Evidence Soup
How to find, use, and explain evidence.

149 posts categorized "healthcare: evidence-based medicine & healthIT"

Wednesday, 09 August 2017

How evidence can guide, not replace, human decisions.

Bad Choices book cover

1. Underwriters + algorithms = Best of both worlds.
We hear so much about machine automation replacing humans. But several promising applications are designed to supplement complex human knowledge and guide decisions, not replace them: Think primary care physicians, policy makers, or underwriters. Leslie Scism writes in the Wall Street Journal that AIG “pairs its models with its underwriters. The approach reflects the company’s belief that human judgment is still needed in sizing up most of the midsize to large businesses that it insures.” See Insurance: Where Humans Still Rule Over Machines [paywall] or the podcast Insurance Rates Set by ... Machine Intelligence?

Who wants to be called a flat liner? Does this setup compel people to make changes to algorithmic findings - necessary or not - so their value/contributions are visible? Scism says “AIG even has a nickname for underwriters who keep the same price as the model every time: ‘flat liners.’” This observation is consistent with research we covered last week, showing that people are more comfortable with algorithms they can tweak to reflect their own methods.

AIG “analysts and executives say algorithms work well for standardized policies, such as for homes, cars and small businesses. Data scientists can feed millions of claims into computers to find patterns, and the risks are similar enough that a premium rate spit out by the model can be trusted.” On the human side, analytics teams work with AIG decision makers to foster more methodical, evidence-based decision making, as described in the excellent Harvard Business Review piece How AIG Moved Toward Evidence-Based Decision Making.


2. Another gem from Ali Almossawi.
An Illustrated Book of Bad Arguments was a grass-roots project that blossomed into a stellar book about logical fallacy and barriers to successful, evidence-based decisions. Now Ali Almossawi brings us Bad Choices: How Algorithms Can Help You Think Smarter and Live Happier.

It’s a superb example of explaining complex concepts in simple language. For instance, Chapter 7 on ‘Update that Status’ discusses how crafting a succinct Tweet draws on ideas from data compression. Granted, not everyone wants to understand algorithms - but Bad Choices illustrates useful ways to think methodically, and sort through evidence to solve problems more creatively. From the publisher: “With Bad Choices, Ali Almossawi presents twelve scenes from everyday life that help demonstrate and demystify the fundamental algorithms that drive computer science, bringing these seemingly elusive concepts into the understandable realms of the everyday.”


3. Value guidelines adjusted for novel treatment of rare disease.
Like it or not, oftentimes the assigned “value” of a health treatment depends on how much it costs, compared to how much benefit it provides. Healthcare, time, and money are scarce resources, and payers must balance effectiveness, ethics, and equity.

Guidelines for assessing value are useful when comparing alternative treatments for common diseases. But they fail when considering an emerging treatment or a small patient population suffering from a rare condition. ICER, the Institute for Clinical and Economic Review, has developed a value assessment framework that’s being widely adopted. However, acknowledging the need for more flexibility, ICER has proposed a Value Assessment Framework for Treatments That Represent a Potential Major Advance for Serious Ultra-Rare Conditions.

In a request for comments, ICER recognizes the challenges of generating evidence for rare treatments, including the difficulty of conducting randomized controlled trials, and the need to validate surrogate outcome measures. “They intend to calculate a value-based price benchmark for these treatments using the standard range from $100,000 to $150,000 per QALY [quality adjusted life year], but will [acknowledge] that decision-makers... often give special weighting to other benefits and to contextual considerations that lead to coverage and funding decisions at higher prices, and thus higher cost-effectiveness ratios, than applied to decisions about other treatments.”

Monday, 31 July 2017

Resistance to algorithms, evidence for home visits, and problems with wearables.

Kitty with laptop

I'm back, after time away from the keyboard. Yikes! Evidence is facing an uphill battle. Decision makers still resist handing control to others, even when new methods or machines make better predictions. And government agencies continue to, ahem, struggle with making evidence-based policy.  — Tracy Altman


1. Evidence-based home visit program loses funding.
The evidence base has developed over 30+ years: Advocates for home visit programs - where professionals visit at-risk families - cite immediate and long-term benefits for parents and for children. Things like positive health-related behavior, fewer arrests, community ties, lower substance abuse [Long-term Effects of Nurse Home Visitation on Children's Criminal and Antisocial Behavior: 15-Year Follow-up of a Randomized Controlled Trial (JAMA, 1998)]. Or Nobel Laureate-led findings that "Every dollar spent on high-quality, birth-to-five programs for disadvantaged children delivers a 13% per annum return on investment" [Research Summary: The Lifecycle Benefits of an Influential Early Childhood Program (2016)].

