Collaboration chapter

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 inferencepred.md
 Interpreting_Your_Results.md
 communication.md
+collaboration.md
 conclusion.md
 backmatter.txt
 about.md

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 Successful collaboration is an important determinant of career success and, for many people, an enormous source of career satisfaction. In the current environment in which large, complex datasets and sophisticated quantitative and data visualization methods are becoming increasingly common, collaboration with data scientists and statisticians is increasingly necessary to harness the full potential of your data and to have the greatest impact. In some cases, not engaging a data scientist collaborator may put you at risk of making statistical missteps that could result in erroneous results and poor decisions.
 
-* **Be respectful of time**. This tenet, of course, is applicable to all collaborations, but may be a more common stumbling block for clinical investigators working with biostatisticians. Most power estimates and sample size calculations, for example, are more complex than appreciated by the clinical investigator.  A discussion about the research question, primary outcome, etc. is required and some thought has to go into determining the most appropriate approach before your biostatistician collaborator has even laid hands on the keyboard and fired up R.  At a minimum, engage your biostatistician collaborator earlier than you might think necessary, and ideally, solicit their input during the planning stages.  Engaging a biostatistician sooner rather than later not only fosters good will, but will also improve your science. A biostatistician’s time, like yours, is valuable, so respect their time by allocating an appropriate level of salary support on grants.  Most academicians I come across appreciate that budgets are tight, so they understand that they may not get the level of salary support that they think is most appropriate.  However, “finding room” in the budget for 1% salary support for a biostatistician sends the message that the biostatistician is an afterthought, a necessity for a sample size calculation and a competitive grant application, but in the end, just a formality.   Instead, dedicate sufficient salary support in your grant to support the level of biostatistical effort that will be needed.  This sends the message that you would like your biostatistician collaborator to be an integral part of the investigator team and provides an opportunity for the kind of regular, ongoing interactions that are needed for productive collaborations.
+Over time, we have both learned some key lessons regarding how to best cultivate a productive relationship with a data scientists. The following points are made from the point of view of the scientist or business partner.
+
+## Be Respectful of Time
+
+This tenet, of course, is applicable to all collaborations, but may be a more common stumbling block for clinical investigators working with biostatisticians. Most power estimates and sample size calculations, for example, are more complex than appreciated by the clinical investigator.  A discussion about the research question, primary outcome, etc. is required and some thought has to go into determining the most appropriate approach before your biostatistician collaborator has even laid hands on the keyboard and fired up R.  At a minimum, engage your biostatistician collaborator earlier than you might think necessary, and ideally, solicit their input during the planning stages.  Engaging a biostatistician sooner rather than later not only fosters good will, but will also improve your science. A biostatistician’s time, like yours, is valuable, so respect their time by allocating an appropriate level of salary support on grants.  Most academicians I come across appreciate that budgets are tight, so they understand that they may not get the level of salary support that they think is most appropriate.  However, “finding room” in the budget for 1% salary support for a biostatistician sends the message that the biostatistician is an afterthought, a necessity for a sample size calculation and a competitive grant application, but in the end, just a formality.   Instead, dedicate sufficient salary support in your grant to support the level of biostatistical effort that will be needed.  This sends the message that you would like your biostatistician collaborator to be an integral part of the investigator team and provides an opportunity for the kind of regular, ongoing interactions that are needed for productive collaborations.
 
-* **Understand that a biostatistician is not a computational tool**.  Although sample size and power calculations are probably the most common service solicited from biostatisticians, and biostatisticians can be enormously helpful in this arena, they have the most impact when they are engaged in discussions about study designs and analytic approaches for a scientific question. Their quantitative approach to scientific problems provides a fresh perspective that can increase the scientific impact of your work.  My sense is that this is also much more interesting work for a biostatistician than sample size and power calculations, and engaging them in interesting work goes a long way towards cementing a mutually productive collaboration.
+## The Data Scientist is Not a Computational Tool
+
+Although sample size and power calculations are probably the most common service solicited from biostatisticians, and biostatisticians can be enormously helpful in this arena, they have the most impact when they are engaged in discussions about study designs and analytic approaches for a scientific question. Their quantitative approach to scientific problems provides a fresh perspective that can increase the scientific impact of your work.  My sense is that this is also much more interesting work for a biostatistician than sample size and power calculations, and engaging them in interesting work goes a long way towards cementing a mutually productive collaboration.
+
+## Learn the Lingo of Statistics
+
+Technical jargon is a serious impediment to successful collaboration. Again, this is true of all cross-discipline collaborations, but may be particularly true in collaborations with biostatisticians.  The field has a penchant for eponymous methods (Hosmer-Lemeshow, Wald, etc.) and terminology that is entertaining, but not intuitive (jackknife, bootstrapping, lasso).  While I am not suggesting that a clinical investigator needs to enroll in biostatistics courses (why gain expertise in a field when your collaborator provides this expertise), I am advocating for educating yourself about the basic concepts and terminology of statistics.  Know what is meant by: distribution of a variable, predictor variable, outcome variable, and variance, for example. There are some terrific “Biostatistics 101”-type lectures and course materials online that are excellent resources.  But also lean on your biostatistician collaborator by asking him/her to explain terminology and teach you these basics and do not be afraid to ask questions.
+
+## When All Else Fails (and Even When All Else Doesn’t Fail), Draw Pictures
+
+In truth, this is often the place where I start when I first engage a biostatistician. Showing your biostatistician collaborator what you expect your data to look like in a figure or conceptual diagram simplifies communication as it avoids use of jargon and biostatisticians can readily grasp the key information they need from a figure or diagram to come up with a sample size estimate or analytic approach.
 
