Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models

By E. Vittinghoff, D. Glidden, S. Shiboski, Charles E. McCulloch. 2005

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Associations between over-winter survival and resting metabolic rate in juvenile North American red squirrels (2010, unknown): “As per the methods described in Vittinghoff et al. (2004), we cal- culated odds ratios to reflect the change in odds across the 25th and 75th percentile for a given variable.”
The role of administrative data in the big data revolution in social science research. (2016, background): “…relatively straightforward steps that researchers can take to move beyond simply interpreting a p value, for example researchers can present a measure of effect size alongside a p value, such as a marginal effect (see Connelly et al., Forthcoming, Long and Freese, 2014; Vittinghoff et al., 2005).”
Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK): Explanation and Elaboration (2012, methodology): “More extensive discussions of statistical analysis methods for binary outcome and for survival data can be found elsewhere [74,102,114-125].”
Research Commentary - Too Big to Fail: Large Samples and the p-Value Problem (2013, background): “…like the probit, one has to specify whether an effect size is being calculated Table 2 Interpreting Effect Sizes for Common Regression Models (Vittinghoff et al. 2005) Functional form Effect size interpretation (where (cid:130) is the coefﬁcient) The odds of Y = 1 at x = 1 are higher than at…”