The Nurse-Family Partnership (@NFP_nursefamily), a well-known provider of home visit programs, is getting the word out in the New York Times and on NPR.

AEI_funnel_27jul17

Yet this bipartisan, evidence-based policy is now defunded. @Jyebreck explains that advocates are “staring down a Sept. 30 deadline.... The Maternal, Infant and Early Childhood Home Visiting program, or MIECHV, supports paying for trained counselors or medical professionals” where they establish long-term relationships.

It’s worth noting that the evidence on childhood programs is often conflated. AEI’s Katharine Stevens and Elizabeth English break it down in their excellent, deep-dive report Does Pre-K Work? They illustrate the dangers of drawing sweeping conclusions about research findings, especially when mixing studies about infants with studies of three- or four-year olds. And home visit advocates emphasize that disadvantage begins in utero and infancy, making a standard pre-K program inherently inadequate. This issue is complex, and Congress’ defunding decision will only hurt efforts to gather evidence about how best to level the playing field for children.

AEI Does Pre-K Work

2. Why do people reject algorithms?
Researchers want to understand our ‘irrational’ responses to algorithmic findings. Why do we resist change, despite evidence that a machine can reliably beat human judgment? Berkeley J. Dietvorst (great name, wasn’t he in Hunger Games?) comments in the MIT Sloan Management Review that “What I find so interesting is that it’s not limited to comparing human and algorithmic judgment; it’s my current method versus a new method, irrelevant of whether that new method is human or technology.”

Job-security concerns might help explain this reluctance. And Dietvorst has studied another cause: We lose trust in an algorithm when we see its imperfections. This hesitation extends to cases where an ‘imperfect’ algorithm remains demonstrably capable of outpredicting us. On the bright side, he found that “people were substantially more willing to use algorithms when they could tweak them, even if just a tiny amount”. Dietvorst is inspired by the work of Robyn Dawes, a pioneering behavioral decision scientist who investigated the Man vs. Machine dilemma. Dawes famously developed a simple model for predicting how students will rank against one another, which significantly outperformed admissions officers. Yet both then and now, humans don’t like to let go of the wheel.

Wearables Graveyard by Aaron Parecki

3. Massive data still does not equal evidence.
For those who doubted the viability of consumer health wearables and the notion of the quantified self, there’s plenty of validation: Jawbone liquidated, Intel dropped out, and Fitbit struggles. People need a compelling reason to wear one (such as fitness coach, or condition diagnosis and treatment).

Rather than a data stream, we need hard evidence about something actionable: Evidence is “the available body of facts or information indicating whether a belief or proposition is true or valid (Google: define evidence).” To be sure, some consumers enjoy wearing a device that tracks sleep patterns or spots out-of-normal-range values - but that market is proving to be limited.

But Rock Health points to positive developments, too. Some wearables demonstrate specific value: Clinical use cases are emerging, including assistance for the blind.

Photo credit: Kitty on Laptop by Ryan Forsythe, CC BY-SA 2.0 via Wikimedia Commons.
Photo credit: Wearables Graveyard by Aaron Parecki on Flickr.

Tuesday, 03 January 2017

Valuing patient perspective, moneyball for tenure, visualizing education impacts.

Patient_value
1. Formalized decision process → Conflict about criteria

It's usually a good idea to establish a methodology for making repeatable, complex decisions. But inevitably you'll have to allow wiggle room for the unquantifiable or the unexpected; leaving this gray area exposes you to criticism that it's not a rigorous methodology after all. Other sources of criticism are the weighting and the calculations applied in your decision formulas - and the extent of transparency provided.

How do you set priorities? In healthcare, how do you decide who to treat, at what cost? To formalize the process of choosing among options, several groups have created so-called value frameworks for assessing medical treatments - though not without criticism. Recently Ugly Research co-authored a post summarizing industry reaction to the ICER value framework developed by the Institute for Clinical and Economic Review. Incorporation of patient preferences (or lack thereof) is a hot topic of discussion.

