Former UK National Adult Cardiac Surgery Audit Statistician (2012-14) • Researcher who has published in cardiothoracic journals • Assistant Editor (Statistical Consultant) for the EJCTS and ICVTS (2012— present) 900 papers reviewed to-date
use to transform complex raw data into meaningful results • We live in a world of evidence-based medicine, and statistics is the lingua franca • Choice of statistical methods will depend on several things, including: – Clinical question – Study design – Outcomes
of one patient; a mistake in statistical analysis or interpretation can lead to hundreds of early deaths. So it is perhaps odd that, while we allow a doctor to conduct surgery only after years of training, we give SPSS® (SPSS, Chicago, IL) to almost anyone.” Vickers A. Nat Clin Pract Urol. 2005;2(9):404-405.
of blood transfused after surgery • Dichotomous / binary – E.g. 30-day mortality status (dead versus alive) • Time-to-event – E.g. time from surgery to death or re-intervention • Ordinal – E.g. MV regurgitation grade at 12-months post-surgery • Count – E.g. number of infections in first post-treatment year
= 20.0% 30.7% in the TAVI group 50.7% in the standard therapy group • HR uses all data at each time point • Not robust to departures from proportionality Source: Makkar et al. N Engl J Med 2012; 366:1696-1704.
– ⍺ = 0.05 (or 5%) – β = 0.20 (corresponding to a power of 0.8, or 80%) • To estimate sample size needed, we also need the minimum clinically relevant difference (MCRD) – Pilot studies – Published evidence – Clinical knowledge • Essential that sample size calculation is reported + parameters used
specified statistical model (null hypothesis) that a statistical summary of the data would be equal to or more extreme than its observed value • Absence of evidence is not evidence of absence Source: https://xkcd.com/1478/
with a specified statistical model 2. P-values do not measure the probability that the studied hypothesis is true, or the probability that the data were produced by random chance alone 3. Scientific conclusions and business or policy decisions should not be based only on whether a P-value passes a specific threshold 4. Proper inference requires full reporting and transparency 5. A P-value, or statistical significance, does not measure the size of an effect or the importance of a result 6. By itself, a P-value does not provide a good measure of evidence regarding a model or hypothesis Source: Wasserstein & Lazar. The American Statistician. 2016; 70(2): 129-133.
– Allows for either treatment to be superior • One-tailed tests only try to detect effect in one direction of interest – Can be abused; e.g. two-tailed P=0.06, one-tailed P=0.03 • One-tailed tests useful if: – treatment effect possible in only one direction; and – it would not be irresponsible or unethical to miss an effect in the opposite direction
CI for the mean outcome • Imagine that you could then independently sample another n subjects and re-calculate the 95% CI • Do this lots and lots of times • 95% of those intervals will contain the true population mean • It does not mean that there is a 95% probability that the population parameter lies within the interval We can use the CI to gauge plausible estimates and assess if clinically relevant Figure source: http://www.propharmagroup.com/blog/understanding- statistical-intervals-part-1-confidence-intervals
size increase • Which is more clinically significant? – Length of stay recorded for n patients randomized to open or EVAR surgery – Scenario 1: n = 16, difference 1-day (SD = 1-days) P=0.065 – Scenario 2: n = 2000, difference = 0.1-days (SD = 1-day); P=0.026 • Clinical significance ≠ statistical significance • Interpret the confidence interval rather than the P-value
suspected acute MI to intravenous streptokinase, oral aspirin, both, or neither – Aspirin produced a highly significant reduction in 5-week vascular mortality relative to placebo – Subgroup analysis: patients were divided into 12 astrological star sign groups – In the Gemini and Libra groups, aspirin had a non-significant adverse effect • Subgroup analyses should only be considered as hypothesis generating, rather than hypothesis testing • A non-significant effect in a subgroup does not mean no effect is present → studies usually not powered for subgroup analyses
of a treatment on subjects, where the assignment of subjects into a treated group versus a control group is outside the control of the investigator Designs: • Case-control studies • Cohort studies • Cross-sectional studies
kidney stones were non-randomly assigned to either open surgery (Group O; n = 350) or percutaneous nephrolithotomy (PN) (Group P; n = 350) • Successfully treated: – Group O: 273 patients (78%) – Group PN: 289 patients (83%) • Conclusion: PN is preferable to O • What if the patients are separated into those with small and large kidney stones? Source: Charig CR et al. BMJ, 1986; 292(6524): 879–882.
Stones ³2cm 73% (192/263) 69% (55/80) Total 78% (273/350) 83% (289/350) • Confounder: a variable associated with both exposure and outcome • 270/357 (76%) patients with small stones were assigned to PN, whereas 263/343 (77%) patients with large stones were assigned to open surgery • Simpson’s paradox: confounding reverses effect of exposure
1. the primary predictor (mechanical vs. biological valve) 2. and the outcome(s) under consideration 3. after the potential distortion through covariates has been eliminated
(e.g. aneurysm diameter) Multiple linear regression Expected increase in outcome Binary (e.g. in-hospital mortality) Multiple logistic regression Log odds ratio Time-to-event (e.g. time to all-cause mortality) Multiple Cox proportional hazards regression Log hazard ratio *Other regression models exist as well
– Depends on the number of events (not the sample size) – Rule-of-thumb: 1 covariate per 10 events • How do I decide which covariates to include? – Univariable pre-screening – Stepwise regression – Clinical knowledge • How do I model continuous covariates? – E.g. very large BMI is usually associated with increased hospital mortality, but so is very low BMI ⇒ U-shape • What model assumptions am I making, and how do I check them? – E.g. Cox regression depends on the assumption of “proportional hazards” • How to handle missing data? Picture source: Strauss V. The Washington Post. March 27, 2013
(or more) controls Covariance adjustment •Include the PS as a covariate along with the treatment variable Inverse probability treatment weights (IPTW) •Weight every observation according to the PS Stratification •Split the data up in 5 (or more) groups using quantiles of the PS • The propensity score (PS) is defined as a subject’s probability of treatment assignment conditional on measured covariates • Can usually estimate the PS using multiple logistic regression • Different methods available to estimate the treatment effects
1181 biological implant patients • Confirmed that they were well-balanced groups on known confounders • Compared in-hospital mortality using simple univariable analysis • Question: should we account for the paired nature of the data? – Chi-square test vs. McNemar test? Source: Dimarakis et al. Heart 2014;100:500–507.
propensity score model often requires several iterations – Interaction terms – Higher-order terms – What if a known confounder is not measured (cf. frailty for TAVI) • What if we have missing data? • N-to-1 matching • Matching with or without replacement? • …
chance • Can be caused by: – clinical differences (e.g. all-comers vs. octogenarians) – methodological differences (RCT vs. observational study) • Usual assessment involves: – I2-statistic: the percentage of total variation across studies that is due to heterogeneity rather than chance – Cochran’s Q-test: significant values (P < 0.1) provide evidence against homogeneity
than medicine • NRS subject to inherent selection bias • Present separate meta-analyses; avoid pooling RCTs and NRSs • When pooling NRSs, consider what effect is being pooled: – crude (unadjusted) – multivariable regression adjusted – propensity score adjusted – then ask whether they are sufficiently homogeneous to combine Higgins JPT, Green S (editors). Cochrane Handbook for Systematic Reviews of Interventions Version 5.1.0 [updated March 2011]. The Cochrane Collaboration, 2011. Available from www.handbook.cochrane.org.
value of published health research literature • To encourage this there are several transparent and accurate reporting guidelines available • Checklists often required by journals at time of submission http://www.equator-network.org