Forest Plot: graphical representation of a meta-analysis
- usually big studies draw the report
- Bigger studies tend to have a smaller standard error
FOREST PLOTS IN META-ANALYSIS
- Know what outcome you are measuring
- Horizontal entries represents studies included but does not mean all the studies that were found in the search
- Tips of the diamond refer to the confidence interval
- Size of square = weight in analysis
- Weighting is based on inverse variance – low variance = high precision = higher weight
- Narrower CI = proportional to N
- Intention to Treat Analysis (ITT):
- all participants that are randomized must be included in the final analysis and analyzed according to the treatment group to which they were originally assigned, regardless of the treatment received, withdrawals, loss to f/u or cross-overs
- However…in many instances in randomized trials the term intention-to-treat was inappropriately described and participants improperly excluded.
- Generally preferred as they are unbiased, and also because they address a more pragmatic and clinically relevant question.
Keep participants in the intervention groups to which they were randomized, regardless of the intervention they actually received.
Measure outcome data on all participants.
Include all randomized participants in the analysis.
There is no clear consensus on whether all criteria should be applied (Hollis 1999). While the first is widely agreed, the second is often impossible and the third is contentious, since to include participants whose outcomes are unknown (mainly through loss to follow-up) involves imputing (‘filling-in’) the missing data.
- Modified Intention to Treat Analysis (mITT):
- No clear definition and can vary from trial to trial
- Primary factor that characterizes the mITT: Post-randomization exclusion
- Lies between true ITT and per-protocol analyses
- E.g. patient receives at least 1 dose of medication and 1 efficacy measurement…as long as that happens they were included in that study
- Also consider the number that was censored
- More likely to report post-randomization exclusion and more likely to be associated with industry sponsorship
- As-Treated: subjects analyzed according to whether they got treatment or not (regardless of allocation) → May be used for safety events
- Sensitivity analysis: a repeat of the primary analysis or meta-analysis, substituting alternative decisions or ranges of values for decisions that were arbitrary or unclear
- Asks the question: “Are the findings robust to the decisions made in the process of obtaining them?”
For dichotomous outcomes, should odds ratios, risk ratios or risk differences be used?
And for continuous outcomes, where several scales have assessed the same dimension, should results be analysed as a standardized mean difference across all scales or as mean differences individually for each scale?
- Different from subgroup analysis:
I. sensitivity analyses do not attempt to estimate the effect of the intervention in the group of studies removed from the analysis, whereas in subgroup analyses, estimates are produced for each subgroup.
II. in sensitivity analyses, informal comparisons are made between different ways of estimating the same thing, whereas in subgroup analyses, formal statistical comparisons are made across the subgroups.
Intention to Treat and Per Protocol
|Intention to Treat
|All subjects who were randomized are analyzed at the end of the trial according to the treatment to which they were originally assigned
- Included if dropped out
- Included if non-compliant (with treatment or following up monitoring)
- Included in original group even if treatment changed
This does not fix problems related to loss to f/u
Superiority studies: Conservative
– since the groups are made more artificially similar = makes it harder to show a difference
– minimizes risk of Type I error
Non-inferiority studies: Less conservative
– “is the drug non-inferior = no different than the other”
– difference will not be as visible
|All subjects are included only if they received the intended intervention in accordance with the protocol
= ideal patients
- Excluded if dropped out
- Excluded if non-compliant (with treatment or follow-up monitoring)
- Analyzed in new group if treatment changed
- Does not test for practical benefit of an intervention
Superiority studies: less conservative
– if there is a benefit, it would be the most visible difference
Non-inferiority studies: conservative
– easier to find difference = we want to exacerbate the difference when doing non-inferiority study
I will be starting my Medication Use Evaluation rotation today and my personal learning objectives are:
- Improve and build a systematic approach to performing literature searches and critically appraising
- E.g. Gain more familiarity and use at least 2 different tools to perform critical appraisal
- Develop an approach to reviewing Cochrane Reviews and a template to submitting feedback for literature
Will continue to add objectives as the rotation progresses! 🙂
The following are the objectives from the manual:
- MUE Roles & Responsibilities
- Describe the roles and responsibilities of MUE within LMPS, including, but not limited to:
- Regional and local Pharmacy / Drugs and Therapeutics Committee support
- Pre-printed Orders creation and management
- Drug policy / protocol / guideline creation and regulation
- Evaluation of drug use through retrospective chart review, drug usage reports, etc.
- Formulary Management
- Define formulary management and discuss the components necessary for success.
- Describe the steps necessary for addition/deletion of formulary items.
- Outline possible methods of alerting pharmacy/medical staff of changes to formulary items, therapeutic alternatives, and cost saving strategies
- Drug Data Procurement
- Explain how drug data procurement can support decisions and improve pharmacotherapy in the institution
- Understand the DUE process including prioritizing target drugs, establishing DUE criteria, methods of intervention, implementation of programs, evaluation of patient outcomes & economic outcomes.
I won’t be doing my MUE rotation until much later on in my residency, so it was nice to get an introduction into MUE early on in the residency. Before the start of my MUE rotation, I will review the following notes:
- Main Purpose: Evaluate medications for the formulary
- for new drugs/therapies or new indications for an existing drug
- 3 layers of PNT (Pharmacy And Therapeutics):
- top layer = provincial layer (MUE pharmacists who review and evaluate drugs for the entire province)
- Indications, Restrictions, Exclusions = are done by provincial
- middle layer = regional layer (each health authority (e.g. VCH, providence, VIHA))
- anything that is formulary has to make into policies and decisions on how to use it
- – if we list a drug from formulary, what can we institute to make sure it is effective and safe – e.g. PPO or restrict to certain prescribers or restriction criteria by indication – e.g. dabigatran = restrict for only post-op ortho ß this restriction is determined at a provincial layer…if at VGH we are determining that only ortho docs can order ß determined at regional layer and can turn into a PPO, drug use evaluations = put out memos, quarterly newsletter, policies. Some policies are corporate policies (two things you do in hospital for everybody who is admitted = assess HIV status, DVT prophylaxis).
- Restrictions by area, by prescriber = done by regional
- bottom layer = local PNT (for each hospital…make sure that provincial and regional policies are all followed, and to get feedback for the upper layers).
- If a drug is restricted = possible ways of restriction are by indication, by prescriber or by area
- Excluded = for health authorities, does not make sense for us to carry it…but it does not mean it cannot be used…it can be brought in on a case by case basis
- If something has increased in budget = PNT will evaluate them
- Therapeutic Interchange: done to minimize spending since can’t carry everything
- Most common ARB in the community: cand, val, and telmisartan