To address this proactively, Faster Cures has led creation of the Patient Perspective Value Framework to inform other frameworks about what's important to patients (cost? impact on daily life? outcomes?). They're asking for comments on their draft report; comment using this questionnaire.

2. Analytics → Better tenure decisions
New analysis in the MIT Sloan Management Review observes "Using analytics to improve hiring decisions has transformed industries from baseball to investment banking. So why are tenure decisions for professors still made the old-fashioned way?"

Ironically, academia often proves to be one of the last fields to adopt change. Erik Brynjolfsson and John Silberholz explain that "Tenure decisions for the scholars of computer science, economics, and statistics — the very pioneers of quantitative metrics and predictive analytics — are often insulated from these tools." The authors say "data-driven models can significantly improve decisions for academic and financial committees. In fact, the scholars recommended for tenure by our model had better future research records, on average, than those who were actually granted tenure by the tenure committees at top institutions."

Education_evidence

3. Visuals of research findings → Useful evidence
The UK Sutton Trust-EEF Teaching and Learning Toolkit is an accessible summary of educational research. The purpose is to help teachers and schools more easily decide how to apply resources to improve outcomes for disadvantaged students. Research findings on selected topics are nicely visualized in terms of implementation cost, strength of supporting evidence, and the average impact on student attainment.

4. Absence of patterns → File-drawer problem
We're only human. We want to see patterns, and are often guilty of 'seeing' patterns that really aren't there. So it's no surprise we're uninterested in research that lacks significance, and disregard findings revealing no discernible pattern. When we stash away projects like this, it's called the file-drawer problem, because this lack of evidence could be valuable to others who might have otherwise pursued a similar line of investigation. But Data Colada says the file-drawer problem is unfixable, and that’s OK.

5. Optimal stopping algorithm → Practical advice?
In Algorithms to Live By, Stewart Brand describes an innovative way to help us make complex decisions. "Deciding when to stop your quest for the ideal apartment, or ideal spouse, depends entirely on how long you expect to be looking.... [Y]ou keep looking and keep finding new bests, though ever less frequently, and you start to wonder if maybe you refused the very best you’ll ever find. And the search is wearing you down. When should you take the leap and look no further?"

Optimal Stopping is a mathematical concept for optimizing a choice, such as making the right hire or landing the right job. Brand says "The answer from computer science is precise: 37% of the way through your search period." The question is, how can people translate this concept into practical steps guiding real decisions? And how can we apply it while we live with the consequences?

Tuesday, 20 December 2016

Choices, policy, and evidence-based investment.

Badarguments

1. Bad Arguments → Bad Choices
Great news. There will be a follow-on to the excellent Bad Arguments book by @alialmossawi. The book of Bad Choices will be released this April by major publishers. You can preorder now.

2. Evidence-based decisions → Effective policy outcomes
The conversative think tank, Heritage Foundation, is advocating for evidence-based decisions in the Trump administration. Their recommendations include resurrection of PART (the Program Assessment Rating Tool) from the George W. Bush era, which ranked federal programs according to effectiveness. "Blueprint for a New Administration offers specific steps that the new President and the top officers of all 15 cabinet-level departments and six key executive agencies can take to implement the long-term policy visions reflected in Blueprint for Reform." Read a nice summary here by Patrick Lester at the Social Innovation Research Center (@SIRC_tweets).

Pharmagellan

3. Pioneer drugs → Investment value
"Why do pharma firms sometimes prioritize 'me-too' R&D projects over high-risk, high-reward 'pioneer' programs?" asks Frank David at Pharmagellan (@Frank_S_David). "[M]any pharma financial models assume first-in-class drugs will gain commercial traction more slowly than 'followers.' The problem is that when a drug’s projected revenues are delayed in a financial forecast, this lowers its net present value – which can torpedo the already tenuous investment case for a risky, innovative R&D program." Their research suggests that pioneer drugs see peak sales around 6 years, similar to followers: "Our finding that pioneer drugs are adopted no more slowly than me-too ones could help level the economic playing field and make riskier, but often higher-impact, R&D programs more attractive to executives and investors."

Details appear in the Nature Reviews article, Drug launch curves in the modern era. Pharmagellan will soon release a book on biotech financial modeling.

