RESEARCH SUPPORT
Stata Statistical Modeling for Research
Event-Time and Clustered Data Models
Stata survival analysis with Cox and parametric models for follow-up times, and mixed-effects models for clustered data, with the assumptions examined and every estimate reported with a confidence interval, so that each number in your tables can be traced back to a model.
No Commitment Required
Strictly Confidential
In-House PhD-Level Statisticians
Fixed Project Fee
Same-Day Response
What Does Stata Statistical Modeling Involve?
Stata statistical modeling for research covers two families that ordinary regression does not handle: models for the time until an event, where some participants are still event-free when follow-up ends, and models for data in which participants share a clinic, school or household. Survival models in Stata declare the time and event variables once and then fit hazard models, while Stata mixed-effects models add random effects so that observations within a cluster are not treated as independent. Both kinds of model are fitted and checked by an in-house PhD-level statistician, which is how OnlineSPSS, a specialist statistical consultancy for academic, clinical and professional research, carries out its analyses. What a hazard ratio or a variance component means for your field is a judgment for you to argue.
WHEN TO ENGAGE
When Follow-Up Times or Clusters Shape the Model
A model for follow-up or clustered data is seldom the first one tried, and the need often follows an attempt that a supervisor, a reviewer or a convergence message has rejected.
Event Times With Censored Cases Some participants have not had the event when follow-up ends, so the time scale, the time origin and the handling of censoring are settled before any hazard ratio is estimated, since the origin decides who is counted at risk from the first day.
Participants Nested in Sites Patients, pupils or households share a clinic, school or area, so their outcomes are correlated, and an ordinary regression would understate the uncertainty of effects that vary at the level of the cluster.
A Model That Will Not Converge A mixed model fails to converge or returns a variance estimate at zero, and the cause, whether too few clusters, an over-specified random part or badly scaled predictors, has to be found and addressed before any estimate is trusted.
Hazards That Do Not Behave A reviewer asks whether the hazards are proportional, or an exposure seems to act differently early and late in follow-up, and the model must be stratified, re-specified or given time-varying terms, with the choice reported.
HAZARD AND CLUSTER MODELS
Time-to-Event and Mixed Models Fitted in Stata
Stata has dedicated commands for both families, and each expects the data to be declared in a form those commands understand before any model is fitted.
Declaring Time and Events
The time origin, the end of follow-up and the event indicator are declared with stset, and censoring is described before any estimate is read, in line with Survival Analysis.
Cox and Parametric Models
After stset, stcox in Stata fits a Cox model that leaves the baseline hazard unspecified, whereas streg fits a Weibull or similar parametric model that needs a distribution that fits, so the choice follows the question and the shape of the hazard.
Checking the Hazard Assumption
Tests and plots based on Schoenfeld residuals examine proportionality, and a covariate that violates it is stratified or given a time-varying effect, with the choice reported.
Random Effects for Clusters
A linear mixed model for a clustered outcome begins with a random intercept for each cluster, and a slope is added only if the data support it, as set out for Linear Mixed Models (Multilevel Modeling).
Binary and Count Outcomes
Mixed-effects logistic and Poisson models handle clustered binary and count outcomes, with the between-cluster variance reported so that the size of the clustering is visible.
Reporting for Clinical Work
Hazard ratios, variance components and the numbers of clusters and events are tabulated with intervals, and the wider setting is described under Clinical, Survival & Epidemiological.
MODELING RISKS
Faults That Slip Into Event-Time and Cluster Models
These models run without complaint when the setup is wrong, so the faults below are found by inspecting the data and the model, not by waiting for an error message.
Time Scale Chosen by Default Follow-up is counted from study entry when the natural origin is diagnosis or randomization, which changes who is at risk at each time, so the origin is chosen from the research question and stated in the methods section.
Censoring That Carries Information Participants who leave because they are getting worse are treated as ordinary censored cases, which biases the hazards when withdrawal relates to prognosis, so the reasons for leaving are examined before the hazards are interpreted.
Immortal Time in an Exposure A treatment defined by something received after follow-up begins gives exposed participants time in which the event could not occur, which flatters the treatment unless exposure is modeled as time-varying.
Clusters Counted as Individuals Clustered observations are analyzed as if independent, so standard errors are too small, whereas a mixed model separates the variation between clusters from the variation within them and states the uncertainty accordingly.
Few Clusters, Many Random Effects A handful of clusters is asked to support several variance components, and the estimates become unstable, so the random part is kept as simple as the number of clusters allows, and the reason is recorded.
Many Ties in Event Times Event times recorded to the month or the year produce many ties, and the method used to handle them can shift the estimates, so the approach is stated and compared when ties are frequent.
DELIVERABLES
Fitted Models, Checks and Tables Delivered
The files follow the order of the modeling decisions, from the stset declaration to the last table, so a hazard ratio or a variance component can be traced to its source.
Do-File From stset to Tables One do-file declares the time and event variables, fits the agreed models and saves the results, so that every estimate can be regenerated from the data in the order it was produced.
Follow-Up and Censoring Summary A summary of follow-up time, events, censoring patterns and numbers at risk, which shows how much information each stretch of follow-up contributes to the estimates.
Proportionality Check Record The residual tests and plots used to examine proportional hazards, with the stratified or time-varying alternative shown wherever the assumption did not hold.
Variance Component Table Random-effect variances, the intraclass correlation where the model supports one, and the numbers of clusters and observations, laid out beside the fixed effects and their confidence intervals.
Hazard Ratio Tables Hazard ratios with 95% confidence intervals and the numbers of participants and events behind each model, formatted for the target journal or the thesis template.
Model Choice Memo A short memo on the time scale, the baseline-hazard choice, the random-effects structure and the alternatives tried, which the methods section can draw on.
