Different effects for different questions: An illustration using short cervix and the risk of preterm birth

To illustrate the difference between exposure effects and population attributable effects.

[1]  S. Cole,et al.  Maternal HIV Infection and Spontaneous Versus Provider-Initiated Preterm Birth in an Urban Zambian Cohort , 2021, Journal of acquired immune deficiency syndromes.

[2]  D. Westreich.  Epidemiology by Design: A Causal Approach to the Health Sciences , 2019 .

[3]  D. Westreich,et al.  Number (of Whom?) Needed to Treat (with What?): Exposures, Population Interventions, and the Number Needed to Treat. , 2019, Epidemiology.

[4]  B. Chi,et al.  Adverse birth outcomes and their clinical phenotypes in an urban Zambian cohort , 2019, Gates open research.

[5]  B. Chi,et al.  The Zambian Preterm Birth Prevention Study (ZAPPS): Cohort characteristics at enrollment. , 2018, Gates open research.

[6]  Eric J Tchetgen Tchetgen,et al.  Multiple Imputation for Incomplete Data in Epidemiologic Studies , 2018, American journal of epidemiology.

[7]  D. Altman,et al.  Population attributable fraction , 2018, British Medical Journal.

[8]  D. Altman,et al.  Ultrasound‐based gestational‐age estimation in late pregnancy , 2016, Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology.

[9]  J. Ahern.  Population Intervention Measures to Connect Research Findings to Policy. , 2016, American journal of public health.

[10]  S. Galea,et al.  Predicting the Population Health Impacts of Community Interventions: The Case of Alcohol Outlets and Binge Drinking. , 2016, American journal of public health.

[11]  Catherine R. Lesko,et al.  An Illustration of Inverse Probability Weighting to Estimate Policy-Relevant Causal Effects. , 2016, American journal of epidemiology.

[12]  S. Cole,et al.  Causal Impact: Epidemiological Approaches for a Public Health of Consequence. , 2016, American journal of public health.

[13]  K. Nicolaides,et al.  A Randomized Trial of a Cervical Pessary to Prevent Preterm Singleton Birth. , 2016, The New England journal of medicine.

[14]  D. Altman,et al.  International standards for early fetal size and pregnancy dating based on ultrasound measurement of crown–rump length in the first trimester of pregnancy , 2014, Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology.

[15]  D. Westreich.  From exposures to population interventions: pregnancy and response to HIV therapy. , 2014, American journal of epidemiology.

[16]  G. Saade,et al.  17 alpha-hydroxyprogesterone caproate to prevent prematurity in nulliparas with cervical length less than 30 mm. , 2012, American journal of obstetrics and gynecology.

[17]  Jessica G. Young,et al.  The parametric g‐formula to estimate the effect of highly active antiretroviral therapy on incident AIDS or death , 2012, Statistics in medicine.

[18]  T. Leung,et al.  Cerclage Pessary for Preventing Preterm Birth in Women with a Singleton Pregnancy and a Short Cervix at 20 to 24 Weeks: A Randomized Controlled Trial , 2012, American Journal of Perinatology.

[19]  E. Carreras,et al.  Cervical pessary in pregnant women with a short cervix (PECEP): an open-label randomised controlled trial , 2012, The Lancet.

[20]  L. Sullivan,et al.  Vaginal progesterone reduces the rate of preterm birth in women with a sonographic short cervix: a multicenter, randomized, double‐blind, placebo‐controlled trial , 2011, Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology.

[21]  Sherri Rose,et al.  Implementation of G-computation on a simulated data set: demonstration of a causal inference technique. , 2011, American journal of epidemiology.

[22]  A. Laupacis,et al.  A Tutorial on Methods to Estimating Clinically and Policy-Meaningful Measures of Treatment Effects in Prospective Observational Studies: A Review , 2011, The international journal of biostatistics.

[23]  S. Cole,et al.  Invited commentary: positivity in practice. , 2010, American journal of epidemiology.

[24]  S. Galea,et al.  Estimating the effects of potential public health interventions on population disease burden: a step-by-step illustration of causal inference methods. , 2009, American journal of epidemiology.

[25]  Anthonius de Boer,et al.  Systematic differences in treatment effect estimates between propensity score methods and logistic regression. , 2008, International journal of epidemiology.

[26]  M. Robins James,et al.  Estimation of the causal effects of time-varying exposures , 2008 .

[27]  Alan E Hubbard,et al.  Population intervention models in causal inference. , 2008, Biometrika.

[28]  K. Nicolaides,et al.  Progesterone and the risk of preterm birth among women with a short cervix. , 2007, The New England journal of medicine.

[29]  J. Avorn,et al.  Variable selection for propensity score models. , 2006, American journal of epidemiology.

[30]  K. Nicolaides,et al.  Prediction of patient‐specific risk of early preterm delivery using maternal history and sonographic measurement of cervical length: a population‐based prospective study , 2006, Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology.

[31]  Donna Spiegelman,et al.  Easy SAS calculations for risk or prevalence ratios and differences. , 2005, American journal of epidemiology.

[32]  Paula R Williamson,et al.  Cervical cerclage for prevention of preterm delivery in woman with short cervix:randomised controlled trial , 2004, The Lancet.

[33]  P. Taipale,et al.  Predicting Delivery Date by Ultrasound and Last Menstrual Period in Early Gestation , 2001, Obstetrics and gynecology.

[34]  J. Pearl,et al.  Confounding and Collapsibility in Causal Inference , 1999 .

[35]  B. Blondel,et al.  [The length of the cervix and the risk of spontaneous premature delivery]. , 1996, Revue d'epidemiologie et de sante publique.

[36]  H. Andersen,et al.  Prediction of risk for preterm delivery by ultrasonographic measurement of cervical length. , 1990, American journal of obstetrics and gynecology.

[37]  S Greenland,et al.  Interpretation and choice of effect measures in epidemiologic analyses. , 1987, American journal of epidemiology.

[38]  J M Robins,et al.  Identifiability, exchangeability, and epidemiological confounding. , 1986, International journal of epidemiology.

[39]  D. Rubin,et al.  Multiple Imputation for Interval Estimation from Simple Random Samples with Ignorable Nonresponse , 1986 .

[40]  G Rose,et al.  Sick individuals and sick populations. , 1985, International journal of epidemiology.

[41]  O. Miettinen,et al.  Confounding: essence and detection. , 1981, American journal of epidemiology.

[42]  Ewout W. Steyerberg,et al.  Focus on : Contemporary Methods in Biostatistics ( I ) Regression Modeling Strategies , 2017 .

[43]  Stephen R Cole,et al.  The consistency statement in causal inference: a definition or an assumption? , 2009, Epidemiology.

[44]  H. Morgenstern,et al.  A method for using epidemiologic data to estimate the potential impact of an intervention on the health status of a target population , 2005, Journal of Community Health.