4. Unrealistic expectations → Questioning 'evidence-based medicine'
As we've noted before, @EvidenceLive has a manifesto addressing how to make healthcare decisions, and how to communicate evidence. The online comments are telling: Evidence-based medicine is perhaps more of a concept than a practical thing. The spot-on @trishgreenhalgh says "The world is messy. There is no view from nowhere, no perspective that is free from bias."

Evidence & Insights Calendar.

Jan 23-25, London: Advanced Pharma Analytics 2017. Spans topics from machine learning to drug discovery, real-world evidence, and commercial decision making.

Feb 1-2, San Francisco. Advanced Analytics for Clinical Data 2017. All about accelerating clinical R&D with data-driven decision making for drug development.

Tuesday, 27 September 2016

Improving vs. proving, plus bad evidence reporting.

Turtle slow down and learn something

If you view gathering evidence as simply a means of demonstrating outcomes, you’re missing a trick. It’s most valuable when part of a journey of iterative improvement. - Frances Flaxington

1. Immigrants to US don't disrupt employment.
There is little evidence that immigration significantly affects overall employment of native-born US workers. This according to an expert panel's 500-page report. We thought you might like this condensed version from PepperSlice.

Bad presentation alert: The report, The Economic and Fiscal Consequences of Immigration, offers no summary visuals and buries its conclusions deep within dense chapters. Perhaps methodology is the problem, documenting the "evidence-based consensus of an authoring committee of experts". People need concise synthesis and actionable findings: What can policy makers do with this information?

Bad reporting alert: Perhaps unsatisfied with these findings, Julia Preston of the New York Times slipped her own claim into the coverage, saying the report "did not focus on American technology workers [true], many of whom have been displaced from their jobs in recent years by immigrants on temporary visas [unfounded claim]". Rather sloppy reporting, particularly when covering an extensive economic study of immigration impacts.


Immigration

Key evidence: "Empirical research in recent decades suggests that findings remain by and large consistent with those in The New Americans (National Research Council, 1997) in that, when measured over a period of 10 years or more, the impact of immigration on the wages of natives overall is very small." [page 204]

Immigration also contributes to the nation’s economic growth.... Perhaps even more important than the contribution to labor supply is the infusion by high-skilled immigration of human capital that has boosted the nation’s capacity for innovation and technological change. The contribution of immigrants to human and physical capital formation, entrepreneurship, and innovation are essential to long-run sustained economic growth. [page 243]

Author: @theNASEM, the National Academies of Sciences, Engineering, and Medicine.

Relationship: immigration → sustains → economic growth


2. Improving vs. proving.
On @A4UEvidence: "We often assume that generating evidence is a linear progression towards proving whether a service works. In reality the process is often two steps forward, one step back." Ugly Research supports the 'what works' concept, but wholeheartedly agrees that "The fact is that evidence rarely provides a clear-cut truth – that a service works or is cost-beneficial. Rather, evidence can support or challenge the beliefs that we, and others, have and it can point to ways in which a service might be improved."


3. Who should make sure policy is evidence-based and transparent?
Bad PR alert? Is it government's responsibility to make policy transparent and balanced? If so, some are accusing the FDA of not holding up their end on drug and medical device policy. A recent 'close-held embargo' of an FDA announcement made NPR squirm. Scientific American says the deal was this: "NPR, along with a select group of media outlets, would get a briefing about an upcoming announcement by the U.S. Food and Drug Administration a day before anyone else. But in exchange for the scoop, NPR would have to abandon its reportorial independence. The FDA would dictate whom NPR's reporter could and couldn't interview.

"'My editors are uncomfortable with the condition that we cannot seek reaction,' NPR reporter Rob Stein wrote back to the government officials offering the deal. Stein asked for a little bit of leeway to do some independent reporting but was turned down flat. Take the deal or leave it."


Evidence & Insights Calendar

November 9-10, Philadelphia: Real-World Evidence & Market Access Summit 2016. "No more scandals! Access for Patients. Value for Pharma."

29 Oct-2 Nov, Vienna, Austria: ISPOR 19th Annual European Congress. Plenary: "What Synergies Could Be Created Between Regulatory and Health Technology Assessments?"

October 3-6, National Harbor, Maryland. AMCP Nexus 2016. Special topic: "Behavioral Economics - What Does it All Mean?"


Photo credit: Turtle on Flickr.

Tuesday, 20 September 2016

Social program science, gut-bias decision test, and enough evidence already.

