meta-analysis   556

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Blinding and Patient-Reported Outcome Completion Rates in US Food and Drug Administration Cancer Trial Submissions, 2007–2017 | JNCI: Journal of the National Cancer Institute | Oxford Academic
Patient-reported outcomes (PROs) are commonly included in submissions to the United States Food and Drug Administration (FDA). Open-label designs are frequent in cancer trials. Between-arm differences in PRO missingness may affect results. We sought to compare PRO completion rates between study arms in randomized open-label and double-blind cancer trials.
PRO  meta-analysis  healthcare 
8 weeks ago by PieroRivizzigno
The contact hypothesis re-evaluated | Behavioural Public Policy | Cambridge Core
This paper evaluates the state of contact hypothesis research from a policy perspective. Building on Pettigrew and Tropp's (2006) influential meta-analysis, we assemble all intergroup contact studies that feature random assignment and delayed outcome measures, of which there are 27 in total, nearly two-thirds of which were published following the original review. We find the evidence from this updated dataset to be consistent with Pettigrew and Tropp's (2006) conclusion that contact “typically reduces prejudice.” At the same time, our meta-analysis suggests that contact's effects vary, with interventions directed at ethnic or racial prejudice generating substantially weaker effects. Moreover, our inventory of relevant studies reveals important gaps, most notably the absence of studies addressing adults' racial or ethnic prejudices, an important limitation for both theory and policy. We also call attention to the lack of research that systematically investigates the scope conditions suggested by Allport (1954) under which contact is most influential. We conclude that these gaps in contact research must be addressed empirically before this hypothesis can reliably guide policy.
discrimination  intervention  meta-analysis  social_psychology  bias  via:sunstein 
december 2018 by rvenkat
How Much Should We Trust Estimates from Multiplicative Interaction Models? Simple Tools to Improve Empirical Practice | Political Analysis | Cambridge Core
Multiplicative interaction models are widely used in social science to examine whether the relationship between an outcome and an independent variable changes with a moderating variable. Current empirical practice tends to overlook two important problems. First, these models assume a linear interaction effect that changes at a constant rate with the moderator. Second, estimates of the conditional effects of the independent variable can be misleading if there is a lack of common support of the moderator. Replicating 46 interaction effects from 22 recent publications in five top political science journals, we find that these core assumptions often fail in practice, suggesting that a large portion of findings across all political science subfields based on interaction models are fragile and model dependent. We propose a checklist of simple diagnostics to assess the validity of these assumptions and offer flexible estimation strategies that allow for nonlinear interaction effects and safeguard against excessive extrapolation. These statistical routines are available in both R and STATA.

--Not clear if transfers to log-linear interaction models...
regression  statistics  estimation  meta-analysis  for_friends 
december 2018 by rvenkat

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