-* **Make an effort to learn the language of biostatistics**. Technical jargon is a serious impediment to successful collaboration.  Again, this is true of all cross-discipline collaborations, but may be particularly true in collaborations with biostatisticians.  The field has a penchant for eponymous methods (Hosmer-Lemeshow, Wald, etc.) and terminology that is entertaining, but not intuitive (jackknife, bootstrapping, lasso).  While I am not suggesting that a clinical investigator needs to enroll in biostatistics courses (why gain expertise in a field when your collaborator provides this expertise), I am advocating for educating yourself about the basic concepts and terminology of statistics.  Know what is meant by: distribution of a variable, predictor variable, outcome variable, and variance, for example. There are some terrific “Biostatistics 101”-type lectures and course materials online that are excellent resources.  But also lean on your biostatistician collaborator by asking him/her to explain terminology and teach you these basics and do not be afraid to ask questions.
+## Teach Them Your Language
 
-* **When all else fails (and even when all else doesn’t fail), draw pictures**. In truth, this is often the place where I start when I first engage a biostatistician. Showing your biostatistician collaborator what you expect your data to look like in a figure or conceptual diagram simplifies communication as it avoids use of jargon and biostatisticians can readily grasp the key information they need from a figure or diagram to come up with a sample size estimate or analytic approach.
+Clinical medicine is also rife with jargon, and just as biostatistical jargon can make it difficult to communicate clearly with a biostatistician, so can clinical jargon.  Avoid technical jargon where possible, and define terminology where it is not possible.  Educate your collaborator about the background, context and rationale for your scientific question and encourage questions.
 
-* **Teach them your language**.  Clinical medicine is also rife with jargon, and just as biostatistical jargon can make it difficult to communicate clearly with a biostatistician, so can clinical jargon.  Avoid technical jargon where possible, and define terminology where it is not possible.  Educate your collaborator about the background, context and rationale for your scientific question and encourage questions.
+## Generously Share Your Data and Ideas
 
-* **Generously share your data and ideas**.  In many organizations, biostatisticians are very interested in developing new methods, applying more sophisticated methods to an “old” problem, and/or answering their own scientific questions. Do what you can to support these career interests, such as sharing your data and your ideas. Sharing data opens up avenues for increasing the impact of your work, as your biostatistician collaborator has opportunities to develop quantitative approaches to answering research questions related to your own interests.  Sharing data alone is not sufficient, though. Discussions about what you see as the important, unanswered questions will help provide the necessary background and context for the biostatistician to make the most of the available data.  As highlighted in a recent <a href="http://www.nytimes.com/2013/03/31/magazine/is-giving-the-secret-to-getting-ahead.html?ref=magazine&pagewanted=all&_r=0">book</a>, giving may be an important and overlooked component of success, and I would argue, also a cornerstone of a successful collaboration.
+In many organizations, biostatisticians are very interested in developing new methods, applying more sophisticated methods to an “old” problem, and/or answering their own scientific questions. Do what you can to support these career interests, such as sharing your data and your ideas. Sharing data opens up avenues for increasing the impact of your work, as your biostatistician collaborator has opportunities to develop quantitative approaches to answering research questions related to your own interests.  Sharing data alone is not sufficient, though. Discussions about what you see as the important, unanswered questions will help provide the necessary background and context for the biostatistician to make the most of the available data.  As highlighted in a recent [book](http://www.nytimes.com/2013/03/31/magazine/is-giving-the-secret-to-getting-ahead.html), giving may be an important and overlooked component of success, and I would argue, also a cornerstone of a successful collaboration.