Paperwork

"The driving force behind MDRC is a conviction that reliable evidence, well communicated, can make an important difference in social policy." -Gordon L. Berlin, President, MDRC

1. Slice of the week: Can behavioral science improve the delivery of child support programs? Yes. Understanding how people respond to communications has improved outcomes. State programs shifted from heavy packets of detailed requirements to simple emails and postcard reminders. (Really, did this require behavioral science? Not to discount the excellent work by @CABS_MDRC, but it seems pretty obvious. Still, a promising outcome.)

Applying Behavioral Science to Child Support: Building a Body of Evidence comes to us from MRDC, a New-York based institute that builds knowledge around social policy.

Data: Collected using random assignment and analyzed with descriptive statistics.

Evidence: Support payments increased with reminders. Simple notices (email or postcards) sent to people not previously receiving them increased by 3% the number of parents making at least one payment.

Relationship: behaviorally informed interventions → solve → child support problems


“A commitment to using best evidence to support decision making in any field is an ethical commitment.”
-Dónal O’Mathuna @DublinCityUni

2. How to test your decision-making instincts.
McKinsey's Andrew Campbell and Jo Whitehead have studied decision-making for execs. They suggest asking yourself these four questions to ensure you're drawing on appropriate experiences and emotions. "Leaders cannot prevent gut instinct from influencing their judgments. What they can do is identify situations where it is likely to be biased and then strengthen the decision process to reduce the resulting risk."

Familiarity test: Have we frequently experienced identical or similar situations?
Feedback test: Did we get reliable feedback in past situations?
Measured-emotions test: Are the emotions we have experienced in similar or related situations measured?
Independence test: Are we likely to be influenced by any inappropriate personal interests or attachments?

Relationship: Test of instincts → reduces → decision bias


3. When is enough evidence enough?
At what point should we agree on the evidence, stop evaluating, and move on? Determining this is particularly difficult where public health is concerned. Despite all the available findings, investigators continue to study the costs and benefits of statin drugs. A new Lancet review takes a comprehensive look and makes a strong case for this important drug class. "Large-scale evidence from randomised trials shows that statin therapy reduces the risk of major vascular events" and "claims that statins commonly cause adverse effects reflect a failure to recognise the limitations of other sources of evidence about the effects of treatment".

The insightful Richard Lehman (@RichardLehman1) provides a straightforward summary: The treatment is so successful that the "main adverse effect of statins is to induce arrogance in their proponents." And Larry Husten explains that Statin Trialists Seek To Bury Debate With Evidence.


Photo credit: paperwork by Camilo Rueda López on Flickr.

Tuesday, 13 September 2016

Battling antimicrobial resistance, visualizing data, and value in health.

Dentist-antibiotic-board

PepperSlice Board of the Week: Dentists will slow down on antibiotics if you show them a chart of their prescribing numbers. 

Antimicrobial resistance is a serious public health concern. PLOS Medicine has published findings from an RCT studying whether quantitative feedback and intervention about prescribing patterns will reduce dentists' antibiotic RXs. An intervention group prescribed substantially fewer antibiotics per 100 cases.

The Evidence. Peer-reviewed: An Audit and Feedback Intervention for Reducing Antibiotic Prescribing in General Dental Practice.

Data: Collected using RAPiD Cluster Randomised Controlled Trial, and analyzed with ANCOVA.

Relationship: historical data ➞ influence ➞ dentist antibiotic prescribing rates

This study evaluated the impact of providing general-practice dentists with individualised feedback consisting of a line graph of their monthly antibiotic prescribing rate. Rates in the intervention group were substantially lower than in the control group.

From the authors: "The feedback provided in this study is a relatively straightforward, low-cost public health and patient safety intervention that could potentially help the entire healthcare profession address the increasing challenge of antimicrobial resistance." Authors: Paula Elouafkaoui et al.

#: evidentista, antibiotics, evidence-based practice


Distribution-plots1

2. Visualizing data distributions.
Nathan Yau's fantastic blog, Flowing Data, offers a simple explanation of distributions - the spread of a dataset - and how to compare them. Highly recommended. "Single data points from a large dataset can make it more relatable, but those individual numbers don’t mean much without something to compare to. That’s where distributions come in."


