Software

The development of the software provided here has been supported by the following grants: NIH ES009411, CA050597, CA081345, and CA055075.

betacomp.f Implementing Spiegelman D, Rosner B. “Estimation and inference for binary data with covariate measurement error and misclassification for main study/validation study designs.” Submitted for publication, Journal of the American Statistical Association, June, 1997.

%blinplus macro Implementing Rosner, Spiegelman, Willett “Correction of logistic regression relative risk estimates and confidence intervals for measurement error: the case of multiple covariates measured with error”. American Journal of Epidemiology 1990;132: 734-735 etc. (click the link to see all the reference papers).

ge_int.f Implementing Foppa I, Spiegelman D. “Power and sample size calculations for case-control studies of gene-environment interactions with a polytomous exposure variable”. American Journal of Epidemiology, 1997; 146:596-604.

ge_trend_v2 Implementing power and sample size calculations developed in Spiegelman D. and Logan R. “Power and sample size for case-control studies of gene-environment interactions: a new method with comparison to old.”Submitted for publication, American Journal of Epidemiology, January, 2002.

goodwin.f77 Implementing Crouch EAC, Spiegelman D. “The evaluation of integrals of the form f(t)exp{-t²}dt: Application to logistic-normal models”. Journal of the American Statistical Association 85: 464-469, 1990.

holcroft.f77 Implementing Holcroft C, Spiegelman D. “Design of validation studies for estimating the odds ratio of exposure-disease relationships when exposure is misclassified”. Biometrics, 1999; 55:1193-1201.

%int2way Makes all the 2-way interaction variables from a list of variables.

%kmplot9 Makes publication-quality Kaplan-Meier plots of survival data, following the JAMA guidelines.Produces numerical output of the

censoring summary, as well as of tests among subgroups (e.g. log-rank).

%lgtphcurv9 Implementing Durrleman and Simon's restricted cubic spline methodology to fit possibly non-linear exposure response curves in Cox and logistic regression models. Publication quality graphs are provided and a stepwise knot selection procedure is available to enhance the flexibility of the method. Govindarajulu U, Spiegelman D, Thurston SW, Eisen EA. “Comparing smoothing techniques for modeling exposure-response curves in Cox models”. Submitted for publication, Statistics in Medicine, January, 2006.

%mediate Calculates the point and interval estimates of the percent of treatment (exposure) effect (PTE) explained by an intermediate variable.

%metaanal produces Laird-Der Simonian estimators for fixed and random effects models in meta- and pooled analysis.

%metadose SAS macro for meta-analysis of dose-response. It is used when only limited data are available from research reports studying on the same dose-response relationship with different exposure or treatment levels. It is a two step macro: First, for each study, it uses the Greenland method (AJE, 1992) to get a single pooled estimate and its variance estimate across different exposure or treatment levels; Second, it does meta analysis for all relevant studies using the pooled numbers.

Multsurr Method Implementing Weller E, Milton D, Eisen E, Spiegelman D. method in “Regression calibration for logistic regression with multiple surrogates for one exposure.” In press, Journal of Statistical Planning and Inference, 2006.

optitxs Implementing sample size and power calculations for longitudinal (repeated measures) studies method in “The design of observational longitudinal study”

%par macro Computing full and partial population attributable risks and their confidence intervals, for cohort studies. The manuscript can be downloaded here(in pdf).

%relibpls8 macro Implementing Rosner, Spiegelman, Willett “Correction of logistic regression relative risk estimates and confidence intervals for random within person measurement error”, American Journal of Epidemiology 1992; 136: 1400-1413

%relrisk8 macro Implementing log-binomial and log-Poisson models to get risk, prevalence and rate ratios and risk, prevalence and rate differences.

%table1 Produces publication quality MS Word table with a breakdown of study/cohort characteristics, typically by categories of an exposure variable.

tcs Implementing Takkouche B, Cardarso-Su_rez C, Spiegelman D. “An evaluation of old and new tests for heterogeneity in meta-analysis for epidemiologic research”. American Journal of Epidemiology, 1999;150:206-215.

%icc9 macro Intraclass correlation coefficients (ICC) and their 95 percent confidence intervals. SAS Documentation Instructions for using icc9. ( in pdf format. ) SAS Program SAS code to run icc9 macro. -->

rrc_timevarying_method.f_ _Implementing the new method developed in the paper of_ "Survival analysis with error-prone time-varying covariates: a risk set calibration approach" by Xiaomei Liao, David Zucker, Yi Li, Donna Spiegelman.

ElenaBen

DonnaJennyEllenJames

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