ILLUSTRATIVE EXAMPLE
A Center Effect and a Changing Hazard
An illustration of how a time-to-event model for claimants treated in several clinics is specified, checked and reported.
ILLUSTRATIVE EXAMPLE · WORKERS' COMPENSATION RETURN-TO-WORK RESEARCH · UNITED STATES
An occupational health researcher wanted to know whether time to sustained return to work after a back injury differed between claimants managed under a direct-access physical therapy pathway and those referred through a physician, using claims records from many rehabilitation clinics.
Return-to-work rates differed between clinics, so the plan specified a Cox model with a shared frailty for clinic, with pathway, age, job demands and injury severity chosen from clinical and occupational reasoning. The complication appeared in the diagnostics: the Schoenfeld residual test and plot showed that the association with pathway changed over follow-up. The model was re-specified so that the hazard ratio could differ before and after the first three months, the cut point was justified clinically and compared with alternatives, and the variation between clinics was reported beside the fixed effects. The result was worded as an association, since pathways were not randomized, and differences in how clinics coded injury severity were documented as a limitation.
Outcome: The researcher received the hazard ratio for each period with its 95% confidence interval, the clinic-level variance, the residual diagnostics, the do-file and the model choice memo.
This example does not describe a real client or project. We never share details of actual client work, in line with the confidentiality we promise every researcher. This illustrative example is included to show the type, scope, and standard of work involved in this kind of analysis.
SOFTWARE
Stata, R, SAS and Mplus for These Models
The models are fitted in Stata, though a coauthor's existing code or a latent-variable extension can place the same analysis in another program.
HOW WE ASSESS YOUR PROJECT
We Review Your Actual Research
Your assessment is based on a review of your actual research materials and analytical requirements, not a generic project category.
You Submit Your Project Details
Share whatever is available at the current stage, such as the research aim, protocol or proposal, dataset, variable list, manuscript or statistical requirements.
We Review Your Research and Define the Scope
Your statistically trained Research Coordinator reviews the materials to understand the study design, research questions, data structure, analytical requirements and expected deliverables. If clarification is needed, additional documents or information can be requested before the assessment is finalized.
Your Analysis Is Completed and Delivered
Once the scope and fixed project fee are agreed, the in-house statistician completes the work and the Research Coordinator remains the communication point through delivery, with the analysis, deliverables and timeline as agreed.
RESEARCH RESPONSIBILITY
The Researcher Weighs What the Hazards Mean
Whether a hazard ratio or a between-cluster variance is large enough to matter is a question about patients, schools or services, and the researcher answers it.
Agreed Statistical Support OnlineSPSS provides the agreed methodological, analytical and statistical reporting support, defined in a scope that is confirmed before any work begins.
No Intellectual Contribution OnlineSPSS does not claim any intellectual contribution to your article, protocol or thesis chapter. Authorship remains the researcher's.
Your Research Decisions The choice of outcome and exposure, the clinical or policy meaning of the estimates, the discussion and the conclusions remain yours, with overall responsibility.
Confidential Handling Research data and project details are handled confidentially, project information is not published, and an NDA is available where required.
FREQUENTLY ASKED QUESTIONS
Questions About Follow-Up and Clustered Models in Stata
Stata's survival setup decides what a frailty term, a late entry date or a covariate that changes during follow-up can do in a model.
Which model families does the Stata work at OnlineSPSS include?
OnlineSPSS fits time-to-event models and mixed-effects models for clustered data in Stata, from declaring the data structure to the diagnostics and the reporting of intervals. A project may need only one of the two families, or both when patients are followed within centers. The mix is settled once the follow-up and cluster structure of your data are known.
Is a frailty term the same as a random intercept?
They are close relatives. A shared frailty term is a cluster-level random effect on the hazard, so a Cox model with one plays the role that a random intercept plays in a linear mixed model. The frailty is often assumed to be gamma distributed and the intercept normal, and the hazard ratios then compare participants within a cluster. Cluster-robust standard errors are an alternative when the variation between clusters is not itself of interest.
What if participants entered the study at different times after diagnosis?
This is delayed entry, also called left truncation. People who joined later were not under observation before they entered, so they must not count as being at risk before that time. Ignoring entry times distorts the risk sets and favors those who survived long enough to be enrolled. Stata’s stset accepts an entry time, and the time scale, such as age or time since diagnosis, is chosen with the entry pattern in view.
Can one Stata dataset hold covariates that change during follow-up?
Yes. Stata’s survival commands accept several records per participant, each covering a stretch of follow-up with the covariate values that applied then, once stset is given the participant identifier through id(). A treatment started midway then enters the model when it began, which keeps immortal time out of the exposed group, and stsplit divides follow-up so that an effect can differ between periods.
How do you protect my research data and confidentiality?
Your data and project details are handled confidentially, and project information is not published. Researcher names and institutions are not used publicly without permission. An NDA is available where required and can be arranged before sensitive materials are shared. Read more about confidentiality and data security.
What happens when I request an assessment, and how long does it take?
You share your research question, design and relevant materials, such as the dataset, the follow-up and event definitions, any do-files and the model output you already have, and a statistically trained Research Coordinator reviews the request and responds the same day. The analysis, deliverables, timeline and one fixed project fee are then confirmed before any work begins, so the timeline follows from the agreed scope.
Still have a question? Find more answers in our full FAQ.
NEXT STEP
A Model That Respects
Follow-Up and Clustering
Which events count, when the clock starts and which units form the clusters are settled before the first Stata model is fitted.
No Commitment Required
Strictly Confidential
Fixed Project Fee
In-House PhD-Level Statisticians
Same-Day Response
Serving Researchers Since 2014