3. Calculating 'expected value' of health interventions.
Frank David provides a useful reminder of the realities of computing 'expected value'. Sooner or later, we must make simplifying assumptions, and compare costs and benefits on similar terms (usually $). On Forbes he walks us through a straightforward calculation of the value of an Epi-pen. (Frank's firm, Pharmagellan, is coming out with a book on biotech financial modeling, and we look forward to that.)


G20-bayes-johnoliver

4. What is Bayesian, really?
In the Three Faces of Bayes, @burrsettles beautifully describes three uses of the term Bayesian, and wonders "Why is it that Bayesian networks, for example, aren’t considered… y’know… Bayesian?" Recommended for readers wanting to know more about these algorithms for machine learning and decision analysis.


Fun Fact: Everyone can stop carrying around fake babies. Evidence tells us baby simulators don't deter teen pregnancy after all.


Evidence & Insights Calendar:

September 19-21; Boston. FierceBiotech Drug Development Forum. Evaluate challenges, trends, and innovation in drug discovery and R&D. Covering the entire drug development process, from basic research through clinical trials.

September 13-14; Palo Alto, California. Nonprofit Management Institute: The Power of Network Leadership to Drive Social Change, hosted by Stanford Social Innovation Review.

September 20-22; Newark, New Jersey. Advanced Pharma Analytics. How to harness real-world evidence to optimize decision-making and improve patient-centric strategies.

Tuesday, 30 August 2016

Social determinants of health, nonfinancial performance metrics, and satisficers.

Dear reader: Evidence Soup is starting a new chapter. Our spotlight topics are now accompanied by a 'newsletter' version of a PepperSlice, the capsule form of evidence-based analysis we've created at PepperSlice.com. Let me know what you think, and thanks for your continued readership. - Tracy Altman

1. Is social services spending associated with better health outcomes? Yes.
Socialhealth-pepperslice-thumbnail Evidence has revealed a significant association between healthcare outcomes and the ratio of social service to healthcare spending in various OECD countries. Now a new study, published in Health Affairs, finds this same pattern within the US. The health differences were substantial. For instance, a 20% change in the median social-to-health spending ratio was associated with 85,000 fewer adults with obesity and more than 950,000 adults with mental illness. Elizabeth Bradley and Lauren Taylor explain on the RWJF Culture of Health blog.

This is great, but we wonder: Where/what is the cause-effect relationship?

The Evidence. Peer-reviewed: Variation In Health Outcomes: The Role Of Spending On Social Services, Public Health, And Health Care, 2000-09.

Data: Collected using longitudinal state-level spending data and analyzed with repeated measures multivariable modeling, retrospective.

Relationship: Social : medical spending → associated → better health outcomes

From the authors: "Reorienting attention and resources from the health care sector to upstream social factors is critical, but it’s also an uphill battle. More research is needed to characterize how the health effects of social determinants like education and poverty act over longer time horizons. Stakeholders need to use information about data on health—not just health care—to make resource allocation decisions."

#: statistical_modeling social_determinants population_health social_services health_policy

2. Are nonfinancial metrics good leading indicators of financial performance? Maybe.
Nonfinancial-metrics During the '90s and early 00's we heard a lot about Kaplan and Norton's Balanced Scorecard. A key concept was the use of nonfinancial management metrics such as customer satisfaction, employee engagement, and openness to innovation. This was thought to encourage actions that increased a company’s long-term value, rather than maximizing short-term financials.

The idea has taken hold, and nonfinancial metrics are often used in designing performance management systems and executive compensation plans. But not everyone is a fan: Some argue this can actually be harmful; for instance, execs might prioritize customer sat over other performance areas. Recent findings in the MIT Sloan Management Review look at whether these metrics truly are leading indicators of financial performance.

The Evidence. Business journal: Are Nonfinancial Metrics Good Leading Indicators of Future Financial Performance?

Data: Collected from American Customer Satisfaction Index, ExecuComp, and Compustat and analyzed with econometrics: panel data analysis.

Relationship: Nonfinancial metrics → predict → Financial performance

From the authors: "We found that there were notable variations in the lead indicator strength of customer satisfaction in a sample of companies drawn from different industries. For instance, for a chemical company in our sample, customer satisfaction’s lead indicator strength was negative; this finding is consistent with prior research suggesting that in many industries, the expense required to increase customer satisfaction can’t be justified. By contrast, for a telecommunications company we studied, customer satisfaction was a strong leading indicator; this finding is consistent with evidence showing that in many service industries, customer satisfaction reduces customer churn and price sensitivity. For a professional service firm in our sample, the lead indicator strength of customer satisfaction was weak; this is consistent with evidence showing that for such services, measures such as trust provide a clearer indication of the economic benefits than customer satisfaction.... Knowledge of whether a nonfinancial metric such as customer satisfaction is a strongly positive, weakly positive, or negative lead indicator of future financial performance can help companies avoid the pitfalls of using a nonfinancial metric to incentivize the wrong behavior."

