Choose the model from the outcome, not from the software menu.
Work through these six prompts in order. Each one supplies what the next one needs.
1 · Before you start
Choose one later outcome and the quantity you want to estimate. Start with six-month disability if this is your first regression; the other outcome routes then reuse the same decision pattern.
Words used on this page
Dependent variable
another name for the outcome being modelled.
Model
a mathematical description linking an outcome to an exposure and other variables.
Estimand
the exact quantity the analysis aims to estimate, such as an adjusted odds ratio.
Covariate
an additional recorded variable included in a model.
Adjusted association
the exposure–outcome comparison after accounting for named covariates; it is not automatically causal.
2 · Why this comes now
Outcome types encode different questions and distributions. Regression comes after unadjusted measures so you can see what adjustment changes and keep the cohort design—not the command—at the centre.
3 · Do this now
Classify the outcome as continuous, binary, ordered, count or time-to-event.
Choose the matching model and select baseline covariates before seeing which result you prefer, using clinical knowledge about likely causes.
Fit the model, check its assumptions and interpret the medication-review coefficient on the correct scale.
Same step in Stata and SPSS
Run Section 05 in Stata or SPSS. Stata and SPSS use different procedures, but the outcome, reference group, covariates and target interpretation must remain identical.
4 · Read this
Find the medication-review estimate, 95% confidence interval and analysis n, then read the model-specific diagnostic. Exp(B) in SPSS and an exponentiated Stata coefficient are ratios; an unexponentiated linear coefficient is a mean difference.
5 · Ready to continue when
You can justify the model from the outcome, explain every adjustment variable, name the effect scale and report an adjusted association without claiming an effect.
Every model compares participants who did and did not receive medication
review at baseline, then relates that exposure to a later six-month
outcome. The target is an adjusted association under the assumptions that
the model is correctly specified and that the recorded baseline variables
adequately address confounding. It is not automatically a causal effect.
Continuous
Linear
Adjusted mean difference. Check linearity, residual pattern, influential cases and dependence.
Binary
Logistic
Adjusted odds ratio. Add predicted probabilities when absolute interpretation matters.
Ordered
Ordered logistic
Cumulative odds across ordered thresholds. Check proportional odds.
Count
Poisson
Incidence-rate ratio. Check exposure time, overdispersion and excess zeros.
Time to event
Cox
Hazard ratio with censoring. Check the proportional-hazards assumption.
Model choice does not end at “continuous or binary”. Repeated measures,
clustering, competing risks, non-linearity and survey design can all
require a different model or variance estimator.
Adjustment rationale
Why are these baseline variables in the models?
The teaching adjustment set is chosen before fitting the outcome models.
Each variable was measured before medication review and is a plausible
cause, or marker of causes, of both review allocation and later health.
The same set makes the five worked models comparable; a real protocol
should justify the set separately for its precise estimand and outcome.
Baseline variable
Why it may affect review
Why it may affect outcomes
Age
Older adults may trigger closer medicines review.
Age predicts frailty, disability, falls and admission.
Sex
Prescribing and care pathways may differ.
Outcome risks and health trajectories may differ.
Living alone
Medication support needs may prompt review.
Social support can affect disability and hospital use.
Deprivation
Access, prescribing burden and clinical need may differ.
Deprivation is associated with later health.
Medication count
It is a direct reason to consider a review.
Treatment burden and multimorbidity predict adverse outcomes.
Baseline frailty
Frailer participants are more likely to be reviewed.
It strongly predicts every later outcome considered here.
Baseline disability
Existing functional problems can prompt review.
It predicts later disability and wider health outcomes.
Not included twice: polypharmacy is derived from medication
count, so including both would encode the same information twice. Variables
recorded after review would not belong in a total-effect adjustment set if
they could be mediators.
Five outcome routes
Work from the outcome to the interpretation.
Continuous outcome
Quality of life at six months
What are we interested in?
The adjusted difference in mean EQ-5D-style score between reviewed and unreviewed participants.
Model: linear regression because the outcome is a bounded but approximately continuous score.
Reference: no medication review.
Check: residual pattern, linearity of continuous covariates, influential observations and whether the bounded scale causes serious misfit.
medication review coefficient = +0.047
95% CI = +0.034 to +0.059
analysis n = 704
Interpret exactly
After adjustment for the pre-specified baseline variables, reviewed participants had a mean six-month quality-of-life score 0.047 points higher than unreviewed participants (95% CI 0.034 to 0.059). This is an adjusted cohort association, not proof that review caused the difference.
Binary outcome
Disability by six months
What are we interested in?
The adjusted relative odds of disability for reviewed versus unreviewed participants.
Model: logistic regression because disability is recorded yes/no.
Reference: no medication review; an odds ratio of 1 means equal adjusted odds.
Check: outcome coding, linearity on the logit for continuous covariates, influential observations and sparse cells.
adjusted odds ratio = 0.64
95% CI = 0.41 to 1.01
analysis n = 712
Interpret exactly
After adjustment, the reviewed group had an estimated 36% lower odds of six-month disability than the unreviewed group (OR 0.64, 95% CI 0.41 to 1.01). The interval includes 1 and is compatible with substantially lower odds through to almost no difference. Odds are not risks.
Ordered outcome
Robust, pre-frail or frail
What are we interested in?
The adjusted odds of being in a worse frailty category rather than a better category.
Model: ordered logistic regression because the three categories have a meaningful order.
Reference: no medication review; categories remain ordered from robust to frail.
Check: the proportional-odds assumption, which requires one exposure odds ratio to describe every cumulative cut-point.
adjusted common odds ratio = 0.43
95% CI = 0.28 to 0.66
analysis n = 705
Interpret exactly
Assuming proportional odds, reviewed participants had 57% lower adjusted odds of being at or above a worse frailty category than unreviewed participants (common OR 0.43, 95% CI 0.28 to 0.66). The interpretation applies across both category thresholds.
Count outcome
Number of falls during follow-up
What are we interested in?
The adjusted rate of recorded falls in the reviewed group relative to the unreviewed group.
Model: Poisson regression because participants can experience zero, one or several falls.
Follow-up: all participants share the same six-month window here; unequal time would require an exposure offset.
Check: overdispersion, excess zeros and whether a negative-binomial or other count model is more credible.
adjusted incidence-rate ratio = 0.79
95% CI = 0.63 to 0.98
analysis n = 710
Interpret exactly
The adjusted fall rate in reviewed participants was 0.79 times that in unreviewed participants—about 21% lower (IRR 0.79, 95% CI 0.63 to 0.98). This concerns the event rate, not the proportion of people who fell.
Time-to-event outcome
Time to first emergency admission
What are we interested in?
The adjusted admission hazard at each observed time among participants still admission-free.
Model: Cox regression because the timing of admission and right-censoring both matter.
Data: follow-up days plus an indicator of first admission; 134 admissions occurred among 720 participants.
Check: proportional hazards, influential observations, censoring assumptions and competing events.
adjusted hazard ratio = 0.70
95% CI = 0.47 to 1.07
participants / events = 720 / 134
Interpret exactly
At any observed time, reviewed participants had an estimated 30% lower admission hazard than unreviewed participants after adjustment (HR 0.70, 95% CI 0.47 to 1.07). The interval includes 1. This does not mean that 30% fewer people were admitted.
Run the models
Equivalent syntax, one epidemiological interpretation.
Stata, SPSS and R arrange their output differently. The coefficient,
reference group, adjustment set and interpretation should nevertheless
answer the same cohort question.
Stata
The Stata command changes with the outcome. Factor-variable prefixes keep binary and categorical predictors explicit.