume dropout occurs at random 41 and ignore the possibility that unmeasured variables driving missing data might interact with interventions to worsen outcomes. Thus, currently available methods for handling missing data do not address and may actually compound bias because of nonrandom missing data.…”

Section: Per S Ona L I T Y a S Pr Edic Tor O F Mis Sing Datamentioning

confidence: 99%

Is Personality a Key Predictor of Missing Study Data? An Analysis From a Randomized Controlled Trial

Jerant¹,

Chapman²,

Duberstein³

et al. 2009

The Annals of Family Medicine

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PURPOSE Little is known regarding the effects of psychological factors on data collection in research studies. We examined whether Five Factor Model (FFM) personality factors-Neuroticism, Extraversion, Openness, Agreeableness, and Conscientiousness-predicted missing data in a randomized controlled trial (RCT).METHODS Individuals (N = 415) aged 40 years and older with various chronic conditions, plus basic activity impairment, depressive symptoms, or both, were recruited from a primary care network and enrolled in a 6-week RCT of an illness self-management intervention, delivered by means of home visits or telephone calls or usual care. Random effects logistic regression modeling was used to examine whether FFM factors predicted missing illness management self-effi cacy data at any scheduled follow-up (2, 4, and 6 weeks, and 6 and 12 months), controlling for disease burden, study arm, and sociodemographic characteristics.RESULTS Across all follow-up points, the missing data rate was 4.5%. Higher levels of Openness (adjusted odds ratio [AOR] for 1-SD increase = 0.24; 95% CI, 0.12-0.46; P <.001), Agreeableness (AOR = 0.29; CI 0.14-0.60; P = .001), and Conscientiousness (AOR = 0.24; CI 0.15-0.50; P <.001) were independently associated with fewer missing data. Accuracy of the missing data prediction model increased when personality variables were added (change in area under the receiver operating characteristic curve from 0.71 to 0.77; χ 2 1 = 6.6; P = .01).CONCLUSIONS Personality was a powerful predictor of missing study data in this RCT. Assessing personality could inform efforts to enhance data completion and adjust analyses for bias caused by missing data. Ann Fam Med 2009;7:148-156. DOI: 10.1370/afm.920. INTRODUCTIOND espite advantages compared with observational studies, randomized controlled trials (RCTs) have limitations.1 One threat to their validity is bias resulting from nonrandom missing data. Commonly measured sociodemographic [2][3][4][5][6] and health status 7-9 variables have been associated with missing data caused by study attrition. Because such variables may also affect study outcomes, bias in the assessment of treatment effects may occur, though statistical adjustment can mitigate bias.Psychological factors also likely affect study participants' decisions to drop out from, keep data collection appointments for, or complete all questionnaire items in clinical research studies, yet they are rarely measured. This omission may be critical in assessing the validity and applicability of studies, since adherence to study protocols, including placebos, can have profound effects on outcomes, comparable to active treatment effects. [10][11][12] Although earlier studies have explored attrition resulting from psychological factors in patients with psychiatric disorders [13][14][15] [17][18][19] The result of more than 7 decades of research, 17 FFM factors are empirically derived, broad clusters of behavioral and dispositional tendencies (Table 1). They capture the major axes of psychological and beh...

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“…Unfortunately, random assignment will not always equalize preexisting study enrollee characteristics across experimental groups (2), and this will always be the case when study enrollees tend to prefer one service condition over another. Common sense tells us that service assignment preference must first be balanced within the total enrollee group for it to be distributed equitably across experimental conditions.…”

mentioning

confidence: 99%

Impact of Referral Source and Study Applicants’ Preference for Randomly Assigned Service on Research Enrollment, Service Engagement, and Evaluative Outcomes

Macias¹,

Barreira²,

Hargreaves³

et al. 2005

AJP

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Objective-The inability to blind research participants to their experimental conditions is the Achilles' heel of mental health services research. When one experimental condition receives more disappointed participants, or more satisfied participants, research findings can be biased in spite of random assignment. The authors explored the potential for research participants' preference for one experimental program over another to compromise the generalizability and validity of randomized controlled service evaluations as well as cross-study comparisons.Method-Three Cox regression analyses measured the impact of applicants' service assignment preference on research project enrollment, engagement in assigned services, and a service-related outcome, competitive employment.Results-A stated service preference, referral by an agency with a low level of continuity in outpatient care, and willingness to switch from current services were significant positive predictors of research enrollment. Match to service assignment preference was a significant positive predictor of service engagement, and mismatch to assignment preference was a significant negative predictor of both service engagement and employment outcome.Conclusions-Referral source type and service assignment preference should be routinely measured and statistically controlled for in all studies of mental health service effectiveness to provide a sound empirical base for evidence-based practice.Participants in research on mental health services are rarely blind to their experimental assignments, and so attrition caused by disappointment in service assignment is a wellrecognized risk. Researchers can minimize overall attrition, as well as the threat of differential attrition (1), by screening out applicants with a priori service preferences. However, some applicants may choose not to disclose their preference to ensure that they have a chance to be assigned to a favored program, or they may feel they do not have a personal preference over and above the obvious one posed by the study design. For instance, when the experimental comparison is between a new intervention and services as usual, many applicants may prefer the new program even before they learn much about it.Unfortunately, random assignment will not always equalize preexisting study enrollee characteristics across experimental groups (2), and this will always be the case when study NIH-PA Author ManuscriptNIH-PA Author Manuscript NIH-PA Author Manuscript enrollees tend to prefer one service condition over another. Common sense tells us that service assignment preference must first be balanced within the total enrollee group for it to be distributed equitably across experimental conditions. For example, if 60% of enrollees have a preference for condition A and 40% have a preference for condition B, then, with true equivalence across conditions, service A would have 60% pleased and 40% disappointed assignees, while service B would have 40% pleased and 60% disappointed assignees. To the extent that pa...

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What random assignment does and does not do

Cited by 53 publications

References 11 publications

A conceptual framework for drug treatment process and outcomes

A conceptual framework for drug treatment process and outcomes

Is Personality a Key Predictor of Missing Study Data? An Analysis From a Randomized Controlled Trial

Impact of Referral Source and Study Applicants’ Preference for Randomly Assigned Service on Research Enrollment, Service Engagement, and Evaluative Outcomes

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