#: customer_satisfaction nonfinancial balanced_scorecard CEO_compensation performance_management

3. Reliable evidence about p values.
Daniël Lakens (@lakens) puts it very well, saying "One of the most robust findings in psychology replicates again: Psychologists misinterpret p-values." This from Frontiers in Psychology.

4. Satisficers are happier.
Fast Company's article sounds at first just like clickbait, but they have a point. You can change how you see things, and reset your expectations. The Surprising Scientific Link Between Happiness And Decision Making.

Evidence & Insights Calendar:

September 19-21; Boston. FierceBiotech Drug Development Forum. Evaluate challenges, trends, and innovation in drug discovery and R&D. Covering the entire drug development process, from basic research through clinical trials.

September 13-14; Palo Alto, California. Nonprofit Management Institute: The Power of Network Leadership to Drive Social Change, hosted by Stanford Social Innovation Review.

September 20-22; Newark, New Jersey. Advanced Pharma Analytics. How to harness real-world evidence to optimize decision-making and improve patient-centric strategies.


Photo credit: Fat cat by brokinhrt2 on Flickr.

Tuesday, 23 August 2016

Science of CEO success?, drug valuation kerfuffle, and event attribution science.

  Fingerpointing


1. Management research: Alchemy → Chemistry?
McKinsey's Michael Birshan and Thomas Meakin set out to "take a data-driven look" at the strategic moves of newly appointed CEOs, and how those moves influenced company returns. The accompanying podcast (with transcript), CEO transitions: The science of success, says "A lot of the existing literature is quite qualitative, anecdotal, and we’ve been able to build a database of 599 CEO transitions and add a bunch of other sources to it and really try and mine that database hard for what we hope are new insights. We are really trying to move the conversation from alchemy to chemistry, if you like."

The research was first reported in How new CEOs can boost their odds of success. McKinsey's evidence says new CEOs make similar moves, with similar frequency, whether they're taking over a struggling company or a profitable one (see chart). For companies not performing well, Birshan says the data support his advice to be bold, and make multiple moves at once. Depending how you slice the numbers, both external and internal hires fared well in the CEO role (8).

  CEO-science-success

Is this science? CEO performance was associated with the metric excess total returns to shareholders, "which is the performance of one company over or beneath the average performance of its industry peers over the same time period". Bottom line, can you attribute CEO activities directly to excess TRS? Organizational redesign was correlated with significant excess TRS (+1.9 percent) for well-performing companies. The authors say "We recognize that excess TRS CAGR does not prove a causal link; too many other variables, some beyond a CEO’s control, have an influence. But we do find the differences that emerged quite plausible." Hmm, correlation still does not equal causation.

Examine the evidence. The report's end notes answer some key questions: Can you observe or measure whether a CEO inspires the top team? Probably not (1). Where do you draw the line between a total re-org and a management change? They define 'management reshuffle' as 50+% turnover in first two years (5). But we have other questions: How were these data collected and analyzed? Some form of content analysis would likely be required to assign values to variables. How were the 599 CEOs chosen as the sample? Selection bias is a concern. Were some items self-reported? Interviews or survey results? Were findings validated by assigning a second team to check for internal reliability? External reliability?


2. ICER + pharma → Fingerpointing.
There's a kerfuffle between pharma companies and the nonprofit ICER (@ICER_review). The Institute for Clinical and Economic Review publishes reports on drug valuation, and studies comparative efficacy. Biopharma Dive explains that "Drugmakers have argued ICER's reviews are driven by the interests of insurers, and fail to take the patient perspective into account." The National Pharmaceutical Council (@npcnow) takes issue with how ICER characterizes its funding sources.

ICER has been doing some damage control, responding to a list of 'myths' about its purpose and methods. Its rebuttal, Addressing the Myths About ICER and Value Assessment, examines criticisms such as "ICER only cares about the short-term cost to insurers, and uses an arbitrary budget cap to suggest low-ball prices." Also, ICER's economic models "use the Quality-Adjusted Life Year (QALY) which discriminates against those with serious conditions and the disabled, 'devaluing' their lives in a way that diminishes the importance of treatments to help them."


Cupid-lesser-known-arrow

3. Immortal time bias → Overstated findings.
You can't get a heart transplant after you're dead. The must-read Hilda Bastian writes on Statistically Funny about immortal time bias, a/k/a event-free time or competing risk bias. This happens when an analysis mishandles events whose occurrence precludes the outcome of interest - such as heart transplant outcomes. Numerous published studies, particularly those including Kaplan-Meier analyses, may suffer from this bias.


4. Climate change → Weird weather?
This week the US is battling huge fires and disastrous floods: Climate change, right? Maybe. There's now a thing called event attribution science, where people apply probabilistic methods in an effort to determine whether an extreme weather resulted from climate change. The idea is to establish/predict adverse impacts.


Evidence & Insights Calendar:

September 20-22; Newark, New Jersey. Advanced Pharma Analytics. How to harness real-world evidence to optimize decision-making and improve patient-centric strategies.

September 13-14; Palo Alto, California. Nonprofit Management Institute: The Power of Network Leadership to Drive Social Change, hosted by Stanford Social Innovation Review.

September 19-23; Melbourne, Australia. International School on Research Impact Assessment. Founded in 2013 by the Agency of Health Quality and Assessment (AQuAS), RAND Europe, and Alberta Innovates.


Photo credit: Fingerpointing by Tom Hilton.

Tuesday, 09 August 2016

Health innovation, foster teens, NBA, Gwyneth Paltrow.

Foster_care_youth

1. Behavioral economics → Healthcare innovation.
Jaan Sidorov (@DisMgtCareBlog) writes on the @Health_Affairs blog about roadblocks to healthcare innovation. Behavioral economics can help us truly understand resistance to change, including unconscious bias, so valuable improvements will gain more traction. Sidoro offers concise explanations of hyperbolic discounting, experience weighting, social utility, predictive value, and other relevant economic concepts. He also recommends specific tactics when presenting a technology-based innovation to the C-Suite.

2. Laptops → Foster teen success.
Nobody should have to type their high school essays on their phone. A coalition including Silicon Valley leaders and public sector agencies will ensure all California foster teens can own a laptop computer. Foster Care Counts reports evidence that "providing laptop computers to transition age youth shows measurable improvement in self-esteem and academic performance". KQED's California Report ran a fine story.

For a year, researchers at USC's School of Social Work surveyed 730 foster youth who received laptops, finding that "not only do grades and class attendance improve, but self-esteem and life satisfaction increase, while depression drops precipitously."

3. Analytical meritocracy → Better NBA outcomes.
The Innovation Enterprise Sports Channel explain how the NBA draft is becoming an analytical meritocracy. Predictive models help teams evaluate potential picks, including some they might have overlooked. Example: Andre Roberson, who played very little college ball, was drafted successfully by Oklahoma City based on analytics. It's tricky combining projections for active NBA teams with prospects who may never take the court. One decision aid is ESPN’s Draft Projection model, using Statistical Plus/Minus to predict how someone would perform through season five of a hypothetical NBA career. ESPN designates each player as a Superstar, Starter, Role Player, or Bust, to facilitate risk-reward assessments.

4. Celebrity culture → Clash with scientific evidence.
Health law and policy professor Timothy Caulfield (@CaulfieldTim) examines the impact of celebrity culture on people's choices of diet and healthcare. His new book asks Is Gwyneth Paltrow Wrong About Everything?: How the Famous Sell Us Elixirs of Health, Beauty & Happiness. Caulfield cites many, many peer-reviewed sources of evidence.

Evidence & Insights Calendar:

September 13-14; Palo Alto, California. Nonprofit Management Institute: The Power of Network Leadership to Drive Social Change, hosted by Stanford Social Innovation Review.

September 19-23; Melbourne, Australia. International School on Research Impact Assessment. Founded in 2013 by the Agency of Health Quality and Assessment (AQuAS), RAND Europe, and Alberta Innovates.

February 22-23; London UK. Evidence Europe 2017. How pharma, payers, and patients use real-world evidence to understand and demonstrate drug value and improve care.

Photo credit: Foster Care Counts.